Published: 2025-11-02 07:59:31
Authors: Xinsong Li, Ning Jiang, Jay Selvaraj
Categories: cs.HC
Abstract:
The rapid appearance of large language models (LLMs) has led to systems that
turn natural-language intent into real user interfaces (UIs). Free-form code
generation maximizes expressiveness but often hurts reliability, security, and
design-system compliance. In contrast, fully static UIs are easy to govern but
lack adaptability. We present the Portal UX Agent, a practical middle way that
makes bounded generation work: an LLM plans the UI at a high level, and a
deterministic renderer assembles the final interface from a vetted set of
components and layout templates. The agent maps intents to a typed
composition-template and component specifications-constrained by a schema. This
enables auditability, reuse, and safety while preserving flexibility. We also
introduce a mixed-methods evaluation framework that combines automatic checks
(coverage, property fidelity, layout, accessibility, performance) with an
LLM-as-a-Judge rubric to assess semantic alignment and visual polish.
Experiments on multi-domain portal scenarios show that the Portal UX Agent
reliably turns intent into coherent, usable UIs and performs well on
compositionality and clarity. This work advances agentic UI design by combining
model-driven representations, plug-and-play rendering, and structured
evaluation, paving the way for controllable and trustworthy UI generation.
Published: 2025-11-02 07:53:24
Authors: David Amaro-Alcalá
Categories: quant-ph
Abstract:
We reduce the cost of the current bosonic randomized benchmarking proposal.
First, we introduce a filter function using immanants. With this filter, we
avoid the need to compute Clebsch-Gordan coefficients. Our filter uses the same
data as the original, although we propose a distinct data collection process
that requires a single type of measurement. Furthermore, we argue that weak
coherent states and intensity measurements are sufficient to proceed with the
characterization. Our work could then allow simpler platforms to be
characterized and simplify the data analysis process.
Published: 2025-11-02 07:46:41
Authors: Xuerui Yang
Categories: math.CA, math.NT, 42B37
Abstract:
Let $R^{\frac{1}{2}}$ be a large integer, and $\omega$ be a nonnegative
weight in the $R$-ball $B_R=[0,R]^2$ such that $\omega(B_R)\le R$. For any
complex sequence $\{a_n\}$, define the quadratic exponential sum \[
G(x,t)=\sum_{n=1}^{R^{\frac{1}{2}}} a_n e\big(\frac{n}{R^{\frac{1}{2}}}
x+\frac{n^2}{R} t\big). \] It holds that \[ \int |G|^2 \omega \lessapprox
\sup_{T}\omega(T)^{\frac{1}{2}}\cdot R \,\|a_n\|_{l^2}^2 \] where $T$ ranges
over $R\times R^{\frac{1}{2}}$ tubes in $B_R$. The proof is established through
exploring the distributions of superlevel sets of the $G$ function. It is based
on the $TT^*$ method and the circle method.
Published: 2025-11-02 07:43:36
Authors: William Suliman, Ekaterina Chaikovskaia, Egor Davydenko, Roman Gorbachev
Categories: cs.RO
Abstract:
This work presents an extended framework for learning-based bipedal
locomotion that incorporates a heuristic step-planning strategy guided by
desired torso velocity tracking. The framework enables precise interaction
between a humanoid robot and its environment, supporting tasks such as crossing
gaps and accurately approaching target objects. Unlike approaches based on full
or simplified dynamics, the proposed method avoids complex step planners and
analytical models. Step planning is primarily driven by heuristic commands,
while a Raibert-type controller modulates the foot placement length based on
the error between desired and actual torso velocity. We compare our method with
a model-based step-planning approach -- the Linear Inverted Pendulum Model
(LIPM) controller. Experimental results demonstrate that our approach attains
comparable or superior accuracy in maintaining target velocity (up to 80%),
significantly greater robustness on uneven terrain (over 50% improvement), and
improved energy efficiency. These results suggest that incorporating complex
analytical, model-based components into the training architecture may be
unnecessary for achieving stable and robust bipedal walking, even in
unstructured environments.
Published: 2025-11-02 07:42:51
Authors: John Yang, Kilian Lieret, Joyce Yang, Carlos E. Jimenez, Ofir Press, Ludwig Schmidt, Diyi Yang
Categories: cs.SE, cs.AI
Abstract:
Current benchmarks for coding evaluate language models (LMs) on concrete,
well-specified tasks such as fixing specific bugs or writing targeted tests.
However, human programmers do not spend all day incessantly addressing isolated
tasks. Instead, real-world software development is grounded in the pursuit of
high-level goals, like improving user retention or reducing costs. Evaluating
whether LMs can also iteratively develop code to better accomplish open-ended
objectives without any explicit guidance remains an open challenge. To address
this, we introduce CodeClash, a benchmark where LMs compete in multi-round
tournaments to build the best codebase for achieving a competitive objective.
Each round proceeds in two phases: agents edit their code, then their codebases
compete head-to-head in a code arena that determines winners based on
objectives like score maximization, resource acquisition, or survival. Whether
it's writing notes, scrutinizing documentation, analyzing competition logs, or
creating test suites, models must decide for themselves how to improve their
codebases both absolutely and against their opponents. We run 1680 tournaments
(25,200 rounds total) to evaluate 8 LMs across 6 arenas. Our results reveal
that while models exhibit diverse development styles, they share fundamental
limitations in strategic reasoning. Models also struggle with long-term
codebase maintenance, as repositories become progressively messy and redundant.
These limitations are stark: top models lose every round against expert human
programmers. We open-source CodeClash to advance the study of autonomous,
goal-oriented code development.
Published: 2025-11-02 07:42:42
Authors: Avijit Pal, Bhaskar Paul
Categories: math.FA
Abstract:
A fundamental theorem of Sz.-Nagy states that a contraction $T$ on a Hilbert
space can be dilated to an isometry $V.$ A more multivariable context of recent
significance for these concepts involves substituting the unit disk with
$\Gamma_{E(3; 3; 1, 1, 1)}, \Gamma_{E(3; 2; 1, 2)},$ and pentablock. We
demonstrate the necessary conditions for the existence of $\Gamma_{E(3; 3; 1,
1, 1)}$-isometric dilation, $\Gamma_{E(3; 2; 1, 2)}$-isometric dilation and
pentablock-isometric dilation. We construct a class of $\Gamma_{E(3; 3; 1, 1,
1)}$-contractions and $\Gamma_{E(3; 2; 1, 2)}$-contractions that are always
dilate . We create an example of a $\Gamma_{E(3; 3; 1, 1, 1)}$-contraction that
has a $\Gamma_{E(3; 3; 1, 1, 1)}$-isometric dilation such that $[F_{7-i}^*,
F_j] \ne [F_{7-j}^*, F_i] $ for some $i,j$ with $1\leq i ,j\leq 6,$ where $F_i$
and $F_{7-i}, 1\leq i \leq 6$ are the fundamental operators of $\Gamma_{E(3; 3;
1, 1, 1)}$-contraction $\textbf{T}=(T_1, \dots, T_7).$ We also produce an
example of a $\Gamma_{E(3; 2; 1, 2)}$-contraction that has a $\Gamma_{E(3; 2;
1, 2)}$-isometric dilation by which $$[G^*_1, G_1] \neq [\tilde{G}^*_2,
\tilde{G}_2]~{\rm{ and }}~[2G^*_2, 2G_2] \neq
[2\tilde{G}^*_1, 2\tilde{G}_1],$$ where $G_1, 2G_2, 2\tilde{G}_1,
\tilde{G}_2$ are the fundamental operators of $\textbf{S}$. As a result, the
set of sufficient conditions for the existence of a $\Gamma_{E(3; 3; 1, 1,
1)}$-isometric dilation and $\Gamma_{E(3; 2; 1; 2)} $-isometric dilations
presented in Theorem \ref{conddilation} and Theorem \ref{condilation1},
respectively, are not generally necessary. We construct explicit $\Gamma_{E(3;
3; 1, 1, 1)} $-isometric, $\Gamma_{E(3; 2; 1; 2)} $-isometric dilations and
$\mathcal{\bar{P}}$-isometric dilation of $\Gamma_{E(3; 3; 1, 1,
1)}$-contraction, $\Gamma_{E(3; 2; 1; 2)}$-contraction and
$\mathcal{\bar{P}}$-contraction, respectively.
Published: 2025-11-02 07:42:09
Authors: Kohei Mukasa, Yusuke Masaki
Categories: cond-mat.supr-con
Abstract:
We numerically investigate finite-momentum superconductivity in
noncentrosymmetric metallic altermagnets with $d$-wave spin-splitting and
strong Rashba-type spin-orbit coupling. Focusing on a stripe phase in which
Cooper pairs acquire multiple center-of-mass momenta, we construct phase
diagrams that reveal phase boundaries between the stripe phase and a helical
phase characterized by a single center-of-mass momentum. Our results show that
the stripe phase emerges at low temperatures and exhibits a reentrant behavior
as a function of the strength of the altermagnetic splitting. We further
analyze the stripe phase within a linearized gap equation, and uncover the
mechanism of the pairing formation unique to the stripe phase. This mechanism
originates from the anisotropic deformation of the Fermi surfaces induced by
the altermagnetic splitting, highlighting the intriguing interplay between the
spin-orbit coupling and the altermagnets.
Published: 2025-11-02 07:37:06
Authors: Xin Liu, Yichen Yang, Kun He, John E. Hopcroft
Categories: cs.CV, cs.AI
Abstract:
Though deep neural networks exhibit superior performance on various tasks,
they are still plagued by adversarial examples. Adversarial training has been
demonstrated to be the most effective method to defend against adversarial
attacks. However, existing adversarial training methods show that the model
robustness has apparent oscillations and overfitting issues in the training
process, degrading the defense efficacy. To address these issues, we propose a
novel framework called Parameter Interpolation Adversarial Training (PIAT).
PIAT tunes the model parameters between each epoch by interpolating the
parameters of the previous and current epochs. It makes the decision boundary
of model change more moderate and alleviates the overfitting issue, helping the
model converge better and achieving higher model robustness. In addition, we
suggest using the Normalized Mean Square Error (NMSE) to further improve the
robustness by aligning the relative magnitude of logits between clean and
adversarial examples rather than the absolute magnitude. Extensive experiments
conducted on several benchmark datasets demonstrate that our framework could
prominently improve the robustness of both Convolutional Neural Networks (CNNs)
and Vision Transformers (ViTs).
Published: 2025-11-02 07:19:10
Authors: Taikun Zhu, Kai Jin, Ruixi Luo, Song Cao
Categories: cs.GT
Abstract:
We investigate optimal social welfare allocations of $m$ items to $n$ agents
with binary additive or submodular valuations. For binary additive valuations,
we prove that the set of optimal allocations coincides with the set of
so-called \emph{stable allocations}, as long as the employed criterion for
evaluating social welfare is strongly Pigou-Dalton (SPD) and symmetric. Many
common criteria are SPD and symmetric, such as Nash social welfare, leximax,
leximin, Gini index, entropy, and envy sum. We also design efficient algorithms
for finding a stable allocation, including an $O(m^2n)$ time algorithm for the
case of indivisible items, and an $O(m^2n^5)$ time one for the case of
divisible items. The first is faster than the existing algorithms or has a
simpler analysis. The latter is the first combinatorial algorithm for that
problem. It utilizes a hidden layer partition of items and agents admitted by
all stable allocations, and cleverly reduces the case of divisible items to the
case of indivisible items.
In addition, we show that the profiles of different optimal allocations have
a small Chebyshev distance, which is 0 for the case of divisible items under
binary additive valuations, and is at most 1 for the case of indivisible items
under binary submodular valuations.
Published: 2025-11-02 07:04:15
Authors: Jing-Qi Lai, Jia-Geng Jiao, Cai-Ying Shao, Jun-Xi Shi, Yu Tian
Categories: gr-qc, astro-ph.IM, physics.data-an
Abstract:
We introduce DenoiseGapFiller (DGF), a deep-learning framework specifically
designed to reconstruct gravitational-wave ringdown signals corrupted by data
gaps and instrumental noise. DGF employs a dual-branch encoder-decoder
architecture, which is fused via mixing layers and Transformer-style blocks.
Trained end-to-end on synthetic ringdown waveforms with gaps up to 20% of the
segment length, DGF can achieve a mean waveform mismatch of 0.002. The residual
amplitudes of the Time-domain shrink by roughly an order of magnitude and the
power spectral density in the 0.01-1 Hz band is suppressed by 1-2 orders of
magnitude, restoring the peak of quasi-normal mode(QNM) in the time-frequency
representation around 0.01-0.1 Hz. The ability of the model to faithfully
reconstruct the original signals, which implies milder penalties in the
detection evidence and tighter credible regions for parameter estimation, lay a
foundation for the following scientific work.
Published: 2025-11-02 07:04:12
Authors: Yifan Pu, Jixuan Ying, Qixiu Li, Tianzhu Ye, Dongchen Han, Xiaochen Wang, Ziyi Wang, Xinyu Shao, Gao Huang, Xiu Li
Categories: cs.CV, cs.AI
Abstract:
Vision Transformers (ViTs) have become a universal backbone for both image
recognition and image generation. Yet their Multi-Head Self-Attention (MHSA)
layer still performs a quadratic query-key interaction for every token pair,
spending the bulk of computation on visually weak or redundant correlations. We
introduce Visual-Contrast Attention (VCA), a drop-in replacement for MHSA that
injects an explicit notion of discrimination while reducing the theoretical
complexity from O(N N C) to O(N n C) with n << N. VCA first distils each head's
dense query field into a handful of spatially pooled visual-contrast tokens,
then splits them into a learnable positive and negative stream whose
differential interaction highlights what truly separates one region from
another. The module adds fewer than 0.3M parameters to a DeiT-Tiny backbone,
requires no extra FLOPs, and is wholly architecture-agnostic. Empirically, VCA
lifts DeiT-Tiny top-1 accuracy on ImageNet-1K from 72.2% to 75.6% (+3.4) and
improves three strong hierarchical ViTs by up to 3.1%, while in
class-conditional ImageNet generation it lowers FID-50K by 2.1 to 5.2 points
across both diffusion (DiT) and flow (SiT) models. Extensive ablations confirm
that (i) spatial pooling supplies low-variance global cues, (ii) dual
positional embeddings are indispensable for contrastive reasoning, and (iii)
combining the two in both stages yields the strongest synergy. VCA therefore
offers a simple path towards faster and sharper Vision Transformers. The source
code is available at https://github.com/LeapLabTHU/LinearDiff.
Published: 2025-11-02 06:58:21
Authors: Yuchao Yi, Yang Zhang
Categories: math.DG, 53C24, 53C50
Abstract:
In this work, we prove the following three rigidity results: (i) in a
real-analytic globally hyperbolic spacetime $(M,g)$ without boundary, the time
separation function restricted to a thin exterior layer of a unknown compact
subset $K \subset M$ determines $K$ up to an analytic isometry, assuming no
lightlike cut points in $K$; (ii) in a real-analytic globally hyperbolic
spacetime $(M,g)$ with timelike boundary, the boundary time separation function
determines $M$ up to an analytic isometry, assuming no lightlike cut points
near $M$ and lightlike geodesics are non-trapping; (iii) in a real-analytic
Lorentzian manifold $(M,g)$ with timelike boundary, the interior and complete
scattering relations near the light cone, each determines $M$ up to an analytic
isometry, assuming that lightlike geodesics are non-trapping. We emphasize in
all of these three cases we do not assume the convexity of the boundary of the
subset or the manifold. Moreover, in (iii) we do not assume causality of the
Lorentzian manifold, and allow the existence of cut points. Along the way, we
also prove some boundary determination results, the connections between the
interior and complete scattering relations, and the connections between the
lens data and the scattering relation, for Riemannian manifolds and Lorentzian
manifolds with boundaries.
Published: 2025-11-02 06:55:49
Authors: Xin Liu, Aoyang Zhou, Aoyang Zhou
Categories: cs.CV, cs.AI
Abstract:
Visual-Language Pre-training (VLP) models have achieved significant
performance across various downstream tasks. However, they remain vulnerable to
adversarial examples. While prior efforts focus on improving the adversarial
transferability of multimodal adversarial examples through cross-modal
interactions, these approaches suffer from overfitting issues, due to a lack of
input diversity by relying excessively on information from adversarial examples
in one modality when crafting attacks in another. To address this issue, we
draw inspiration from strategies in some adversarial training methods and
propose a novel attack called Local Shuffle and Sample-based Attack (LSSA).
LSSA randomly shuffles one of the local image blocks, thus expanding the
original image-text pairs, generating adversarial images, and sampling around
them. Then, it utilizes both the original and sampled images to generate the
adversarial texts. Extensive experiments on multiple models and datasets
demonstrate that LSSA significantly enhances the transferability of multimodal
adversarial examples across diverse VLP models and downstream tasks. Moreover,
LSSA outperforms other advanced attacks on Large Vision-Language Models.
Published: 2025-11-02 06:52:22
Authors: Siddheswar Kundu
Categories: math.CO, math.RT, 05E05
Abstract:
In this article, we offer a new way to prove the Murnaghan-Nakayama type rule
for the stable Grothendieck polynomials, originally established by
Nguyen-Hiep-Son-Thuy. Additionally, we establish a Murnaghan-Nakayama type rule
for cannoical stable Grothendieck functions.
Published: 2025-11-02 06:52:19
Authors: Olivier Geoffroy, David Saint-Martin
Categories: physics.ao-ph
Abstract:
The objective of this paper is twofold. First, it documents the second
version of the global atmospheric model ARP-GEM and its calibration at
kilometer-scale resolution. The model is currently able to run simulations at a
resolution of up to 1.3 km. Second, this paper focus on multi-year global
atmospheric simulations at a 2.6 km resolution with and without parameterized
convection and associated calibration. Simulations without deep convection tend
to be similar to those with infinite, or at least large, entrainment values.
Consistently, entrainment and detrainment are used as primary drivers for the
gradual reduction of convection as resolution increases. The results indicate
that, with this hydrostatic model, parameterized convection still plays a
significant role in the correct representation of the mean state at the
kilometer scale. Additionally, they suggest some added value of high resolution
in representing climate variability. However, a compromise between the adequate
representation of the mean state and variability is necessary, as both are
differently favored by the degree of parameterized convection. Finally, it is
likely that even higher resolutions are necessary to achieve an unequivocal
added value.
Published: 2025-11-02 06:47:56
Authors: Huiyao Dong, Igor Kotenko
Categories: cs.CR, cs.AI
Abstract:
The Control Area Network (CAN) protocol is essential for in-vehicle
communication, facilitating high-speed data exchange among Electronic Control
Units (ECUs). However, its inherent design lacks robust security features,
rendering vehicles susceptible to cyberattacks. While recent research has
investigated machine learning and deep learning techniques to enhance network
security, their practical applicability remains uncertain. This paper presents
a lightweight intrusion detection technique based on Binarized Neural Networks
(BNNs), which utilizes payload data, message IDs, and CAN message frequencies
for effective intrusion detection. Additionally, we develop hybrid binary
encoding techniques to integrate non-binary features, such as message IDs and
frequencies. The proposed method, namely the BNN framework specifically
optimized for in-vehicle intrusion detection combined with hybrid binary
quantization techniques for non-payload attributes, demonstrates efficacy in
both anomaly detection and multi-class network traffic classification. The
system is well-suited for deployment on micro-controllers and Gateway ECUs,
aligning with the real-time requirements of CAN bus safety applications.
Published: 2025-11-02 06:39:20
Authors: Wen Yang, Qiming Ye, Onur Ascigil, Saidu Sokoto, Leonhard Balduf, Michał Król, Gareth Tyson
Categories: cs.SI, H.4.0; J.4; K.4.0
Abstract:
This paper presents the first empirical analysis of how diverse token-based
reward mechanisms impact platform dynamics and user behaviors. For this, we
gather a unique, large-scale dataset from Farcaster. This blockchain-based,
decentralized social network incorporates multiple incentive mechanisms
spanning platform-native rewards, third-party token programs, and peer-to-peer
tipping. Our dataset captures token transactions and social interactions from
574,829 wallet-linked users, representing 64.25% of the platform's user base.
Our socioeconomic analyses reveal how different tokenomics design shape varying
participation rates (7.6%--70%) and wealth concentration patterns (Gini
0.72--0.94), whereas inter-community tipping (51--75% of all tips) is 1.3--2x
more frequent among non-following pairs, thereby mitigating echo chambers. Our
causal analyses further uncover several critical trade-offs: (1) while most
token rewards boost content creation, they often fail to enhance -- sometimes
undermining -- content quality; (2) token rewards increase follower acquisition
but show neutral or negative effects on outbound following, suggesting
potential asymmetric network growth; (3) repeated algorithmic rewards
demonstrate strong cumulative effects that may encourage strategic
optimization. Our findings advance understanding of cryptocurrency integration
in social platforms and highlight challenges in aligning economic incentives
with authentic social value.
Published: 2025-11-02 06:36:19
Authors: Shatha Algarni, Boris Glavic, Seokki Lee, Adriane Chapman
Categories: cs.DB
Abstract:
In many real-world scenarios, query results must satisfy domain-specific
constraints. For instance, a minimum percentage of interview candidates
selected based on their qualifications should be female. These requirements can
be expressed as constraints over an arithmetic combination of aggregates
evaluated on the result of the query. In this work, we study how to repair a
query to fulfill such constraints by modifying the filter predicates of the
query. We introduce a novel query repair technique that leverages bounds on
sets of candidate solutions and interval arithmetic to efficiently prune the
search space. We demonstrate experimentally, that our technique significantly
outperforms baselines that consider a single candidate at a time.
Published: 2025-11-02 06:32:06
Authors: Hanif Golchin, Hamid R. Bakhtiarizadeh, Mohammad Reza Mehdizadeh
Categories: gr-qc, hep-th
Abstract:
We perform the first study of traversable wormhole solutions in the
background of Einstein-Aether theory. We show that the field equations admit
some wormhole geometries, for several combinations of values of the aether
coupling constants. We investigating the null and weak energy conditions for
wormhole solutions with three types of the worm hole shape function. In
contrast to Einstein gravity, we find that by choosing adequate values for the
parameters of the models, wormhole geometries respect the energy conditions at
the wormhole throat and also throughout the whole space. Satisfaction of these
energy conditions led to some constraints on the value of the Einstein-Aether
theory. Comparing these constraints with those previously obtained from
theoretical and observational considerations, we find that the satisfaction of
energy condition put more limitations on the values of the Einstein-Aether
couplings.
Published: 2025-11-02 06:27:06
Authors: Yang Cao, Yijin Wang
Categories: math.NT, math.AG, 14G12
Abstract:
Let $G$ be a connected linear algebraic group over a number field $K$. We
study the almost strong approximation property (ASA) of $G$ raised by Rapinchuk
and Tralle, and we give a necessary and sufficient condition for (ASA) to hold
in terms of the Brauer group of $G$, by using Demarche's results on strong
approximation with Brauer-Manin obstruction. Using the criteria, it turns out
that (ASA) can be completely controlled by the Dirichlet density of the places
and the splitting field of $G$, which generalizes a result of Rapinchuk and
Tralle.
Published: 2025-11-02 06:26:22
Authors: Qi Xia, Hu Xia, Isaac Amankona Obiri, Adjei-Arthur Bonsu, Grace Mupoyi Ntuala, Ansu Badjie, Tienin Bole Wilfried, Jiaqin Liu, Lan Ma, Jianbin Gao, Feng Yao
Categories: cs.NI, cs.DC
Abstract:
Blockchain technology facilitates the development of decentralized systems
that ensure trust and transparency without the need for expensive centralized
intermediaries. However, existing blockchain architectures particularly
consortium blockchains face critical challenges related to scalability and
efficiency. State sharding has emerged as a promising approach to enhance
blockchain scalability and performance. However, current shard-based solutions
often struggle to guarantee fair participation and a balanced workload
distribution among consortium members. To address these limitations, we propose
Trusted Intelligent NetChain (TINC), a multi-plane sharding architecture
specifically designed for consortium blockchains. TINC incorporates intelligent
mechanisms for adaptive node assignment and dynamic workload balancing,
enabling the system to respond effectively to changing network conditions while
maintaining equitable shard utilization. By decoupling the control and data
planes, TINC allows control nodes to focus on consensus operations, while data
nodes handle large-scale storage, thus improving overall resource efficiency.
Extensive experimental evaluation and formal analysis demonstrate that TINC
significantly outperforms existing shard-based blockchain frameworks. It
achieves higher throughput, lower latency, balanced node and transaction
distributions, and reduced transaction failure rates. Furthermore, TINC
maintains essential blockchain security guarantees, exhibiting resilience
against Byzantine faults and dynamic network environments. The integration of
Dynamic Decentralized Identifiers (DDIDs) further strengthens trust and
security management within the consortium network.
Published: 2025-11-02 06:21:28
Authors: Masroor Bashir, Pravabati Chingangbam, Stephen Appleby
Categories: astro-ph.CO, astro-ph.GA, gr-qc
Abstract:
The cosmological principle, which asserts a statistically homogeneous and
isotropic universe on large scales, is a foundational assumption of the
standard cosmological model. A critical test of this principle involves the
kinematic interpretation of the Cosmic Microwave Background temperature dipole,
conventionally attributed to our peculiar motion relative to the cosmic rest
frame. The Ellis-Baldwin test provides a probe of this kinematic interpretation
by searching for a matching Doppler-driven dipole in the number counts of
extragalactic radio sources. Recent measurements from the CatWISE2020 quasar
catalog have reported a dipole amplitude significantly exceeding the kinematic
expectation, with a claimed significance of $4.9\sigma$. We present a
comprehensive reassessment of this test using the same dataset, incorporating
major sources of uncertainty in the statistical inference. We use a simulation
framework based on the FLASK package, incorporating lognormal realizations of
the large-scale structure, the quasar clustering bias, the survey's radial
selection function, and its exact sky coverage. Our simulations account for the
kinematic dipole, the intrinsic clustering dipole, shot noise, and survey
geometry effects. The analysis yields a revised significance of the kinematic
dipole excess of $3.63\sigma$ in the absence of a clustering dipole, and
$3.44\sigma$ in the presence of a randomly oriented clustering dipole. When the
clustering dipole is aligned with the kinematic dipole direction, the
significance decreases further to $3.27\sigma$. Our analysis demonstrates that
although the anomaly is reduced in significance, it cannot be explained solely
as a result of the clustering dipole or mode coupling arising from the survey
mask.
Published: 2025-11-02 06:19:44
Authors: Ruoxiang Huang, Xindian Ma, Rundong Kong, Zhen Yuan, Peng Zhang
Categories: cs.CV
Abstract:
Vision-Language Models (VLMs) have demonstrated strong performance across
various multimodal tasks, where position encoding plays a vital role in
modeling both the sequential structure of textual information and the spatial
structure of visual information. However, current VLMs commonly adopt
modality-unified 1D or 2D positional indexing strategies, which treat textual
and visual tokens uniformly without accounting for their distinct structural
properties and sequential continuity for text and spatial coherence for vision.
To address this limitation, we propose OMEGA, a novel position encoding
framework that employs Modality-Specific Position Encoding (MSPE) to assign
positional indices while preserving the inherent structures of each modality
across separate coordinate dimensions. Additionally, to align the information
density of multimodal data in the positional index space, OMEGA introduces
Global Adaptive Encoding Step Scaling (GAESS), which adaptively adjusts the
position encoding step size of visual tokens based on the embedding entropy of
both modalities. Experimental results demonstrate that OMEGA consistently
enhances VLM performance across diverse architectures and VQA benchmarks. On
visual-intensive tasks, OMEGA achieves up to 3.43% improvement over baseline
position encoding strategies on Qwen2.5-VL-3B, with consistent gains observed
across larger models including Qwen2.5-VL-7B and LLaVA-v1.5-7B.
Published: 2025-11-02 06:16:29
Authors: Isaac Gibbs, John J. Cherian, Emmanuel J. Candès
Categories: stat.ME, math.ST, stat.TH
Abstract:
We develop a collection of methods for adjusting the predictions of quantile
regression to ensure coverage. Our methods are model agnostic and can be used
to correct for high-dimensional overfitting bias with only minimal assumptions.
Theoretical results show that the estimates we develop are consistent and
facilitate accurate calibration in the proportional asymptotic regime where the
ratio of the dimension of the data and the sample size converges to a constant.
This is further confirmed by experiments on both simulated and real data. A key
component of our work is a new connection between the leave-one-out coverage
and the fitted values of variables appearing in a dual formulation of the
quantile regression problem. This facilitates the use of cross-validation in a
variety of settings at significantly reduced computational costs.
Published: 2025-11-02 06:15:14
Authors: Yuxuan Hu, Jianchao Tan, Jiaqi Zhang, Wen Zan, Pingwei Sun, Yifan Lu, Yerui Sun, Yuchen Xie, Xunliang Cai, Jing Zhang
Categories: cs.CL
Abstract:
In this work, we conduct a systematic analysis of Native Sparse Attention
(NSA) and propose targeted improvements that enhance long-context modeling. A
key insight is that alternating between local (sliding-window) and global
(compression, selective) attention across layers, rather than using fixed
patterns, enables more effective propagation of long-range dependencies and
substantially boosts performance on long-sequence tasks. Meanwhile, we further
refine NSA's branches with Latent Attention that the sliding-window branch is
enhanced with Multi-head Latent Attention (MLA) while compression and selective
branches adopt Group-head Latent Attention (GLA). These changes reduce KV-cache
memory by 50\% versus NSA while improving the model's common-sense reasoning
and long-text understanding capabilities. Experiments on models from 340M to
1.3B parameters (trained on 15B and 100B tokens) show our method matches or
exceeds full attention and native sparse attention in both common-sense
reasoning and long-context understanding tasks.
Published: 2025-11-02 06:10:27
Authors: Lingyao Li, Zhijie Duan, Xuexin Li, Xiaoran Xu, Zhaoqian Xue, Siyuan Ma, Jin Jin
Categories: cs.SI, q-bio.OT
Abstract:
Large language models (LLMs) are increasingly transforming biomedical
discovery and clinical innovation, yet their impact extends far beyond
algorithmic revolution-LLMs are restructuring how scientific collaboration
occurs, who participates, and how resources shape innovation. Despite this
profound transformation, how this rapid technological shift is reshaping the
structure and equity of scientific collaboration in biomedical LLM research
remains largely unknown. By analyzing 5,674 LLM-related biomedical publications
from PubMed, we examine how collaboration diversity evolves over time, identify
institutions and disciplines that anchor and bridge collaboration networks, and
assess how resource disparities underpin research performance. We find that
collaboration diversity has grown steadily, with a decreasing share of Computer
Science and Artificial Intelligence authors, suggesting that LLMs are lowering
technical barriers for biomedical investigators. Network analysis reveals
central institutions, including Stanford University and Harvard Medical School,
and bridging disciplines such as Medicine and Computer Science that anchor
collaborations in this field. Furthermore, biomedical research resources are
strongly linked to research performance, with high-performing
resource-constrained institutions exhibiting larger collaboration volume with
the top 1% most connected institutions in the network. Together, these findings
reveal a complex landscape, where democratizing trends coexist with a
persistent, resource-driven hierarchy, highlighting the critical role of
strategic collaboration in this evolving field.
Published: 2025-11-02 06:02:21
Authors: Yunxiao Zhang, Zhaozheng Lyu, Xiang Wang, Yukun Shi, Duolin Wang, Xiaozhou Yang, Enna Zhuo, Bing Li, Yuyang Huang, Zenan Shi, Anqi Wang, Heng Zhang, Fucong Fei, Xiaohui Song, Peiling Li, Bingbing Tong, Ziwei Dou, Jie Shen, Guangtong Liu, Fanming Qu, Fengqi Song, Li Lu
Categories: cond-mat.mes-hall, cond-mat.supr-con, quant-ph
Abstract:
Majorana zero modes are anyons obeying non-Abelian exchange statistics
distinct from fermions or bosons. While significant progresses have been
achieved in the past two decades in searching for these exotic excitations in
solid-state systems, their non-Abelian nature remains unverified, as definitive
proof requires braiding operations. Here, we report preliminarily experimental
advances in creating, manipulating, and exchanging the presumed Majorana zero
modes in an envelope-shaped Josephson device composed of multiple trijunctions
on a topological insulator surface. We observed the signatures of in-gap states
migration consistent with the expectations of the Fu-Kane model, supporting the
realization of an exchange operation. This work would establish a critical
pathway toward ultimately braiding Majorana zero modes in the Fu-Kane scheme of
topological quantum computation.
Published: 2025-11-02 06:00:31
Authors: Fumiya Tokoro, Hideki Takayama, Shinji Deguchi, Andreas Zöttl, Daiki Matsunaga
Categories: physics.bio-ph, cond-mat.soft
Abstract:
In nature, many unicellular organisms are able to swim with the help of
beating filaments, where local energy input leads to cooperative undulatory
beating motion. Here, we investigate by employing reinforcement learning how
undulatory microswimmers modeled as a discretized bead-bend-spring filament
actuated by torques which are constrained locally. We show that the competition
between actively applied torques and intrinsic bending stiffness leads to
various optimal beating patterns characterized by distinct frequencies,
amplitudes, and wavelengths. Interestingly, the optimum solutions depend on the
action interval, i.e.\ the time scale how fast the microswimmer can \rev{change
the applied torques} based on its internal state. We show that optimized
stiffness- and action-interval-dependent beating is realized by bang-bang
solutions of the applied torques with distinct optimum time-periodicity and
phase shift between consecutive joints, which we analyze in detail by a
systematic study of possible bang-bang wave solution patterns of applied
torques. Our work not only sheds light on how efficient beating patterns of
biological microswimmers can emerge based on internal and local constraints,
but also offers actuation policies for potential artificial elastic
microswimmers.
Published: 2025-11-02 05:57:12
Authors: Yue Gou, Fanghui Song, Yuming Xing, Shengzhu Shi, Zhichang Guo, Boying Wu
Categories: cs.CV
Abstract:
Pancreas segmentation in medical image processing is a persistent challenge
due to its small size, low contrast against adjacent tissues, and significant
topological variations. Traditional level set methods drive boundary evolution
using gradient flows, often ignoring pointwise topological effects. Conversely,
deep learning-based segmentation networks extract rich semantic features but
frequently sacrifice structural details. To bridge this gap, we propose a novel
model named TA-LSDiff, which combined topology-aware diffusion probabilistic
model and level set energy, achieving segmentation without explicit geometric
evolution. This energy function guides implicit curve evolution by integrating
the input image and deep features through four complementary terms. To further
enhance boundary precision, we introduce a pixel-adaptive refinement module
that locally modulates the energy function using affinity weighting from
neighboring evidence. Ablation studies systematically quantify the contribution
of each proposed component. Evaluations on four public pancreas datasets
demonstrate that TA-LSDiff achieves state-of-the-art accuracy, outperforming
existing methods. These results establish TA-LSDiff as a practical and accurate
solution for pancreas segmentation.
Published: 2025-11-02 05:54:30
Authors: Stella Kombo, Masih Haseli, Skylar Wei, Joel W. Burdick
Categories: cs.RO, cs.LG, cs.SY, eess.SY, 93C41, 93E11, 37M10, I.2.9; I.2.6; I.2.8
Abstract:
Autonomous systems often must predict the motions of nearby agents from
partial and noisy data. This paper asks and answers the question: "can we
learn, in real-time, a nonlinear predictive model of another agent's motions?"
Our online framework denoises and forecasts such dynamics using a modified
sliding-window Hankel Dynamic Mode Decomposition (Hankel-DMD). Partial noisy
measurements are embedded into a Hankel matrix, while an associated Page matrix
enables singular-value hard thresholding (SVHT) to estimate the effective rank.
A Cadzow projection enforces structured low-rank consistency, yielding a
denoised trajectory and local noise variance estimates. From this
representation, a time-varying Hankel-DMD lifted linear predictor is
constructed for multi-step forecasts. The residual analysis provides
variance-tracking signals that can support downstream estimators and risk-aware
planning. We validate the approach in simulation under Gaussian and
heavy-tailed noise, and experimentally on a dynamic crane testbed. Results show
that the method achieves stable variance-aware denoising and short-horizon
prediction suitable for integration into real-time control frameworks.
Published: 2025-11-02 05:53:01
Authors: Maximilian Mangold, Lorenz Bauriedl, Johanna Berger, Chang Yu-Cheng, Thomas N. G. Meier, Matthias Kronseder, Pertti Hakonen, Christian H. Back, Christoph Strunk, Dhavala Suri
Categories: cond-mat.supr-con
Abstract:
We explore the role of interfacial Rashba spin-orbit coupling (SOC) for the
Josephson diode effect in all-metal diffusive Josephson junctions. Devices with
Fe/Pt and Cu/Pt weak links between Nb leads reveal a Josephson diode effect in
an in-plane magnetic field with magneto-chiral anisotropy according to the
symmetry of Rashba SOC. The Rashba SOC originates from inversion symmetry
breaking at the metal-metal interfaces. A control sample with a plain Cu-layer
as weak link exhibits also a finite diode efficiency that, in contrast, is
independent of the angle between current and field. The Fraunhofer patterns
display an apparent inverted hysteresis which can be traced back to stray
fields resulting from the conventional hysteretic vortex pinning in the Nb
contacts.
Published: 2025-11-02 05:51:48
Authors: Shashank Nag, Alan T. L. Bacellar, Zachary Susskind, Anshul Jha, Logan Liberty, Aishwarya Sivakumar, Eugene B. John, Krishnan Kailas, Priscila M. V. Lima, Neeraja J. Yadwadkar, Felipe M. G. Franca, Lizy K. John
Categories: cs.LG, cs.CV
Abstract:
Vision Transformers have been tremendously successful in computer vision
tasks. However, their large computational, memory, and energy demands are a
challenge for edge inference on FPGAs -- a field that has seen a recent surge
in demand. We recognize the benefits of recent works on logic and Look Up Table
(LUT) based networks, such as LogicNets, NeuraLUT, DWN, among others, in
offering models that simultaneously reduce both the memory and compute
footprints. However, these models natively do not perform well on common vision
tasks, such as CIFAR-10/100. In this work, we propose LL-ViT, a novel edge
optimized vision transformer design that integrates layers of LUT neurons
within the transformer architecture. Based on our characterization that reveals
that a majority of model weights and computations are from the channel mixer
(MLP layer), we design an alternate LUT-based channel mixer, and simultaneously
develop an FPGA-based accelerator for LL-ViT. Contrary to some attempts to
replace each multiplication with a table lookup, our architecture utilizes a
neural learning approach which natively learns the LUT functions. This approach
allows for reduced model sizes, and a computational and energy-efficient
inference solution for vision transformer models. Evaluating on edge-suitable
workloads, we achieve accuracies of 95.5% on CIFAR-10, 78.8% on CIFAR-100, and
60.9% on Tiny-ImageNet datasets, comparable to the baseline transformer. LL-ViT
eliminates over 60% of the model weights and 50% of the multiplications in the
model, and achieves 1.9x energy efficiency and 1.3x lower latency over an
integer quantized ViT accelerator, while also offering superior throughput
against prior works at a 10.9W power budget.
Published: 2025-11-02 05:45:27
Authors: Runyu Lu, Peng Zhang, Ruochuan Shi, Yuanheng Zhu, Dongbin Zhao, Yang Liu, Dong Wang, Cesare Alippi
Categories: cs.LG
Abstract:
Equilibrium learning in adversarial games is an important topic widely
examined in the fields of game theory and reinforcement learning (RL).
Pursuit-evasion game (PEG), as an important class of real-world games from the
fields of robotics and security, requires exponential time to be accurately
solved. When the underlying graph structure varies, even the state-of-the-art
RL methods require recomputation or at least fine-tuning, which can be
time-consuming and impair real-time applicability. This paper proposes an
Equilibrium Policy Generalization (EPG) framework to effectively learn a
generalized policy with robust cross-graph zero-shot performance. In the
context of PEGs, our framework is generally applicable to both pursuer and
evader sides in both no-exit and multi-exit scenarios. These two
generalizability properties, to our knowledge, are the first to appear in this
domain. The core idea of the EPG framework is to train an RL policy across
different graph structures against the equilibrium policy for each single
graph. To construct an equilibrium oracle for single-graph policies, we present
a dynamic programming (DP) algorithm that provably generates pure-strategy Nash
equilibrium with near-optimal time complexity. To guarantee scalability with
respect to pursuer number, we further extend DP and RL by designing a grouping
mechanism and a sequence model for joint policy decomposition, respectively.
Experimental results show that, using equilibrium guidance and a distance
feature proposed for cross-graph PEG training, the EPG framework guarantees
desirable zero-shot performance in various unseen real-world graphs. Besides,
when trained under an equilibrium heuristic proposed for the graphs with exits,
our generalized pursuer policy can even match the performance of the fine-tuned
policies from the state-of-the-art PEG methods.
Published: 2025-11-02 05:34:21
Authors: Shijie Zhou, Viet Dac Lai, Hao Tan, Jihyung Kil, Wanrong Zhu, Changyou Chen, Ruiyi Zhang
Categories: cs.CV, cs.AI, cs.CL, cs.HC, cs.LG
Abstract:
Graphical user interface (GUI) grounding is a key function of computer-use
agents, which maps natural-language instructions to actionable screen regions.
Existing approaches based on Multimodal Large Language Models (MLLMs) typically
formulate it as a text-based coordinate generation task, yet directly
generating precise coordinates from visual inputs remains challenging and
computationally intensive. An intuitive way to implement GUI grounding is to
first select visual patches relevant to the instructions and then determine the
precise click location within those patches. Based on the observations that
general MLLMs have some native grounding capability, nested within their
attentions, we propose GUI-AIMA, an attention-based and coordinate-free
supervised fine-tuning framework for efficient GUI grounding. GUI-AIMA aligns
the intrinsic multimodal attention of MLLMs with patch-wise grounding signals.
These signals are calculated adaptively for diverse user instructions by
multi-head aggregation on simplified query-visual attention matrices. Besides,
its coordinate-free manner can easily integrate a plug-and-play zoom-in stage.
GUI-AIMA-3B was trained with only 85k screenshots, demonstrating exceptional
data efficiency and verifying that light training can trigger the native
grounding capability of MLLMs. It achieves state-of-the-art performance among
3B models, attaining an average accuracy of 58.6% on ScreenSpot-Pro and 62.2%
on OSWorld-G. Project page: https://github.com/sjz5202/GUI-AIMA
Published: 2025-11-02 05:22:36
Authors: Yang Xu, Haibin Kan, Guangyue Han
Categories: cs.IT, math.IT
Abstract:
In this paper, we characterize the MacWilliams extension property (MEP) and
constant weight codes with respect to $\omega$-weight defined on
$\mathbb{F}^{\Omega}$ via an elementary approach, where $\mathbb{F}$ is a
finite field, $\Omega$ is a finite set, and
$\omega:\Omega\longrightarrow\mathbb{R}^{+}$ is a weight function. Our approach
relies solely on elementary linear algebra and two key identities for
$\omega$-weight of subspaces derived from a double-counting argument. When
$\omega$ is the constant $1$ map, our results recover two well-known results
for Hamming metric code: (1) any Hamming weight preserving map between linear
codes extends to a Hamming weight isometry of the entire ambient space; and (2)
any constant weight Hamming metric code is a repetition of the dual of Hamming
code.
Published: 2025-11-02 05:21:33
Authors: Bowen Fang, Ruijian Zha, Xuan Di
Categories: cs.AI
Abstract:
Predicting public transit incident duration from unstructured text alerts is
a critical but challenging task. Addressing the domain sparsity of transit
operations with standard Supervised Fine-Tuning (SFT) is difficult, as the task
involves noisy, continuous labels and lacks reliable expert demonstrations for
reasoning. While Reinforcement Learning from Verifiable Rewards (RLVR) excels
at tasks with binary correctness, like mathematics, its applicability to noisy,
continuous forecasting is an open question. This work, to our knowledge, is the
first to bridge the gap between RLVR LLM training with the critical, real-world
forecasting challenges in public transit operations. We adapt RLVR to this task
by introducing a tolerance-based, shaped reward function that grants partial
credit within a continuous error margin, rather than demanding a single correct
answer. We systematically evaluate this framework on a curated dataset of NYC
MTA service alerts. Our findings show that general-purpose, instruction-tuned
LLMs significantly outperform specialized math-reasoning models, which struggle
with the ambiguous, real-world text. We empirically demonstrate that the binary
reward is unstable and degrades performance, whereas our shaped reward design
is critical and allows our model to dominate on the most challenging metrics.
While classical regressors are superior at minimizing overall MAE or MSE, our
RLVR approach achieved a 35\% relative improvement in 5-minute accuracy (Acc@5)
over the strongest baseline. This demonstrates that RLVR can be successfully
adapted to real-world, noisy forecasting, but requires a verifier design that
reflects the continuous nature of the problem.
Published: 2025-11-02 05:17:02
Authors: Xuan He, Zequan Fang, Jinzhao Lian, Danny H. K. Tsang, Baosen Zhang, Yize Chen
Categories: cs.DC
Abstract:
The ever-increasing computation and energy demand for LLM and AI agents call
for holistic and efficient optimization of LLM serving systems. In practice,
heterogeneous GPU clusters can be deployed in a geographically distributed
manner, while LLM load also observes diversity in terms of both query traffic
and serving patterns. LLM queries running on advanced GPUs during a
high-emission hour at one location can lead to significantly higher carbon
footprints versus same queries running on mid-level GPUs at a low-emission time
and location. By observing LLM serving requirements and leveraging
spatiotemporal computation flexibility, we consider the joint routing and
scheduling problem, and propose FREESH to cooperatively run a group of data
centers while minimizing user-specified carbon or energy objectives. FREESH
identifies the optimal configurations of balanced load serving by matching
distinct GPU instance's power-throughput characteristics with predictable LLM
query length and workloads. To ensure both latency and fairness requirements,
FREESH identifies optimized parallelism and query routing schedules together
with dynamic GPU frequency scaling for power saving, and Least-Laxity-First
(LLF) serving strategy for query scheduling. During the 1-hour serving on
production workloads, FREESH reduces energy by 28.6% and emissions by 45.45%
together with improvements in SLO attainment and fairness.
Published: 2025-11-02 05:02:17
Authors: Guangxi Wan, Peng Zeng, Xiaoting Dong, Chunhe Song, Shijie Cui, Dong Li, Qingwei Dong, Yiyang Liu, Hongfei Bai
Categories: cs.LG, cs.AI
Abstract:
Cyber-physical systems (CPS) require the joint optimization of discrete cyber
actions and continuous physical parameters under stringent safety logic
constraints. However, existing hierarchical approaches often compromise global
optimality, whereas reinforcement learning (RL) in hybrid action spaces often
relies on brittle reward penalties, masking, or shielding and struggles to
guarantee constraint satisfaction. We present logic-informed reinforcement
learning (LIRL), which equips standard policy-gradient algorithms with
projection that maps a low-dimensional latent action onto the admissible hybrid
manifold defined on-the-fly by first-order logic. This guarantees feasibility
of every exploratory step without penalty tuning. Experimental evaluations have
been conducted across multiple scenarios, including industrial manufacturing,
electric vehicle charging stations, and traffic signal control, in all of which
the proposed method outperforms existing hierarchical optimization approaches.
Taking a robotic reducer assembly system in industrial manufacturing as an
example, LIRL achieves a 36.47\% to 44.33\% reduction at most in the combined
makespan-energy objective compared to conventional industrial hierarchical
scheduling methods. Meanwhile, it consistently maintains zero constraint
violations and significantly surpasses state-of-the-art hybrid-action
reinforcement learning baselines. Thanks to its declarative logic-based
constraint formulation, the framework can be seamlessly transferred to other
domains such as smart transportation and smart grid, thereby paving the way for
safe and real-time optimization in large-scale CPS.
Published: 2025-11-02 05:01:04
Authors: Rishita Agarwal, Himanshu Singhal, Peter Baile Chen, Manan Roy Choudhury, Dan Roth, Vivek Gupta
Categories: cs.IR
Abstract:
Answering natural language queries over relational data often requires
retrieving and reasoning over multiple tables, yet most retrievers optimize
only for query-table relevance and ignore table table compatibility. We
introduce REAR (Retrieve, Expand and Refine), a three-stage, LLM-free framework
that separates semantic relevance from structural joinability for efficient,
high-fidelity multi-table retrieval. REAR (i) retrieves query-aligned tables,
(ii) expands these with structurally joinable tables via fast, precomputed
column-embedding comparisons, and (iii) refines them by pruning noisy or weakly
related candidates. Empirically, REAR is retriever-agnostic and consistently
improves dense/sparse retrievers on complex table QA datasets (BIRD, MMQA, and
Spider) by improving both multi-table retrieval quality and downstream SQL
execution. Despite being LLM-free, it delivers performance competitive with
state-of-the-art LLM-augmented retrieval systems (e.g.,ARM) while achieving
much lower latency and cost. Ablations confirm complementary gains from
expansion and refinement, underscoring REAR as a practical, scalable building
block for table-based downstream tasks (e.g., Text-to-SQL).
Published: 2025-11-02 04:59:38
Authors: Abhiram Kusumba, Maitreya Patel, Kyle Min, Changhoon Kim, Chitta Baral, Yezhou Yang
Categories: cs.LG, cs.CV
Abstract:
Erasing harmful or proprietary concepts from powerful text to image
generators is an emerging safety requirement, yet current "concept erasure"
techniques either collapse image quality, rely on brittle adversarial losses,
or demand prohibitive retraining cycles. We trace these limitations to a myopic
view of the denoising trajectories that govern diffusion based generation. We
introduce EraseFlow, the first framework that casts concept unlearning as
exploration in the space of denoising paths and optimizes it with GFlowNets
equipped with the trajectory balance objective. By sampling entire trajectories
rather than single end states, EraseFlow learns a stochastic policy that steers
generation away from target concepts while preserving the model's prior.
EraseFlow eliminates the need for carefully crafted reward models and by doing
this, it generalizes effectively to unseen concepts and avoids hackable rewards
while improving the performance. Extensive empirical results demonstrate that
EraseFlow outperforms existing baselines and achieves an optimal trade off
between performance and prior preservation.
Published: 2025-11-02 04:49:46
Authors: Sohini Guin, Naresh Shyaga, Jagadish Rajendran, Aryaman Das, Subhransu Kumar Negi, Saisab Bhowmik, Pankaj Bhardwaj, U. Chandni, Dhavala Suri
Categories: cond-mat.supr-con
Abstract:
High-temperature superconductors (high-Tc SCs) host a rich landscape of
electronic phases encompassing the pseudogap, strange metal, superconducting,
antiferromagnetic insulating, and Fermi-liquid regimes. The superconducting
phase is notable for non-dissipative electronic functionality at relatively
high temperatures. These phases are commonly probed in thermodynamic phase
space by varying temperature or current through the sample. They can also be
probed by breaking time-reversal symmetry (TRS) with an external magnetic
field, which yields transition signatures distinct from those arising solely
from temperature or current tuning. Here we show that electron transport in
Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ is primarily governed by two-dimensional
superconductivity consistent with a Berezinskii-Kosterlitz-Thouless (BKT)
topological phase transition, as supported by current-voltage characteristics
measured under temperature variation; these measurements preserve TRS. In
contrast, when an external magnetic field is applied, the superconducting state
is consistently preceded by weak antilocalization (WAL), where bound
vortex-antivortex pairs dissociate into a normal metallic state through an
intermediate localized phase. We further establish that highly disordered films
exhibit transport dominated by three-dimensional weak localization, with
superconductivity entirely suppressed.
Published: 2025-11-02 04:47:17
Authors: Jie JW Wu, Ayanda Patrick Herlihy, Ahmad Saleem Mirza, Ali Afoud, Fatemeh Fard
Categories: cs.SE, cs.CL, cs.LG
Abstract:
With the software industry shifting toward a data-driven culture, online A/B
testing is a key tool for evaluating new technologies. However, deploying such
experiments requires substantial resources, may negatively impact users, and
involves long data collection periods. To address this, \textit{off-policy
evaluation (OPE)}, or offline A/B testing, uses logged data to assess
technologies and is fundamental in Reinforcement Learning, making it crucial in
domains where online testing is costly or risky, such as healthcare,
recommender systems, education, dialog systems, and robotics. Despite advances
in coding LLMs and agentic AI, little is known about leveraging them to
optimize OPE results. We investigate whether LLMs and LLM-based agents can
improve OPE performance via code optimization. We propose
\textit{GrowthHacker}, a benchmark with agent and baseline methods on
large-scale real-world datasets, which iteratively optimizes code, evaluates
results, and begins new optimization cycles. We collected datasets, established
protocols, implemented baselines for OPE on the Open Bandit Pipeline
(OBP)~\cite{saito2021openbanditdatasetpipeline} and
Scope-RL~\cite{kiyohara2023scope}, and developed the \textit{two_agent}
framework, which reduces system complexity while preserving optimization
effectiveness. Results show the two_agent framework achieves 100% reliability
and the highest average improvement of 106.7% among positive outcomes. Both
two_agent and CrewAI reach 45% success rates, outperforming AutoGen's 34%.
These findings demonstrate the feasibility of LLM-based agents as automated
"growth hackers" to enhance OPE systems, with implications for scaling
data-driven decision-making in production.
Published: 2025-11-02 04:46:43
Authors: Zhihui Chen, Mengling Feng
Categories: cs.CV, cs.MM
Abstract:
Recent advances in multimodal large language models have enabled remarkable
medical image editing capabilities. However, the research community's progress
remains constrained by the absence of large-scale, high-quality, and openly
accessible datasets built specifically for medical image editing with strict
anatomical and clinical constraints. We introduce Med-Banana-50K, a
comprehensive 50K-image dataset for instruction-based medical image editing
spanning three modalities (chest X-ray, brain MRI, fundus photography) and 23
disease types. Our dataset is constructed by leveraging Gemini-2.5-Flash-Image
to generate bidirectional edits (lesion addition and removal) from real medical
images. What distinguishes Med-Banana-50K from general-domain editing datasets
is our systematic approach to medical quality control: we employ LLM-as-Judge
with a medically grounded rubric (instruction compliance, structural
plausibility, realism, and fidelity preservation) and history-aware iterative
refinement up to five rounds. Beyond single-turn editing, Med-Banana-50K
includes 37K failed attempts with full conversation logs for preference
learning and alignment research. By providing this large-scale, medically
validated, and fully documented resource, Med-Banana-50K establishes a
foundation for training and evaluating the next generation of medical image
editing models.Our dataset and code are publicly available at
[https://github.com/richardChenzhihui/med-banana-50k].
Published: 2025-11-02 04:45:14
Authors: Abbé M. Whitford, Cullan Howlett, Tamara M. Davis, David Camarena, Francis-Yan Cyr-Racine
Categories: astro-ph.CO
Abstract:
Neutrinos with Standard Model interactions free-stream in the early Universe,
leaving a distinct phase shift in the pattern of baryon acoustic oscillations
(BAO). When isolated, this phase shift allows one to robustly infer the
presence of the cosmic neutrino background in BAO and cosmic microwave
background (CMB) data independently of other cosmological parameters. While in
the context of the Standard Model, this phase shift follows a known
scale-dependent relation, new physics in the cosmic neutrino background could
alter the overall shape of this feature. In this paper, we discuss how changes
in the neutrino phase shift could be used to constrain self-interactions among
neutrinos. We produce simple models for this phase-shift assuming universal
self-interactions, and use these in order to understand what constraining power
is available for the strength of such interactions in BAO and CMB data. We find
that, although challenging, it may be possible to use a detection of the phase
to put a more robust limit on the strength of the self-interaction,
$G_{\mathrm{eff}}$, which at present suffers from bimodality in cosmological
constraints. Our forecast analysis reveals that BAO data alone will not provide
the precision needed to tightly constrain self-interactions; however, the
combined analysis of the phase shift signature in both CMB and BAO can
potentially provide a way to detect the impact of new neutrino interactions.
Our results could be extended upon for models with non-universal interactions.
Published: 2025-11-02 04:44:15
Authors: Haocheng Fan
Categories: math.NT, math.AG
Abstract:
We prove that the coherent cohomological dimension of the Siegel modular
variety $A_{g,\Gamma}$ is at most $g(g+1)/2-2$ for $g\geq 2$. As a corollary,
we show that the boundary of the compactified Siegel modular variety satisfies
the Grothendieck-Lefschetz condition. This implies, in particular, that formal
Siegel modular forms of genus $g\geq2$ are automatically classical Siegel
modular forms. Our result generalizes the work of Bruinier and Raum on the
modularity of formal Siegel modular forms.
Published: 2025-11-02 04:40:36
Authors: Brandon Venville, Anna Bonaldi, David Parkinson, Natasha Hurley-Walker, Tim Galvin, Nick Seymour
Categories: astro-ph.CO
Abstract:
Techniques using multiple tracers of the large scale structure of the
universe show great promise for examining the fundamentals of our Universe's
cosmology. Such techniques rely on the different relationship between the
overdensity of tracers and the broader matter overdensity, enabling
cosmic-variance-free tests of primordial non-Gaussianity in the initial
curvature perturbations. There is a great opportunity for current and future
all-sky extra-galactic radio surveys to make use of this technique to test for
non-Gaussianity at a precision greater than existing all-sky constraints from
the cosmic microwave background. To realize this goal there is a need for
accurate simulations. Previous radio galaxy simulations have either been
realistic but covering only a small area (and so unhelpful for cosmological
forecasts), or all-sky dark matter only cosmological simulations but having no
connection to a real radio galaxy population. In this study, we use the
FLAMINGO suite of cosmological surveys, as well as the matching of dark matter
halos to radio galaxy population, to create an accurate sky simulation in order
to examine the feasibility of multi-tracer techniques. We present an analysis
of the clustering (with a bias model for the simulation), as well as redshift
distributions, source counts and radio luminosity functions, and discuss future
work on non-Gaussianity detection.
Published: 2025-11-02 04:32:41
Authors: Wang Zixian
Categories: cs.LG, cs.AI
Abstract:
Pre-trained Transformers often exhibit over-confidence in source patterns and
difficulty in forming new target-domain patterns during fine-tuning. We
formalize the mechanism of output saturation leading to gradient suppression
through standard cross-entropy and softmax analysis, showing that gradient
suppression at inflection layers confines adaptation to high-level
recombination of existing features while preventing low-level reconstruction.
We introduce a set of layer-wise diagnostic metrics -- attention entropy
(saturation proxy), activation gradient norm, parameter gradient norm, and
Delta-CKA under a shared PCA basis -- to identify inflection layers
characterized by both low attention entropy and steep gradient decay. Building
on these findings, we propose a diagnose-first, inject-light fine-tuning
strategy: selectively inserting LoRA adapters at inflection layers to restore
suppressed backward signals with minimal parameter overhead. Experiments on
BERT-base transfer from SST-2 to Rotten Tomatoes under under-trained and
over-trained source regimes reveal that over-trained initialization benefits
from inflection-layer LoRA injection, while under-trained initialization
suffers performance degradation. When base features are strong, unblocking
inflection layers facilitates high-level compositional adaptation; when base
features are weak, full-pathway unblocking is required for low-level
reconstruction, as supported by joint analysis of layer-wise activation
gradients and Delta-CKA dynamics.
Published: 2025-11-02 04:17:30
Authors: Ran Yan, Youhe Jiang, Tianyuan Wu, Jiaxuan Gao, Zhiyu Mei, Wei Fu, Haohui Mai, Wei Wang, Yi Wu, Binhang Yuan
Categories: cs.DC, cs.LG
Abstract:
Maximizing training throughput and cost-efficiency of RL for LLMs is
essential to democratize this advanced technique. One promising but challenging
approach is to deploy such a computational workflow over heterogeneous GPUs.
Unlike conventional large-scale LLM pretraining, RL training generally
decomposes into three coupled stages, i.e., rollout generation, reward
computation, and policy/value updates, which exhibit markedly different compute
intensities, memory footprints, and communication patterns. Recent research
shows that fully asynchronous RL training can disaggregate these stages across
disjoint hardware pools without sacrificing training stability, creating a
great opportunity for real-world heterogeneous deployment. To this end, we
present AReaL-Hex, a heterogeneity-aware asynchronous RL training system that
effectively schedules how to execute rollout generation and policy model
training over heterogeneous GPUs while enforcing data staleness bounds.
Concretely, we use a two-phase scheduler: (i) a constrained search with MILP to
select per-stage parallelization strategies and workload assignments given a
resource budget, and (ii) a graph-partitioning step that allocates
heterogeneous GPUs and interconnects to maximize end-to-end throughput. Built
atop a fully asynchronous RL architecture, AReaL-Hex maps HBM-I/O-bound
generation and compute-bound optimization to more cost-efficient resources and
balances their producer-consumer interactions to avoid both idleness and stale
rollout trajectories. On the mathematical reasoning task with various model
scales (1.5B, 7B, and 14B), compared to homogeneous deployments of
state-of-the-art asynchronous RL systems: (i) When maintaining the same total
budgets, AReaL-Hex delivers up to 1.50x higher training throughput; (ii) When
achieving the same training throughput, AReaL-Hex results in up to 1.46x
reduction in training cost.
Published: 2025-11-02 04:17:14
Authors: Viswa Chaitanya Marella, Suhasnadh Reddy Veluru, Sai Teja Erukude
Categories: cs.CV, cs.AI
Abstract:
Federated Learning (FL) allows multiple institutions to cooperatively train
machine learning models while retaining sensitive data at the source, which has
great utility in privacy-sensitive environments. However, FL systems remain
vulnerable to membership-inference attacks and data heterogeneity. This paper
presents FedOnco-Bench, a reproducible benchmark for privacy-aware FL using
synthetic oncologic CT scans with tumor annotations. It evaluates segmentation
performance and privacy leakage across FL methods: FedAvg, FedProx, FedBN, and
FedAvg with DP-SGD. Results show a distinct trade-off between privacy and
utility: FedAvg is high performance (Dice around 0.85) with more privacy
leakage (attack AUC about 0.72), while DP-SGD provides a higher level of
privacy (AUC around 0.25) at the cost of accuracy (Dice about 0.79). FedProx
and FedBN offer balanced performance under heterogeneous data, especially with
non-identical distributed client data. FedOnco-Bench serves as a standardized,
open-source platform for benchmarking and developing privacy-preserving FL
methods for medical image segmentation.
Published: 2025-11-02 04:16:47
Authors: Yan Sun, Jia Guo, Stanley Kok, Zihao Wang, Zujie Wen, Zhiqiang Zhang
Categories: cs.LG, cs.AI
Abstract:
Reinforcement learning with verifiable rewards (RLVR) has improved the
reasoning ability of large language models, yet training remains costly because
many rollouts contribute little to optimization, considering the amount of
computation required. This study investigates how simply leveraging intrinsic
data properties, almost free benefit during training, can improve data
efficiency for RLVR. We propose PREPO with two complementary components. First,
we adopt prompt perplexity as an indicator of model adaptability in learning,
enabling the model to progress from well-understood contexts to more
challenging ones. Second, we amplify the discrepancy among the rollouts by
differentiating their relative entropy, and prioritize sequences that exhibit a
higher degree of exploration. Together, these mechanisms reduce rollout demand
while preserving competitive performance. On the Qwen and Llama models, PREPO
achieves effective results on mathematical reasoning benchmarks with up to 3
times fewer rollouts than the baselines. Beyond empirical gains, we provide
theoretical and in-depth analyses explaining the underlying rationale of our
method to improve the data efficiency of RLVR.