arXiv Papers

Published: 2026-02-26 18:59:48

Authors: Erik Gillis, Ryan Cloutier, Emily Pass

Categories: astro-ph.EP

Abstract:
We present the deepest systematic search for planets around mid-to-late M dwarfs to date. We have surveyed 8134 mid-to-late M dwarfs observed by TESS with a custom built pipeline and recover 77 vetted transiting planet candidates. We characterize the sensitivity of our survey via injection-recovery and measure the occurrence rate of planets as a function of orbital period, instellation, and planet radius. We measure a cumulative occurrence rate of $1.10\pm0.16$ planets per star with radii $>1\, R_\oplus$ orbiting within 30 days. This value is consistent with the cumulative occurrence rate around early M dwarfs, making M dwarfs collectively the most prolific hosts of small close-in planets. Unlike the bimodal Radius Valley exhibited by close-in planet population around FGK and early M dwarfs, we recover a unimodal planet radius distribution peaking at $1.25\pm0.05 \, R_\oplus$. We additionally find $0.954\pm0.147$ super-Earths and $0.148\pm0.045$ sub-Neptunes per star, with super-Earths outnumbering sub-Neptunes 5.5:1, firmly demonstrating that the Radius Valley disappears around the lowest mass stars. The dearth of sub-Neptunes around mid-to-late M dwarfs is consistent with predictions from water-rich pebble accretion models that predict a fading Radius Valley with decreasing stellar mass. Our results support the emerging idea that the sub-Neptune population around M dwarfs is composed of water-rich worlds. We find no hot Jupiters in our survey and set an upper limit of 0.012 hot Jupiters per mid-to-late M dwarf within 10 days.

            {
  "tldr": "This study reveals that the Radius Valley disappears around mid-to-late M dwarfs, showing a unimodal planet radius distribution with a peak at 1.25 R⊕. It finds a cumulative occurrence rate of 1.10 ± 0.16 planets per star.",
  "whats_new": [
    "• First systematic search for planets around mid-to-late M dwarfs using TESS.",
    "• Discovery of a unimodal planet radius distribution, contrasting with earlier bimodal findings.",
    "• Measurement of 0.954 super-Earths and 0.148 sub-Neptunes per star, indicating super-Earth dominance.",
    "• Establishment of an upper limit for hot Jupiters at 0.012 per star."
  ],
  "method": "The study surveyed 8134 mid-to-late M dwarfs using a custom transit search pipeline. It employed injection-recovery tests to characterize sensitivity and derived occurrence rates based on detected transiting planet candidates.",
  "results": [
    "• Cumulative occurrence rate of 1.10 ± 0.16 planets per star with radii >1 R⊕ within 30 days.",
    "• Unimodal radius distribution peaking at 1.25 ± 0.05 R⊕.",
    "• 0.954 ± 0.147 super-Earths and 0.148 ± 0.045 sub-Neptunes per star.",
    "• No hot Jupiters detected, with an upper limit of 0.012 hot Jupiters per mid-to-late M dwarf."
  ],
  "limitations": [
    "• Limited sensitivity to sub-Earth-sized planets and planets in the habitable zone.",
    "• The survey only covers planets within 30 days, potentially missing longer-period planets.",
    "• The findings may not generalize to all M dwarfs due to sample selection."
  ],
  "why_useful": "This research enhances understanding of planet formation around low-mass stars, particularly mid-to-late M dwarfs. It provides crucial data for future studies on the habitability of exoplanets and the evolution of planetary systems.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:59:05

Authors: Vaibhav Agrawal, Rishubh Parihar, Pradhaan Bhat, Ravi Kiran Sarvadevabhatla, R. Venkatesh Babu

Categories: cs.CV, cs.AI

Abstract:
We identify occlusion reasoning as a fundamental yet overlooked aspect for 3D layout-conditioned generation. It is essential for synthesizing partially occluded objects with depth-consistent geometry and scale. While existing methods can generate realistic scenes that follow input layouts, they often fail to model precise inter-object occlusions. We propose SeeThrough3D, a model for 3D layout conditioned generation that explicitly models occlusions. We introduce an occlusion-aware 3D scene representation (OSCR), where objects are depicted as translucent 3D boxes placed within a virtual environment and rendered from desired camera viewpoint. The transparency encodes hidden object regions, enabling the model to reason about occlusions, while the rendered viewpoint provides explicit camera control during generation. We condition a pretrained flow based text-to-image image generation model by introducing a set of visual tokens derived from our rendered 3D representation. Furthermore, we apply masked self-attention to accurately bind each object bounding box to its corresponding textual description, enabling accurate generation of multiple objects without object attribute mixing. To train the model, we construct a synthetic dataset with diverse multi-object scenes with strong inter-object occlusions. SeeThrough3D generalizes effectively to unseen object categories and enables precise 3D layout control with realistic occlusions and consistent camera control.

            {
  "tldr": "SeeThrough3D introduces an occlusion-aware method for 3D control in text-to-image generation, enabling realistic object placement and camera control. It effectively models inter-object occlusions, enhancing scene realism.",
  "whats_new": [
    "• Proposes an Occlusion-Aware 3D Scene Representation (OSCR) for better occlusion handling.",
    "• Introduces a method for precise camera viewpoint control in generated images.",
    "• Constructs a synthetic dataset to train the model on complex multi-object scenes.",
    "• Demonstrates strong generalization to unseen object categories."
  ],
  "method": "The approach uses a translucent 3D bounding box representation to encode occlusions and object orientations. It conditions a pretrained text-to-image model on this representation, applying masked self-attention to bind objects to their textual descriptions.",
  "results": [
    "• Achieves a depth ordering score of 1.46, outperforming previous methods.",
    "• Reports a CLIP objectness score of 22.86, indicating high layout adherence.",
    "• Shows a significant reduction in angular error compared to baseline methods.",
    "• User studies indicate a preference for SeeThrough3D outputs in realism and layout adherence."
  ],
  "limitations": [
    "• Performance is limited by the underlying text-to-image model's capabilities.",
    "• Struggles with generating out-of-distribution cases.",
    "• Higher VRAM requirements for multi-subject personalization.",
    "• Does not preserve image consistency under layout changes."
  ],
  "why_useful": "This paper is significant for applications in design, gaming, and architectural visualization, where accurate 3D scene generation is crucial. The ability to control occlusions and camera viewpoints enhances creative possibilities in content creation.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://seethrough3d.github.io"
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:57:36

Authors: Shing-Chi Leung, Henry Yerdon, Seth Walther, Ken'ichi Nomoto, Aurora Simionescu

Categories: astro-ph.GA, astro-ph.HE

Abstract:
The Perseus Cluster has been precisely measured by the legacy Hitomi telescope on the Si-group (Si, S, Ar, Ca) and Fe-group elements (Cr, Mn, Ni). These element abundance ratios provide insight into the typical behaviour of supernovae. In Paper II, we presented new massive star explosion models at various metallicity, assuming spherical explosions. We show that while the fitting is improved, some features (e.g., Ni/Fe) remain to be improved. In this article, we extend our calculation to an aspherical explosion using the jet-induced explosion mechanism. The detailed pre- and post-explosion chemical profiles are calculated with a large post-processing network to capture the production of odd-number elements (V, Mn, Cu) and iron-group elements. We further explore how the jet-driven explosions create the diversity of models which could be compatible with the observed diversity in terms of $^{56}$Ni-mass vs ejecta mass, Ti-V relation, and stellar abundances. Finally, we apply the new collapsar models in the Galactic Chemical Evolution context. We study how the galactic stars, including the Zn-enriched star HE 1327-2326, can put constraints on the relative rates of collapsar and some of its model parameters. We show that collapsar could lead to significant changes in some elements, e.g., Zn. Our study shows that the collapsar is a necessary component to explain multiple elemental trends observed in the Milky Way Galaxy.

            {
  "tldr": "This paper explores the impact of aspherical explosions on the chemical composition of the Perseus Cluster and extends findings to metal-poor stars and galaxies, highlighting the importance of collapsar models in explaining elemental trends.",
  "whats_new": [
    "• Introduces aspherical explosion models to better fit observed elemental ratios in the Perseus Cluster.",
    "• Extends analysis to metal-poor stars, providing new insights into their chemical enrichment.",
    "• Utilizes a jet-induced explosion mechanism to enhance nucleosynthesis predictions.",
    "• Establishes empirical constraints on collapsar rates and their role in supernova populations."
  ],
  "method": "The study employs a two-dimensional special relativistic hydrodynamics code to simulate jet-driven explosions of massive stars. It calculates pre- and post-explosion chemical profiles using a large nuclear reaction network, focusing on the production of various elements.",
  "results": [
    "• New models improve fitting for Si, S, Ar, and Ca ratios in the Perseus Cluster.",
    "• Collapsar models predict significant changes in Zn production, aligning with observations of metal-poor stars.",
    "• Ejecta mass varies from <1 M⊙ to ∼10 M⊙ depending on jet parameters.",
    "• The models show a positive correlation between 56Ni yield and ejecta mass."
  ],
  "limitations": [
    "• Some elemental ratios, such as Ca and Cr, remain underproduced compared to observations.",
    "• The models may not fully account for contributions from other supernova types in high metallicity environments.",
    "• Further studies are needed to refine the role of collapsars in chemical evolution."
  ],
  "why_useful": "This research enhances our understanding of supernova nucleosynthesis and its implications for galactic chemical evolution. It provides a framework for interpreting elemental abundances in both the Perseus Cluster and ancient metal-poor stars, which can inform future studies of cosmic evolution.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:57:26

Authors: Jongwoo Choi, Neil A. Spencer, Jeffrey W. Miller

Categories: stat.ME

Abstract:
Model selection is a central task in statistics, but standard methods are not robust in misspecified settings where the true data-generating process (DGP) is not in the set of candidate models. The key limitation is that existing methods -- including information criteria and Bayesian posteriors -- do not quantify uncertainty about how well each candidate model approximates the true DGP. In this paper, we introduce a novel approach to model selection based on modeling the likelihood values themselves. Specifically, given $K$ candidate models and $n$ observations, we view the $n\times K$ matrix of negative log-likelihood values as a random data matrix and observe that the expectation of each row is equal to the vector of Kullback--Leibler divergences between the $K$ models and the true DGP, up to an additive constant. We use a multivariate normal model to estimate and quantify uncertainty in this expectation, providing calibrated inferences for robust model selection under misspecification. The procedure is easy to compute, interpretable, and comes with theoretical guarantees, including consistency.

            {
  "tldr": "This paper presents a new method for robust model selection using likelihood values as data, addressing limitations of traditional methods under model misspecification.",
  "whats_new": [
    "• Introduces the Likelihood as Data (LaD) framework for model selection.",
    "• Quantifies uncertainty in model approximations to the true data-generating process (DGP).",
    "• Provides theoretical guarantees for consistency and stability under ties.",
    "• Demonstrates effectiveness in various applications, including Gaussian mixture models and population genetics."
  ],
  "method": "The proposed method treats the matrix of negative log-likelihood values as data, using a Bayesian multivariate normal model to estimate the expected log-likelihoods. This allows for robust model selection by quantifying uncertainty in the Kullback-Leibler divergences between candidate models and the true DGP.",
  "results": [
    "• The LaD framework outperforms traditional methods like AIC and BIC in model selection under misspecification.",
    "• Provides calibrated inferences for model selection, achieving stability even with multiple models having similar fits.",
    "• Successfully identifies simpler models that are close to the best-fitting models within a specified tolerance.",
    "• Demonstrated effectiveness in real-data applications, including microbial ecology and population structure models."
  ],
  "limitations": [
    "• Assumes independent observations and finite second moments of log-likelihoods.",
    "• Performance may degrade if the assumptions of model correctness are violated.",
    "• Computationally intensive for large numbers of candidate models."
  ],
  "why_useful": "This paper is significant for statisticians and data scientists as it provides a robust framework for model selection that quantifies uncertainty and addresses common pitfalls in traditional methods. It has potential applications in various fields, including ecology, genetics, and any area requiring model selection under uncertainty.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:55:06

Authors: Simon Roschmann, Paul Krzakala, Sonia Mazelet, Quentin Bouniot, Zeynep Akata

Categories: cs.LG, cs.AI

Abstract:
The Platonic Representation Hypothesis posits that neural networks trained on different modalities converge toward a shared statistical model of the world. Recent work exploits this convergence by aligning frozen pretrained vision and language models with lightweight alignment layers, but typically relies on contrastive losses and millions of paired samples. In this work, we ask whether meaningful alignment can be achieved with substantially less supervision. We introduce a semi-supervised setting in which pretrained unimodal encoders are aligned using a small number of image-text pairs together with large amounts of unpaired data. To address this challenge, we propose SOTAlign, a two-stage framework that first recovers a coarse shared geometry from limited paired data using a linear teacher, then refines the alignment on unpaired samples via an optimal-transport-based divergence that transfers relational structure without overconstraining the target space. Unlike existing semi-supervised methods, SOTAlign effectively leverages unpaired images and text, learning robust joint embeddings across datasets and encoder pairs, and significantly outperforming supervised and semi-supervised baselines.

            {
  "tldr": "SOTAlign introduces a semi-supervised method for aligning unimodal vision and language models using minimal paired data and abundant unpaired data, significantly improving alignment performance.",
  "whats_new": [
    "• Proposes SOTAlign, a two-stage framework for semi-supervised alignment.",
    "• Introduces KLOT, an optimal-transport-based divergence that enhances alignment without overconstraining the target space.",
    "• Demonstrates effective use of unpaired data to achieve state-of-the-art results in alignment tasks.",
    "• Validates the approach across various datasets and encoder combinations."
  ],
  "method": "SOTAlign operates in two stages: first, it uses a linear teacher to establish a coarse alignment from limited paired data. Then, it refines this alignment using an optimal-transport-based divergence on unpaired samples, allowing for flexible learning of joint embeddings.",
  "results": [
    "• SOTAlign outperforms supervised and semi-supervised baselines by up to 10% in zero-shot retrieval tasks.",
    "• Achieves a MeanR@1 score of 46.1% on ImageNet with only 10k paired samples.",
    "• Leverages unpaired data effectively, showing consistent performance gains with up to 1M unpaired samples.",
    "• Demonstrates robustness across various datasets, including COCO and CC3M."
  ],
  "limitations": [
    "• The method's performance may still be limited by the quality of the paired samples.",
    "• Further exploration needed on the scalability of KLOT in larger datasets.",
    "• Potential challenges in defining effective target geometries for diverse datasets."
  ],
  "why_useful": "This paper addresses the challenge of aligning vision and language models with minimal supervision, making it relevant for applications in domains where paired data is scarce. SOTAlign's ability to leverage unpaired data can enhance model adaptability and efficiency in real-world scenarios.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:50:06

Authors: Suat Barış İplikçioğlu

Categories: physics.optics, physics.app-ph

Abstract:
Recent experiments on temporal reflection in transmission line metamaterials and theoretical treatments of dispersive time-varying media have unearthed the fundamental role of modulation mechanisms on the interface conditions, underpinning the introduction of passive photonic time crystals with stable momentum band gaps. Drawing from these concepts, it is shown that temporal metamaterials with simultaneous passive permittivity and permeability switching exhibit wideband absorption with impedance-matching, effectively behaving as one-dimensional perfectly matched layers. Under the effective medium theory, the loss mechanism is attributed to the emergent effective electric and magnetic conductivities, which are used to derive an approximate matching condition for asynchronous modulation and to engineer lossy material properties. The proposed approach and its performance beyond the Rozanov bound are verified with semi-analytical calculations as well as full-wave simulations, and the possibility of realizing a two-dimensional temporal perfectly matched layer is discussed.

            {
  "tldr": "This paper presents a novel approach to temporal metamaterials that utilize passive switching for impedance-matched absorption, achieving wideband absorption similar to perfectly matched layers. The findings suggest potential applications in advanced electromagnetic systems.",
  "whats_new": [
    "• Introduces passive switching in temporal metamaterials for effective absorption.",
    "• Demonstrates performance beyond the Rozanov bound using semi-analytical calculations and simulations.",
    "• Proposes a method for realizing two-dimensional temporal perfectly matched layers."
  ],
  "method": "The study employs effective medium theory to analyze temporal metamaterials with passive permittivity and permeability switching. It uses semi-analytical calculations and full-wave simulations to verify the proposed absorption mechanism and matching conditions.",
  "results": [
    "• Achieves efficient absorption with negligible reflections in simulations.",
    "• Exceeds the Rozanov bound for certain modulation depths.",
    "• Demonstrates that absorption efficiency can be improved with shorter switching periods."
  ],
  "limitations": [
    "• Theoretical models may not fully capture practical implementation challenges.",
    "• Performance may degrade under asynchronous modulation due to impedance mismatches.",
    "• Requires further exploration of multi-dimensional matching conditions."
  ],
  "why_useful": "This research advances the understanding of temporal metamaterials, offering new methods for electromagnetic wave absorption. Its findings could lead to practical applications in radar and communication technologies, enhancing performance in various frequency ranges.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:49:53

Authors: Ruiqi Wang, Pinjun Zheng, Yiming Yang, Xiarui Su, Mohammad Vaseem, Anas Chaaban, Md. Jahangir Hossain, Tareq Y. Al-Naffouri, Atif Shamim

Categories: eess.SY

Abstract:
Reconfigurable intelligent surfaces have emerged as a promising hardware platform for shaping wireless propagation environments at millimeter-wave (mm-Wave) frequencies and beyond. While many existing studies emphasize channel modeling and signal processing, practical RIS deployment is fundamentally governed by hardware design choices and their system-level implications. This paper presents a hardware-centric overview of recent mm-Wave RIS developments, covering wideband realizations, high-resolution phase-quantized designs, fully printed low-cost implementations, optically transparent surfaces, RIS-on-chip solutions, and emerging three-dimensional architectures. Key challenges including mutual coupling, calibration, multi-RIS interaction, and frequency-dependent phase control are discussed to bridge hardware realization with system-level optimization. This overview provides practical design insights and aims to guide future RIS research toward scalable, efficient, and practically deployable intelligent surface architectures.

            {
  "tldr": "This paper provides a comprehensive overview of hardware design and system-level considerations for millimeter-wave reconfigurable intelligent surfaces (RIS), highlighting recent advancements and challenges in practical deployment.",
  "whats_new": [
    "• Focuses on hardware-centric aspects of mm-Wave RIS rather than just theoretical models.",
    "• Discusses wideband designs, phase quantization, and low-cost implementations.",
    "• Explores optically transparent and 3D RIS architectures.",
    "• Addresses practical challenges like mutual coupling and calibration."
  ],
  "method": "The paper reviews recent developments in mm-Wave RIS technology, emphasizing hardware design choices and their implications for system performance. It covers various implementations, including wideband, printed, and transparent surfaces, while discussing challenges in calibration and multi-RIS interactions.",
  "results": [
    "• Demonstrates a wideband RIS prototype achieving stable performance from 22.5 to 29.5 GHz.",
    "• Reports a 7.6 dB reduction in sidelobe levels when moving from 1-bit to 2-bit phase quantization.",
    "• Highlights the cost reduction of fully printed RIS to just a few dollars for a 20x20 array.",
    "• Shows that 3D RIS architectures can enhance gain by up to 14.7 dB."
  ],
  "limitations": [
    "• Practical deployment of RIS is still limited to trial scenarios.",
    "• Calibration and mutual coupling issues remain significant challenges.",
    "• Long-term reliability of new materials and designs is not fully established.",
    "• The integration of multiple functionalities may complicate system design."
  ],
  "why_useful": "This paper is crucial for researchers and engineers working on next-generation wireless systems, particularly in understanding the hardware implications of mm-Wave RIS technology. It provides insights into practical design considerations that can guide future developments in 5G and 6G communications.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:47:06

Authors: Alkis Kalavasis, Anay Mehrotra, Manolis Zampetakis, Felix Zhou, Ziyu Zhu

Categories: cs.LG, cs.DS, math.ST, stat.ML

Abstract:
Coarse data arise when learners observe only partial information about samples; namely, a set containing the sample rather than its exact value. This occurs naturally through measurement rounding, sensor limitations, and lag in economic systems. We study Gaussian mean estimation from coarse data, where each true sample $x$ is drawn from a $d$-dimensional Gaussian distribution with identity covariance, but is revealed only through the set of a partition containing $x$. When the coarse samples, roughly speaking, have ``low'' information, the mean cannot be uniquely recovered from observed samples (i.e., the problem is not identifiable). Recent work by Fotakis, Kalavasis, Kontonis, and Tzamos [FKKT21] established that sample-efficient mean estimation is possible when the unknown mean is identifiable and the partition consists of only convex sets. Moreover, they showed that without convexity, mean estimation becomes NP-hard. However, two fundamental questions remained open: (1) When is the mean identifiable under convex partitions? (2) Is computationally efficient estimation possible under identifiability and convex partitions? This work resolves both questions. [...]

            {
  "tldr": "This paper addresses Gaussian mean estimation from coarse data, providing a geometric characterization for identifiable convex partitions and introducing a polynomial-time algorithm for efficient estimation.",
  "whats_new": [
    "• Characterizes identifiability conditions for convex partitions in Gaussian mean estimation.",
    "• Introduces the first polynomial-time algorithm for estimating the Gaussian mean from coarse samples.",
    "• Applies findings to linear regression with market friction, demonstrating practical relevance."
  ],
  "method": "The authors develop a geometric characterization to determine when a convex partition is identifiable. They then propose a polynomial-time algorithm that uses stochastic gradient descent to estimate the Gaussian mean from coarse samples, ensuring sample efficiency.",
  "results": [
    "• The algorithm achieves ε-accurate estimates with sample complexity of O(d/ε²).",
    "• Identifiability is characterized by the presence of parallel slabs in the partition.",
    "• The proposed method is applicable to linear regression with market friction, achieving similar sample complexity."
  ],
  "limitations": [
    "• The results are limited to convex partitions; non-convex cases remain NP-hard.",
    "• The algorithm's performance may degrade with less informative partitions."
  ],
  "why_useful": "This work is significant for robust machine learning applications where data is often coarse or imprecise. The findings can enhance estimation techniques in various fields, including economics and healthcare analytics.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:40:28

Authors: Dany Haddad, Dan Bareket, Joseph Chee Chang, Jay DeYoung, Jena D. Hwang, Uri Katz, Mark Polak, Sangho Suh, Harshit Surana, Aryeh Tiktinsky, Shriya Atmakuri, Jonathan Bragg, Mike D'Arcy, Sergey Feldman, Amal Hassan-Ali, Rubén Lozano, Bodhisattwa Prasad Majumder, Charles McGrady, Amanpreet Singh, Brooke Vlahos, Yoav Goldberg, Doug Downey

Categories: cs.HC, cs.AI, cs.IR

Abstract:
AI-powered scientific research tools are rapidly being integrated into research workflows, yet the field lacks a clear lens into how researchers use these systems in real-world settings. We present and analyze the Asta Interaction Dataset, a large-scale resource comprising over 200,000 user queries and interaction logs from two deployed tools (a literature discovery interface and a scientific question-answering interface) within an LLM-powered retrieval-augmented generation platform. Using this dataset, we characterize query patterns, engagement behaviors, and how usage evolves with experience. We find that users submit longer and more complex queries than in traditional search, and treat the system as a collaborative research partner, delegating tasks such as drafting content and identifying research gaps. Users treat generated responses as persistent artifacts, revisiting and navigating among outputs and cited evidence in non-linear ways. With experience, users issue more targeted queries and engage more deeply with supporting citations, although keyword-style queries persist even among experienced users. We release the anonymized dataset and analysis with a new query intent taxonomy to inform future designs of real-world AI research assistants and to support realistic evaluation.

            {
  "tldr": "The Asta Interaction Dataset provides insights into how researchers engage with AI-powered scientific tools, revealing complex query patterns and collaborative behaviors. It includes over 200,000 user interactions from two interfaces, highlighting the evolution of user engagement over time.",
  "whats_new": [
    "• First public dataset of real-world interactions with AI-powered research tools.",
    "• Introduces a new taxonomy for query intents, phrasing styles, and criteria.",
    "• Analyzes user behavior changes as they gain experience with the tools.",
    "• Reveals that users treat AI-generated responses as persistent artifacts."
  ],
  "method": "The study analyzes over 200,000 user queries and interaction logs from two AI-powered tools, PaperFinder and ScholarQA. It employs statistical modeling and LLM-based query labeling to characterize user engagement and query patterns.",
  "results": [
    "• Users submit longer and more complex queries compared to traditional search engines.",
    "• 50.5% of users revisit previous reports, treating them as persistent artifacts.",
    "• Experienced users engage more deeply with citations, increasing evidence clicks by 27%.",
    "• Users issue more targeted queries over time, with a drop in broad topic exploration from 61.2% to 53.5%."
  ],
  "limitations": [
    "• Findings may not generalize to other AI tools with different designs or objectives.",
    "• The dataset primarily reflects interactions with Asta, limiting broader applicability.",
    "• User behavior may be influenced by the specific features of the tools analyzed."
  ],
  "why_useful": "This paper is significant for understanding how researchers utilize AI in their workflows, informing the design of future AI research assistants. The dataset and insights can help improve user experience and tool effectiveness in academic settings.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://arxiv.org/abs/2602.23335v1"
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:37:23

Authors: Chen Bo Calvin Zhang, Christina Q. Knight, Nicholas Kruus, Jason Hausenloy, Pedro Medeiros, Nathaniel Li, Aiden Kim, Yury Orlovskiy, Coleman Breen, Bryce Cai, Jasper Götting, Andrew Bo Liu, Samira Nedungadi, Paula Rodriguez, Yannis Yiming He, Mohamed Shaaban, Zifan Wang, Seth Donoughe, Julian Michael

Categories: cs.AI, cs.CL, cs.CR, cs.CY, cs.HC

Abstract:
Large language models (LLMs) perform increasingly well on biology benchmarks, but it remains unclear whether they uplift novice users -- i.e., enable humans to perform better than with internet-only resources. This uncertainty is central to understanding both scientific acceleration and dual-use risk. We conducted a multi-model, multi-benchmark human uplift study comparing novices with LLM access versus internet-only access across eight biosecurity-relevant task sets. Participants worked on complex problems with ample time (up to 13 hours for the most involved tasks). We found that LLM access provided substantial uplift: novices with LLMs were 4.16 times more accurate than controls (95% CI [2.63, 6.87]). On four benchmarks with available expert baselines (internet-only), novices with LLMs outperformed experts on three of them. Perhaps surprisingly, standalone LLMs often exceeded LLM-assisted novices, indicating that users were not eliciting the strongest available contributions from the LLMs. Most participants (89.6%) reported little difficulty obtaining dual-use-relevant information despite safeguards. Overall, LLMs substantially uplift novices on biological tasks previously reserved for trained practitioners, underscoring the need for sustained, interactive uplift evaluations alongside traditional benchmarks.

            {
  "tldr": "This study shows that large language models (LLMs) significantly enhance the performance of novice users on complex biological tasks, often surpassing expert levels. However, novices frequently underutilize LLM capabilities.",
  "whats_new": [
    "• Demonstrates that novices with LLM access are 4.16× more accurate than those using only internet resources.",
    "• Novices with LLMs outperformed experts on three out of four benchmarks.",
    "• Highlights that standalone LLMs often perform better than LLM-assisted novices.",
    "• Provides a comprehensive qualitative analysis of human-LLM interactions."
  ],
  "method": "The study involved a multi-model, multi-benchmark design where novices were divided into two groups: one with access to LLMs and another with internet-only resources. Participants worked on eight biosecurity-relevant tasks over extended periods, up to 13 hours, with performance and qualitative data collected throughout.",
  "results": [
    "• Novices with LLM access achieved 4.16× higher accuracy than controls (95% CI [2.63, 6.87]).",
    "• LLM-assisted novices outperformed expert baselines on three out of four benchmarks.",
    "• On the Human Pathogen Capabilities Test, Treatment participants scored 0.413 compared to 0.104 for Control.",
    "• Most participants (89.6%) reported little difficulty obtaining dual-use information despite safeguards."
  ],
  "limitations": [
    "• The set of models available to participants varied during the study.",
    "• Some participants identified specific questions that were publicly available online.",
    "• The absence of double-blinding may introduce bias in treatment effect estimates.",
    "• The study did not explore physical lab environments."
  ],
  "why_useful": "This research highlights the potential risks of LLMs in enabling novices to perform complex biological tasks, raising concerns about dual-use capabilities. It underscores the need for stronger safeguards in AI deployment within sensitive fields like biology.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:36:14

Authors: Kaizad Rustomji, Nasim Mohammadi Estakhri, Nooshin M. Estakhri

Categories: physics.optics

Abstract:
Through temporal shaping of the excitation signal, the complex-frequency scattering zeros of a lossless structure can be accessed, enabling a storage-release mechanism referred to as coherent virtual absorption. Practical demonstrations of this mechanism, however, have been limited to simple configurations such as slabs and spheres, where analytical solutions allow accurate prediction of the complex-frequency scattering zeros. Here, we extend this concept into the realm of metasurfaces and demonstrate coherent virtual absorption in realistic and dispersive metasurface configurations. Through a combination of full-wave analysis and rational approximation, we present a practical scheme to identify suitable complex-frequency zeros and achieve coherent virtual absorption successfully. Our approach can be implemented in arbitrary metasurface configurations with any number of ports, providing a robust framework for optimized energy storage, memories, optical sensing, and modulation in practical photonic systems.

            {
  "tldr": "This paper presents a method for achieving coherent virtual absorption in dielectric metasurfaces using complex-frequency scattering zeros. The approach extends previous work by applying it to realistic metasurface configurations, enabling applications in energy storage and optical sensing.",
  "whats_new": [
    "• Demonstrates coherent virtual absorption in realistic dielectric metasurfaces.",
    "• Utilizes full-wave analysis combined with rational approximation for complex-frequency zeros.",
    "• Applicable to arbitrary metasurface configurations with multiple ports.",
    "• Provides a framework for optimized energy storage and optical modulation."
  ],
  "method": "The authors employ full-wave analysis and rational approximation techniques to identify complex-frequency scattering zeros in dielectric metasurfaces. They conduct time-domain numerical simulations to analyze the scattering response and achieve coherent virtual absorption without prior approximations.",
  "results": [
    "• Successfully demonstrated coherent virtual absorption in a semi-randomized metasurface with a complex zero at kL = 6.04 + 0.31i.",
    "• Achieved coherent virtual absorption in a patterned metasurface with a complex zero at kL ≈ 9.24 + 0.6i.",
    "• The method allows for precise prediction of complex-frequency behavior with high accuracy.",
    "• Observed significant asymmetry in scattering response based on metasurface configuration."
  ],
  "limitations": [
    "• The approach may be sensitive to numerical noise and limited frequency resolution.",
    "• Potential artifacts like Froissart doublets can occur, requiring careful algorithm setup.",
    "• Experimental validation of the proposed method is still needed."
  ],
  "why_useful": "This research advances the understanding of light-matter interactions in complex photonic systems, enabling new applications in energy storage and optical sensing. The findings could lead to innovative designs in photonic devices and materials.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:36:13

Authors: Venkata D. Pamulaparthy, Rosemary J. Harris

Categories: cond-mat.stat-mech

Abstract:
We analyse a continuous-time random walk model with stochastic reversals of direction. There is no external potential but the reorientation mechanism generates a non-zero current from asymmetry in the forward and backward waiting-time distributions (even when they have the same mean); the system can therefore can be considered as a type of active particle ratchet. We derive an explicit expression for the mean ratchet current with exponentially distributed reorientation times and also develop a general renewal-theory framework to obtain the full large deviations, using this to comment on the possibility of dynamical phase transitions.

            {
  "tldr": "This paper explores a model of active particle ratchets that generates non-zero currents through memory-induced asymmetries in waiting-time distributions. It derives expressions for mean currents and fluctuations, revealing potential dynamical phase transitions.",
  "whats_new": [
    "• Introduces a novel active ratchet model without external potentials.",
    "• Derives explicit expressions for mean ratchet currents with non-exponential waiting times.",
    "• Develops a framework for analyzing large deviations and potential dynamical phase transitions.",
    "• Explores implications for biological transport systems."
  ],
  "method": "The authors analyze a continuous-time random walk model with stochastic direction reversals, focusing on non-exponential waiting-time distributions. They derive mean currents and fluctuations using renewal theory and large-deviation principles.",
  "results": [
    "• Mean current can be non-zero even when forward and backward waiting times have the same mean.",
    "• The mean current is expressed as ⟨j⟩ = r/2 (ψ+(r)/(1 - ψ+(r)) - ψ−(r)/(1 - ψ−(r))).",
    "• Demonstrates the possibility of dynamical phase transitions in certain ratchet models.",
    "• Validates results with phase-type waiting-time distributions."
  ],
  "limitations": [
    "• The model primarily focuses on exponential reorientation times, limiting generalizability.",
    "• Further exploration needed for non-exponential waiting times without hidden-Markov representations.",
    "• Potential challenges in experimentally controlling waiting-time distributions."
  ],
  "why_useful": "This research provides insights into how memory effects can generate directed currents in systems without external potentials, relevant for understanding molecular motors and biological transport. It also opens avenues for exploring non-equilibrium dynamics in various contexts.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:32:51

Authors: Jose M. Betancourt, Matthew P. Leighton, Thierry Emonet, Benjamin B. Machta, Michael C. Abbott

Categories: physics.bio-ph, cond-mat.stat-mech, q-bio.QM

Abstract:
Navigation up a sensory gradient is one of the simplest behaviours, and the simplest strategy is run and tumble. But some organisms use other strategies, such as reversing direction or turning by some angle. Here we ask what drives the choice of strategy, which we frame as maximising up-gradient speed using a given amount of sensory information per unit time. We find that, without directional information on which way to turn, behavioural strategies which make sudden turns perform better than gradual steering. We see various transitions where a different strategy becomes optimal, such as a switch from reversing direction to fully re-orienting tumbles as more information becomes available. And, among more complex re-orientation strategies, we show that discrete turn angles are best, and see transitions in how many such angles the optimal strategy employs.

            {
  "tldr": "This paper investigates how organisms choose navigation strategies under limited sensory information, finding that discrete turn strategies outperform continuous steering in low-information scenarios.",
  "whats_new": [
    "• Introduces a framework for analyzing navigation strategies based on sensory information rates.",
    "• Demonstrates that sudden turns are more effective than gradual steering without directional information.",
    "• Identifies transitions between optimal strategies as information availability changes.",
    "• Shows that optimal strategies utilize a discrete set of turn angles."
  ],
  "method": "The authors model navigation as a problem of maximizing speed up a gradient with limited sensory information. They analyze various strategies, including run-and-tumble and continuous steering, using mathematical optimization and Fokker-Planck equations.",
  "results": [
    "• Sudden turns outperform continuous steering when directional information is absent.",
    "• At low information rates, reversing direction is optimal, while at high rates, tumbling is preferred.",
    "• The optimal strategy employs discrete turn angles, transitioning from two to five angles as information increases.",
    "• Performance metrics indicate that low-information limits yield speeds of approximately √i."
  ],
  "limitations": [
    "• The model does not account for time-dependent or memory-based strategies used by real organisms.",
    "• It simplifies complex biological behaviors into basic mathematical forms, which may not capture all nuances.",
    "• The findings are based on theoretical models and may require empirical validation."
  ],
  "why_useful": "This research provides insights into how organisms optimize navigation strategies under sensory constraints, which could inform the design of autonomous systems and improve our understanding of biological behaviors. It highlights the importance of discrete decision-making in dynamic environments.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:27:18

Authors: Youssef Trifa, Dario Cafasso, Marco Fattori, Luca Pezzè

Categories: quant-ph, cond-mat.quant-gas, gr-qc

Abstract:
Operational probes of the interface between quantum mechanics and general relativity in the Newtonian regime -- via mass-energy equivalence in clocks or spatial superpositions in interferometers -- share a common description in terms of an effective qubit-qubit Ising coupling. Here we generalize both paradigms to interacting $(N+1)$-level effective qudits made of atomic ensembles with particle number, $N$. The many-body enhancement boosts the signal-to-noise and increases the effective interaction rate, facilitating the observation of gravitationally induced entanglement and decoherence, certified by metrological witnesses based on local and collective measurements. Furthermore, we show that quantum effects induced by gravitational interaction can be simulated by trapped bimodal Bose-Einstein condensates with long-range (e.g. dipolar) coupling, providing a programmable analogue platform to explore gravitating quantum dynamics at accessible time and energy scales. Finally, extending the protocol to a sensor network broadens the entanglement-detection window.

            {
  "tldr": "This paper explores using dipolar Bose-Einstein condensates to simulate many-body gravitating quantum systems, enhancing the detection of gravitationally induced entanglement and decoherence. It proposes a programmable platform for studying quantum gravity effects at accessible scales.",
  "whats_new": [
    "• Generalizes existing quantum gravity proposals to many-body systems using dipolar Bose-Einstein condensates.",
    "• Introduces a network of qudits for enhanced sensitivity to gravitational effects.",
    "• Demonstrates a significant increase in signal-to-noise ratio for detecting gravitationally induced phenomena.",
    "• Provides a programmable analogue platform for exploring gravitating quantum dynamics."
  ],
  "method": "The authors develop an effective Hamiltonian for interacting qudits formed by atomic ensembles in Bose-Einstein condensates. They analyze the dynamics of these systems under gravitational interactions, focusing on entanglement and decoherence detection through local and collective measurements.",
  "results": [
    "• The many-body enhancement increases the effective interaction rate by a factor of approximately N.",
    "• Witness criteria for entanglement detection show C1 < 1 and C2 < 1, indicating successful entanglement between ensembles.",
    "• The operational dephasing time scales favorably with N, approximately τ_deph ∼ N^(-1.2).",
    "• For N=1000, the GIE witness C2 reaches its minimum at about 3.2 seconds."
  ],
  "limitations": [
    "• The experimental realization depends on achieving precise control over interactions in Bose-Einstein condensates.",
    "• The scalability of the proposed network architecture for larger systems remains to be fully explored.",
    "• The impact of environmental noise on the coherence of the systems is not fully addressed."
  ],
  "why_useful": "This research provides a novel approach to studying quantum gravity effects using accessible experimental setups. It opens pathways for future experiments that could reveal fundamental insights into the interplay between quantum mechanics and gravity, with potential applications in quantum metrology and fundamental physics.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:24:58

Authors: Nima Alibabaei

Categories: math.DS

Abstract:
We first study i.i.d. products of finitely many invertible $2 \times 2$ matrices with positive entries, and prove that the top Lyapunov exponent admits an explicit, rapidly convergent Neumann-series-type representation involving an infinite matrix. We further show that non-negative invertible $2 \times 2$ matrices are simultaneously conjugate to positive matrices if and only if ``generalized'' heteroclinic connections do not occur among products of length at most $2$. These results yield a series formula for the Hausdorff dimension of the intersection of the middle-$n$th Cantor set with a random translate of itself, for every natural number $n$ except $4$. Furthermore, our method applies to the intersection of thick Cantor sets under random translation. We also determine the almost sure growth rate of i.i.d. three-term recurrences with finitely many positive coefficients.

            {
  "tldr": "This paper presents a new method for calculating Lyapunov exponents for random products of non-negative matrices, providing explicit formulas and faster convergence rates compared to existing methods.",
  "whats_new": [
    "• Introduces a rapidly convergent Neumann-series-type representation for Lyapunov exponents.",
    "• Establishes conditions for simultaneous conjugacy of non-negative matrices to positive matrices.",
    "• Applies findings to determine Hausdorff dimensions of Cantor set intersections.",
    "• Provides polynomial complexity for approximating Lyapunov exponents."
  ],
  "method": "The approach involves studying i.i.d. products of invertible 2×2 matrices and deriving integral representations for the Lyapunov exponent. A kernel expansion technique is utilized to analyze the convergence of series representations.",
  "results": [
    "• The top Lyapunov exponent can be expressed as a convergent series involving an infinite matrix.",
    "• The series can be approximated with O(log(1/ε))^3 arithmetic operations.",
    "• The method outperforms previous transfer-operator approaches in computational efficiency.",
    "• Hausdorff dimensions for intersections of Cantor sets are derived for various classes."
  ],
  "limitations": [
    "• The method's quantitative estimates are currently specific to 2×2 matrices.",
    "• Generalizations to higher dimensions remain unresolved due to complexities in contraction properties."
  ],
  "why_useful": "This paper provides a significant advancement in the computation of Lyapunov exponents, which are crucial in various fields such as dynamical systems and fractal geometry. The explicit formulas and efficient algorithms can facilitate further research and applications in these areas.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:11:10

Authors: Ryan Tiew, Nikolas P. Breuckmann

Categories: quant-ph

Abstract:
We determine conditions on classical group algebra codes so that they have pre-orientation for cup products and copy-cup gates. This defines quantum codes that have constant-depth $\operatorname{CZ}$ and $\operatorname{CCZ}$ gates constructed via tensor products of classical group algebra codes, including hypergraph and balanced products. We show that determining the conditions relies on solving the perfect matching problem in graph theory. Conditions are fully determined for the 2- and 3-copy-cup gates, for group algebra codes up to weight 4, including for codes with odd check weight. These include the bivariate bicycle codes, which we show do not have the pre-orientation for either type of copy-cup gate. We show that abelian weight 4 group algebra codes satisfying the non-associative 3-copy-cup gate necessarily have a code distance of 2, whereas codes that satisfy conditions for the symmetric 3-copy-cup gate can have higher distances, and in fact also satisfy conditions for the 2-copy-cup gate. Finally we find examples of quantum codes from the product of abelian group algebra codes that have inter-code constant-depth $\operatorname{CZ}$ and $\operatorname{CCZ}$ gates.

            {
  "tldr": "This paper explores conditions for classical group algebra codes to support copy-cup gates, enabling constant-depth quantum gates. It identifies constraints through combinatorial methods and numerical searches, revealing insights into the structure of quantum error-correcting codes.",
  "whats_new": [
    "• Establishes conditions for classical group algebra codes to support 2- and 3-copy-cup gates.",
    "• Introduces a combinatorial approach to solving the perfect matching problem in graph theory related to these codes.",
    "• Provides numerical examples of quantum codes with constant-depth gates derived from group algebra codes.",
    "• Extends the analysis to codes with odd check weights, previously unexplored."
  ],
  "method": "The authors analyze classical group algebra codes to determine pre-orientation conditions for copy-cup gates. They employ combinatorial techniques to solve related perfect matching problems and conduct numerical searches for specific code examples.",
  "results": [
    "• Conditions for 2- and 3-copy-cup gates are fully determined for group algebra codes up to weight 4.",
    "• Bivariate bicycle codes do not satisfy pre-orientation for copy-cup gates.",
    "• Found examples of quantum codes with constant-depth CZ and CCZ gates from abelian group algebra codes.",
    "• Weight 4 group algebra codes with pre-orientation for the non-associative 3-copy-cup gate have a maximum distance of 2."
  ],
  "limitations": [
    "• The analysis is primarily focused on abelian group algebra codes, limiting generalizability.",
    "• Computational challenges arise with increasing check weights, complicating the search for valid configurations.",
    "• The results for higher weight codes remain inconclusive due to the complexity of the conditions."
  ],
  "why_useful": "This research is significant for advancing quantum error correction by identifying specific conditions under which classical codes can be effectively utilized in quantum computing. The findings can inform the design of more efficient quantum error-correcting codes and contribute to the development of fault-tolerant quantum computers.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:09:16

Authors: Ilya Balabin, Thomas M. Kaiser

Categories: cs.LG

Abstract:
Machine learning techniques are now routinely encountered in research laboratories across the globe. Impressive progress has been made through ML and AI techniques with regards to large data set processing. This progress has increased the ability of the experimenter to digest data and make novel predictions regarding phenomena of interest. However, machine learning predictors generated from data sets taken from the natural sciences are often treated as black boxes which are used broadly and generally without detailed consideration of the causal structure of the data set of interest. Work has been attempted to bring causality into discussions of machine learning models of natural phenomena; however, a firm and unified theoretical treatment is lacking. This series of three papers explores the union of chemical theory, biological theory, probability theory and causality that will correct current causal flaws of machine learning in the natural sciences. This paper, Part 1 of the series, provides the formal framework of the foundational causal structure of phenomena in chemical biology and is extended to machine learning through the novel concept of focus, defined here as the ability of a machine learning algorithm to narrow down to a hidden underpinning mechanism in large data sets. Initial proof of these principles on a family of Akt inhibitors is also provided. The second paper containing Part 2 will provide a formal exploration of chemical similarity, and Part 3 will present extensive experimental evidence of how hidden causal structures weaken all machine learning in chemical biology. This series serves to establish for chemical biology a new kind of mathematical framework for modeling mechanisms in Nature without the need for the tools of reductionism: inferential mechanics.

            {
  "tldr": "This paper introduces a causal framework for machine learning in chemical biology, addressing the limitations of current models that treat data as 'black boxes.' It emphasizes the importance of understanding hidden causal structures to improve predictive accuracy.",
  "whats_new": [
    "• Proposes a novel concept of 'focus' in machine learning to identify hidden mechanisms in data.",
    "• Establishes a causal framework integrating chemical, biological, and probabilistic theories.",
    "• Demonstrates that hidden causal structures can lead to poor machine learning performance.",
    "• Introduces methods to evaluate causal relationships in structure-activity data."
  ],
  "method": "The authors employ causal calculus to analyze the relationships between chemical structures and biological activities. They introduce the concept of 'focus' to guide machine learning models towards relevant mechanistic insights, using ROC AUC for performance evaluation.",
  "results": [
    "• The average ROC AUC peaked at 0.841 for the most similar 25 compounds in a test set.",
    "• Performance declined to 0.791 when including 2225 compounds of varying similarity.",
    "• Hidden mechanistic incongruencies were identified as a cause for decreased predictive accuracy.",
    "• The paper outlines a method for selecting optimal training sets based on mechanistic similarity."
  ],
  "limitations": [
    "• The framework relies on the assumption that mechanisms can be inferred from similarity.",
    "• It does not address how to determine the distribution of unknown mediators.",
    "• The proposed methods may not be applicable to all datasets or biological contexts.",
    "• Further empirical validation is needed to confirm the theoretical claims."
  ],
  "why_useful": "This paper provides a foundational approach to integrating causality into machine learning for chemical biology, which can enhance predictive modeling and drug design. Understanding hidden causal structures can lead to more accurate and reliable machine learning applications in the field.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://github.com/balabin/IM_01"
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:09:02

Authors: Giacomo Bonanno

Categories: cs.AI, cs.LO, math.LO

Abstract:
For each axiom of KM belief update we provide a corresponding axiom in a modal logic containing three modal operators: a unimodal belief operator $B$, a bimodal conditional operator $>$ and the unimodal necessity operator $\square$. We then compare the resulting logic to the similar logic obtained from converting the AGM axioms of belief revision into modal axioms and show that the latter contains the former. Denoting the latter by $\mathcal L_{AGM}$ and the former by $\mathcal L_{KM}$ we show that every axiom of $\mathcal L_{KM}$ is a theorem of $\mathcal L_{AGM}$. Thus AGM belief revision can be seen as a special case of KM belief update. For the strong version of KM belief update we show that the difference between $\mathcal L_{KM}$ and $\mathcal L_{AGM}$ can be narrowed down to a single axiom, which deals exclusively with unsurprising information, that is, with formulas that were not initially disbelieved.

            {
  "tldr": "This paper shows that the logic of KM belief update is a subset of AGM belief revision logic, establishing AGM as a special case of KM. It provides modal axioms for both frameworks and highlights their relationship.",
  "whats_new": [
    "• Establishes that every axiom of KM belief update is a theorem of AGM belief revision.",
    "• Provides modal axioms corresponding to KM belief update.",
    "• Clarifies the conceptual relationship between KM and AGM frameworks.",
    "• Identifies a single axiom that differentiates strong KM updates from AGM revisions."
  ],
  "method": "The paper translates each axiom of KM belief update into a modal logic framework using three modal operators. It then compares this logic with the modal axioms derived from AGM belief revision, demonstrating that the former is contained within the latter.",
  "results": [
    "• Every axiom of KM belief update is shown to be a theorem of AGM belief revision.",
    "• The difference between strong KM updates and AGM revisions is reduced to a single axiom regarding unsurprising information.",
    "• Modal axioms for KM belief update are explicitly defined and compared with AGM axioms."
  ],
  "limitations": [
    "• The paper primarily focuses on the strong version of KM belief update.",
    "• It does not explore potential applications of the modal logic framework in practical scenarios."
  ],
  "why_useful": "This work clarifies the theoretical foundations of belief change, providing a unified framework for understanding belief updates and revisions. It can inform future research in artificial intelligence and logic by offering insights into how beliefs can be systematically altered.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 18:07:45

Authors: Quang-Huy Nguyen, Jiaqi Wang, Wei-Shinn Ku

Categories: cs.LG, cs.AI

Abstract:
Federated learning (FL) faces challenges in uncertainty quantification (UQ). Without reliable UQ, FL systems risk deploying overconfident models at under-resourced agents, leading to silent local failures despite seemingly satisfactory global performance. Existing federated UQ approaches often address data heterogeneity or model heterogeneity in isolation, overlooking their joint effect on coverage reliability across agents. Conformal prediction is a widely used distribution-free UQ framework, yet its applications in heterogeneous FL settings remains underexplored. We provide FedWQ-CP, a simple yet effective approach that balances empirical coverage performance with efficiency at both global and agent levels under the dual heterogeneity. FedWQ-CP performs agent-server calibration in a single communication round. On each agent, conformity scores are computed on calibration data and a local quantile threshold is derived. Each agent then transmits only its quantile threshold and calibration sample size to the server. The server simply aggregates these thresholds through a weighted average to produce a global threshold. Experimental results on seven public datasets for both classification and regression demonstrate that FedWQ-CP empirically maintains agent-wise and global coverage while producing the smallest prediction sets or intervals.

            {
  "tldr": "This paper presents FedWQ-CP, a novel framework for uncertainty quantification in federated learning that effectively addresses both data and model heterogeneity. It achieves reliable coverage while minimizing prediction set sizes across diverse datasets.",
  "whats_new": [
    "• Introduces FedWQ-CP, a federated calibration method for uncertainty quantification.",
    "• Balances empirical coverage performance with efficiency in heterogeneous settings.",
    "• Achieves reliable agent-level and global coverage in a single communication round.",
    "• Outperforms existing federated uncertainty quantification methods in terms of efficiency."
  ],
  "method": "FedWQ-CP computes local quantile thresholds based on conformity scores from calibration data. Each agent sends its quantile threshold and calibration sample size to a server, which aggregates these to produce a global threshold for evaluation.",
  "results": [
    "• Maintains agent-wise and global coverage around the nominal level (e.g., 95%) across seven datasets.",
    "• FedWQ-CP reduces prediction set sizes significantly compared to existing methods, achieving up to 68.7% reduction.",
    "• Outperforms state-of-the-art methods like DP-FedCP, which exhibited severe under-coverage.",
    "• Achieves competitive runtime, with a one-shot procedure requiring minimal communication."
  ],
  "limitations": [
    "• Theoretical guarantees are asymptotic and may not hold in finite samples.",
    "• Performance may vary with different levels of data and model heterogeneity.",
    "• Requires careful calibration to ensure effective coverage across agents."
  ],
  "why_useful": "This paper is significant for developing reliable federated learning systems, especially in high-stakes environments like healthcare. FedWQ-CP provides a scalable and privacy-preserving solution for uncertainty quantification, enhancing decision-making across diverse institutions.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 17:55:22

Authors: Mike Y. Michelis, Nana Obayashi, Josie Hughes, Robert K. Katzschmann

Categories: cs.RO

Abstract:
Mimicking the graceful motion of swimming animals remains a core challenge in soft robotics due to the complexity of fluid-structure interaction and the difficulty of controlling soft, biomimetic bodies. Existing modeling approaches are often computationally expensive and impractical for complex control or reinforcement learning needed for realistic motions to emerge in robotic systems. In this work, we present a tendon-driven fish robot modeled in an efficient underwater swimmer environment using a simplified, stateless hydrodynamics formulation implemented in the widespread robotics framework MuJoCo. With just two real-world swimming trajectories, we identify five fluid parameters that allow a matching to experimental behavior and generalize across a range of actuation frequencies. We show that this stateless fluid model can generalize to unseen actuation and outperform classical analytical models such as the elongated body theory. This simulation environment runs faster than real-time and can easily enable downstream learning algorithms such as reinforcement learning for target tracking, reaching a 93% success rate. Due to the simplicity and ease of use of the model and our open-source simulation environment, our results show that even simple, stateless models -- when carefully matched to physical data -- can serve as effective digital twins for soft underwater robots, opening up new directions for scalable learning and control in aquatic environments.

            {
  "tldr": "This paper presents a simplified fluid model for tendon-driven underwater robots, enabling efficient simulations that match real-world swimming behavior. The model outperforms traditional analytical approaches and facilitates reinforcement learning for control tasks.",
  "whats_new": [
    "• Introduces a stateless fluid model for underwater swimming robots.",
    "• Achieves accurate simulation with only two real-world trajectories.",
    "• Outperforms classical models like elongated body theory in generalization.",
    "• Demonstrates successful reinforcement learning for target tracking with a 93% success rate."
  ],
  "method": "The authors developed a tendon-driven fish robot using a simplified hydrodynamics model implemented in MuJoCo. They identified five fluid parameters from real swimming data to match experimental behavior and tested the model's generalization across various actuation frequencies.",
  "results": [
    "• The simulation environment runs at 14.58x real-time speed.",
    "• Achieved an average error of 0.019 m/s in forward swimming velocity across different frequencies.",
    "• The digital twin model outperformed the elongated body theory with an average error of 0.134 m/s.",
    "• Reinforcement learning agents achieved a 93% success rate in target tracking."
  ],
  "limitations": [
    "• The model is limited to surface swimming and does not explore full 3D underwater dynamics.",
    "• Requires further complexity for shape optimization and more expressive fluid models.",
    "• The current approach may not generalize well to more complex control tasks."
  ],
  "why_useful": "This research provides a computationally efficient method for simulating soft underwater robots, making it easier to develop and deploy biomimetic swimming robots. The findings can enhance control strategies in aquatic environments and contribute to advancements in soft robotics.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://srl-ethz.github.io/website_fishsim"
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 17:50:56

Authors: Giorgio Mangioni, Alessandro Sisto

Categories: math.GT, math.GR

Abstract:
We introduce a construction that simultaneously yields cusped spaces of relatively hyperbolic groups, and spaces quasi-isometric to Teichmueller metrics. We use this to study Dehn-filling-like quotients of various groups, among which mapping class groups of punctured spheres. In particular, we show that the mapping class group of a five-holed sphere (resp. the braid group on four strands) has infinite hyperbolic quotients (strongly) not isomorphic to hyperbolic quotients of any other given sphere mapping class group (resp. any other braid group). These quotients are obtained by modding out suitable large powers of Dehn twists, and we further argue that the corresponding quotients of the extended mapping class group have trivial outer automorphism groups. We obtain these results by studying torsion elements in the relevant quotients.

            {
  "tldr": "This paper introduces a construction for cusped spaces of hierarchically hyperbolic groups, facilitating the study of Dehn filling quotients. It shows that certain mapping class groups have unique infinite hyperbolic quotients, distinguishing them from others.",
  "whats_new": [
    "• Introduces cusped spaces for hierarchically hyperbolic groups.",
    "• Establishes unique infinite hyperbolic quotients for specific mapping class groups.",
    "• Provides a framework for studying torsion elements in these groups.",
    "• Explores applications to Dehn filling quotients."
  ],
  "method": "The authors construct cusped spaces using a framework of relatively hierarchically hyperbolic spaces. They analyze the properties of these spaces and their actions by groups, particularly focusing on mapping class groups and their quotients.",
  "results": [
    "• Mapping class group MCGpS5q has infinite hyperbolic quotients not isomorphic to those of other groups.",
    "• Any homomorphism from the braid group Bn to these quotients has virtually cyclic image.",
    "• The quotients are obtained by modding out large powers of Dehn twists.",
    "• The outer automorphism groups of certain quotients are trivial."
  ],
  "limitations": [
    "• The construction relies on specific technical conditions that may limit generalizability.",
    "• Some results are delicate and depend on the careful handling of torsion elements.",
    "• The methods may not extend easily to all cases of mapping class groups."
  ],
  "why_useful": "This paper provides a novel approach to understanding the structure of hierarchically hyperbolic groups and their quotients. Its findings can have implications in geometric topology and the study of mapping class groups, particularly in distinguishing between different groups based on their hyperbolic properties.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 17:48:49

Authors: Sanjay Sarkar, Sayan Sarkar, Amit Agarwal

Categories: cond-mat.mes-hall

Abstract:
Altermagnets exhibit momentum-dependent spin splitting despite having zero net magnetization. This enables a spin-splitter effect in which an external electric field generates transverse spin currents by separating oppositely polarized carriers. Here, we develop a unified semiclassical theory of linear extrinsic spin-splitter currents, incorporating impurity-induced side-jump and skew-scattering contributions, and apply it to the $d$-wave altermagnet \ch{FeSb2}. We demonstrate that asymmetric impurity scattering provides a dominant channel for spin-splitter currents. Remarkably, the resulting extrinsic spin conductivity is time-reversal even, in contrast to previously studied spin-splitter responses arising from symmetric scattering.

            {
  "tldr": "This paper presents a semiclassical theory of extrinsic spin-splitter currents in altermagnets, focusing on the role of asymmetric impurity scattering. It highlights that these currents can dominate over intrinsic contributions, particularly in the d-wave altermagnet FeSb2.",
  "whats_new": [
    "• Introduces a unified semiclassical theory for extrinsic spin-splitter currents.",
    "• Demonstrates that asymmetric impurity scattering is a dominant mechanism.",
    "• Shows that extrinsic spin conductivity is time-reversal even, contrasting with previous findings.",
    "• Applies the theory to the d-wave altermagnet FeSb2, revealing significant spin current generation."
  ],
  "method": "The authors develop a semiclassical framework incorporating impurity-induced side-jump and skew-scattering contributions to analyze spin-splitter currents. They derive explicit expressions for spin conductivities using the Boltzmann transport equation.",
  "results": [
    "• Asymmetric impurity scattering can generate sizable spin currents, exceeding intrinsic contributions.",
    "• The spin Hall angle reaches approximately 0.8, indicating efficient spin-charge conversion.",
    "• Extrinsic contributions are governed by band-geometric quantities like Berry curvature.",
    "• The extrinsic spin conductivity is time-reversal even, unlike conventional contributions."
  ],
  "limitations": [
    "• The study focuses on a specific material (FeSb2), limiting generalizability.",
    "• The impact of other potential scattering mechanisms remains unexplored.",
    "• Assumes a minimal disorder model, which may not capture all real-world complexities."
  ],
  "why_useful": "This research enhances understanding of spin transport in altermagnets, providing insights into their potential applications in spintronic devices. The findings suggest new avenues for manipulating spin currents through extrinsic mechanisms.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 17:46:42

Authors: Haotian Zhai, Elias Stengel-Eskin, Pratik Patil, Liu Leqi

Categories: cs.AI

Abstract:
Deep Research Agents (DRAs) are promising agentic systems that gather and synthesize information to support research across domains such as financial decision-making, medical analysis, and scientific discovery. Despite recent improvements in research quality (e.g., outcome accuracy when ground truth is available), DRA system design often overlooks a critical barrier to real-world deployment: stochasticity. Under identical queries, repeated executions of DRAs can exhibit substantial variability in terms of research outcome, findings, and citations. In this paper, we formalize the study of stochasticity in DRAs by modeling them as information acquisition Markov Decision Processes. We introduce an evaluation framework that quantifies variance in the system and identify three sources of it: information acquisition, information compression, and inference. Through controlled experiments, we investigate how stochasticity from these modules across different decision steps influences the variance of DRA outputs. Our results show that reducing stochasticity can improve research output quality, with inference and early-stage stochasticity contributing the most to DRA output variance. Based on these findings, we propose strategies for mitigating stochasticity while maintaining output quality via structured output and ensemble-based query generation. Our experiments on DeepSearchQA show that our proposed mitigation methods reduce average stochasticity by 22% while maintaining high research quality.

            {
  "tldr": "This paper analyzes stochasticity in Deep Research Agents (DRAs), showing that variability in outputs can be significantly reduced by addressing early-stage stochasticity and improving inference methods.",
  "whats_new": [
    "• Introduces a formal model of DRAs as information acquisition Markov Decision Processes.",
    "• Develops multi-level metrics to quantify stochasticity in research outputs.",
    "• Identifies early-stage stochasticity and inference as major contributors to output variance.",
    "• Proposes effective mitigation strategies that reduce stochasticity by 22% while maintaining output quality."
  ],
  "method": "The authors model DRAs as Markov Decision Processes to analyze stochasticity. They introduce metrics to measure variability in outputs and conduct experiments to assess the impact of stochasticity on research quality. Mitigation strategies are proposed based on structured outputs and ensemble query generation.",
  "results": [
    "• Average stochasticity reduced by 22% with proposed methods.",
    "• Inference module contributes most to output variance.",
    "• Early-stage stochasticity has a stronger influence on final output variance than late-stage stochasticity.",
    "• Findings exhibit greater stochasticity than citations, with average dTV(B) at 0.76 compared to dTV(C) at 0.44."
  ],
  "limitations": [
    "• The study primarily focuses on specific DRA frameworks, limiting generalizability.",
    "• Future work is needed to explore a broader range of mitigation strategies.",
    "• The empirical framework may not capture all sources of stochasticity."
  ],
  "why_useful": "This research is crucial for improving the reliability of DRAs in high-stakes applications like finance and healthcare. By addressing stochasticity, the findings can enhance user trust and the overall effectiveness of these systems.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 17:38:24

Authors: Domagoj Jelić, Piotr Oprocha

Categories: math.DS

Abstract:
The paper studies the structure of $ω$-limit sets of map $\tilde{f}$ induced on the hyperspace $C(G)$ of all connected compact sets, by dynamical system $(G,f)$ acting on a topological graph $G$. In the case of the base space being a topological tree we additionally show that $\tilde{f}$ is always almost equicontinuous and characterize its Birkhoff center.

            {
  "tldr": "This paper explores the dynamics of ω-limit sets in the hyperspace of continua, particularly focusing on topological graphs and trees. It establishes new results on equicontinuity and the structure of these sets under induced mappings.",
  "whats_new": [
    "• Characterizes ω-limit sets for induced maps on continua in topological graphs and trees.",
    "• Proves that the induced map is almost equicontinuous on the hyperspace of continua.",
    "• Generalizes previous results on periodic and wandering sets in the context of topological trees."
  ],
  "method": "The authors extend continuous self-maps on compact metric spaces to induced mappings on the hyperspace of continua. They analyze the dynamics of these mappings using topological and dynamical systems techniques, focusing on the structure of ω-limit sets and equicontinuity points.",
  "results": [
    "• For any continuum A in C(G), it is either asymptotically periodic, wandering, or contained in a circumferential set.",
    "• The set of equicontinuity points is dense Gδ in the hyperspace of subcontinua.",
    "• The paper establishes that the entropy of the induced map remains bounded under certain conditions."
  ],
  "limitations": [
    "• The results are primarily applicable to topological graphs and trees, limiting generalizability to more complex spaces.",
    "• Open questions remain regarding the behavior of induced maps on general topological graphs."
  ],
  "why_useful": "This paper contributes to the understanding of dynamical systems on continua, particularly in one-dimensional spaces. Its findings have implications for the study of chaotic behavior and the structure of limit sets, which are relevant in various fields including topology and dynamical systems.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 17:36:48

Authors: Jasmine Bayrooti, Weiwei Kong, Natalia Ponomareva, Carlos Esteves, Ameesh Makadia, Amanda Prorok

Categories: cs.CV, cs.CR

Abstract:
Generative models trained on sensitive image datasets risk memorizing and reproducing individual training examples, making strong privacy guarantees essential. While differential privacy (DP) provides a principled framework for such guarantees, standard DP finetuning (e.g., with DP-SGD) often results in severe degradation of image quality, particularly in high-frequency textures, due to the indiscriminate addition of noise across all model parameters. In this work, we propose a spectral DP framework based on the hypothesis that the most privacy-sensitive portions of an image are often low-frequency components in the wavelet space (e.g., facial features and object shapes) while high-frequency components are largely generic and public. Based on this hypothesis, we propose the following two-stage framework for DP image generation with coarse image intermediaries: (1) DP finetune an autoregressive spectral image tokenizer model on the low-resolution wavelet coefficients of the sensitive images, and (2) perform high-resolution upsampling using a publicly pretrained super-resolution model. By restricting the privacy budget to the global structures of the image in the first stage, and leveraging the post-processing property of DP for detail refinement, we achieve promising trade-offs between privacy and utility. Experiments on the MS-COCO and MM-CelebA-HQ datasets show that our method generates images with improved quality and style capture relative to other leading DP image frameworks.

            {
  "tldr": "This paper presents DP-Wavelet, a novel framework for generating private images using a coarse-to-fine wavelet modeling approach that improves image quality while ensuring differential privacy.",
  "whats_new": [
    "• Introduces DP-Wavelet, the first autoregressive method for text-to-image synthesis focusing on low-frequency wavelet components.",
    "• Allocates privacy budgets preferentially to low-frequency components, enhancing signal-to-noise ratio.",
    "• Achieves competitive image quality and style consistency on MS-COCO and MM-CelebA-HQ datasets compared to existing methods."
  ],
  "method": "DP-Wavelet operates in two stages: first, it finetunes a wavelet-based model on low-frequency components of sensitive images under differential privacy. Then, it uses a pretrained model to upscale these components into high-resolution images, allowing for detail refinement without additional privacy cost.",
  "results": [
    "• DP-Wavelet achieves an LPIPS score of 0.666 on MS-COCO under non-private conditions, outperforming other methods.",
    "• Maintains competitive performance with LPIPS scores close to DP-LlamaGen under privacy budgets (ε=10, 1).",
    "• On MM-CelebA-HQ, DP-Wavelet achieves the lowest FID of 18.2 under ε=∞, indicating superior quality."
  ],
  "limitations": [
    "• The performance gap in image quality compared to some baselines suggests potential improvements in the upsampling model.",
    "• The method relies on the assumption that high-frequency details can be generated without accessing private data, which may not hold in all cases."
  ],
  "why_useful": "DP-Wavelet addresses critical privacy concerns in generative modeling by ensuring that sensitive information is not leaked while still producing high-quality images. This framework is particularly relevant for applications in medical imaging and other fields where data privacy is paramount.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 17:28:14

Authors: Daniele Rogantini, Erin Kara, Luigi Gallo, S Komossa, Peter Kosec, Dan Wilkins, Ehud Behar, Joheen Chakraborty, Dirk Grupe, Missagh Mehdipour, Christos Panagiotou, Ciro Pinto, Irina Zhuravleva

Categories: astro-ph.HE, astro-ph.GA

Abstract:
Transient X-ray obscuration in Seyfert 1 galaxies is thought to arise from clumpy accretion-disk winds near the broad-line region (BLR), but the wind structure and its short-timescale variability are difficult to measure because high-resolution spectra are often suppressed during deep low states. We analyse a coordinated XMM-Newton/NuSTAR campaign on Mrk 335 in June 2021, complemented by long-term Swift monitoring, which captured the source in an intermediate-flux state that preserves strong RGS absorption features. We first model the broadband spectral energy distribution to determine the ionising continuum and then perform self-consistent photoionisation modelling of the RGS spectra. The stacked RGS spectrum requires three photoionised absorbers with time-averaged log xi approx 3.63, 3.10, and 2.01 and outflow velocities |v_out| approx 5820, 3210, and 2140 km/s. Their properties are broadly consistent with the three-phase obscurer reported in the 2009 intermediate state, indicating recurring multi-phase obscuration over decade timescales. Using five consecutive RGS observations, we track the wind evolution on day timescales and find strong variability in column density and ionisation in all phases, together with smaller but coherent changes in outflow velocity. During a flare, the low-ionisation phase shows an extreme drop in N_H, and the subsequent epoch exhibits an increase in outflow velocity in all phases, consistent with rapid restructuring and possible radiative acceleration in a clumpy wind. The high-ionisation phase responds most directly to changes in the ionising luminosity, while the lowest-ionisation phase shows at most a delayed response. Order-of-magnitude constraints place the obscurer at BLR scales (approx 10^3-10^5 R_g), and simple continuity arguments suggest kinetic power that can reach the percent level of L_bol for plausible estimates of geometry and clumpiness.

            {
  "tldr": "This study investigates the variability of the X-ray obscuring wind in Mrk 335 using data from a coordinated XMM-Newton/NuSTAR campaign. It identifies three distinct photoionised absorbers and their dynamic changes over days, suggesting a clumpy wind structure.",
  "whats_new": [
    "• First detailed characterization of the obscuring wind in Mrk 335 on day timescales.",
    "• Identification of three photoionised absorbers with distinct ionization parameters and outflow velocities.",
    "• Evidence of strong variability in column density and ionization during flares.",
    "• Insights into the potential feedback mechanisms of AGN on host galaxies."
  ],
  "method": "The study utilized high-resolution spectra from XMM-Newton and NuSTAR, complemented by long-term Swift monitoring. The authors performed photoionization modeling on the RGS spectra to analyze the obscuring wind's properties and variability.",
  "results": [
    "• Three photoionised absorbers identified with logξ values of 3.63, 3.10, and 2.01.",
    "• Outflow velocities measured at approximately 5820, 3210, and 2140 km/s for the respective phases.",
    "• Significant day-scale variability observed in column density and ionization parameters.",
    "• During a flare, the low-ionization phase showed a drastic drop in column density."
  ],
  "limitations": [
    "• The study relies on a limited number of epochs, which may not capture the full variability.",
    "• Sparse sampling limits the ability to robustly assess time-dependent photoionization effects.",
    "• The inferred distances and energetics depend on assumptions about geometry and clumpiness."
  ],
  "why_useful": "This research enhances understanding of the dynamics of obscuring winds in AGN, which are crucial for studying AGN feedback mechanisms. The findings may inform future observational strategies and theoretical models regarding the impact of these winds on host galaxies.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 17:17:48

Authors: Aleksandr V. Korolev, Evgeny F. Talantsev

Categories: cond-mat.supr-con

Abstract:
For a relatively long time, the observation of the DC diamagnetic state in highly compressed nickelate superconductors [1],[2] has been a challenging experimental problem. And recently Li et al.[3] reported on the measurements of the DC diamagnetism in zero-field-cooled (ZFC) and field-cooled (FC) pressurized single crystal $La_2SmNi_2O_7$. From the analysis of experimental data, Li et al.[3] reported that the superconducting phase fraction in their $La_2SmNi_2O_7$ sample measured in the ZFC mode is 62.1%, and the superconducting phase fraction in the FC mode is 14.4%. It should be clarified that we regard the measurements of the DC diamagnetic state [3] in $La_2SmNi_2O_7$ (and more recently in $Pr_4Ni_3O_{10}$ [4]) as outstanding experimental results confirming bulk superconductivity in pressurized nickelates. However, we should note that Li et al.[3] made a threefold error in their calculations of the superconducting phase fraction in $La_2SmNi_2O_7$. We believe that correcting this and other errors in Ref.[3] will benefit the physics community.

            {
  "tldr": "The authors identify a threefold error in the reported superconducting phase fraction of La2SmNi2O7, correcting it from 62.1% to 22.8%. They argue that the calculation methods used in previous studies are fundamentally flawed.",
  "whats_new": [
    "• Corrects the superconducting phase fraction from 62.1% to 22.8%.",
    "• Highlights the inapplicability of field-cooled data for superconducting phase calculations.",
    "• Discusses the fundamental uncertainties in measuring superconducting phase volume."
  ],
  "method": "The authors analyze the magnetic moment of La2SmNi2O7 using equations related to the Meissner effect. They calculate the demagnetization factor for the sample and derive the superconducting phase fraction based on zero-field cooled data.",
  "results": [
    "• The corrected superconducting phase fraction is 22.8%.",
    "• The original reported value was 62.1%.",
    "• The demagnetization factor calculated is 0.81548, compared to 0.849 used by previous authors."
  ],
  "limitations": [
    "• The study relies on theoretical calculations that may not account for all experimental variables.",
    "• The authors do not provide experimental data to support their claims.",
    "• The implications of the corrected phase fraction on superconductivity are not fully explored."
  ],
  "why_useful": "This paper is significant as it addresses critical errors in the understanding of superconductivity in nickelates, which could impact future research and applications in superconducting materials. It encourages more rigorous methodologies in calculating superconducting phase fractions.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 17:11:26

Authors: Evangelia Christakopoulou, Vivekkumar Patel, Hemanth Velaga, Sandip Gaikwad

Categories: cs.IR, cs.AI, cs.LG

Abstract:
Large-scale commercial search systems optimize for relevance to drive successful sessions that help users find what they are looking for. To maximize relevance, we leverage two complementary objectives: behavioral relevance (results users tend to click or download) and textual relevance (a result's semantic fit to the query). A persistent challenge is the scarcity of expert-provided textual relevance labels relative to abundant behavioral relevance labels. We first address this by systematically evaluating LLM configurations, finding that a specialized, fine-tuned model significantly outperforms a much larger pre-trained one in providing highly relevant labels. Using this optimal model as a force multiplier, we generate millions of textual relevance labels to overcome the data scarcity. We show that augmenting our production ranker with these textual relevance labels leads to a significant outward shift of the Pareto frontier: offline NDCG improves for behavioral relevance while simultaneously increasing for textual relevance. These offline gains were validated by a worldwide A/B test on the App Store ranker, which demonstrated a statistically significant +0.24% increase in conversion rate, with the most substantial performance gains occurring in tail queries, where the new textual relevance labels provide a robust signal in the absence of reliable behavioral relevance labels.

            {
  "tldr": "This paper presents a method to enhance app store ranking by using LLM-generated textual relevance labels, addressing the scarcity of expert labels. The approach significantly improves both behavioral and textual relevance metrics.",
  "whats_new": [
    "• Introduces the 'LLM-as-a-Judge' paradigm for generating textual relevance labels.",
    "• Demonstrates that a fine-tuned LLM outperforms larger pre-trained models in label generation.",
    "• Achieves a +0.24% increase in conversion rate through A/B testing.",
    "• Validates improvements in tail queries where behavioral data is scarce."
  ],
  "method": "The authors fine-tune a specialized LLM on human-generated relevance labels to produce millions of new textual relevance labels. These labels are then integrated into a multi-objective ranker that optimizes for both behavioral and textual relevance.",
  "results": [
    "• Fine-tuned LLM (FT-3B) achieved precision of 0.802, recall of 0.798, and F1 score of 0.800.",
    "• The LLM-augmented model improved NDCG@1 by 0.001 and NDCG@3 by 0.002 compared to the production model.",
    "• A/B testing showed a statistically significant +0.24% increase in conversion rate.",
    "• Improvements were most notable in tail queries, where behavioral signals were weak."
  ],
  "limitations": [
    "• The study primarily focuses on textual relevance without exploring other potential objectives.",
    "• Future work is needed to fine-tune larger models and explore different prompting strategies.",
    "• The impact of LLM-generated labels on long-term user engagement remains untested."
  ],
  "why_useful": "This paper provides a scalable solution to the challenge of label scarcity in search relevance systems, particularly for app stores. The findings can be applied to enhance ranking models in various digital marketplaces, improving user experience and engagement.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 17:04:29

Authors: L. Schio, M. Alagia, T. Moitra, D. Toffoli, A. Ponzi, M. Stener, S. Coriani, P. Decleva, O. Rebrov, V. Zhaunerchyk, M. Larsson, S. Falcinelli, A. A. Dias, D. Catone, S. Turchini, N. Zema, F. Salvador, D. Benedetti, D. Vivoda, B. Botta, S. Stranges

Categories: physics.chem-ph

Abstract:
A peculiar electron correlation effect, leading to orbital rotation upon ionization, theoretically predicted long ago, was never experimentally characterized. The effect is expected to appear prominently in the photoionization of chiral molecules, due to the lack of symmetry constraints to wave-functions mixing. This is observed to have a profound effect on the photoelectron dynamics, as here demonstrated by investigating \b{eta} asymmetry parameters and partial cross-section observables in the Cl 3p Cooper minimum region of epichlorohydrin, a chiral prototype system. Angle-resolved photoelectron spectroscopy with tunable synchrotron radiation allowed measuring Cooper minimum $β$ oscillations, which were observed for solely two valence photoionization channels. The nature and number of channels exhibiting such dynamical behavior, along with the extent of the observed oscillation amplitudes, could not be accounted for by predictions based on Hartree-Fock (HF) and Density Functional Theory (DFT). These features could only be explained by incorporating correlation effects, which mix single-hole configurations of identical symmetry, in the characterization of the four lowest-lying molecular cation states, via equation-of-motion coupled cluster singles and doubles Dyson orbitals.

            {
  "tldr": "This study investigates orbital mixing during ionization in epichlorohydrin, revealing significant effects on photoelectron dynamics through Cooper minimum phenomena. Experimental and theoretical analyses demonstrate that traditional models fail to account for these correlation effects.",
  "whats_new": [
    "• First experimental characterization of orbital mixing upon ionization in chiral molecules.",
    "• Demonstrated profound effects on photoelectron dynamics linked to Cooper minimum.",
    "• Utilized advanced computational methods (EOM-CCSD and TDDFT) to explain observed phenomena.",
    "• Highlighted the inadequacy of traditional Hartree-Fock and DFT predictions in this context."
  ],
  "method": "The study employed angle-resolved photoelectron spectroscopy (ARPES) with synchrotron radiation to measure photoelectron dynamics in epichlorohydrin. Theoretical calculations were performed using equation-of-motion coupled cluster singles and doubles (EOM-CCSD) and time-dependent density functional theory (TDDFT) to analyze correlation effects.",
  "results": [
    "• Observed oscillations in the β asymmetry parameter for two valence photoionization channels.",
    "• Significant deviations from Hartree-Fock and DFT predictions, indicating the importance of correlation effects.",
    "• Theoretical models showed excellent agreement with experimental data, particularly in predicting Cooper minimum features.",
    "• The β parameter reached values of +1.35 for certain channels, indicating strong dynamical behavior."
  ],
  "limitations": [
    "• The study primarily focuses on a single chiral molecule, limiting generalizability.",
    "• Discrepancies in absolute values of cross sections suggest potential experimental or theoretical inaccuracies.",
    "• The complexity of the molecular system may obscure some effects, requiring further investigation."
  ],
  "why_useful": "This research enhances understanding of electron correlation effects in chiral molecules, which is crucial for interpreting photoelectron circular dichroism (PECD) and other phenomena. The findings have implications for future studies in molecular spectroscopy and could inform the design of experiments in related fields.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Data available from the authors upon reasonable request."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 16:55:22

Authors: Nima Motamed, Nina Otter, Emily Roff

Categories: cs.SI, cs.DM, cs.SC, math.CT

Abstract:
The algebraic analysis of social systems, or algebraic social network analysis, refers to a collection of methods designed to extract information about the structure of a social system represented as a directed graph. Central among these are methods to determine the roles that exist within a given system, and the positions. The analysis of roles and positions is highly developed for social systems that involve only pairwise interactions among actors - however, in contemporary social network analysis it is increasingly common to use models that can take into account higher-order interactions as well. In this paper we take a category-theoretic approach to the question of how to lift role and positional analysis from graphs to hypergraphs, which can accommodate higher-order interactions. We use the framework of universal coalgebra - a 'theory of systems' with origins in computer science and logic - to formalize the main concepts of role and positional analysis and extend them to a large class of structures that includes both graphs and hypergraphs. As evidence for the validity of our definitions, we prove a very general functoriality theorem that specializes, in the case of graphs, to a folkloric observation about the compatibility of positional and role analysis.

            {
  "tldr": "This paper extends traditional role and positional analysis from graphs to hypergraphs using a coalgebraic framework. It introduces new definitions and proves their compatibility, addressing higher-order interactions in social systems.",
  "whats_new": [
    "• Introduces a coalgebraic framework for analyzing social systems with higher-order interactions.",
    "• Defines roles and positions in hypergraphs and proves their compatibility with traditional analyses.",
    "• Establishes functoriality theorems for both positional and role analysis in the context of hypergraphs."
  ],
  "method": "The authors employ universal coalgebra to formalize role and positional analysis for social systems represented as hypergraphs. They define new concepts and prove their compatibility through functoriality theorems, extending traditional methods from graphs.",
  "results": [
    "• Established a functoriality theorem showing that every positional reduction induces a role reduction.",
    "• Defined semigroups of tight and loose roles for multirelational hypergraphs, each with distinct properties.",
    "• Demonstrated that positional and role analyses are compatible in hypergraphs, similar to traditional graphs."
  ],
  "limitations": [
    "• The framework is initially restricted to F-hypergraphs, limiting generalizability.",
    "• Future work is needed to address approximate notions of equivalence and non-associative compositions.",
    "• The practical application of the framework in real-world scenarios remains to be fully explored."
  ],
  "why_useful": "This paper provides a robust mathematical framework for analyzing complex social systems, enabling researchers to capture higher-order interactions. Its findings can enhance the understanding of social dynamics in various fields, including sociology and network science.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-02-26 16:40:32

Authors: Yassine Hamoudi, Yvan Le Borgne, Shrinidhi Teganahally Sridhara

Categories: quant-ph, cs.CC, cs.DS

Abstract:
We construct a probability distribution, induced by the Perron--Frobenius eigenvector of an exponentially large graph, which cannot be efficiently sampled by any classical algorithm, even when provided with the best-possible warm-start distribution. In the quantum setting, this problem can be viewed as preparing the ground state of a stoquastic Hamiltonian given a guiding state as input, and is known to be efficiently solvable on a quantum computer. Our result suggests that no efficient classical algorithm can solve a broad class of stoquastic ground-state problems. Our graph is constructed from a class of high-degree, high-girth spectral expanders to which self-similar trees are attached. This builds on and extends prior work of Gilyén, Hastings, and Vazirani [Quantum 2021, STOC 2021], which ruled out dequantization for a specific stoquastic adiabatic path algorithm. We strengthen their result by ruling out any classical algorithm for guided ground-state preparation.

arXiv Page | PDF

Published: 2026-02-26 15:27:49

Authors: David Dirnfeld, Fabien Delattre, Pedro Miraldo, Erik Learned-Miller

Categories: cs.CV, cs.CG, cs.RO

Abstract:
Estimating camera motion from monocular video is a fundamental problem in computer vision, central to tasks such as SLAM, visual odometry, and structure-from-motion. Existing methods that recover the camera's heading under known rotation, whether from an IMU or an optimization algorithm, tend to perform well in low-noise, low-outlier conditions, but often decrease in accuracy or become computationally expensive as noise and outlier levels increase. To address these limitations, we propose a novel generalization of the Hough transform on the unit sphere (S(2)) to estimate the camera's heading. First, the method extracts correspondences between two frames and generates a great circle of directions compatible with each pair of correspondences. Then, by discretizing the unit sphere using a Fibonacci lattice as bin centers, each great circle casts votes for a range of directions, ensuring that features unaffected by noise or dynamic objects vote consistently for the correct motion direction. Experimental results on three datasets demonstrate that the proposed method is on the Pareto frontier of accuracy versus efficiency. Additionally, experiments on SLAM show that the proposed method reduces RMSE by correcting the heading during camera pose initialization.

arXiv Page | PDF

Published: 2026-02-26 15:10:39

Authors: Matthew Sutton, Katrin Amunts, Timo Dickscheid, Christian Schiffer

Categories: cs.CV

Abstract:
Foundation models increasingly offer potential to support interactive, agentic workflows that assist researchers during analysis and interpretation of image data. Such workflows often require coupling vision to language to provide a natural-language interface. However, paired image-text data needed to learn this coupling are scarce and difficult to obtain in many research and clinical settings. One such setting is microscopic analysis of cell-body-stained histological human brain sections, which enables the study of cytoarchitecture: cell density and morphology and their laminar and areal organization. Here, we propose a label-mediated method that generates meaningful captions from images by linking images and text only through a label, without requiring curated paired image-text data. Given the label, we automatically mine area descriptions from related literature and use them as synthetic captions reflecting canonical cytoarchitectonic attributes. An existing cytoarchitectonic vision foundation model (CytoNet) is then coupled to a large language model via an image-to-text training objective, enabling microscopy regions to be described in natural language. Across 57 brain areas, the resulting method produces plausible area-level descriptions and supports open-set use through explicit rejection of unseen areas. It matches the cytoarchitectonic reference label for in-scope patches with 90.6% accuracy and, with the area label masked, its descriptions remain discriminative enough to recover the area in an 8-way test with 68.6% accuracy. These results suggest that weak, label-mediated pairing can suffice to connect existing biomedical vision foundation models to language, providing a practical recipe for integrating natural-language in domains where fine-grained paired annotations are scarce.

arXiv Page | PDF

Published: 2026-02-26 13:52:59

Authors: Mohamed Yaseen Noor, Ryan Siebenaller, Wenhao Liu, Zixin Zhai, Conrad Kuz, Simin Zhang, Mousumi Upadhyay Kahaly, Gergely Nagy, Aamir Mushtaq, Rahul Rao, Emmanuel Rowe, Benjamin S. Conner, Bing Lv, Michael A. Susner, Enam Chowdhury

Categories: cond-mat.mtrl-sci, physics.optics

Abstract:
Single-crystal X-ray diffraction and nonlinear optical measurements, especially second- and third-harmonic generation (SHG/THG) are comprehensively investigated for the van der Waals layered material AgScP2S6 with a non-centrosymmetric P31c (159) space group. Linear optical constants are extracted using spectroscopic ellipsometry and applied in fitting the harmonic generation behavior. Polarization-resolved SHG and THG measurements exhibit pronounced anisotropy, with emission patterns well-described by theoretical models derived from the khi(2) and khi(3) tensor elements. The material demonstrates exceptionally high nonlinear susceptibilities, with khi(2) ~ 10^(-8) m/V and khi(3) ~ 10^(-17) m^2/V^2 which is a few orders of magnitude greater than comparable 2D materials reported in the literature. Temperature-dependent SHG and THG measurements from 300 K to 25 K reveal exponential decay in harmonic signal intensities, attributed to reduced carrier mobility, with no evidence of structural phase transitions, consistent with results from single crystal diffraction and heat capacity measurements. Polarization-resolved SHG and THG measurements also reveal distinct orientation and ellipticity trends, highlighting the anisotropic nonlinear tensor contributions and contrasting polarization selection rules in the material. These results establish AgScP2S6 as a high-performance, thermally stable, and highly anisotropic nonlinear candidate material suitable for compact photonic applications such as ultrafast optical modulators, polarization-sensitive detectors, and wavelength-tunable light sources.

arXiv Page | PDF

Published: 2026-02-26 13:50:37

Authors: Fabian Grander, Tobias Gröfler, Franz Ferdinand Locker, Manuel Rinner, Alexander Kendl

Categories: physics.plasm-ph

Abstract:
Non-linear dynamics of zonal flows is investigated in the context of the gyrofluid modified Hasegawa-Wakatani model. Merging of zonal flows and the chaotic developement of the initial zonal flow pattern is explored. Conservation equations for zonal flow momentum and energy with consistent finite Larmor radius (FLR) effects are derived and used for a quantitative analysis of zonal flow mergers in numerical simulations. The nonlinear local Reynolds stress transfer as opposed to (hyper)viscous dissipation is found to be the main cause of merging. The applicability of the concept of a phase transition in the strict thermodynamical sense is discussed in context of zonal flow transition hysteresis.

arXiv Page | PDF

Published: 2026-02-26 13:50:28

Authors: Arsalan Jawaid, Abdullah Karatas, Jörg Seewig

Categories: stat.ML, cs.LG

Abstract:
Simulating a Gaussian process requires sampling from a high-dimensional Gaussian distribution, which scales cubically with the number of sample locations. Spectral methods address this challenge by exploiting the Fourier representation, treating the spectral density as a probability distribution for Monte Carlo approximation. Although this probabilistic interpretation works for stationary processes, it is overly restrictive for the nonstationary case, where spectral densities are generally not probability measures. We propose regular Fourier features for harmonizable processes that avoid this limitation. Our method discretizes the spectral representation directly, preserving the correlation structure among spectral weights without requiring probability assumptions. Under a finite spectral support assumption, this yields an efficient low-rank approximation that is positive semi-definite by construction. When the spectral density is unknown, the framework extends naturally to kernel learning from data. We demonstrate the method on locally stationary kernels and on harmonizable mixture kernels with complex-valued spectral densities.

arXiv Page | PDF

Published: 2026-02-26 13:49:16

Authors: Man Zhang, Tao Yue, Yihua He

Categories: cs.SE

Abstract:
Applying LLM-based multi-agent software systems in safety-critical domains such as lifespan echocardiography introduces system-level risks that cannot be addressed by improving model accuracy alone. During system operation, beyond individual LLM behavior, uncertainty propagates through agent coordination, data pipelines, human-in-the-loop interaction, and runtime control logic. Yet existing work largely treats uncertainty at the model level rather than as a first-class software engineering concern. This paper approaches uncertainty from both system-level and runtime perspectives. We first differentiate epistemological and ontological uncertainties in the context of LLM-based multi-agent software system operation. Building on this foundation, we propose a lifecycle-based uncertainty management framework comprising four mechanisms: representation, identification, evolution, and adaptation. The uncertainty lifecycle governs how uncertainties emerge, transform, and are mitigated across architectural layers and execution phases, enabling structured runtime governance and controlled adaptation. We demonstrate the feasibility of the framework using a real-world LLM-based multi-agent echocardiographic software system developed in clinical collaboration, showing improved reliability and diagnosability in diagnostic reasoning. The proposed approach generalizes to other safety-critical LLM-based multi-agent software systems, supporting principled operational control and runtime assurance beyond model-centric methods.

arXiv Page | PDF

Published: 2026-02-26 13:47:15

Authors: Filomena De Filippis

Categories: math.AP

Abstract:
We establish local boundedness for solutions to fractional porous medium-type equations in the fast diffusion regime, under optimal tail assumptions.

arXiv Page | PDF

Published: 2026-02-26 13:36:32

Authors: Hiroki Kanda, Tadashi Takayanagi, Zixia Wei

Categories: hep-th

Abstract:
In this paper, we discuss the entanglement phase transition of pseudo entropy in CFTs. We focus on the case where the in-state and the out-state are different boundary states related by boundary condition changing operators. We compute the pseudo entropy with BCFT methods and find a phase transition with respect to the conformal weight of the boundary condition changing operators. For holographic CFTs, we confirm that the CFT results match that evaluated in AdS.

arXiv Page | PDF

Published: 2026-02-26 13:36:18

Authors: Tianqi Song, Black Sun, Jingshu Li, Han Li, Chi-Lan Yang, Yijia Xu, Yi-Chieh Lee

Categories: cs.HC

Abstract:
AI-generated influencers are rapidly gaining popularity on Chinese short-video platforms, often adopting kinship-based roles such as AI grandchildren to attract older adults. Although this trend has raised public concern, little is known about the design strategies behind these influencers, how older adults experience them, and the benefits and risks involved. In this study, we combined social media analysis with interviews to unpack the above questions. Our findings show that influencers use both visual and conversational cues to enact kinship roles, prompting audiences to engage in kinship-based role-play. Interviews further show that these cues arouse emotional resonance, help fulfill older adults' informational and emotional needs, while also raising concerns about emotional displacement and unequal emotional investment. We highlight the complex relationship between virtual avatars and real family ties, shaped by broader sociocultural norms, and discuss how AI might strengthen social support for older adults while mitigating risks within cultural contexts.

arXiv Page | PDF

Published: 2026-02-26 13:35:26

Authors: Alexander Bonora, Anna V. Guglielmi, Davide Scazzoli, Marco Giordani, Maurizio Magarini, Vineeth Teeda, Stefano Tomasin

Categories: eess.SP

Abstract:
Beamforming in multiple-input multiple-output (MIMO) systems should take interference mitigation into account. However, for beamform design, accurate channel state information (CSI) is needed, which is often difficult to obtain due to channel variability, feedback overhead, or hardware constraints. For example, amplify-and-forward (AF) relays passively forward signals without measurement, precluding full CSI acquisition to and from the relay. To address these issues, this paper introduces a novel prediction-assisted optimization (PAO) framework for beamform design in AF relay-assisted multiuser MIMO systems. The proposed solution in the AF relay aims at maximizing the signal-plus-interference-to-noise ratio (SINR). Unlike other methods, PAO relies solely on received power measurements, making it suitable for scenarios where CSI is unreliable or unavailable. PAO consists of two stages: a supervised-learning-based neural network (NN) that predicts the positions of transmitters using signal observations, and an optimization algorithm, guided by a digital twin (DT), that iteratively refines the beam direction of the relay in a simulated radio environment. As a key contribution, we validate the proposed framework using realistic measurements collected on a custom-built experimental millimeter wave (mmWave) platform, which enables training of the NN model under practical wireless conditions. The estimated information is then used to update the digital twin with knowledge of the surrounding environment, enabling online optimization. Numerical results show the trade-off between localization accuracy and beamforming performance and confirm that PAO maintains robustness even in the presence of localization errors while reducing the need for real-world measurements.

arXiv Page | PDF

Published: 2026-02-26 13:26:38

Authors: Dmitri Bykov, Viacheslav Krivorol

Categories: hep-th, math-ph

Abstract:
We present a holomorphic quantization scheme for free point particles on two-dimensional constant curvature Riemannian backgrounds. The procedure is based on a Lagrangian embedding of the particle configuration space into a product of coadjoint orbits of the background isometry group. Examples are provided by particles on the plane, torus, sphere, and hyperbolic plane, with or without a monopole field. We elaborate the method by recovering the Hamiltonian spectrum and the wave functions on such spaces. As a by-product, we obtain a geometric and physical interpretation of Repka's result on the decomposition of tensor products of $\mathbf{SL}(2,\mathbb{R})$ discrete series representations.

arXiv Page | PDF

Published: 2026-02-26 13:25:35

Authors: Xun Huang, Simeng Qin, Xiaoshuang Jia, Ranjie Duan, Huanqian Yan, Zhitao Zeng, Fei Yang, Yang Liu, Xiaojun Jia

Categories: cs.AI, cs.CR

Abstract:
As Large Language Models (LLMs) are increasingly used, their security risks have drawn increasing attention. Existing research reveals that LLMs are highly susceptible to jailbreak attacks, with effectiveness varying across language contexts. This paper investigates the role of classical Chinese in jailbreak attacks. Owing to its conciseness and obscurity, classical Chinese can partially bypass existing safety constraints, exposing notable vulnerabilities in LLMs. Based on this observation, this paper proposes a framework, CC-BOS, for the automatic generation of classical Chinese adversarial prompts based on multi-dimensional fruit fly optimization, facilitating efficient and automated jailbreak attacks in black-box settings. Prompts are encoded into eight policy dimensions-covering role, behavior, mechanism, metaphor, expression, knowledge, trigger pattern and context; and iteratively refined via smell search, visual search, and cauchy mutation. This design enables efficient exploration of the search space, thereby enhancing the effectiveness of black-box jailbreak attacks. To enhance readability and evaluation accuracy, we further design a classical Chinese to English translation module. Extensive experiments demonstrate that effectiveness of the proposed CC-BOS, consistently outperforming state-of-the-art jailbreak attack methods.

arXiv Page | PDF

Published: 2026-02-26 13:19:19

Authors: M. L. Pereira Junior, M. G. E. da Luz, P. Cesana, A. L. da Rosa, M. J. Piotrowski, D. Guedes-Sobrinho, T. A. S. Pereira, E. A. Moujaes, A. C. Dias, R. M. Tromer

Categories: cond-mat.mtrl-sci

Abstract:
Adequate characterization of two-dimensional materials with low energy barriers for impurity adsorption is key for advancing applications based on catalysis, sensing, and surface functionalization. However, first-principles methods, such as DFT, are often computationally extremely expensive for feasible large-scale screenings. Given such a scenario, we address a data-driven approach which integrates the semi-empirical Extended Huckel Method with machine learning techniques to estimate adsorption energy barriers in the case of three relevant chalcogen impurities, S, Se and Te. With this aim, we consider the 4036 2D materials found in the C2DB. The scheme employs the EHM to compute energy profiles along three in-plane migration paths, from which average barriers can be derived. The equilibrium distance between the impurity and the 2D surface is not calculated from a tie-consuming geometry optimization. Instead, it is estimated from a simple effective phenomenological expression. Physicochemical descriptors are then obtained from the Matminer library for curated features. Four different ML models are tested, with the XGBoost leading to the highest performance. We further use SHAP to verify the resulting predictions, focusing on the $\sim1,500$ materials displaying the lowest barrier values. As it could be anticipated, we establish that the average valence electron count, electronegativity, and atomic number are typically the most relevant attributes to validate the ML model. But we also are able to determine, for the different chalcogen atoms, which other few descriptors likewise considerably influence the adsorption properties. Our results show that when combined with interpretable ML protocols, EHM can produce a scalable framework for choosing 2D structures that exhibit the desired capture/release dynamics pertinent in a variety of utilization.

arXiv Page | PDF

Published: 2026-02-26 13:19:06

Authors: Hanchun Wang, Ronojoy Adhikari, Michael E. Cates

Categories: cond-mat.soft, cond-mat.stat-mech, math.DG

Abstract:
We study the free energy and dynamics of a closed elastic filament (a one-dimensional curve in two dimensions) whose local internal state is specified by curvature, stretch, and a scalar density field representing, for example, the concentration of an absorbed species. The density variable has a tendency to phase-separate whereas the local spontaneous curvature is concentration-dependent. There is also a coupling between concentration and the stretching of the filament, although our main interest is in the nearly inextensible regime. We formulate and simulate the dynamics, comprising a coupled Willmore flow and Cahn-Hilliard gradient flow on the full differential geometry of a closed filament, addressing issues that previous work typically sidestepped by restricting to the Monge gauge. We use a numerical strategy for global free energy minimization, presenting the equilibrium shapes and density profiles across a wide range of model parameters. The phase diagram is dominated by a relatively small number of simple shapes that exhibit, as expected, strong coupling between local curvature and concentration. We also find regimes where curvature and/or stretching energies suppress phase separation altogether. For selected parameter values we present fully dynamical results, tracking the time evolution of the various contributions to the free energy. The dynamics often arrive at metastable minima rather than the equilibrium state - for example, at states with more than the minimum number of interfaces between coexisting phases. The metastability of these states is absent for phase separation on a rigid circular domain and thus a direct result of the coupling between geometry and density.

arXiv Page | PDF

Published: 2026-02-26 13:11:58

Authors: Dimitrios P. Panagoulias, Evangelia-Aikaterini Tsichrintzi, Georgios Savvidis, Evridiki Tsoureli-Nikita

Categories: cs.AI

Abstract:
Human-in-the-loop validation is essential in safety-critical clinical AI, yet the transition between initial model inference and expert correction is rarely analyzed as a structured signal. We introduce a diagnostic alignment framework in which the AI-generated image based report is preserved as an immutable inference state and systematically compared with the physician-validated outcome. The inference pipeline integrates a vision-enabled large language model, BERT- based medical entity extraction, and a Sequential Language Model Inference (SLMI) step to enforce domain-consistent refinement prior to expert review. Evaluation on 21 dermatological cases (21 complete AI physician pairs) em- ployed a four-level concordance framework comprising exact primary match rate (PMR), semantic similarity-adjusted rate (AMR), cross-category alignment, and Comprehensive Concordance Rate (CCR). Exact agreement reached 71.4% and remained unchanged under semantic similarity (t = 0.60), while structured cross-category and differential overlap analysis yielded 100% comprehensive concordance (95% CI: [83.9%, 100%]). No cases demonstrated complete diagnostic divergence. These findings show that binary lexical evaluation substantially un- derestimates clinically meaningful alignment. Modeling expert validation as a structured transformation enables signal-aware quantification of correction dynamics and supports traceable, human aligned evaluation of image based clinical decision support systems.

arXiv Page | PDF

Published: 2026-02-26 12:57:38

Authors: Yuejiang Yu, Langwen Huang, Alexandru Calotoiu, Torsten Hoefler

Categories: cs.LG

Abstract:
Data-driven models are revolutionizing weather forecasting. To optimize training efficiency and model performance, this paper analyzes empirical scaling laws within this domain. We investigate the relationship between model performance (validation loss) and three key factors: model size ($N$), dataset size ($D$), and compute budget ($C$). Across a range of models, we find that Aurora exhibits the strongest data-scaling behavior: increasing the training dataset by 10x reduces validation loss by up to 3.2x. GraphCast demonstrates the highest parameter efficiency, yet suffers from limited hardware utilization. Our compute-optimal analysis indicates that, under fixed compute budgets, allocating resources to longer training durations yields greater performance gains than increasing model size. Furthermore, we analyze model shape and uncover scaling behaviors that differ fundamentally from those observed in language models: weather forecasting models consistently favor increased width over depth. These findings suggest that future weather models should prioritize wider architectures and larger effective training datasets to maximize predictive performance.

arXiv Page | PDF

Published: 2026-02-26 12:50:32

Authors: Feng Guo, Jiaxiang Liu, Yang Li, Qianqian Shi, Mingkun Xu

Categories: cs.CV, cs.AI

Abstract:
Accurate brain tumor diagnosis requires models to not only detect lesions but also generate clinically interpretable reasoning grounded in imaging manifestations, yet existing public datasets remain limited in annotation richness and diagnostic semantics. To bridge this gap, we introduce MM-NeuroOnco, a large-scale multimodal benchmark and instruction-tuning dataset for brain tumor MRI understanding, consisting of 24,726 MRI slices from 20 data sources paired with approximately 200,000 semantically enriched multimodal instructions spanning diverse tumor subtypes and imaging modalities. To mitigate the scarcity and high cost of diagnostic semantic annotations, we develop a multi-model collaborative pipeline for automated medical information completion and quality control, enabling the generation of diagnosis-related semantics beyond mask-only annotations. Building upon this dataset, we further construct MM-NeuroOnco-Bench, a manually annotated evaluation benchmark with a rejection-aware setting to reduce biases inherent in closed-ended question formats. Evaluation across ten representative models shows that even the strongest baseline, Gemini 3 Flash, achieves only 41.88% accuracy on diagnosis-related questions, highlighting the substantial challenges of multimodal brain tumor diagnostic understanding. Leveraging MM-NeuroOnco, we further propose NeuroOnco-GPT, which achieves a 27% absolute accuracy improvement on diagnostic questions following fine-tuning. This result demonstrates the effectiveness of our dataset and benchmark in advancing clinically grounded multimodal diagnostic reasoning. Code and dataset are publicly available at: https://github.com/gfnnnb/MM-NeuroOnco

arXiv Page | PDF

Published: 2026-02-26 12:47:04

Authors: Yuan Tang, Bruno V. Adorno, Brendan A. McGrath, Andrew Weightman

Categories: cs.RO

Abstract:
Percutaneous dilatational tracheostomy (PDT) is frequently performed on patients in intensive care units for prolonged mechanical ventilation. The needle puncture, as the most critical step of PDT, could lead to adverse consequences such as major bleeding and posterior tracheal wall perforation if performed inaccurately. Current practices of PDT puncture are all performed manually with no navigation assistance, which leads to large position and angular errors (5 mm and 30 degree). To improve the accuracy and reduce the difficulty of the PDT procedure, we propose a system that automates the needle insertion using a velocity-controlled robotic manipulator. Guided using pose data from two electromagnetic sensors, one at the needle tip and the other inside the trachea, the robotic system uses an adaptive constrained controller to adapt the uncertain kinematic parameters online and avoid collisions with the patient's body and tissues near the target. Simulations were performed to validate the controller's implementation, and then four hundred PDT punctures were performed on a mannequin to evaluate the position and angular accuracy. The absolute median puncture position error was 1.7 mm (IQR: 1.9 mm) and midline deviation was 4.13 degree (IQR: 4.55 degree), measured by the sensor inside the trachea. The small deviations from the nominal puncture in a simulated experimental setup and formal guarantees of collision-free insertions suggest the feasibility of the robotic PDT puncture.

arXiv Page | PDF

Published: 2026-02-26 12:42:56

Authors: Raphael M. Tromer, Isaac M. Felix, Rafael Besse, Marcelo L. Pereira Junior, Marcos G. E. da Luz

Categories: cond-mat.mtrl-sci

Abstract:
In materials science, the selection of structural descriptors for machine learning protocols strongly influences predictive performance and the degree of physical interpretability that can be achieved from the derived models. Although more complex descriptors may improve numerical accuracy, they often represent extra computational load, also reducing transparency into the underlying structural information. A framework called the Dynamic Collision Fingerprint (DCF) was recently proposed with the goal of producing concise, physically significant representations, generating descriptors via dynamical probing of atomic structures. In this work, we benchmark DCF using a dataset composed of 120 two-dimensional carbon allotropes and compare its performance with the widely considered Matminer library. The analysis employs three regression models, linear regression, decision tree, and XGBoost, evaluated over train and test partitions ranging from 10\% to 90\% and repeated over multiple random seeds in order to characterize statistical variability. The obtained results demonstrate that DCF easily matches Matminer in terms of predicting accuracy across all learning algorithms. However, it accomplishes this using descriptors that are significantly lower dimensional, pointing to manageable computing costs. Moreover, compared to the rather technical Matminer descriptions, the DCF exhibits considerably clearer physical interpretability. These findings suggest that DCF is a significant substitute for high-dimensional descriptor libraries as structural representation since it is both computationally flexible and physically grounded.

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