Published: 2026-02-19 18:59:55
Authors: Gabriel Malavé, Rodrigo Soto-Garrido, Bitan Roy, Vladimir Juričić
Categories: cond-mat.str-el, cond-mat.mes-hall, hep-th
Abstract:
Owing to the power-law anisotropy in the quasiparticle dispersion, yielding an enhanced density of states, the effects of long range Coulomb interaction get amplified in three-dimensional generalized Weyl semimetals, characterized by integer monopole charge $n>1$ of the underlying Weyl nodes. Using a Wilsonian renormalization group approach controlled by a large-$N$ expansion with $N$ as the number of Weyl fermion flavors and a gauge-consistent regularization fixed by the Ward-Takahashi identity, we uncover for $n\ge 2$ an extended interaction-dominated scaling regime with intrinsically anisotropic dynamic Coulomb screening, a finite fermionic anomalous dimension, and a power-law suppression of the quasiparticle residue, yielding an \emph{anisotropic} marginal non-Fermi liquid at intermediate energies. Ultimately, the effective fine structure constant flows to zero, albeit only logarithmically slowly, so the marginal Fermi liquid phenomenology emerges as a broad crossover, controlled by a slowly running coupling. By contrast, for $n=1$ the system retains an isotropic marginal Weyl-liquid character. These predictions can be tested via scaling in thermodynamics (specific heat and compressibility), direction-dependent optical conductivity, and by anisotropic broadening of the single-particle spectral function in angle-resolved photoemission spectroscopy.
Published: 2026-02-19 18:56:36
Authors: Jiaqi Xi, Raghav Saboo, Luming Chen, Martin Wang, Sudeep Das
Categories: cs.IR, cs.LG
Abstract:
We propose a two-stage "Mine and Refine" contrastive training framework for semantic text embeddings to enhance multi-category e-commerce search retrieval. Large scale e-commerce search demands embeddings that generalize to long tail, noisy queries while adhering to scalable supervision compatible with product and policy constraints. A practical challenge is that relevance is often graded: users accept substitutes or complements beyond exact matches, and production systems benefit from clear separation of similarity scores across these relevance strata for stable hybrid blending and thresholding. To obtain scalable policy consistent supervision, we fine-tune a lightweight LLM on human annotations under a three-level relevance guideline and further reduce residual noise via engagement driven auditing. In Stage 1, we train a multilingual Siamese two-tower retriever with a label aware supervised contrastive objective that shapes a robust global semantic space. In Stage 2, we mine hard samples via ANN and re-annotate them with the policy aligned LLM, and introduce a multi-class extension of circle loss that explicitly sharpens similarity boundaries between relevance levels, to further refine and enrich the embedding space. Robustness is additionally improved through additive spelling augmentation and synthetic query generation. Extensive offline evaluations and production A/B tests show that our framework improves retrieval relevance and delivers statistically significant gains in engagement and business impact.
Published: 2026-02-19 18:54:06
Authors: Dhruv Talwar, Harsh Desai, Wendong Yin, Goutam Mohanty, Rafael Reveles
Categories: cs.LG
Abstract:
Traditional electronic recycling processes suffer from significant resource loss due to inadequate material separation and identification capabilities, limiting material recovery. We present A.R.I.S. (Automated Recycling Identification System), a low-cost, portable sorter for shredded e-waste that addresses this efficiency gap. The system employs a YOLOx model to classify metals, plastics, and circuit boards in real time, achieving low inference latency with high detection accuracy. Experimental evaluation yielded 90% overall precision, 82.2% mean average precision (mAP), and 84% sortation purity. By integrating deep learning with established sorting methods, A.R.I.S. enhances material recovery efficiency and lowers barriers to advanced recycling adoption. This work complements broader initiatives in extending product life cycles, supporting trade-in and recycling programs, and reducing environmental impact across the supply chain.
Published: 2026-02-19 18:43:23
Authors: Dilawer Singh, Antoni J. Wojcik, Timothy D. Wilkinson
Categories: physics.optics
Abstract:
Phase-only computer-generated holography (CGH) seeks a phase pattern for a spatial light modulator (SLM) whose propagated optical field reproduces a desired intensity distribution. In the far-field (Fraunhofer) regime, optical propagation reduces to a Fourier transform, such that each hologram pixel contributes to the entire reconstructed intensity distribution. When restricted to phase-only modulation, intensity must be shaped through global phase interference effects, making the inverse mapping from target intensity to phase highly non-linear and sensitive to local minima. We present a proof-of-concept physics-in-the-loop approach in which a transformer maps a target intensity image to a phase-only SLM field and is trained end-to-end through exact FFT-based propagation embedded directly within optimization. We further observe that patch tokenization strongly shapes the optimization geometry: coarse tokenization acts as an implicit spectral regularizer that stabilizes training and suppresses checkerboard-like attractors, while finer tokenization increases spatial degrees of freedom but benefits from curriculum or hierarchical refinement. Despite training on limited primitives and restricted digit subsets, the learned generator exhibits out-of-distribution (OOD) generalization to unseen digits and hand-drawn target patterns. These results suggest that transformer architectures, whose self-attention enables global token interactions, are a natural fit for far-field holography and provide a viable foundation for scalable physics-grounded hologram generation.
Published: 2026-02-19 14:43:48
Authors: René Brinkhege, Prahlad Menon
Categories: cs.CR, cs.CL
Abstract:
In current inter-organizational data spaces, usage policies are enforced mainly at the asset level: a whole document or dataset is either shared or withheld. When only parts of a document are sensitive, providers who want to avoid leaking protected information typically must manually redact documents before sharing them, which is costly, coarse-grained, and hard to maintain as policies or partners change. We present DAVE, a usage policy-enforcing LLM spokesperson that answers questions over private documents on behalf of a data provider. Instead of releasing documents, the provider exposes a natural language interface whose responses are constrained by machine-readable usage policies. We formalize policy-violating information disclosure in this setting, drawing on usage control and information flow security, and introduce virtual redaction: suppressing sensitive information at query time without modifying source documents. We describe an architecture for integrating such a spokesperson with Eclipse Dataspace Components and ODRL-style policies, and outline an initial provider-side integration prototype in which QA requests are routed through a spokesperson service instead of triggering raw document transfer. Our contribution is primarily architectural: we do not yet implement or empirically evaluate the full enforcement pipeline. We therefore outline an evaluation methodology to assess security, utility, and performance trade-offs under benign and adversarial querying as a basis for future empirical work on systematically governed LLM access to multi-party data spaces.
Published: 2026-02-19 14:34:04
Authors: Glen Hjelmerud Mørkbak Sørensen, Torleiv H. Bryne, Kristoffer Gryte, Tor Arne Johansen
Categories: eess.SY, cs.RO
Abstract:
Phased-array Bluetooth systems have emerged as a low-cost alternative for performing aided inertial navigation in GNSS-denied use cases such as warehouse logistics, drone landings, and autonomous docking. Basing a navigation system off of commercial-off-the-shelf components may reduce the barrier of entry for phased-array radio navigation systems, albeit at the cost of significantly noisier measurements and relatively short feasible range. In this paper, we compare robust estimation strategies for a factor graph optimisation-based estimator using experimental data collected from multirotor drone flight. We evaluate performance in loss-of-GNSS scenarios when aided by Bluetooth angular measurements, as well as range or barometric pressure.
Published: 2026-02-19 13:54:35
Authors: Yuchang Jiang, Anton Raichuk, Xiaoye Tong, Vivien Sainte Fare Garnot, Daniel Ortiz-Gonzalo, Dan Morris, Konrad Schindler, Jan Dirk Wegner, Maxim Neumann
Categories: cs.CV
Abstract:
Monitoring tree crop expansion is vital for zero-deforestation policies like the European Union's Regulation on Deforestation-free Products (EUDR). However, these efforts are hindered by a lack of highresolution data distinguishing diverse agricultural systems from forests. Here, we present the first 10m-resolution tree crop map for South America, generated using a multi-modal, spatio-temporal deep learning model trained on Sentinel-1 and Sentinel-2 satellite imagery time series. The map identifies approximately 11 million hectares of tree crops, 23% of which is linked to 2000-2020 forest cover loss. Critically, our analysis reveals that existing regulatory maps supporting the EUDR often classify established agriculture, particularly smallholder agroforestry, as "forest". This discrepancy risks false deforestation alerts and unfair penalties for small-scale farmers. Our work mitigates this risk by providing a high-resolution baseline, supporting conservation policies that are effective, inclusive, and equitable.
Published: 2026-02-19 10:19:44
Authors: Quan Shi, Yang Wang, Huaiqing Zuo
Categories: math.AG
Abstract:
Spectrum is an important numerical invariant of an isolated hypersurface singularity, connecting its topological and analytic structures. The well-known Hertling conjecture tells the relation of range and variance of exponents i.e. elements of spectrum. For trimodal singularities, we compute their spectra and verify Hertling conjecture for them.
Jung, Kim, Saito and Yoon recently defined Tjurina spectrum, stemming from Hodge ideals. This set of numerical invariants is a subset of spectrum in Steenbrink's sense. We give an estimation of exponents not in Tjurina spectrum and propose a similar Generalized Hertling Conjecture for Tjurina Spectrum. Moreover, we prove the conjecture for singularities of modality $\leq 3$.
Published: 2026-02-18 22:44:43
Authors: Farnaz Zamiri Zeraati, Yang Trista Cao, Yuehan Qiao, Hal Daumé, Hernisa Kacorri
Categories: cs.HC, cs.AI
Abstract:
Prompting and steering techniques are well established in general-purpose generative AI, yet assistive visual question answering (VQA) tools for blind users still follow rigid interaction patterns with limited opportunities for customization. User control can be helpful when system responses are misaligned with their goals and contexts, a gap that becomes especially consequential for blind users that may rely on these systems for access. We invite 11 blind users to customize their interactions with a real-world conversational VQA system. Drawing on 418 interactions, reflections, and post-study interviews, we analyze prompting-based techniques participants adopted, including those introduced in the study and those developed independently in real-world settings. VQA interactions were often lengthy: participants averaged 3 turns, sometimes up to 21, with input text typically tenfold shorter than the responses they heard. Built on state-of-the-art LLMs, the system lacked verbosity controls, was limited in estimating distance in space and time, relied on inaccessible image framing, and offered little to no camera guidance. We discuss how customization techniques such as prompt engineering can help participants work around these limitations. Alongside a new publicly available dataset, we offer insights for interaction design at both query and system levels.
Published: 2026-02-18 22:03:59
Authors: Kiana Farhadyar, Maren Hackenberg, Kira Ahrens, Charlotte Schenk, Bianca Kollmann, Oliver Tüscher, Klaus Lieb, Michael M. Plichta, Andreas Reif, Raffael Kalisch, Martin Wolkewitz, Moritz Hess, Harald Binder
Categories: stat.ME, cs.LG, stat.ML
Abstract:
Modeling of longitudinal cohort data typically involves complex temporal dependencies between multiple variables. There, the transformer architecture, which has been highly successful in language and vision applications, allows us to account for the fact that the most recently observed time points in an individual's history may not always be the most important for the immediate future. This is achieved by assigning attention weights to observations of an individual based on a transformation of their values. One reason why these ideas have not yet been fully leveraged for longitudinal cohort data is that typically, large datasets are required. Therefore, we present a simplified transformer architecture that retains the core attention mechanism while reducing the number of parameters to be estimated, to be more suitable for small datasets with few time points. Guided by a statistical perspective on transformers, we use an autoregressive model as a starting point and incorporate attention as a kernel-based operation with temporal decay, where aggregation of multiple transformer heads, i.e. different candidate weighting schemes, is expressed as accumulating evidence on different types of underlying characteristics of individuals. This also enables a permutation-based statistical testing procedure for identifying contextual patterns. In a simulation study, the approach is shown to recover contextual dependencies even with a small number of individuals and time points. In an application to data from a resilience study, we identify temporal patterns in the dynamics of stress and mental health. This indicates that properly adapted transformers can not only achieve competitive predictive performance, but also uncover complex context dependencies in small data settings.
Published: 2026-02-18 21:46:41
Authors: Hideyasu Yamashita
Categories: gr-qc, hep-th, math-ph, quant-ph
Abstract:
This article is a sequel to our previous paper (arXiv:2511.12311), where we considered the conceptual problem on the empirical laws for the Klein\textendash Gordon quantum field theory in curved spacetime (QFTCS), and we will consider the similar problems for the Majorana field on curved spacetime here. A ``law'' in theoretical physics is said to be observable or empirical only if it can be verified/falsified by some experimental procedure. The notion of empiricality/observability becomes far more unclear in QFTCS, than in QFT in Minkowski (flat) spacetime (QFTM), mainly because QFTCS lacks the notion of vacuum. This could potentially undermine the status of QFTCS as a physical (not only mathematical) theory. We consider this problem for the Majorana field in curved spacetime, and examine some examples of the empirical laws.
Published: 2026-02-18 21:33:59
Authors: Juliusz Ziomek, William Bankes, Lorenz Wolf, Shyam Sundhar Ramesh, Xiaohang Tang, Ilija Bogunovic
Categories: cs.AI, cs.LG
Abstract:
We introduce LLM-Wikirace, a benchmark for evaluating planning, reasoning, and world knowledge in large language models (LLMs). In LLM-Wikirace, models must efficiently navigate Wikipedia hyperlinks step by step to reach a target page from a given source, requiring look-ahead planning and the ability to reason about how concepts are connected in the real world. We evaluate a broad set of open- and closed-source models, including Gemini-3, GPT-5, and Claude Opus 4.5, which achieve the strongest results on the easy level of the task and demonstrate superhuman performance. Despite this, performance drops sharply on hard difficulty: the best-performing model, Gemini-3, succeeds in only 23\% of hard games, highlighting substantial remaining challenges for frontier models. Our analysis shows that world knowledge is a necessary ingredient for success, but only up to a point, beyond this threshold, planning and long-horizon reasoning capabilities become the dominant factors. Trajectory-level analysis further reveals that even the strongest models struggle to replan after failure, frequently entering loops rather than recovering. LLM-Wikirace is a simple benchmark that reveals clear limitations in current reasoning systems, offering an open arena where planning-capable LLMs still have much to prove. Our code and leaderboard available at https:/llmwikirace.github.io.
Published: 2026-02-18 21:11:12
Authors: Artem Dudko, Constantine Medynets
Categories: math.GR
Abstract:
Let $(X,T)$ be a Cantor minimal system, and let $Γ$ denote either its associated topological full group or the full group of a Bratteli diagram associated with $(X,T)$. In this paper we describe the structure of indecomposable (extreme) characters and the associated $\textrm{II}_1$-factor representations for the group $Γ$ and its commutator subgroup $Γ'$. In particular, we prove that: (1) for every nontrivial indecomposable character $χ$ of $Γ'$, there exists a finite collection (with repetitions allowed) $\{μ_i\}_{i\in I}$ of $T$-invariant ergodic measures on $X$ such that $χ(γ) = \prod_{i\in I} μ_i(Fix(γ))$, for every $γ\in Γ'$, where $Fix(γ) = \{x\in X : γx = x\}$; and (2) each indecomposable character of $Γ$ is the product of an indecomposable character of the form $\prod_{i\in I} μ_i(Fix(γ))$ and a homomorphism from $Γ$ into the unit circle.
As a consequence, we show that any finite-type unitary representation of $Γ'$ that does not contain a regular subrepresentation is automatically continuous with respect to the uniform topology on $Γ'$.
We also establish a general result on automatic continuity of finite-type unitary representations of infinite groups, which we use in our proofs.
Published: 2026-02-18 21:11:09
Authors: M. Cufari, M. Gatu Johnson, C. K. Li, J. A. Frenje, P. W. Moloney, A. J. Crilly, P. V. Heuer, J. R. Davies
Categories: physics.plasm-ph
Abstract:
Hard x-ray emission, associated with hot electron preheat, in direct-drive implosions was observed to be enhanced by a factor of $1.5\pm0.1$ by application of a $10$ T magnetic field. The applied magnetic field reaches a quasi steady-state aligned with the ablation flow prior to the onset of laser-plasma instabilities in the corona. Hot electrons that would otherwise escape the corona and lead to capsule charging in unmagnetized implosions are confined in a mirror-mode of the magnetic field in magnetized implosions. These hot electrons are shown to subsequently pitch-angle scatter from the mirror onto the capsule, thereby leading to the observed hard x-ray generation in magnetized implosions. Consequently, the energy of charged-fusion products, associated with the capsule charging, are observed to decrease when the implosion is magnetized. These results intensify the need to mitigate laser-plasma instabilities -- particularly for magnetized implosions -- to maximize fusion gain and implosion efficiency.
Published: 2026-02-18 21:07:00
Authors: Bekir Can Lütfüoğlu
Categories: gr-qc
Abstract:
We investigate particle motion in regular and asymptotically flat black hole spacetimes supported by Dehnen-type dark-matter halos. Two analytic models are analyzed, allowing a systematic study of circular geodesics, photon-sphere properties, shadow radius, Lyapunov exponent, ISCO frequency, binding energy, and Hawking temperature. The corrected numerical results show that the halo scale parameter can significantly modify strong-field observables. In both models, for moderate density slopes, increasing the halo parameter reduces characteristic radii while enhancing orbital instability and accretion efficiency. For steeper density falloff, however, deviations from the Schwarzschild case remain small. These results demonstrate that halo-induced modifications of optical and dynamical black hole signatures are strongly controlled by the density profile parameters.
Published: 2026-02-18 21:05:05
Authors: Eduardo Martínez-Pedroza, Diana Vizcaíno Torres
Categories: math.GR
Abstract:
We address a question from \cite{BKV25} regarding the finiteness of the homological $R$-isoperimetric function. Let $R$ be a subfield of the complex numbers $\mathbb{C}$ with the absolute value norm. We prove that for any group $G$ that admits a finite $(n+1)$-dimensional model for $K(G,1)$, the homological $n$-isoperimetric function of $G$ over $R$ is either linear or takes infinite values. In particular, by results of Gersten and Mineyev, in the class of groups admitting a finite $2$-dimensional classifying space, the homological $1$-dimensional isoperimetric function over $R$ only captures hyperbolicity. This follows as a particular case of a more general result proved in this note.
Published: 2026-02-18 21:01:05
Authors: Yaroslav Solovko
Categories: cs.LG, stat.ML
Abstract:
Modern datasets often contain ballast as redundant or low-utility information that increases dimensionality, storage requirements, and computational cost without contributing meaningful analytical value. This study introduces a generalized, multimodal framework for ballast detection and reduction across structured, semi-structured, unstructured, and sparse data types. Using diverse datasets, entropy, mutual information, Lasso, SHAP, PCA, topic modelling, and embedding analysis are applied to identify and eliminate ballast features. A novel Ballast Score is proposed to integrate these signals into a unified, cross-modal pruning strategy. Experimental results demonstrate that significant portions of the feature space as often exceeding 70% in sparse or semi-structured data, can be pruned with minimal or even improved classification performance, along with substantial reductions in training time and memory footprint. The framework reveals distinct ballast typologies (e.g. statistical, semantic, infrastructural), and offers practical guidance for leaner, more efficient machine learning pipelines.
Published: 2026-02-18 21:00:05
Authors: Geunbin Yu
Categories: cs.MA, cs.AI
Abstract:
As large language models from diverse providers converge toward comparable benchmark performance, the traditional paradigm of selecting a single best model per task yields diminishing returns. We argue that orchestration topology -- the structural composition of how multiple agents are coordinated, parallelized, and synthesized -- now dominates system-level performance over individual model capability. We present AdaptOrch, a formal framework for task-adaptive multi-agent orchestration that dynamically selects among four canonical topologies (parallel, sequential, hierarchical, and hybrid) based on task dependency graphs and empirically derived domain characteristics. Our framework introduces three key contributions: (1) a Performance Convergence Scaling Law, formalizing conditions under which orchestration selection outweighs model selection; (2) a Topology Routing Algorithm that maps task decomposition DAGs to optimal orchestration patterns in O(|V| + |E|) time; and (3) an Adaptive Synthesis Protocol with provable termination guarantees and heuristic consistency scoring for parallel agent outputs. We validate AdaptOrch across coding (SWE-bench), reasoning (GPQA), and retrieval-augmented generation tasks, demonstrating that topology-aware orchestration achieves 12-23% improvement over static single-topology baselines, even when using identical underlying models. Our results establish orchestration design as a first-class optimization target independent of model scaling.
Published: 2026-02-18 20:42:32
Authors: Andy Au
Categories: math.OC, q-fin.PM
Abstract:
We study entropy-regularized mean-variance portfolio optimization under Bayesian drift uncertainty. Gaussian policies remain optimal under partial information, the value function is quadratic in wealth, and belief-dependent coefficients admit closed-form solutions. The mean control is identical to deterministic Bayesian Markowitz feedback; entropy regularization affects only the policy variance. Additionally, this variance does not affect information gain, and instead provides belief-dependent robustness. Notably, optimal policy variance increases with posterior conviction $|m_t|$, forcing greater action randomization when mean position is most aggressive.
Published: 2026-02-18 20:42:03
Authors: EunJeong Cheon, Do Yeon Shin
Categories: cs.RO, cs.HC
Abstract:
As the presence of autonomous robots in public spaces increases-whether navigating campus walkways or neighborhood sidewalks-understanding how to carefully study these robots becomes critical. While HRI research has conducted field studies in public spaces, these are often limited to controlled experiments with prototype robots or structured observational methods, such as the Wizard of Oz technique. However, the autonomous mobile robots we encounter today, particularly delivery robots, operate beyond the control of researchers, navigating dynamic routes and unpredictable environments. To address this challenge, a more deliberate approach is required. Drawing inspiration from public realm ethnography in urban studies, geography, and sociology, this paper proposes the Walk-Along with Robots (WawR) methodology. We outline the key features of this method, the steps we applied in our study, the unique insights it offers, and the ways it can be evaluated. We hope this paper stimulates further discussion on research methodologies for studying autonomous robots in public spaces.
Published: 2026-02-18 20:38:51
Authors: Adam Reddy, Asma Karami, Hussein Nassar
Categories: math.DG, cond-mat.soft
Abstract:
Plates generally admit six deformation modes: three of which are high in strain energy, stretch the plate's midsurface and are called membrane modes; and three are low-energy, bend the midsurface without stretching it and are called bending modes. For origami tessellations, and other corrugated compliant thin shells, the modes are mixed and it is no longer clear what modes, if any, are low in energy in the sense that they are inextensional. Here, it is shown, by direct construction of closed-form solutions, that when the midsurface is a surface of translation, there exists three infinitesimally inextensional deformation modes that correspond to (1) stretching, with an effective Poisson's effect; (2) bending, with an effective synclastic or anti-clastic effect; and to (3) twisting. The provided expressions are valid irrespective of surface regularity and, in particular, properly handle any creases be them straight or curved. The results provide a powerful benchmark for the validation of numerical methods and further insight into the elastic stiffness of thin corrugated compliant shells.
Published: 2026-02-18 20:34:24
Authors: Boda Lin, Yongjie Zhu, Wenyu Qin, Meng Wang, Pengfei Wan
Categories: cs.CV
Abstract:
Video Quality Assessment (VQA) is evolving beyond single-number mean opinion score toward richer, multi-faceted evaluations of video content. In this paper, we present a large-scale multi-dimensional VQA dataset UltraVQA that encompasses diverse User-Generated Content~(UGC) annotated across five key quality dimensions: Motion Quality, Motion Amplitude, Aesthetic Quality, Content Quality, and Clarity Quality. Each video in our dataset is scored by over 3 human raters on these dimensions, with fine-grained sub-attribute labels, and accompanied by an explanatory rationale generated by GPT based on the collective human judgments. To better leverage these rich annotations and improve discrete quality score assessment, we introduce Analytic Score Optimization (ASO), a theoretically grounded post-training objective derived for multi-dimensional VQA. By reframing quality assessment as a regularized decision-making process, we obtain a closed-form solution that naturally captures the ordinal nature of human ratings, ensuring alignment with human ranking preferences. In experiments, our method outperforms most baselines including closed-source APIs and open-source models, while also reducing mean absolute error (MAE) in quality prediction. Our work highlights the importance of multi-dimensional, interpretable annotations and reinforcement-based alignment in advancing video quality assessment.
Published: 2026-02-18 20:32:37
Authors: Derek Levinson, Nam Trang, Trevor Wilson
Categories: math.LO
Abstract:
This paper makes significant progress towards resolving a conjecture relating strong forcing axioms like $PFA$ and the derived model at a limit of Woodin cardinals $κ$. In particular, using a concept called Covering Matrices, we show that the $Θ$ of the derived model at $κ$ is strictly less than $κ^+$ under various circumstances; in particular, this shows that the conclusion holds under $PFA$ if $κ$ is a limit of Woodin cardinals of cofinality $ω$ and the derived model does not satisfy $LSA$. Assuming a form of mouse capturing, we show that the derived model satisfies $AD_{\mathbb{R}}$ under $PFA$ when $κ$ is a regular limit of Woodin cardinals. If $κ$ is an indestructibly $(κ,κ^+)$-weakly compact limit of Woodin cardinals, then the derived model outright satisfies $AD_{\mathbb{R}}$.
Published: 2026-02-18 20:13:07
Authors: Ahmed Rafid, Rumman Adib, Fariya Ahmed, Ajwad Abrar, Mohammed Saidul Islam
Categories: cs.CL
Abstract:
Evaluating factual consistency is essential for reliable text summarization, particularly in high-stakes domains such as healthcare and news. However, most existing evaluation metrics overlook Bangla, a widely spoken yet under-resourced language, and often depend on reference summaries. We introduce BanglaSummEval, a reference-free, question-answering-based framework for evaluating factual consistency in Bangla summarization. The proposed method assesses both factual accuracy and content coverage through automatically generated questions and answers derived from the source document and the summary. A single multilingual instruction-tuned language model handles question generation, question answering, candidate answer extraction, and question importance weighting. This unified design reduces system complexity and computational cost. To capture semantic consistency beyond surface-level overlap, we use BERTScore-Recall for answer comparison. We validate BanglaSummEval on 300 human-written summaries from educational and medical domains, demonstrating strong correlation with expert human judgments (Pearson's $r = 0.694$, Spearman's $ρ= 0.763$). By providing interpretable, step-wise diagnostics alongside reliable evaluation scores, BanglaSummEval offers a practical and transparent solution for factual consistency evaluation in low-resource language settings.
Published: 2026-02-18 20:03:38
Authors: Zeliang Zhang, Xiaodong Liu, Hao Cheng, Hao Sun, Chenliang Xu, Jianfeng Gao
Categories: cs.LG, cs.CL
Abstract:
Large reasoning models (LRMs) excel on complex problems but face a critical barrier to efficiency: reinforcement learning (RL) training requires long rollouts for outcome-based rewards, where autoregressive decoding dominates time and memory usage. While sliding-window cache strategies can bound memory, they disrupt long-context reasoning and degrade performance. We introduce Progressive Thought Encoding, a parameter-efficient fine-tuning method that enables LRMs to reason effectively under fixed-size caches. By progressively encoding intermediate reasoning into fixed-size vector representations, our approach eliminates the need to backpropagate through full-cache rollouts, thereby reducing memory usage, while maintaining constant memory during inference. Experiments on three models, including Qwen2.5-3B-Instruct, Qwen2.5-7B-Instruct, and DeepSeek-R1-Distill-Llama-8B, on six widely used challenging mathematical benchmarks show consistent gains: our method achieves +19.3% improvement over LoRA-based fine-tuning and +29.9% over LRMs without fine-tuning on average, with up to +23.4 accuracy improvement on AIME2024/2025 under the same tight cache budgets. These results demonstrate that Progressive Thought Encoding not only improves reasoning accuracy but also makes RL training of LRMs substantially more efficient and scalable under real-world memory constraints.
Published: 2026-02-18 19:52:20
Authors: Omer Burak Iskender, Keck Voon Ling, Mengu Cho, Sangkyun Kim, Necmi Cihan Orger
Categories: eess.SY, astro-ph.EP, astro-ph.IM
Abstract:
This paper presents a low-thrust trajectory optimization strategy to achieve a near-circular lunar orbit for a CubeSat injected into a lunar flyby trajectory. The 12U CubeSat HORYU-VI is equipped with four Hall-effect thrusters and designed as a secondary payload on NASA's Space Launch System under the Artemis program. Upon release, the spacecraft gains sufficient energy to escape the Earth-Moon system after a lunar flyby. The proposed trajectory is decomposed into three phases: (1) pre-flyby deceleration to avoid heliocentric escape, (2) lunar gravitational capture, and (3) orbit circularization to the science orbit. For each phase, an impulsive-burn solution is first computed as an initial guess, which is then refined through finite-burn optimization using Sequential Quadratic Programming (SQP). The dynamical model incorporates Earth-Moon-Sun-Jupiter gravitational interactions and a high-fidelity lunar gravity field. All trajectories are independently verified with NASA's General Mission Analysis Tool (GMAT). Results demonstrate that HORYU-VI achieves lunar capture within 200 days, establishes a stable science orbit at 280 days, and can spiral down to a near-circular 100 km orbit by 450 days, using a total Delta-V of 710 m/s, well within the capability of the electric propulsion system.
Published: 2026-02-18 19:50:56
Authors: Elan Schonfeld, Elias Wisnia
Categories: cs.LG, cs.AI, cs.NE
Abstract:
When feedback is absorbed faster than task structure can be evaluated, the learner will favor feedback over truth. A two-timescale model shows this feedback-truth gap is inevitable whenever the two rates differ and vanishes only when they match. We test this prediction across neural networks trained with noisy labels (30 datasets, 2,700 runs), human probabilistic reversal learning (N = 292), and human reward/punishment learning with concurrent EEG (N = 25). In each system, truth is defined operationally: held-out labels, the objectively correct option, or the participant's pre-feedback expectation - the only non-circular reference decodable from post-feedback EEG. The gap appeared universally but was regulated differently: dense networks accumulated it as memorization; sparse-residual scaffolding suppressed it; humans generated transient over-commitment that was actively recovered. Neural over-commitment (~0.04-0.10) was amplified tenfold into behavioral commitment (d = 3.3-3.9). The gap is a fundamental constraint on learning under noisy supervision; its consequences depend on the regulation each system employs.
Published: 2026-02-18 19:30:55
Authors: Yiqing Xie, Emmy Liu, Gaokai Zhang, Nachiket Kotalwar, Shubham Gandhi, Sathwik Acharya, Xingyao Wang, Carolyn Rose, Graham Neubig, Daniel Fried
Categories: cs.SE, cs.CL, cs.LG
Abstract:
When assessing the quality of coding agents, predominant benchmarks focus on solving single issues on GitHub, such as SWE-Bench. In contrast, in real use, these agents solve more various and complex tasks that involve other skills such as exploring codebases, testing software, and designing architecture. In this paper, we first characterize some transferable skills that are shared across diverse tasks by decomposing trajectories into fine-grained components, and derive a set of principles for designing auxiliary training tasks to teach language models these skills. Guided by these principles, we propose a training environment, Hybrid-Gym, consisting of a set of scalable synthetic tasks, such as function localization and dependency search. Experiments show that agents trained on our synthetic tasks effectively generalize to diverse real-world tasks that are not present in training, improving a base model by 25.4% absolute gain on SWE-Bench Verified, 7.9% on SWT-Bench Verified, and 5.1% on Commit-0 Lite. Hybrid-Gym also complements datasets built for the downstream tasks (e.g., improving SWE-Play by 4.9% on SWT-Bench Verified). Code available at: https://github.com/yiqingxyq/Hybrid-Gym.
Published: 2026-02-18 19:28:44
Authors: The HAWC Collaboration, R. Alfaro, C. Alvarez, A. Andrés, E. Anita-Rangel, M. Araya, J. C. Arteaga-Velázquez, D. Avila Rojas, H. A. Ayala Solares, R. Babu, E. Belmont-Moreno, A. Bernal, K. S. Caballero-Mora, T. Capistrán, F. Carreón, S. Casanova, J. Cotzomi, S. Coutiño de León, C. de León, E. De la Fuente, P. Desiati, N. Di Lalla, R. Diaz Hernandez, M. A. DuVernois, J. C. Díaz-Vélez, K. Engel, C. Espinoza, N. Fraija, S. Fraija, A. Galván-Gámez, J. A. García-González, F. Garfias, N. Ghosh, A. Gonzalez Muñoz, M. M. González, J. A. González, J. A. Goodman, D. Guevel, J. Gyeong, J. P. Harding, S. Hernández-Cadena, I. Herzog, J. Hinton, D. Huang, F. Hueyotl-Zahuantitla, P. Hüntemeyer, A. Iriarte, S. Kaufmann, D. Kieda, K. Leavitt, J. Lee, W. H. Lee, H. León Vargas, J. T. Linnemann, A. L. Longinotti, G. Luis-Raya, K. Malone, O. Martinez, J. Martínez-Castro, J. A. Matthews, P. Miranda-Romagnoli, P. E. Mirón-Enriquez, E. Moreno, M. Mostafá, M. Najafi, A. Nayerhoda, L. Nellen, M. U. Nisa, R. Noriega-Papaqui, N. Omodei, M. Osorio-Archila, E. Ponce, Y. Pérez Araujo, E. G. Pérez-Pérez, C. D. Rho, D. Rosa-González, M. Roth, H. Salazar, D. Salazar-Gallegos, A. Sandoval, M. Schneider, J. Serna-Franco, M. Shin, A. J. Smith, Y. Son, R. W. Springer, O. Tibolla, K. Tollefson, I. Torres, R. Torres-Escobedo, E. Varela, L. Villaseñor, X. Wang, Z. Wang, I. J. Watson, H. Wu, S. Yu, H. Zhou
Categories: astro-ph.HE
Abstract:
Neutrino emission from astrophysical sources has long been considered a signature of cosmic-ray acceleration. The IceCube neutrino observatory has observed a diffuse flux of TeV-PeV neutrinos, but very few confirmed sources have emerged. With the recent publication of IceCube Event Catalog (IceCat-1), IceCube has released a list of the most promising astrophysical neutrino events since May 2011. Using the archival data from the High Altitude Water Cherenkov (HAWC) $γ$-ray observatory, we perform a coincidence search for gamma rays and neutrinos using a Bayesian Block algorithm with the public IceCube alerts from IceCat-1, along with additional alerts issued later. In this work, we consider 368 alerts, up to July 8, 2025, that are within HAWC's field of view. We observe approximately a 5\% coincident detection rate, which is consistent with expectations from background. Two of these detections contain the Active Galactic Nuclei (AGN) Markarian 421 and Markarian 501. We discuss the likelihood that the neutrino/$γ$-ray coincidences are false positives and a brief overview of the results.
Published: 2026-02-18 19:28:37
Authors: Debabrata Mondal, Lea F. Santos, S. Sinha
Categories: quant-ph, cond-mat.quant-gas, cond-mat.stat-mech, nlin.CD
Abstract:
Dissipation is commonly regarded as an obstacle to quantum control, as it induces decoherence and irreversibility. Here we demonstrate that dissipation can instead be exploited as a resource to reshape the dynamics of interacting quantum systems. Using an experimentally realizable Bose-Josephson junction containing two bosonic species, we demonstrate that dissipation enables distinct dynamical behaviors: synchronized phase-locked oscillations, transient chaos with long-time coherence recovery, and steady-state chaos. The emergence of each behavior is determined by experimentally tunable parameters. At weak interactions, the two components synchronize despite dissipation, exhibiting long-lived coherent oscillations reminiscent of a boundary time crystal. Stronger interactions induce a dissipative phase transition into a self-trapped regime accompanied by chaotic dynamics. Remarkably, dissipation regulates the lifetime of chaos and enables the recovery of coherence at long times. By introducing a controlled tilt between the wells, transient chaos can be converted into persistent steady-state chaos. We further show that standard spectral diagnostics fail to distinguish between the two chaotic regimes, revealing that spectral statistics primarily reflect short-time instability. These results establish dissipation as a powerful tool for engineering dynamical phases, restoring quantum coherence, and controlling the duration of chaotic behavior and information scrambling.
Published: 2026-02-18 19:13:29
Authors: Paulo R. Pereira, Jose N. Oliveira
Categories: cs.PL
Abstract:
Since its birth as a new scientific body of knowledge in the late 1950s, computer programming has become a fundamental skill needed in many other disciplines. However, programming is not easy, it is prone to errors and code re-use is key for productivity. This calls for high-quality documentation in software libraries, which is quite often not the case. Taking a few Haskell functions available from the Hackage repository as case-studies, and comparing their descriptions with similar functions in other languages, this paper shows how clarity and good conceptual design can be achieved by following a so-called easy-hard-split formal strategy that is quite general and productive, even if used informally. This strategy is easy to use in functional programming and can be applied to both program analysis and synthesis.
Published: 2026-02-18 19:04:02
Authors: Maryam Khodadad, Noel Walkington, Suresh Kalyanam, Matteo Pozzi, Kaushik Dayal
Categories: cond-mat.mtrl-sci
Abstract:
Conventional phase-field models often drive solid-solid interfaces to coalesce when in close proximity. This feature limits their use for processes like diffusion bonding, where the interfaces might need to remain distinct under certain thermodynamic conditions. We develop a kinetic phase-field model to address this problem, using an evolution equation based on a geometric conservation law for interfaces, rather than the gradient descent evolution that is typical in phase-field modeling. This formulation enables us to specify complex kinetic laws, and we use this to incorporate a physically-motivated geometric criterion to control interface merging. This criterion, based on nonlocal higher-derivative curvature invariants of the phase field, can be temperature-dependent, allows for a range of behaviors from complete coalescence to the preservation of distinct boundaries. Simulations show controlled bonding kinetics, demonstrating capabilities that are not available with existing methods for modeling interfaces that must remain distinct under given thermodynamic conditions.
Published: 2026-02-18 19:00:18
Authors: Prem Seetharaman, Oriol Nieto, Justin Salamon
Categories: cs.SD, cs.MM
Abstract:
In audio-related creative tasks, sound designers often seek to extend and morph different sounds from their libraries. Generative audio models, capable of creating audio using examples as references, offer promising solutions. By masking the noisy latents of a DiT and applying a novel variant of classifier-free guidance on such masked latents, we demonstrate that: (i) given an audio reference, we can extend it both forward and backward for a specified duration, and (ii) given two audio references, we can morph them seamlessly for the desired duration. Furthermore, we show that by fine-tuning the model on different types of stationary audio data we mitigate potential hallucinations. The effectiveness of our method is supported by objective metrics, with the generated audio achieving Fréchet Audio Distances (FADs) comparable to those of real samples from the training data. Additionally, we validate our results through a subjective listener test, where subjects gave positive ratings to the proposed model generations. This technique paves the way for more controllable and expressive generative sound frameworks, enabling sound designers to focus less on tedious, repetitive tasks and more on their actual creative process.
Published: 2026-02-18 19:00:07
Authors: Victoria Lin, Xinnuo Xu, Rachel Lawrence, Risa Ueno, Amit Sharma, Javier Gonzalez, Niranjani Prasad
Categories: cs.LG, cs.CL
Abstract:
Despite their strong performance on reasoning benchmarks, large language models (LLMs) have proven brittle when presented with counterfactual questions, suggesting weaknesses in their causal reasoning ability. While recent work has demonstrated that labeled counterfactual tasks can be useful benchmarks of LLMs' causal reasoning, producing such data at the scale required to cover the vast potential space of counterfactuals is limited. In this work, we introduce double counterfactual consistency (DCC), a lightweight inference-time method for measuring and guiding the ability of LLMs to reason causally. Without requiring labeled counterfactual data, DCC verifies a model's ability to execute two important elements of causal reasoning: causal intervention and counterfactual prediction. Using DCC, we evaluate the causal reasoning abilities of various leading LLMs across a range of reasoning tasks and interventions. Moreover, we demonstrate the effectiveness of DCC as a training-free test-time rejection sampling criterion and show that it can directly improve performance on reasoning tasks across multiple model families.
Published: 2026-02-18 19:00:05
Authors: Wolfram Brenig
Categories: cond-mat.str-el
Abstract:
We show that the concept of coherent phonon generation by second order response to incident electric laser fields, which is a hallmark of pump-probe spectroscopy on conventional solids, can be expanded to include frustrated quantum magnets. For that purpose, we analyze the Raman force on the shear phonons of a frustrated magnetoelectric bilayer spin system. The bilayer is a stacked triangular magnet, motivated by recently emerging type-II van der Waals multiferroic transition metal dihalides and comprises a spin system which allows for incommensurate spiral order. The magnon excitations are treated by linear spin wave theory. In the spiral state, a finite electric polarization is obtained from the spin-current interaction which induces a coupling of the magnons to the electric field. Scattering of the bilayer shear phonons from the magnons is derived from a magnetoelastic energy. In this scenario, a mixed three-point response function for the Raman force is evaluated. We find it to be strongly anisotropic and very sensitive to the magnon lifetime.
Published: 2026-02-18 19:00:02
Authors: S Rahul, Pasquale Marra
Categories: quant-ph, cond-mat.dis-nn, cond-mat.mes-hall, cond-mat.quant-gas, hep-th
Abstract:
Non-Hermitian systems exhibit unique spectral properties, including the non-Hermitian skin effect and exceptional points, often influenced by boundary conditions. The modulation of these phenomena by generalized boundary conditions remains unexplored and not understood. Here, we analyze the Hatano-Nelson model with generalized boundary conditions induced by complex hopping amplitudes at the boundary. Using similarity transformations, we determine the conditions yielding real energy spectra and skin effect, and identify the emergence of exceptional points where spectra transition from real to complex. We demonstrate that tuning the boundary hopping amplitudes precisely controls the non-Hermitian skin effect, i.e., the localization of eigenmodes at the lattice edges. These findings reveal the sensitivity of spectral and localization properties to boundary conditions, providing a framework for engineering quantum lattice models with tailored spectral and localization features, with potential applications in quantum devices.
Published: 2026-02-18 19:00:02
Authors: Shin Toriumi
Categories: astro-ph.SR, astro-ph.EP
Abstract:
The solar-stellar connection provides a unique framework for understanding magnetic activity and atmospheric heating across a broad spectrum of stars. Solar Dynamics Observatory (SDO) of NASA, equipped with the Helioseismic and Magnetic Imager, Atmospheric Imaging Assembly, and Extreme ultraviolet Variability Experiment, has enabled detailed Sun-as-a-star studies that bridge solar and stellar physics. Integrating spatially resolved solar observations into disk-integrated datasets, these studies provide insights into magnetic activity occurring in distant stars. This review highlights key results from recent analyses that employed all three SDO instruments to characterize active regions, quantify universal heating relationships, and reconstruct stellar X-ray and ultraviolet spectra. We discuss how these findings advance our understanding of stellar magnetic activity, provide predictive tools for exoplanetary environments, and outline future directions for applying solar-based frameworks to diverse stellar populations.
Published: 2026-02-18 19:00:02
Authors: Yi-Lin Tsao, Zhu-Xi Luo
Categories: quant-ph, cond-mat.str-el
Abstract:
Many quantum phases, from topological orders to superfluids, are destabilized at finite temperature by the proliferation and motion of topological defects such as anyons or vortices. Conventional protection mechanisms rely on energetic gaps and fail once thermal fluctuations exceed the gap scale. Here we examine a complementary mechanism of entropic protection, in which defect nucleation is suppressed by coupling to mesoscopic auxiliary reservoirs of dimension $M$, generating an effective free-energy barrier that increases with temperature. In the Ising chain, this produces a characteristic three-regime evolution of the correlation length as a function of temperature - linear growth, entropy-controlled plateau, and eventual breakdown - indicating a general modification of defect behavior. Focusing on two spatial dimensions, where true finite-temperature topological order is forbidden in the thermodynamic limit, we show that entropic protection can nevertheless strongly enhance stabilization at finite system size, the regime directly relevant for quantum memory and experiments. Owing to the topological character of the defects, creation and transport are independently suppressed, yielding a double parametric reduction of logical errors in the entropic toric code and enhanced coherence when the framework is extended to Berezinskii-Kosterlitz-Thouless transitions. Entropic barriers thus provide a passive and scalable route to stabilizing quantum phases in experimentally relevant regimes. We propose an experimental setup for entropic toric code using dual species Rydberg arrays with dressing.
Published: 2026-02-18 19:00:00
Authors: Cory Padgett, Jeffrey Fung
Categories: astro-ph.EP
Abstract:
Protoplanetary disks can become eccentric when planets open deep gaps within, but how eccentric are they? We answer this question by analyzing two-dimensional hydrodynamical simulations of planet-disk interaction. The steady state eccentricity of the outer disk (outside of the planet's orbit) is described as a balance between eccentricity excitation by the 1:3 eccentric Lindblad resonance and eccentricity damping by gas pressure. This eccentricity scales with $q(\frac{h_p}{r_p})^{(-1)}(\frac{r_{gap}}{r_p})^{(a-\frac{b}{2}-2)}$, where $q$ is the planet-to-star mass ratio, $\frac{h_p}{r_p}$ is the disk aspect ratio, $\frac{r_{gap}}{r_p}$ is the radial position of the outer gap edge divided by the planet's position, and $a$ and $b$ are the negative exponents in the disk's surface density and temperature power law profiles, respectively. We derive a semi-analytic eccentricity profile that agrees with numerical simulations to within 30%. Our result is a first step to quantitatively interpret observations of eccentric protoplanetary disks, such as MWC 758, HD 142527, IRS 48, and CI Tau.
Published: 2026-02-18 18:57:12
Authors: Nils Palumbo, Sarthak Choudhary, Jihye Choi, Prasad Chalasani, Somesh Jha
Categories: cs.CR, cs.AI, cs.MA
Abstract:
LLM-based agents are increasingly being deployed in contexts requiring complex authorization policies: customer service protocols, approval workflows, data access restrictions, and regulatory compliance. Embedding these policies in prompts provides no enforcement guarantees. We present PCAS, a Policy Compiler for Agentic Systems that provides deterministic policy enforcement.
Enforcing such policies requires tracking information flow across agents, which linear message histories cannot capture. Instead, PCAS models the agentic system state as a dependency graph capturing causal relationships among events such as tool calls, tool results, and messages. Policies are expressed in a Datalog-derived language, as declarative rules that account for transitive information flow and cross-agent provenance. A reference monitor intercepts all actions and blocks violations before execution, providing deterministic enforcement independent of model reasoning.
PCAS takes an existing agent implementation and a policy specification, and compiles them into an instrumented system that is policy-compliant by construction, with no security-specific restructuring required. We evaluate PCAS on three case studies: information flow policies for prompt injection defense, approval workflows in a multi-agent pharmacovigilance system, and organizational policies for customer service. On customer service tasks, PCAS improves policy compliance from 48% to 93% across frontier models, with zero policy violations in instrumented runs.
Published: 2026-02-18 18:51:28
Authors: Shen Zhou Hong, Alex Kleinman, Alyssa Mathiowetz, Adam Howes, Julian Cohen, Suveer Ganta, Alex Letizia, Dora Liao, Deepika Pahari, Xavier Roberts-Gaal, Luca Righetti, Joe Torres
Categories: cs.CY, cs.AI
Abstract:
Large language models (LLMs) perform strongly on biological benchmarks, raising concerns that they may help novice actors acquire dual-use laboratory skills. Yet, whether this translates to improved human performance in the physical laboratory remains unclear. To address this, we conducted a pre-registered, investigator-blinded, randomized controlled trial (June-August 2025; n = 153) evaluating whether LLMs improve novice performance in tasks that collectively model a viral reverse genetics workflow. We observed no significant difference in the primary endpoint of workflow completion (5.2% LLM vs. 6.6% Internet; P = 0.759), nor in the success rate of individual tasks. However, the LLM arm had numerically higher success rates in four of the five tasks, most notably for the cell culture task (68.8% LLM vs. 55.3% Internet; P = 0.059). Post-hoc Bayesian modeling of pooled data estimates an approximate 1.4-fold increase (95% CrI 0.74-2.62) in success for a "typical" reverse genetics task under LLM assistance. Ordinal regression modelling suggests that participants in the LLM arm were more likely to progress through intermediate steps across all tasks (posterior probability of a positive effect: 81%-96%). Overall, mid-2025 LLMs did not substantially increase novice completion of complex laboratory procedures but were associated with a modest performance benefit. These results reveal a gap between in silico benchmarks and real-world utility, underscoring the need for physical-world validation of AI biosecurity assessments as model capabilities and user proficiency evolve.
Published: 2026-02-18 18:42:29
Authors: Huan Souza, Pankaj Mehta
Categories: q-bio.GN, cs.LG, q-bio.QM
Abstract:
Single-cell RNA sequencing (scRNA-seq) data exhibit strong and reproducible statistical structure. This has motivated the development of large-scale foundation models, such as TranscriptFormer, that use transformer-based architectures to learn a generative model for gene expression by embedding genes into a latent vector space. These embeddings have been used to obtain state-of-the-art (SOTA) performance on downstream tasks such as cell-type classification, disease-state prediction, and cross-species learning. Here, we ask whether similar performance can be achieved without utilizing computationally intensive deep learning-based representations. Using simple, interpretable pipelines that rely on careful normalization and linear methods, we obtain SOTA or near SOTA performance across multiple benchmarks commonly used to evaluate single-cell foundation models, including outperforming foundation models on out-of-distribution tasks involving novel cell types and organisms absent from the training data. Our findings highlight the need for rigorous benchmarking and suggest that the biology of cell identity can be captured by simple linear representations of single cell gene expression data.
Published: 2026-02-18 17:23:14
Authors: Alex Moody, Penina Axelrad, Rebecca Russell
Categories: cs.LG, cs.RO, eess.SY
Abstract:
Low Earth orbit (LEO) satellites are leveraged to support new position, navigation, and timing (PNT) service alternatives to GNSS. These alternatives require accurate propagation of satellite position and velocity with a realistic quantification of uncertainty. It is commonly assumed that the propagated uncertainty distribution is Gaussian; however, the validity of this assumption can be quickly compromised by the mismodeling of atmospheric drag. We develop a machine learning approach that corrects error growth in the argument of latitude for a diverse set of LEO satellites. The improved orbit propagation accuracy extends the applicability of the Gaussian assumption and modeling of the errors with a corrected mean and covariance. We compare the performance of a time-conditioned neural network and a Gaussian Process on datasets computed with an open source orbit propagator and publicly available Vector Covariance Message (VCM) ephemerides. The learned models predict the argument of latitude error as a Gaussian distribution given parameters from a single VCM epoch and reverse propagation errors. We show that this one-dimensional model captures the effect of mismodeled drag, which can be mapped to the Cartesian state space. The correction method only updates information along the dimensions of dominant error growth, while maintaining the physics-based propagation of VCM covariance in the remaining dimensions. We therefore extend the utility of VCM ephemerides to longer time horizons without modifying the functionality of the existing propagator.
Published: 2026-02-18 13:44:37
Authors: Miltiadis Paschalis
Categories: math.DG, gr-qc, math-ph
Abstract:
Motivated by recent developments in the theory of gravitation, we revisit the idea of topological variations, originally introduced by Wheeler and Hawking, from a rigorous perspective. Starting from a localized version of the Einstein-Hilbert variational principle, we encode the key aspects of the variational procedure in the form of a topology on a suitable space of variational configurations with low Sobolev regularity. This structure is the final topology with respect to the admissible variational maps and naturally lends itself to generalizations. We rigorously introduce two distinct types of topological variations, corresponding to the infinitesimal addition of disconnected components and to infinitesimal surgeries, both motivated by related physical concepts. Using tools from the theory of Sobolev spaces and precise asymptotics, we establish dimensional obstructions for the continuity and differentiability of the Einstein-Hilbert action with respect to these variations, and show that in the extended variational framework the action does not admit critical points in dimension $n=4$, while higher dimensions are free of this problem. Finally, we demonstrate the non-trivial effect of higher order curvature terms on the critical dimension.
Published: 2026-02-18 13:38:27
Authors: Zamin Mamiyev, Niclas Tilgner, Narmina O. Balayeva, Dietrich R. T. Zahn, Thomas Seyller, Christoph Tegenkamp
Categories: cond-mat.mes-hall, cond-mat.mtrl-sci
Abstract:
Confinement epitaxy beneath graphene stabilizes exotic material phases by restricting vertical growth and altering lateral diffusion, conditions unattainable on bare substrates. However, achieving long-range interfacial order while maintaining high-quality graphene remains a significant challenge. Here, we demonstrate the synthesis of large-area quasi-free-standing monolayer graphene (QFMLG) via the intercalation of a two-dimensional (2D) Sn. While the triangular Sn(1x1) interface exhibits a robust metallic band structure, the decoupled QFMLG maintains charge neutrality, confirmed by photoemission spectroscopy. Using high-resolution Raman spectroscopy and microscopy, we distinguish between direct intercalation and diffusion-driven expansion, identifying the latter as the critical pathway to superior QFMLG crystalline quality. Temperature-dependent analysis reveals dynamical structural coupling between the decoupled QFMLG and the Sn interface, providing a novel degree of freedom for strain engineering. Beyond uncovering the diffusion-driven mechanism, this work establishes metal intercalation as an effective strategy for tailoring durable graphene-metal heterostructures with tunable properties for next-generation quantum materials platforms.
Published: 2026-02-18 13:19:11
Authors: Eva Paraschou, Line Harder Clemmensen, Sneha Das
Categories: cs.LG, cs.AI
Abstract:
Conventional large language model (LLM) fairness alignment largely focuses on mitigating bias along single sensitive attributes, overlooking fairness as an inherently multidimensional and context-specific value. This approach risks creating systems that achieve narrow fairness metrics while exacerbating disparities along untargeted attributes, a phenomenon known as bias spillover. While extensively studied in machine learning, bias spillover remains critically underexplored in LLM alignment. In this work, we investigate how targeted gender alignment affects fairness across nine sensitive attributes in three state-of-the-art LLMs (Mistral 7B, Llama 3.1 8B, Qwen 2.5 7B). Using Direct Preference Optimization and the BBQ benchmark, we evaluate fairness under ambiguous and disambiguous contexts. Our findings reveal noticeable bias spillover: while aggregate results show improvements, context-aware analysis exposes significant degradations in ambiguous contexts, particularly for physical appearance ($p< 0.001$ across all models), sexual orientation, and disability status. We demonstrate that improving fairness along one attribute can inadvertently worsen disparities in others under uncertainty, highlighting the necessity of context-aware, multi-attribute fairness evaluation frameworks.
Published: 2026-02-18 13:15:56
Authors: Yongsung Kwon, Deok-Sun Lee, Mi Jin Lee, Seung-Woo Son
Categories: physics.soc-ph
Abstract:
In South Korea, age-disaggregated tuberculosis (TB) data at the district level are not publicly available due to privacy constraints, limiting fine-scale analyses of healthcare accessibility. To address this limitation, we present a high-resolution, district-level dataset on tuberculosis (TB) fatalities and hospital accessibility in South Korea, covering the years 2014 to 2022 across 228 districts. The dataset is constructed using a reconstruction method that infers age-disaggregated TB cases and fatalities at the district level by integrating province-level age-specific statistics with district-level spatial and demographic data, enabling analyses that account for both spatial heterogeneity and age structure. Building on an existing hospital allocation framework, we extend the objective function to an age-weighted formulation and apply it to the reconstructed dataset to minimize TB fatalities under different age-weighting schemes. We demonstrate that incorporating age structure can give rise to distinct optimized hospital allocation patterns, even when the total number of minimized fatalities is similar, revealing trade-offs between efficiency and demographic targeting. In addition, the dataset supports temporal analyses of TB burden, hospital availability, and demographic variation over time, and provides a testbed for spatial epidemiology and optimization studies that require high-resolution demographic and healthcare data.
Published: 2026-02-18 13:10:35
Authors: Nilesh jain, Rohit Yadav, Andrej Karpathy
Categories: cs.ET
Abstract:
Large language models are increasingly integrated into academic writing workflows; however, the most widely used \LaTeX\ editors remain AI-peripheral -- offering compilation and collaboration, but no native intelligence. This separation forces researchers to leave their editing environment for AI assistance, fragmenting document context and interrupting writing flow. We present Bibby AI (trybibby.com), a native, AI-first \LaTeX\ editor that unifies the complete research writing lifecycle within a single interface. Bibby embeds an AI writing assistant, smart citation search, AI table and equation generation, an AI paper reviewer, abstract generator, literature review drafting, a deep research assistant, and real-time \LaTeX\ error detection and auto-fix -- all natively, without plugins or copy-paste workflows. We introduce LaTeXBench-500, a benchmark of 500 real-world compilation errors across six categories. Bibby achieves 91.4\% detection accuracy and 83.7\% one-click fix accuracy, outperforming Overleaf's native diagnostics (61.2\%) and OpenAI Prism (78.3 / 64.1\%) by large margins. Bibby demonstrates that a privacy-preserving, research-first AI editor can meaningfully accelerate every stage of academic manuscript preparation. We found that Bibby AI is a far superior alternative to overleaf latex and better than OpenAI Prism functionalities and AI.
Published: 2026-02-18 12:55:20
Authors: Ahmet Halici, Ece Tugba Cebeci, Musa Balci, Mustafa Cini, Serkan Sokmen
Categories: eess.IV, cs.AI, cs.CV
Abstract:
Generating diagnostic text from histopathology whole slide images (WSIs) is challenging due to the gigapixel scale of the input and the requirement for precise, domain specific language. We propose a hierarchical vision language framework that combines a frozen pathology foundation model with a Transformer decoder for report generation. To make WSI processing tractable, we perform multi resolution pyramidal patch selection (downsampling factors 2^3 to 2^6) and remove background and artifacts using Laplacian variance and HSV based criteria. Patch features are extracted with the UNI Vision Transformer and projected to a 6 layer Transformer decoder that generates diagnostic text via cross attention. To better represent biomedical terminology, we tokenize the output using BioGPT. Finally, we add a retrieval based verification step that compares generated reports with a reference corpus using Sentence BERT embeddings; if a high similarity match is found, the generated report is replaced with the retrieved ground truth reference to improve reliability.
Published: 2026-02-18 12:23:14
Authors: J. G. Patel
Categories: math.FA
Abstract:
Let $\mathcal{A}$ be an algebra, and let $\mathcal{A}^2 =$ span$\{ab : a, b \in \mathcal{A}\}$ be a subalgebra of $\mathcal{A}$. In this paper, we prove that if $\mathcal{A}^2$ has infinite codimension in $\mathcal{A}$ iff $\mathcal{A}$ has discontinuous square annihilation property (DSAP). In fact, in this case, the algebra $\mathcal{A}$ admits infinitely many non-equivalent algebra norms.