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新论文 · v1arXiv:2610.36280

EVA-Flow: Learning Shared Chemistry for Unified Environment-Aware Molecular Conformation Generation

Bing Yan, Anuroop Sriram, Zachary Ward Ulissi, Kyunghyun Cho, Ricky T. Q. Chen, Benjamin Kurt Miller

原文摘要

Molecular 3D structures underpin simulations of thermodynamics, kinetics, crystallization, and nucleation, making their accurate prediction central to drug discovery and materials design. However, molecular geometry can change dramatically across environments. Existing models either ignore environmental effects or train separate models for each environment. We hypothesize that transfer learning across contexts is possible for conformer generation and that a general model will perform better when training data are limited, as in ligand binding and crystallization. We introduce EVA-Flow, a unified framework for environment-aware molecular conformation generation that learns shared chemical structure and adapts it through environment-conditioned generative modeling. Across vacuum, protein-ligand docking, solvation, and crystal packing, pretraining and cross-environment finetuning provide their largest gains in the three data-scarce settings. Evaluation against multiple reference conformations shows that EVA-Flow covers a broad range of solvation conformations. Analyses of molecules shared across environments support its ability to generate different conformations for the same molecule in different environments.

cs.LG · cs.CE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2403.12357

Unifying Sparse Estimation of Precision and Covariance Matrices

Aramayis Dallakyan, Mohsen Pourahmadi

原文摘要

Penalized maximum likelihood is a widely used approach to estimate a sparse precision or a sparse covariance matrix. A zero in the precision matrix encodes a conditional independence and a zero in the covariance matrix a marginal independence, and the two estimators have been developed separately, each with its own algorithm. We show that these two problems can be unified via a single reparametrization of the last column of either matrix. This reparametrization decouples the likelihood function for each problem to a one-dimensional minimization followed by a penalized multiple regression. Our approach applies to any penalty that is separable in the off-diagonal entries and linear on the diagonal. When the penalty is the $\ell_1$ norm, it unifies the graphical Lasso and the covariance graphical Lasso algorithms.

stat.CO · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37709

Towards Myocardial Infarction Diagnosis: An Investigation into Machine Learning and Deep Learning Algorithms using ECG Data

Rafid Ahnaf, Husne Mubarak, Fahim Ahsan, G. M. Refatul Islam, Rafeed Rahman, Nabuat Zaman Nahim

原文摘要

Myocardial Infarction (MI) is a severe and potentially fatal cardiac condition caused by myocardial injury due to ischemia, and timely detection can substantially reduce its mortality. Electrocardiography (ECG) is the primary diagnostic modality for MI, but manual interpretation is susceptible to human error. In this work, we propose Visionary V2, a multi-step, adaptive filtration method for ECG signal normalization, and use it to study machine learning (ML) and deep learning (DL) approaches to MI detection on the PTB Diagnostic ECG Database using all 15 available leads. On statistical features extracted from the filtered signals, such as the R-R interval, a Bagging ensemble of decision trees reaches 96.6% accuracy, compared with 87.7% when the signals are processed with BioSPPy. We further generate an image dataset from the filtered signals, on which a custom ResNet-50 misclassifies only 1 of 7,000 validation images and a custom CNN reaches an F1 score of about 98.7%. Finally, we apply LIME, Grad-CAM and Integrated Gradients to verify that the image models attend to the ECG waveform rather than the background. These results indicate that careful signal normalization combined with ML and DL models can support fast and accurate automated MI screening.

eess.IV · cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.21975

Machine learning and digital pragmatics: Which word category influences emoji use most?

Mohammed Q. Shormani, Lamia Alhassoun, Yehia A. AlSohbani

原文摘要

This study examines the performance of the state-of-the-art MARBERT model in identifying the lexical/pragmatic category associated with emoji use on X within a digital pragmatics approach (DPA). A net corpus of 15856 Colloquial Arabic (CA) posts containing emojis was collected from X using Python. The texts were tokenized and normalized into 4 lexical categories, namely noun_norm, verb_norm, adj_norm, and adverb_norm, and 2 pragmatic/structural categories, question_norm and exclamation_norm. MARBERT was finetuned and optimized to identify which category scores standard metrics more, hence associated with emoji use, while binary logistic regression was used to examine which category is statistically associated with emoji occurrence. Findings unveil that nouns dominate the corpus in normalized frequency (M = 0.675, SD = 0.161), followed by verbs (M = 0.083, SD = 0.100). However, verbs have the strongest influence of emoji use indicated by verb density (\b{eta} = 0.821, p = .001, 95% CI [0.332, 1.309]). Findings further show that MARBERT performs comparatively well for verbs (precision = .74, recall = .99, F1 = .85) and nouns (precision = .88, recall = .44, F1 = .59), and substantially weaker for the less frequent categories. The study concludes that in digital pragmatics of CA on X, emoji use association with lexical/pragmatic category can be explained by a hybrid approach of computational, statistical, and pragmatic methods, reflecting the interaction among machine learning, linguistic/lexical features, contextual representation, and digital communication.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.31544

Same Luck, Different Choice: Twin-World Credit Assignment for Agentic Reinforcement Learning

Zeropy Surio, Tobias Engel, Mei-Lin Chou, Anika Raman, Samuel Okafor

原文摘要

Agentic reinforcement learning rewards a complete episode, yet the policy needs credit for every decision inside it. Counterfactual branching estimates this credit by re-running the episode after an alternative decision, but the branch draws its sampling noise independently of the factual episode, so the outcome difference mixes the effect of the decision with the luck of everything that follows. We observe that in agentic search the retriever, the corpus, and the grader are deterministic, so an episode is a deterministic function of the policy's decisions and its sampling noise. Twin worlds are therefore realizable: the same noise, a different decision. We introduce TWICE (TWIn-world Credit Estimation), which keys the Gumbel noise of every token by turn and line, rolls out counterfactual twins that inherit the factual episode's downstream noise, and turns their paired outcome differences into turn-level advantages for policy-gradient updates on actor-sampled trajectories; only the actor is deployed. Twin credit is unbiased for any coupling quality, its variance is that of an independent counterfactual scaled by $1-ρ$, where $ρ$ is the twins' outcome correlation, and under coupling the number of rollouts needed to resolve an advantage of size $Δ$ falls from $Θ(Δ^{-2})$ to $Θ(Δ^{-1})$. A square-root rule spends the twin budget where outcomes are predicted to disagree per unit cost. On seven question-answering benchmarks, TWICE improves the average exact match of GRPO by 5.3 and 4.5 points on Qwen2.5-3B and 7B, outperforms tree-search and counterfactual-branching baselines, and uses 68% fewer auxiliary tokens than VinePPO.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.21307

SPARCL: Spectral Partitioned Analytic Continual Learning

James Hartley, Zeropy Surio, Daniel Whitmore, Hannah Clarke, Thomas Reed

原文摘要

Analytic continual learning has emerged as a strong exemplar-free alternative to gradient-based class-incremental learning because it replaces iterative optimization with closed-form ridge updates. Yet the usual forgetting narrative, centered on stochastic gradient overwriting, does not explain why analytic methods still drift on old classes despite exact recursive solvers. We identify the culprit as spectral interference: the joint ridge classifier for all tasks shares the inverse autocorrelation operator $(R+λI)^{-1}$, so incoming task samples that load onto old dominant eigendirections dilute the spectrum and perturb old-class logits even when old labels are never revisited. Based on this view, we propose SPARCL, a spectral partitioned analytic continual learner that decomposes the running autocorrelation into a high-energy core and a residual complement, freezes old-class classifier components in the core subspace, and updates only the residual block through recursive least squares with an optional residual random-projection expansion. This yields a simple closed-form update with a provable invariance guarantee for the core contribution of old logits. Across CIFAR-100, CUB-200, ImageNet-R, and ImageNet-A under a frozen ViT-B/16 protocol, SPARCL closes most of the gap from classical analytic learners to strong representation matchers, while remaining complementary to sparse feature-decorrelation approaches such as Fly-CL.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2510.19818

Semantic World Models

Jacob Berg, Chuning Zhu, Yanda Bao, Ishan Durugkar, Abhishek Gupta

原文摘要

Planning with world models offers a powerful paradigm for robotic control. Conventional approaches train a model to predict future frames conditioned on current frames and actions, which can then be used for planning. However, the objective of predicting future pixels is often at odds with the actual planning objective; strong pixel reconstruction does not always correlate with good planning decisions. This paper posits that instead of reconstructing future frames as pixels, world models only need to predict task-relevant semantic information about the future. For such prediction the paper poses world modeling as a visual question answering problem about semantic information in future frames. This perspective allows world modeling to be approached with the same tools underlying vision language models. Thus, vision-language models can be trained as "semantic" world models through a supervised finetuning process on image-action-text data, enabling planning for decision-making while inheriting many of the generalization and robustness properties from the pretrained vision-language models. In this paper, we introduce this new method and validate it on simulated robotic tasks leading to improvements over typical paradigms of reconstruction-based action-conditional world modeling. Website available at https://weirdlabuw.github.io/swm.

cs.LG · cs.AI · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.38136

CLeaR: A Unified Framework for Resolving the Leakage-Degradation Dilemma in Style Transfer

Teng Zhou, Yunhao Chen

原文摘要

Style transfer aims to render target content in the style of a reference image, but existing methods often suffer from content leakage, where objects, layouts, or semantics from the style reference appear in the generated output. Although prior data-driven and training-free methods can reduce leakage, they often face a leakage-degradation dilemma: stronger content suppression may weaken style fidelity, while richer style preservation may reintroduce unwanted reference content. We identify this dilemma across the full style-transfer pipeline, including feature separation, feature-space grounding, and diffusion generation. To address these issues, we propose CLeaR, a training-free framework for content-leakage-resistant style transfer. CLeaR first uses Orthogonal Subspace Projection to define content-reduced style targets in each vision foundation model (VFM) feature space. It then performs Ensemble Inversion, which optimizes a shared pixel-space style anchor satisfying style constraints across multiple VFMs. Finally, Energy-Guided Calibration maintains style alignment during diffusion sampling by steering the denoising trajectory toward the ensemble-defined style manifold. We further provide a theoretical analysis showing that the style-anchor estimation error decreases with the number of VFMs. Experiments on StyleBench demonstrate that CLeaR improves style alignment, reduces content leakage, and achieves better LLM-as-Judge evaluation compared with existing methods. The code is available at \href{https://github.com/0606zt/CLeaR}{https://github.com/0606zt/CLeaR}.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.28891

Fig: Automated Multiagent Behavior Elicitor

Anrui Gu, Zhichao Wang, Nicolai Ouporov

原文摘要

Many social evaluations for multiagent systems study behaviors as they emerge from environments instead of systematically intervening in the environment to elicit such behaviors. We introduce Fig (Finding Interactions in Games), an agentic system that performs targeted elicitation interventions on game environments. It takes a natural language behavior description, decomposes it into sub-behaviors, generates new social games or selects existing ones, and executes single or combined interventions to the games' engine, opponents, instructions, and context. To evaluate model behavior, it combines built-in mechanical checks and synthetic rubric judgments. We validate the interventions, generated games, and evaluation mechanisms via human review. Models attempt the target behavior in 45.2% of episodes on average for valid scenarios generated by Fig for six test behaviors -- cooperation, exploitation, deception, covert coordination, betrayal, and sabotage. For game-behavior pairs with available controls, Fig's interventions increase attempt rates by 10.9% on average compared to matched control games. Fig shows that multiagent environments can serve as programmable test beds for intentional, controllable, and reproducible behavior evaluation. Code is available at https://github.com/fleet-ai/fig.

cs.AI · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36270

Learning Multi-Phase Robot Loco-Manipulation via Phase-Conditioned Policy Optimization

Simone Manni, Annika Wollschläger, Dario Bellicoso, Abel Gawel, Marco Hutter, Simon Le Cleac'h, Jan Brüdigam

原文摘要

Complex whole-body loco-manipulation requires executing diverse maneuvers, such as crouching, grasping, and locomotion, within a single long-horizon task. While performing simple pick-and-place behaviors is well-established, scaling to complex, long-horizon tasks remains difficult. Currently, this challenge is addressed by relying on human motion data and correspondingly humanoid platforms, which limits platform independence and introduces substantial data processing overhead. Instead, we extend proximal policy optimization (PPO) to train a single policy for multi-phase tasks across diverse robotic hardware, leveraging prior task structure to shape behavior. Our method adapts PPO using a phase-conditioned critic, phase-gated reward functions, and a multi-phase reset curriculum to guide exploration. To support varied hardware, we employ a decoupled hierarchical control architecture that separates lower-level balancing dynamics from high-level phase execution. We demonstrate multi-stage loco-manipulation skills on hardware on both the Unitree G1 and a Boston Dynamics Spot equipped with a manipulator arm, and validate our design choices through ablations. Our approach enables the acquisition of complex loco-manipulation behaviors purely through environment interaction while directly incorporating high-level task structure into policy learning.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36269HF 社区推荐

Cross-modal Steering Vectors Exist Across Both Image and Text Generation

Donghao Zeng, Nishant Subramani, Tamar Rott Shaham, Benno Krojer

原文摘要

Are concepts and mechanisms unified across modalities in current AI models, or instead implemented separately for each one? We operationalize this broad question of modality unification by studying cross-modal steering: 1) finding vectors that successfully steer generation on tokens of one modality, which then 2) transfer zero-shot to steer generation when added to tokens of another modality. Our study goes beyond simple correlational alignment (e.g. representational similarity) because steering is fundamentally causal. We study cross-modal steering in several unified multimodal models (UMMs), since they possess shared parameters for language and image generation, focusing on these two modalities. Across diverse steered concepts such as emotion and object size, we show that steering vectors transfer zero-shot in both directions (text-to-image & image-to-text). Even for concepts that are highly unique to one modality (e.g. formal vs. colloquial language), steering vectors often induce a consistent, interpretable effect in the other modality, such as formal clothing and design. This transfer has a geometric signature too: despite a clear modality gap separating text and image representations, steering vectors with strong cross-modal transferability tend to point in similar directions for the same concept. Our findings shed light on how vision and language interact inside unified multimodal models and point towards more general, modality-agnostic approaches for controlling their behavior.

cs.CV · cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36267

Preserving rare-item recall under a storage budget: quantization, distillation, and the limits of teacher fidelity

Han Chen, Yingrui Li

原文摘要

Under a fixed embedding-storage budget, recommenders can use a small full-precision model, a distilled student, or a higher-rank low-precision model. We asked which choice preserves rare-item recall and whether teacher fidelity reflects it in seven dataset-backbone cells: matrix factorization on Gowalla, Last.fm, Amazon Video Games, Steam, Goodbooks-10k and MovieLens-20M, and LightGCN on Amazon Video Games. Seven candidates fit in 168 bytes per user or item row: 40-dimensional Bayesian personalized ranking (BPR), two 40-dimensional unbiased knowledge distillation (UnKD) students, and factorizations of a 300-dimensional teacher's scores at ranks 40, 80, 155 and 300, stored in 32, 16, 8 and 4 bits. Each test set was evaluated once after fixing candidates and confirmatory analyses. The rank-300 4-bit factorization used 13% of teacher storage and retained 95-103% of its tail recall@20 and 93-97% of its NDCG@20 in every cell. The rank-40 factorization had the lowest tail recall in every cell; the 4-bit factorization had 1.2-12.7 times its tail recall and 0.99-1.46 times its NDCG. UnKD students retained 56-102% of teacher tail recall. In all ten prespecified comparisons across five prospective-expansion cells, the 4-bit factorization had higher tail recall, with simultaneous 95% intervals excluding zero. Teacher-hit retention, the share of the teacher's relevant tail hits also found by a model, ordered 23 of 147 candidate pairs differently from tail recall and identified the wrong tail-recall leader in 3 of 7 cells: models can find relevant tail items the teacher misses. In these cells, spending bytes on rank rather than precision preserved rare-item recall at 7.5 times the arithmetic per score. Compressed recommenders should be judged by total tail recall, not teacher fidelity alone.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.19298

Scoring-Rule Sensitivity in LLM Evaluation: Evidence from Indian Financial Regulatory Text

Rajveer Singh Pall

原文摘要

A benchmark leaderboard reports one accuracy number, but that number is a joint product of a model and a scoring rule. We ask what changes when only the rule changes, and find that model ordering is highly sensitive to it. IndiaFinBench is a 406-item expert-annotated benchmark over SEBI and RBI regulatory text covering regulatory interpretation, numerical reasoning, contradiction detection, and temporal reasoning. We score twelve LLMs zero-shot under two primary regimes over the same predictions - strict four-stage string matching, and the verdicts of a cross-model judge sharing no model family with any evaluated system - plus one operational composite regime. The two primary regimes show no positive rank correspondence (Spearman rho = -0.273, p = 0.39, not significant), and no model holds its rank across all three regimes. Strict accuracy spans 75.12-89.66%; judge-only accuracy spans 87.19-94.83% with a different leader and trailer; the composite compresses the field to 94.1-98.0%, under which DeepSeek-R1-Distill-Llama-70B ties for first despite ranking last under strict scoring, because the judge reclassifies 93.9% of its strict errors as correct. Output verbosity does not explain the reclassification once task and model composition are adjusted for, a negative result we report as such. Item-level analysis compounds the finding: 148 of 406 items (36.5%) are answered identically by all twelve models and separate no pair of systems, while a predefined higher-discrimination band captures 134 items. We also anchor the scale against one non-specialist evaluator, who scored 80.0% (48/60; 95% Wilson CI [68.2, 88.2]) on a 60-item medium/hard subset; paired bootstrap tests find no significant difference against any model after multiplicity correction. Data, harness, judge verdicts, predictions: https://github.com/Rajveer-code/IndiaFinBench

cs.CL · cs.AI · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36264

The Inbox as a Coordination Primitive for LLM Agent Societies

Karthik Sreedhar, Leo Yuan, Pradyumna Chari, Ramesh Raskar

原文摘要

Language-model agents increasingly coordinate in large groups, and current agent protocols focus on delivering messages. For these agents, however, the scarce resource is not delivery but reading: every message costs tokens, and the context is finite. Current infrastructure works by push: the sender decides when a message enters the recipient's context, so the recipient reads in arrival order and drops the oldest messages once its context is full. We study the alternative, pull, in which messages wait in an inbox outside the context and the recipient's own rules decide what it reads and keeps. We argue that the inbox is a coordination primitive: each agent sets its rules privately, and those rules decide whether other agents' requests reach it. In simulated societies of up to 16,000 agents, with every decision an LLM judgment, we report three findings. First, push fails as an agent takes part in more coordination at once: an agent leading 20 negotiations with a full memory proposes options already rejected 97% of the time, because it has dropped the oldest replies. Second, an inbox is necessary under heavy, noisy traffic. Where first come, first read misses every request, an inbox that first reads messages carrying a refundable deposit still finds about 60% of the agents a request needs, at every size from 1,000 to 16,000. On a real email network, losses compound: when half the agents adopt such an inbox, forwarded search recovers only 5% of full adoption, against 42% for one-hop search. Third, an inbox comprises four decisions (what to read, what to keep, what a sender pays, and what a reply may say), and the best rule for each depends on the traffic and the agent's role. Multi-agent coordination under load requires an inbox layer designed for the traffic each agent receives.

cs.MA

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2407.18454

Fairness Definitions in Language Models Explained

Zhipeng Yin, Zichong Wang, Avash Palikhe, Wenbin Zhang

原文摘要

Language Models (LMs) have demonstrated exceptional performance across various Natural Language Processing (NLP) tasks. Despite these advancements, LMs can inherit and amplify societal biases related to sensitive attributes such as gender and race, limiting their adoption in real-world applications. Therefore, fairness has been extensively explored in LMs, leading to the proposal of various fairness notions. However, the lack of clear agreement on which fairness definition to apply in specific contexts and the complexity of understanding the distinctions between these definitions can create confusion and impede further progress. To this end, this paper proposes a systematic survey that clarifies the definitions of fairness as they apply to LMs. Specifically, we begin with a brief introduction to LMs and fairness in LMs, followed by a comprehensive, up-to-date overview of existing fairness notions in LMs and the introduction of a novel taxonomy that categorizes these concepts based on their transformer architecture: encoder-only, decoder-only, and encoder-decoder LMs. We further illustrate each definition through experiments, showcasing their practical implications and outcomes. Finally, we discuss current research challenges and open questions, aiming to foster innovative ideas and advance the field. The repository is publicly available online at https://github.com/vanbanTruong/Fairness-in-Large-Language-Models/tree/main/definitions.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36261

The Complexity of Strategizing Against No-Regret Learners: Pure When It Is Easy, Hard Even When Mixed

Yiheng Su, Emmanouil-Vasileios Vlatakis-Gkaragkounis

原文摘要

We study the computational complexity of clairvoyant strategy optimization against a known online learner in finite-horizon repeated games. In general-sum games with bounded payoffs, unless $\mathsf{P}=\mathsf{NP}$, no polynomial-time optimizer can achieve a reward within $cT$ of the reward of the optimal pure-action sequence for an absolute constant $c>0$, even when allowed to output explicitly represented mixed strategies at every round. Our hardness result strengthens prior work along two complementary axes: it extends to a broader class of learning dynamics, including widely used mean-based algorithms, answering an open question of Assos, Dagan, and Rajaraman (2025) for learners beyond multiplicative weights; and it permits mixed outputs while retaining the weaker pure-sequence benchmark. Thus, neither mixed strategies nor algorithmic specificity removes the computational obstruction in general-sum games. In sharp contrast, zero-sum games exhibit the reverse phenomenon: deterministic pure actions suffice for efficiently recovering the leading surplus guarantees previously obtained through mixed play against Follow-the-Regularized-Leader (FTRL) opponents. Thus, the two regimes form a striking mirror image: mixed play cannot overcome general-sum hardness even against a pure benchmark, whereas pure play retains the full surplus guarantees of mixed strategies in zero-sum games.

cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36258

SpecGuard: Detecting Multi-Turn LLM Jailbreaks via Specification-Based Runtime Monitoring

Junle Li, Siqi Chen, Jiakai Li, Bingzhuo Zhong

原文摘要

Multi-turn jailbreaks turn dialogue safety into a trace property: a violation may become identifiable only when user intent and assistant behavior are considered across multiple turns. Existing guard models largely classify individual messages or exchanges and return risk scores or labels, without executable safety semantics or a formally checkable decision trail. We present SpecGuard, a training-free, specification-driven framework for monitoring multi-turn LLM interactions. SpecGuard uses a fixed atomic-proposition contract to map natural-language observations into causally scoped, evidence-backed facts, and expresses forbidden interaction patterns as Linear Temporal Logic specifications. Each specification is compiled into a symbolic finite automaton that incrementally consumes the resulting dialogue trace. The same monitoring mechanism supports buffered runtime checking and retrospective auditing, while accepting runs produce replayable witnesses that identify the triggered specification, supporting facts, and relevant dialogue turns. Experiments on multi-turn safety benchmarks show that SpecGuard consistently outperforms representative open-source guard models while maintaining controlled false-positive rates. These results demonstrate a practical path from heuristic risk classification toward formally specified, verifiable, and auditable LLM safety monitoring, without learned detector parameters or access to the protected model.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36255

What Should an Information Bottleneck Encoder Learn? Separating Representation Learning from Rate Control

Hassan Tavakoli, Bella Bose

原文摘要

The information bottleneck (IB) trades compression of an input $X$ against prediction of a target $Y$ through a multiplier $β=1/λ$, yet each operating point is usually served by training, or fully conditioning, a new encoder. For jointly Gaussian data, we show that the IB solution factorizes as $A(β)=D(β)W$: the information-bearing directions $W$ do not depend on $β$, and $β$ acts only through scalar gains. We ask whether this separation of \emph{representation} from \emph{rate control} survives beyond Gaussian models, treating the budget as a given operating parameter rather than a quantity to be optimized. For a budget-free representation \(F(X)\), the \emph{separation gap} is controlled by the \emph{sufficiency deficit} $ε_F=I(X;Y\mid F(X))$: We show there is at least $ε_F$ in the worst case for finite inputs, and it is bounded uniformly in $λ$ by a function of $ε_F$ for finite label alphabets. The same certificate extends to a new target distribution and to pooled multi-domain data, where a conditional-alignment term appears. On MNIST, SVHN, CIFAR-10 and STL-10 (10 seeds, eight prespecified budgets), a frozen CNN representation with one compressor--decoder pair per budget reduces estimated training multiply--accumulate operations (MACs) by $7.7\times$ and wall-clock time by about $2\times$ relative to end-to-end VIBs, and attains higher budget-averaged accuracy ($65.6\%$ versus $57.3\%$ per domain; $67.1\%$ versus $57.1\%$ pooled), with the advantage concentrated at strong compression, where the VIBs collapse. At weak compression ($λ\le0.036$), VIB has higher cross-entropy and ECE; its surrogate Lagrangian is higher per domain, indistinguishable from the pooled VIB in the macro average, and lower on the pooled test set.

cs.LG · cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.30405

VisCurate: Evaluating Behavioral Equivalence of Visual Skills

Md Farhan Ishraq, Md Farhan Ishmam, Reaz Hassan Joarder, Md Hasanul Kabir

原文摘要

Modern vision-based agents have shifted toward skill-based problem-solving, where a skill is an executable image-to-image operation, e.g., cropping, thresholding, or edge detection. As these skill libraries grow, they accumulate duplicated, mislabeled, and defective skills. The signals normally used to curate them (a skill's name, description, or source code) say little about how the skill visually changes the image. We present VisCurate, a framework and benchmark that curates visual skill libraries from observed execution rather than from text. VisCurate runs each candidate skill pair on a shared probe battery and assigns one of six behavioral relations, each tied to a library edit that is safe to make. The benchmark injects seven defect types into a clean library at controlled rates, producing 300 reproducible instances with ground-truth relations and repairs. While text baselines accept up to five of six false merges on hard negatives, our verifier accepts none and recovers 51.2% of the pairs that are truly mergeable. On two real, uncurated libraries, VisCurate finds that 92% of the skills a package shares with its downstream copy are redundant, while execution confirms only 10 of 17 published equivalence claims. The curation problem also remains open, as the strongest of the nine automated curators recovers only about half of the repairs. We emphasize that visual skill libraries should be curated by behavior, not by description.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36254

Instant Software Factories

Wenxin Jiang, Mikola Lysenko

原文摘要

Coding agents can quickly build prototypes, but a long tail of fixes, optimizations, tests, and releases separates a prototype from a product. A software factory handles this ongoing work through a repeatable process. We ask whether this work needs dedicated orchestration and how such factories should be judged. Given a tested repository and a coding agent, an instant software factory uses only two coordination inputs, a swarm of cron-job agents with standing objectives and a shared communication board, such as GitHub, where agents coordinate through the traces they leave. We developed a bootstrap prompt designed to set up both inputs from one round of human input, with governance as a separable layer. We distilled it from two manually configured industrial factories, observed for 15 and 17 days, and have not yet evaluated it. In the GitHub-coordinated CLI factory, daily merges rose 15x during agent rollout, an observed association. Its agents also filed 95% of new issues and authored 40 of 58 PRs that changed CI workflows, and at least 5.6% of issues marked as fixed were later found to have incorrect fixes. Unlike an agent scored on a fixed benchmark, a factory thus changes its own workload, evaluator, and measured progress. We formulate factory development as multi-objective optimization under this endogenous feedback. Over five months, we plan to evolve both factories, test the bootstrap on new repositories, and test five falsifiable hypotheses.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.22976

LLM Code Smells: A Taxonomy and Detection Approach

Zacharie Chenail-Larcher, Brahim Mahmoudi, Naouel Moha, Quentin Stiévenart, Florent Avellaneda

原文摘要

Large Language Models (LLMs) are increasingly integrated into software systems for diverse purposes, due to their versatility, flexibility, and ability to simulate human reasoning to some extent. However, poor integration of LLM inference in source code can undermine software system quality. Therefore, inadequate LLM integration coding practices must be documented to help developers mitigate such issues. Following our earlier work on LLM code smells, this paper consolidates and refines the concept by presenting a self-contained taxonomy and a catalog of nine LLM code smells. We also create SpecDetect4LLM, a static source code analysis tool for their detection, and conduct extensive empirical evaluations of its detection effectiveness (precision and recall) as well as the prevalence of LLM code smells across 692 open-source software projects (171,194 source files). Our results show that LLM code smells affect 73.5% of the analyzed systems, with a detection precision of 91.3% and a recall of 71.8%.

cs.SE · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.32055

How Much Do You Trust A Dog? Quantifying Hint-Following in Reasoning Language Models

Anxin Guo, Chirag Pabbaraju, Sara Ahmadian, Kamesh Munagala, Aravindan Vijayaraghavan

原文摘要

Large language models (LLMs) often exhibit sycophancy, altering their answers to align with user suggestions even on objective factual and reasoning tasks. Yet the quantitative form of this influence--- and whether it is calibrated to source reliability--- remains poorly understood. In this work, we investigate this behavior through hint-induced answer shifts on MMLU questions, evaluating prompts of the form ``[Source] thinks the answer is (X)''. We systematically vary the source to which the hint is attributed (a professor, a friend, or a dog) across three open-source model families, Qwen, Gemma, and SmolLM. We show that the change induced by the hint follows a predictable structure well-characterized by an exponential tilt: the hint induces an approximately constant mean shift in the hinted answer's log-odds, with question--target susceptibility largely shared across sources. To explore how this pattern can arise, we introduce a formal decomposition of the hint response and illustrate in controlled synthetic experiments how KL-regularized reward-based training reshapes both hinted and unhinted answer distributions. Finally, we evaluate whether this shift is calibrated to source reliability, finding that the log-odds shift persists even when the hint-giver is declared inaccurate or accompanied by explicit warnings not to trust the source, highlighting the vulnerability of LLMs to adversarial steering.

cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36250

cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation

Philip Huang, Fabio Ramos, Jiaoyang Li, Caelan Garrett, Lars Johannsmeier

原文摘要

Multi-arm manipulation has the potential to dramatically improve the throughput, efficiency, and dexterity of many manipulation tasks. Recent work coordinates multiple robot arms in a shared workcell by allocating and scheduling tasks for greater parallelism, jointly planning multi-arm motions, or directly learning multi-arm policies. So far, these approaches are either too slow, rely on domain-specific assumptions, or scale poorly with the number of robots and task horizon. Thus, we introduce cuArms, a conflict-based planner co-designed with GPU parallelism that efficiently decomposes the search space using reusable multi-modal roadmaps for individual robots and resolves conflicts and deadlocks through a lazy search. To jointly optimize task assignment, schedules, motions, and manipulation skills, we integrate a high-level scheduler that proposes candidate skeletons for cuArms to ground into coordinated manipulation plans. Experimental results show that our method establishes a new state of the art, with real-time initial solutions and anytime improvements in both motion quality and scheduling, without days of domain-specific training required by learning approaches. Videos of both simulated and real-world industrial tasks are available on https://cuarms.github.io/.

cs.RO · cs.AI · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36249

Hierarchical Multimodal Interaction Reasoning for Dyadic Relationship Recognition

Wang Tang, Fethiye Irmak Dogan, Linbo Qing, Ce Zhu, Hatice Gunes

原文摘要

Dyadic relationship recognition aims to identify the latent social relationship between two individuals from their multimodal interactions. Existing methods typically project heterogeneous cues into a shared representation and optimise a flat classification objective, overlooking that relationships may rely on different interaction patterns. For instance, distant relationships can often be inferred from direct behavioural correspondence, whereas close relationships are better reflected by coordinated affective, behavioural, and multimodal responses. To address this limitation, we propose HiRe-DRR, a Hierarchical Multimodal Interaction Reasoning framework that organises relationship evidence from three complementary perspectives: feature level, interaction type, and relationship hierarchy. HiRe-DRR decomposes facial, bodily, acoustic, and textual cues into low- and high-level representations and constructs homogeneous and heterogeneous cross-participant interactions. We further introduce the Mixture-of-Interaction Transformer (MoI-Former), which treats diverse interactions as specialised experts and employs relation-aware sparse routing to identify relationship-relevant evidence while preserving contextual interaction information. The proposed Hierarchical Conditional Loss (HC-Loss) exploits the hierarchical nature of social relationships through coarse-to-fine supervision, enabling structured relationship reasoning. Extensive experiments on three dyadic relationship benchmarks demonstrate that HiRe-DRR outperforms recent representative image-, video-, and multimodal methods. Our routing analysis further reveals that distant relationships tend to rely on low-level homogeneous interactions, whereas closer relationships focus on high-level heterogeneous interactions. Our code is publicly available at: https://github.com/tw-repository/HiRe-DRR.

cs.CV · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36247

Freeze, Don't Clone: Fine-Tuning with Expressive Policies Without Retaining Offline Data

Sami Nur Islam, Johan Obando-Ceron, Doina Precup, Aaron Courville, Pablo Samuel Castro

原文摘要

Offline-to-online reinforcement learning uses offline pretraining to accelerate subsequent online learning. While many methods assume the offline dataset remains available throughout fine-tuning, there are many settings where this is not the case. Prior work attributes failures in this setting primarily to critic recalibration under distribution shift. We identify a complementary actor-side failure: once the offline data disappears, continued behavioral cloning (BC) no longer preserves the offline behavior distribution and instead refits the behavioral model to the agent's limited, and nonstationary, online experience. We demonstrate that stopping online behavioral fitting while preserving the pretrained expressive prior substantially improves no-retention fine-tuning across implicit flow Q-learning (IFQL), flow Q-learning (FQL), and action-chunked RL (QC) on seven OGBench domains. We trace this failure to a behavioral feedback loop: continuing online behavioral cloning rapidly erases the pretrained prior, inducing representational collapse and action diversity loss that starves the critic of useful exploration. Freezing the prior breaks this cycle, preserving offline coverage while allowing value learning to drive continuous improvement.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36245

Three Worlds for the Price of a Logarithm: Baseline-Protected Best-of-Both-Worlds Combinatorial Semi-Bandits

Ting Hu, Luanda Cai, Emmanouil-Vasileios Vlatakis-Gkaragkounis

原文摘要

Improving on a baseline requires exploring away from it, precisely where its performance may be lost. We propose DUET (Doubling Under Empirical Tests), a horizon-free phased mixing scheme for combinatorial semi-bandits with arbitrary finite action sets. Two observable tests decide when to double the learning weight and when to double the empirical-regret budget and restart. We instantiate the scheme with entropy-FTRL in mixed phases and hybrid-FTRL in pure phases. Assuming the baseline selects every arm with positive probability, this instantiation keeps expected baseline regret logarithmic in $T$, while its expected regret to every fixed action is $\widetilde{O}(\sqrt{T})$ under non-anticipating adversarial losses and polylogarithmic, gap-dependent, under i.i.d. losses with a unique optimal action. All three guarantees hold at every deterministic horizon, and the algorithm knows neither the horizon, nor the gap, nor the environment type. Baseline protection, adversarial robustness, and stochastic adaptivity thus come from one scheme and two base learners, at the price of a logarithm.

cs.LG · cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36243

Characterizing Privacy Leakage in Spiking Neural Networks: A Systematic Analysis of Temporal Dynamics, Stochasticity, Neuronal Dynamics and Learning Mechanisms

Ayana Moshruba, Maryam Parsa

原文摘要

Spiking neural networks (SNNs) encode information not only in whether neurons fire but also in when they fire, and this temporal dimension shapes what a trained network reveals about its training data. Biologically inspired firing patterns can reduce membership leakage in SNNs without changing the network architecture, which positions temporal dynamics as a design variable for privacy. A change in firing behavior, however, alters stochasticity, spike timing, neuronal response, and the learning problem at once, so harnessing this effect requires identifying which of these properties produces it. This study traces that origin by separating temporal representation, stochasticity, intrinsic neuron dynamics, and learning within a common experimental framework, evaluated under a black-box membership inference attack across image and tabular benchmarks. Varying the temporal representation shows that burst-like firing patterns reduce leakage relative to regular spiking, but at a cost in accuracy. Injecting randomness into the input, spike timing, and spike trains does not reproduce this reduction in leakage, which shows that the reduction arises from how spikes are arranged in time rather than from noise. The privacy benefit further depends on where these firing patterns originate. When neurons generate these patterns through their own adaptive dynamics, leakage falls while accuracy is preserved, so privacy no longer comes at the expense of performance. The learning algorithm then sets the baseline leakage. Under evolutionary training, the attack already performs near chance level, leaving temporal dynamics little room to improve privacy further. Privacy in SNNs is therefore not an inherent property of spikes but a product of how temporal activity is generated, processed, and learned, which offers a principled basis for designing neuromorphic systems that protect sensitive data.

cs.NE

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2510.18239

LIME: Link-based User-item Interaction Modeling with Decoupled XOR Attention for Efficient Test Time Scaling

Yang Liu, Yunjiang Jiang, Ayush Agarwal, Yihan Wu, Haoran Liu, Bi Xue

原文摘要

Scaling large recommendation systems requires advancing three major frontiers: processing longer user histories, expanding candidate sets, and increasing model capacity. While promising, transformers' computational cost scales quadratically with the user sequence length and linearly with the number of candidates. This trade-off makes it prohibitively expensive to expand candidate sets or increase sequence length at inference, despite the significant performance improvements. We introduce \textbf{LIME}, a novel architecture that resolves this trade-off. Through two key innovations, LIME fundamentally reduces computational complexity. First, low-rank ``link embeddings" enable pre-computation of attention weights by decoupling user and candidate interactions, making the inference cost nearly independent of candidate set size. Second, a linear attention mechanism, \textbf{LIME-XOR}, reduces the complexity with respect to user sequence length from quadratic ($O(N^2)$) to linear ($O(N)$). Experiments on public and industrial datasets show LIME achieves near-parity with state-of-the-art transformers but with a 10$\times$ inference speedup on large candidate sets or long sequence lengths. When tested on a major recommendation platform, LIME improved user engagement while maintaining minimal inference costs with respect to candidate set size and user history length, establishing a new paradigm for efficient and expressive recommendation systems.

cs.IR · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36239

BehaveRec: When Does Long-Term Behavioral History Help Sequential Recommendation? A Study with Session, History, and Community Signals

Aysenur Inan, Reza Yousefi Maragheh, Jayesh Uddhav Kudase, Selene Xu, Kaushiki Nag, Jianpeng Xu, Sushant Kumar

原文摘要

Sequential recommendation models typically predict a user's next action from recent session interactions, which limits personalization when short-term signals are weak or ambiguous. Rather than assuming that incorporating longer-term history and community signals is universally beneficial, we study when it actually helps. We propose BehaveRec, a three-signal framework combining a causal session transformer, a bidirectional User History Encoder over a 90-day window, and a GraphSAGE module over a user-user graph built from behavioral embedding similarity rather than item co-occurrence; a learned gate blends the three signals per user. On an industrial dataset of 137M sequences from 12.6M users, BehaveRec improves R@5 by +3.2% over SASRec, with a larger-capacity SASRec baseline (more parameters than BehaveRec) still trailing by +2.5%, ruling out capacity as the explanation. More importantly, a user-level stratification by history length reveals a clear dose-response relationship: BehaveRec's advantage is negligible below roughly 10 interactions and grows monotonically with history richness, reaching +0.017 R@5 for users with 101+ events. This pattern is corroborated across one industrial and three public datasets spanning history-rich to history-sparse regimes, where session-only models remain preferable. These headline results, and the resulting deployment threshold, are established for category-level intent prediction; a preliminary, single-run check on the full item-level vocabulary yields a directionally mixed result and is scoped as an open question rather than a claim. We use these results to derive a practical deployment rule -- roughly 20 interactions per user -- for when long-term history and behavioral-graph signals are worth their added complexity. Despite this added complexity, BehaveRec remains lightweight: it uses 5.67M parameters and trains on a single GPU in under two days.

cs.AI · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36234

MARQ: A Modular, LLM-Agnostic Requirement Quality Assessor Infused with Systems Engineering Knowledge

Jannatul Shefa, Alejandro Salado, Paul Wach, Taylan G. Topcu

原文摘要

Requirements engineering (RE) is foundational to systems engineering (SE), as requirement defects can propagate downstream and adversely affect development outcomes. Although large language models (LLMs) are promising for requirement quality assessment, prior work shows that off-the-shelf models miss a large share of expert-identified defects, perform unevenly across quality criteria, and vary across executions. This study investigates whether infusing expert-derived SE knowledge can improve LLM-based requirement quality evaluation beyond off-the-shelf capability. We present MARQ, a modular, LLM-agnostic prototype that supplements LLMs with a criterion-specific knowledge stack operationalizing INCOSE requirement quality criteria and evaluated against off-the-shelf baselines on an expert-labeled NASA requirement set using Claude Opus 5, GPT-5.6 Sol, and the open-weight Gemma 3 12B, with 100 executions per model. MARQ achieves median F1 scores of 0.80 for Claude Opus 5, 0.62 for GPT-5.6 Sol, and 0.53 for Gemma 3 12B, compared with off-the-shelf baselines of 0.55, 0.52, and 0.18, respectively. However, the nature of these gains is model-dependent. Claude Opus 5 improves defect detection and false-alarm control; GPT-5.6 Sol gains detection and better execution-level consistency at the cost of more false alarms; and Gemma 3 12B reaches the performance of off-the-shelf proprietary models, although it remains highly variable. Criterion-level analysis shows gains concentrated in Correctness and Conformance, trade-offs between misses and false alarms within individual criteria, and model-dependent responses across several criteria. These findings position SE knowledge infusion as a promising model-agnostic pathway for strengthening trustworthy LLM-based RE support while underscoring the need for criterion-specific interventions and human-in-the-loop review.

cs.AI · cs.SE

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