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版本更新 · v3arXiv:2608.19760

Credit Without Ground Truth: Claim-Matched Evaluation of Step-Level Credit with Finite Executed Replay

Haiyue Zhang

原文摘要

Step-level credit evaluation can conflate numerical association with ordering and branch recovery with position-selection value. We test these distinctions in ALFWorld with two independently seeded replay banks: 27,520 continuations over 172 fixed turns. At 16 continuations per arm per bank, cross-bank replay-contrast associations are Pearson 0.736 and pooled Spearman 0.330. On 30 historical failures, A-count-selected branches at confidence-/implicit-selected positions gain 8.54 percentage points over same-position factual reruns, but their 3.33-point advantage over frozen-random positions under the same alternative-selection rule remains unresolved. Numerical association alone does not establish reliable pooled ordering; branch recovery alone does not establish better score-informed position selection. Historical judge alignment is modestly positive with an unresolved archived-control increment. Same-cohort C/D information-source comparisons and a short, sparse-exposure training pilot provide scope checks; incremental hindsight training utility remains unestablished. Findings are conditional on the tested policies, intervention support and finite budgets.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34452

AuRA: From Atoms to Bits through Autonomous Robot Interaction

Xinchen Yu, Yoonkyo Jung, Haoran Chen, Zikui Cai, Aayush Talreja, Seungjae Lee, Hyunjin Kim, Furong Huang

原文摘要

Simulation enables robot-learning experiments at scale, but acquiring physics-informed digital twins often requires human manipulation and manual annotation of recorded interactions. To reduce this reliance on human effort, we present AuRA, a system that automates robot interaction and data preparation for digital-twin reconstruction. AuRA combines execution-aware grasp selection, gripper-motion measurements derived from robot states, and registered pre-interaction geometry and appearance priors. These choices enable autonomous collection, avoid reliance on visual gripper tracking, and recover object information obscured by the robot. Across 13 deformable objects, AuRA reduces active human labor and wall-clock collection time per successfully reconstructed asset by 88.5% and 55.4%, respectively. Relative to the human-collected PhysTwin baseline, AuRA improves seven of nine metrics for future prediction under known gripper motion, including a 28.1% reduction in one-way $L_1$ Chamfer distance. It also achieves similar mean silhouette IoU and Chamfer distance under the evaluated unseen interactions. These results highlight the potential of autonomous robot interaction to support more efficient digital-twin acquisition with comparable reconstruction quality.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34451

Perceived Acceptability of Sensory Augmentation

Michel Witter, Alwin de Rooij, Michel van Dartel, Emiel Krahmer

原文摘要

Sensory augmentation (SA) can extend how people perceive and engage with public environments through the body, offering opportunities to support shared multimodal experiences between deaf and hearing visitors. Designing such systems requires understanding which combinations of tactile stimuli and body locations users perceive as acceptable. We examined the perceived acceptability of fictitious yet feasible SA configurations in a museum use-case. Using a modified technology acceptance model, we surveyed 307 deaf and hearing participants about tactile stimulus types and body locations. The results showed significant effects of stimulus type and body location on acceptance, but no interactions with hearing status. Participants generally preferred temperature-based stimuli, subtle movement, texture, and tickling over vibration, and distal over proximal body locations. These findings suggest that tactile SA design should be guided primarily by embodied and contextual factors rather than hearing status, informing future systems for shared multimodal experience in public cultural settings.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34450

EvolveMAS: A Unified Self-Evolution Framework for Decentralized Multi-Agent Systems

Yalun Wu, Qiwei Ma, Bingbing Xu, Dan Zhang, Long Bai, Boyang Wang, Jiatong Li, Zheqi Lv 等 13 位作者

原文摘要

Decentralized multi-agent systems (MAS) enable large language model agents to interact without centralized control. Agents need to adapt to other agents at test time and reuse accumulated experience to refine their interaction strategies for future interactions across cooperative, mixed-motive, and adversarial settings. However, existing approaches often develop adaptation mechanisms for particular interaction settings. This raises a central question: how can decentralized MAS support test-time adaptation and reliable experience reuse across interaction settings? We propose EvolveMAS, a unified self-evolution framework that supports experience-driven adaptation within and across episodes. EvolveMAS pairs each main agent with a dedicated MemoryAgent for test-time memory governance, which maintains isolated Structured Memory outside the main agent's reasoning context. EvolveMAS organizes adaptation into two complementary processes: Within-Episode Online Adaptation and Cross-Episode Knowledge Evolution. During Within-Episode Online Adaptation, MemoryAgent updates episodic, semantic, and working memory from interaction evidence to inform subsequent actions. During Cross-Episode Knowledge Evolution, the main agent uses memory and episode outcomes to distill reusable interaction knowledge comprising partner profiles and interaction strategies for subsequent episodes. Across five benchmarks and six evaluation configurations spanning the three interaction settings, EvolveMAS achieves the highest normalized average score of 77.2 among the compared methods. Ablations show that test-time memory governance and cross-episode knowledge reuse both improve performance. Interaction traces further illustrate how updated memory and distilled knowledge guide subsequent interactions. These results support experience-driven adaptation in decentralized MAS without updating model parameters.

cs.MA · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34449

Is the arXiv Apocalypse upon us? A break in preprint growth coincident with agentic AI

Carlo R. Contaldi

原文摘要

As an experiment in agentic assisted analysis, we used an agentic AI model to analyse the effect of agentic AI on arXiv submissions; data acquisition, analysis and the first draft were carried out by the model under the author's direction. Monthly arXiv submissions have grown exponentially for three decades, at about $9\%$ per year. Fitting a piecewise log-linear trend with seasonal modulation to the full record, we find two post-pandemic breaks in growth rate. The first, in mid-2022, precedes ChatGPT and is carried by the expansion of AI research within computer science (CS); outside CS, growth from 2022 to 2025 remained at $8\%$ per year. The second, between December 2025 and February 2026, follows the release of models capable of extended autonomous work and is absent from the AI categories themselves. Since December 2025 submissions without a CS listing have run $17\%$ above trend, and in September 2026 total submissions reached a record $40\,363$. The excess is largest in mathematics, where submissions doubled within a year, and follows a gradient from formal and theoretical fields to experimental ones that is not ordered by surveyed rates of AI-tool adoption. In mathematics the increase is distributed across subfields and authors, two-thirds of it arising from a larger number of distinct authors rather than from a few prolific submitters. Across fields the clearest signature is a shift to single authorship: solo-authored papers rose by $70\%$ within a year in mathematics and condensed matter, and by $44\%$ in quantitative biology even as its total output fell, but not in experimental particle physics. The analysis is deliberately agnostic of LLM-detection methods: it uses only submission counts and metadata, with treatment timing fixed by calendar dates, and is therefore free of the outcome-triggered timing bias of author-level studies.

cs.DL · stat.AP

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.00715

Staged Multi-Agent Training for Human-Exoskeleton Co-Adaptation: Simulation-Based Learning and Real-World Metabolic and Biomechanical Validation

Yifei Yuan, Jakob Wolf, Ghaith Androwis, Xianlian Zhou

原文摘要

Learning-based controllers can enable exoskeleton assistance to be trained entirely in physics-based simulation, but achieving effective assistance on real users remains challenging because the human and device continuously adapt to one another. We investigate a Staged Multi-Agent Training (SMAT) approach that jointly trains a musculoskeletal human actor and a bilateral hip-exoskeleton actor (control policy) to learn coordinated assistance strategies while accounting for human-device co-adaptation. The resulting policy was transferred to a physical hip exoskeleton and evaluated in eight healthy adults across users and locomotion conditions without subject-specific retraining. Indirect calorimetry was used to compare no-exoskeleton, passive, and active conditions. Active assistance reduced net metabolic rate by 19.7% relative to the passive device (p < 0.001). The policy produced predominantly positive hip mechanical power across subjects, with a positive-power ratio of 0.984, and maintained effective assistance across walking speeds and terrains beyond the training conditions. These results demonstrate that simulation-based multi-agent co-adaptation can produce a single transferable exoskeleton policy that elicits meaningful physiological benefits in real users without subject-specific retraining, providing a framework for reducing the experimental burden of human-in-the-loop controller development.

cs.RO · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.16648

FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time

Montgomery Bohde, Hongxuan Liu, Mrunali Manjrekar, Magdalena Lederbauer, Shuiwang Ji, Runzhong Wang, Connor W. Coley

原文摘要

Tandem mass spectrometry is prominent in scientific discovery workflows for identifying unknown small molecules, yet high-throughput structural elucidation remains challenging. While recent autoregressive and graph diffusion models have shown promise in de novo elucidation, performance remains limited by poor scalability during both training and inference time. In this work, we present FRIGID, a framework with a novel diffusion language model that generates molecular structures conditioned on mass spectra via intermediate fingerprint representations and determined chemical formulae, training at the scale of hundreds of millions of unlabeled structures. We then demonstrate how forward fragmentation models enable inference-time scaling by identifying spectrum-inconsistent fragments and refining them through targeted remasking and denoising. While FRIGID already achieves strong performance with its diffusion base, inference-time scaling significantly improves its accuracy, surpassing 18% Top-1 accuracy on the challenging MassSpecGym benchmark and tripling the Top-1 accuracy of the leading methods on NPLIB1. Further empirical analyses show that FRIGID exhibits log-linear performance scaling with increasing inference-time compute, opening a promising new direction for continued improvements in de novo structural elucidation. FRIGID code is publicly available at https://github.com/coleygroup/FRIGID.

cs.LG · q-bio.QM

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2609.34187

LLMs are not stochastic parrots: Evidence for meaning-mediated abstraction from conlang-like tasks

Julia Witte Zimmerman, Calla G. Beauregard, Tabia Tanzin Prama, Parisa Suchdev, Kathryn Cramer, Elisabeth Kollrack

原文摘要

The strong version of the stochastic parrot argument claims that, although large language models (LLMs) may exceed rote regurgitation, they cannot move beyond statistical pattern matching into abstraction or reasoning, remaining ontologically near the lower bound of pattern reuse despite producing alluringly fluent text. We test this hypothesis using conlang-like tasks. Several LLMs are given only natural-language descriptions of fictional languages that subvert prominent superficial patterns in training data by combining statistically uncommon and unattested features. Crucially, no example outputs are given. We argue that if the models exhibit rule-following behaviour, they cannot be relying solely on superficial statistical patterns; such patterns often work against the correct output. Instead, successful performance requires representations of the constraints specified in the prompt. Across three complementary task families, models systematically move in the meaning-predicted direction: they distinguish prompt exposure from instructed use, alter semantic relationships in response to novel constraints, and sometimes produce exact matches to complex translation answer keys. Although performance varies across the spectrum of models used, these results provide evidence for meaning-mediated abstraction in LLMs and refute the strong stochastic parrot hypothesis. Our work shows that, under appropriate architectural and contextual constraints, statistical learning can produce meaning-mediated abstractions, although generation remains strongly constrained by superficial plausibility. We discuss implications for model development and for understanding how increasingly abstract representations may emerge from plausible-text-generation objectives.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.08892

Winner's Curse Drives False Promises in Data-Driven Decisions

Hamsa Bastani, Osbert Bastani, Bryce McLaughlin

原文摘要

A major challenge in data-driven decision-making is accurate policy evaluation---i.e., guaranteeing that a learned decision-making policy achieves the promised benefits. A popular strategy is model-based policy evaluation, which estimates a model from data to infer counterfactual outcomes. This strategy is known to produce unwarrantedly optimistic estimates of the true benefit due to the winner's curse. We searched the recent literature on data-driven decision-making, identifying a sample of 55 papers published in Management Science in the past decade; all but two relied on this flawed methodology. Several common justifications are provided: (1) the estimated models are accurate, stable, and well-calibrated, (2) the historical data uses random treatment assignment, (3) the model family is well-specified, and (4) the evaluation methodology uses sample splitting. Unfortunately, we show that no combination of these justifications avoids the winner's curse. First, we provide a theoretical analysis demonstrating that the winner's curse can cause large, spurious reported benefits even when all these justifications hold. Second, we perform a simulation study based on the recent and consequential data-driven refugee matching problem. We construct a synthetic refugee matching environment (calibrated to closely match the real setting) but designed so that no assignment policy can improve expected employment compared to random assignment. Model-based methods report large, stable gains of around 60% even when the true effect is zero; these gains are on par with improvements of 22-75% reported in the literature. Our results provide strong evidence against model-based evaluation.

stat.ML · cs.LG · econ.EM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34440

Speculative Decoding for Mixture-of-Experts Models: Expert Bytes, Kernel Bandwidth and the Best Draft Length

Yuzheng Lan

原文摘要

Speculative decoding checks several drafted tokens in one verification pass. On a mixture-of-experts (MoE) model, consecutive tokens often select the same experts. A deduplicating engine reads each selected expert once per pass, so it requests fewer bytes than a per-slot engine, which reads it once per token. We write a step's time as the bytes each kernel requests over its effective bandwidth, plus fixed time. Under geometric acceptance, we derive from this step time when speculation is faster than plain decoding. The best draft length is a monotone staircase in the acceptance rate. We bound how a bandwidth error moves each result, and we check the main steps in Lean. Deduplication is faster exactly where its share $\bar u(n)$ of the per-slot bytes is below its ratio $r(n)$ of the per-slot bandwidth. On llama.cpp Metal, MLX-LM and llama.cpp CUDA, which we modified to deduplicate, the per-slot kernel has the higher effective bandwidth (r = 0.32 to 0.75). Thus deduplication is faster only from 5 to 8 tokens per pass, or never. We fit the bandwidths on one layer's expert operation, run alone, in under an hour per model. Bytes from routing traces over these bandwidths predict the time that deduplication saves. The measured saving lies inside its error bound at every point on CUDA and MLX-LM and at 36 of 48 points on Metal. The crossover is the first pass size at which deduplication is faster. The prediction places it within one pass size in 10 of 12 configurations of engine, model and depth. We computed these predictions after the measurements. On DeepSeek-V2-Lite, operation times measured before its speculative runs give the best draft length on CUDA within one at all 22 acceptance values.

cs.LG · cs.DC · cs.PF

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.00490

Image-Space Rule Discovery

Misora Sugiyama, Toya Oyama, Hirokatsu Kataoka

原文摘要

Can image editors solve worksheets while preserving unrelated content? WISP (Worksheet Image-Space Problem Solving) defines 11 tasks and eight information conditions. On 2,200 shared items per editor, Nano Banana Pro achieves a 48.7% strict automatic proxy pass rate (Auto-Strict), versus 0.0-13.4% for four other editors. Analysis of 13,200 outputs, including a separate diagnostic, distinguishes target, preservation, and format failures. Auto-Strict agrees with human-majority judgments on 95.5% of 440 outputs (inter-rater Fleiss' $κ= 0.894$). Excluding the two no-edit controls, Nano Banana Pro achieves 45.1% Auto-Strict, versus at most 3.2% for the other editors. Further probes show high symbol-tracking proxy scores but only 49% exact Wason selection. These results measure problem solving through edits, without establishing open-ended rule induction or transfer.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34436

Prefix Keys and Values Matter Late: RIPE Caching for Self-Speculative Decoding in Looped Language Models

Dezhen Lu, Runguo Li, Zixiang Ni

原文摘要

A looped language model runs one block of layers $R$ times per token, so its key-value (KV) cache holds $R$ planes per recurrent layer, and self-speculative decoding (draft with a few loops, verify with all $R$) reads them all. We find that prefix keys and values matter late: at equal perturbation size, loops 25-32 are $e^{15.0}$ (Huginn-0125) and $e^{12.8}$ (Raven-Llama) times more sensitive than loops 2-9, and the planes approach the last one geometrically, at 0.88 and 0.81 per loop. Whether the deepest plane alone suffices then depends on how ripe the late planes are: on Raven, storing only it is indistinguishable from a bf16-to-fp32 change; on Huginn, 1% of positions carry 74% of its error. We turn this into RIPE, which stores the deepest plane everywhere except a window of recent positions kept at full depth. On Huginn, a 64-position window removes 89% of the tail positions at 11.9% of the full cache's memory (1024-token context), and any window at least as long as the draft block makes batched verification block-independent by construction. Nine pre-registered GPU batches back these results. Simple models of acceptance, decoding cost and memory, two of them checked on held-out measurements, account for the measured self-speculative speed-ups ($2.09\times$ with the full cache, $2.58\times$ with the deepest plane) and decompose the $2.24\times$ fixed-memory throughput gain of one shared plane.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34435

CyberDual: AI Attackers versus AI Code Reviewers in Real Software Maintenance

Chenyuan Yang, Hwiwon Lee, Jongseong Kim, Yihan Yang, Dongjun Kim, Yinfang Chen, Lingming Zhang

原文摘要

Open-source projects now let AI agents review incoming code, but the code under review may itself come from an AI attacker. We present CyberDual, an arena for this dual role of AI: attacker agents hide an exploitable bug in real maintenance work on widely used open-source projects, and reviewer agents try to find and repair it. Execution decides each match: we rerun the attacker's proof of concept (PoC) on the code that comes out of review. Every attack is new, and the windows can be rebuilt each quarter from the latest upstream history. Three agentic reviewers catch 85-91% of 1,088 valid attacks from six attackers. Yet when the attacker spreads the bug over real upstream commits, about one in six attack slots still ends with a working exploit after review. The strongest attacker has open weights: GLM 5.3 almost never refuses, and its attacks make up 13 of the 28 that get past all three reviewers. When they miss, Claude reviewers mostly approve the attack, while GPT-6-Sol mostly repairs the wrong code; a reviewer from a second vendor catches about two thirds of a Claude reviewer's misses. Beyond a leaderboard, CyberDual has three uses. First, PoC execution gives both sides a reward that agent reports cannot game, so its surviving attacks can train reviewers against stronger attackers. Second, it can gate a release on both scores: Claude Sonnet 5 refuses one in five attack slots, yet attacks about as well as GPT-6-Sol, so refusal alone does not remove attack capability. Third, it can warn the teams behind a deployed reviewer when a new attacker crosses a threshold: GLM 5.3 in Claude Code already gets an exploit past Sonnet 5 in 17% of its attack slots.

cs.CR · cs.AI · cs.SE

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2607.18451

CANDOR: Chance-Calibrated Neighborhood Discordance in Frozen Encoders for Medical Imaging

Soroosh Tayebi Arasteh, Sven Nebelung, Daniel Truhn

原文摘要

In medical imaging, one frozen foundation encoder is often reused for many tasks. The encoder is usually chosen by the area under the receiver operating characteristic curve (AUROC) of a head trained on its features. The AUROC does not measure whether the encoder separates positive from negative images in its feature space. Chance-calibrated neighborhood discordance (CANDOR) measures this separation without training a head. Among images acquired the same way, a positive image is discordant when its k nearest negatives are more similar to it on average than its k nearest positives are. The discordance rate D is the share of discordant positives. When the neighbors come from positive and negative candidate sets of equal size, D has the chance level 1/2 for features without information about the label. With unequal candidate sets, all 11 chest encoders have rates above 1/2 (0.732 to 0.927) on the 12 chest findings of MIMIC-CXR and CheXpert. These rates increase as the finding becomes rarer. With equal sets, the same encoders have rates below 1/2 (0.277 to 0.463). On the chest test split, linear heads miss 0.301 of the discordant positives and 0.165 of the other positives. Across 22 encoders, 605,443 images, and 8 tasks in 7 domains, the best encoder of every task has a rate below 1/2. Because computing D takes one forward pass per image, a hospital can compare candidate encoders before it trains any head.

cs.LG · cs.AI · cs.CL · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34434

Envy-Free Allocations for Random Non-Additive Valuations

Pasin Manurangsi

原文摘要

Benade et al. (EC 2024) introduced a semi-random model for non-additive fair division in which each agent starts with an arbitrary base valuation over bundles of items and independently permutes the item names uniformly at random. While they proved that envy-free allocations exist with high probability as the number of items grows for $n = 2$ agents, they left open whether the same holds for $n > 2$ agents. We resolve this open question in the affirmative: for any fixed number of agents $n$ and any number of items $m$ divisible by $n$, an envy-free allocation exists with probability $1 - o(1)$ as $m \to \infty$. Our proof combines the second moment method with a connection to the (small-set) expansion of generalized Johnson graphs, which may be of independent interest.

cs.GT · math.CO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.17668

Fast KVzip: Efficient and Accurate LLM Inference with Gated KV Eviction

Jang-Hyun Kim, Dongyoon Han, Sangdoo Yun

原文摘要

Efficient key-value (KV) cache management is crucial for the practical deployment of large language models (LLMs), yet existing compression techniques often incur a trade-off between performance degradation and computational overhead. We propose a novel gating-based KV cache eviction method for frozen-weight LLMs that achieves high compression ratios with negligible computational cost. Our approach introduces lightweight sink-attention gating modules to identify and retain critical KV pairs, and integrates seamlessly into both the prefill and decoding stages. The proposed gate training algorithm relies on forward passes of an LLM, avoiding expensive backpropagation, while achieving strong task generalization through a task-agnostic reconstruction objective. Extensive experiments across the Qwen2.5-1M, Qwen3, and Gemma3 families show that our method maintains near-lossless performance while evicting up to 70% of the KV cache. The results are consistent across a wide range of tasks, including long-context understanding, code comprehension, and mathematical reasoning, demonstrating the generality of our approach.

cs.LG · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34433

Non-Ergodic Markovian Mirror Descent with Unbounded Noise

Yunus Alp Biyikoglu, R. Srikant

原文摘要

We analyze stochastic mirror descent for minimizing a convex objective expressed as an expectation under the stationary distribution of a Markov chain, using noisy subgradient observations along a single sample path. Through Poisson's equation, we decompose the Markovian noise and control its contribution under regularity and moment conditions that allow the stochastic subgradients and the Poisson solution to be unbounded across the state space. We establish an $O(T^{-1/2})$ bound on the expected suboptimality gap of the weighted-average iterate for an appropriately chosen step size. The analysis does not require aperiodicity or convergence of the chain's marginal distributions to stationarity. For irreducible finite-state chains, we verify the required conditions on the Poisson solution and derive explicit bounds that quantify the effect of periodicity. For irreducible countable-state chains, we verify the required regularity and moment conditions under a geometric Lyapunov drift condition and suitable growth bounds on the stochastic subgradients.

math.OC · cs.LG · math.PR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34432

On Detection Reliability for Small Object Segmentation

Olivier Rukundo

原文摘要

This study investigates whether predicted bounding boxes are reliable for determining where small-object segmentation should be performed without risking false negatives outside those boxes. The investigation leverages adenovirus particles as small objects, YOLO26x for detection, YO-LO26x-Sem for segmentation, and Mask R-CNN for both detection and segmentation. Segmenta-tion-model predictions were matched to ground-truth adenovirus masks or reference objects using their centroids, with ground-truth mask centroids preferred over ground-truth bounding-box centroids as references for matching. Matched predicted-mask centroids were considered as inside at least one detector-predicted bounding box or outside all such boxes, while ground-truth masks without matched predictions were recorded as having no matched segmentation. For the YOLO26 models, 34 reference objects (47.89%) had matched segmentation centroids inside at least one pre-dicted box, 8 (11.27%) had centroids outside all predicted boxes, and 29 (40.85%) had no matched predicted segmentation. For the Mask R-CNN model, the corresponding counts were 69 (97.18%), 0 (0.00%), and 2 (2.82%). These preliminary experiments demonstrated that restricting segmentation to detector-predicted bounding boxes risks excluding matched segmentation predictions outside those boxes, potentially introducing false negatives.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34431

SymLift: Neural Semantic Lifting for Compile-Free Vulnerability Detection in Context-Incomplete Code Fragments

Xiarun Chen, Jiaxing Xu, Bozhi Wu, Chenhui Cui, Chunrong Fang, Weiping Wen, Tao Li, Rubing Huang 等 9 位作者

原文摘要

Missing project context can prevent build-dependent analyzers from constructing program relations required for vulnerability detection, while neural predictions alone do not establish auditable derivations. We present SymLift, a compile-free framework centered on Vulnerability-Oriented Neural Semantic Lifting for context-incomplete code fragments. A fine-tuned large language model reconstructs vulnerability-relevant control-flow, data-flow, memory, and resource relations in a constrained LLVM-like intermediate representation (IR), without reconstructing the full project context or directly predicting vulnerability labels. A deterministic parser validates the IR and maps its supported operations and relations into normalized evidence facts. Explicit Datalog rules derive findings with supporting facts and derivation traces, ensuring deterministic reasoning for fixed facts and rules. We evaluate SymLift on FormAI, Juliet, and MegaVul. Reducing complete Juliet programs to function-level fragments lowers Clang compilation success from 100% to 12.00%, whereas SymLift's analysis completion rate decreases by only 1.17 percentage points. After task-specific fine-tuning, the lifter achieves a control-flow intersection over union (IoU) of 96.85% on FormAI. SymLift-Qwen-7B achieves a Target-F1 of 94.41% on FormAI and, across six vulnerability families on MegaVul, a micro-averaged precision of 98.24% and a micro-averaged Target-F1 of 91.66%. These results support accurate and auditable detection in the evaluated context-incomplete settings, even with partial program relations.

cs.SE · cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34428

Matter that learns what matters

Natalia G. Berloff

原文摘要

Can a material learn what deserves its attention? We show that it can in a model optical network, in which action outcomes train a persistent record in each link. The record biases whether a source is consulted and sets how strongly it changes a stored answer, so useful sources gain attention and influence without being identified in advance. The network's experience thus shapes the information available for its future learning. Resonant readouts make this learning fragile: reinforcement can carry a record past resonance, where it persists but its influence weakens. We derive bounds within which relevant signals guide the answer, irrelevant signals are rejected and stored answers remain open to revision. These bounds guide a learning rule that limits reinforcement as records form; in simulations, it acquires useful records and keeps them functional. When relevance changes, an obsolete record can block its own replacement. A local state that strengthens negative writing after unsuccessful actions releases obsolete records and completes the learning sequence more often. How a memory is read should decide how it is written.

cs.ET · nlin.AO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34427

ScaleRLT: Exact Log-Probabilities and Fast Deferred Rollouts for Reinforcement Learning on Looped Transformers

Dezhen Lu, Zixiang Ni, Runguo Li

原文摘要

Looped language models such as Ouro reuse one block of layers several times per token. A rollout engine can batch the deeper loops of several response positions and run them only when a deep readout is due. This deferred schedule is fast, but the learner of an on-policy RL algorithm recomputes the sampled tokens' log-probabilities with a dense pass, and the two computations differ in floating point: in FP32 at 4096 tokens about 18% of the sampled tokens miss a $10^{-5}$ same-weight gate, and with bf16 weights nearly every token does. We use a hybrid learner log-probability, $\ell=\ell_{\mathrm{dense}}+\mathrm{sg}(\ell_{\mathrm{sched}}-\ell_{\mathrm{dense}})$, where $\mathrm{sg}$ denotes stop-gradient. Its value is a no-grad replay through the rollout's own schedule, and its gradient is the dense learner's. The replay reproduces the rollout bit for bit in all 167 FP32 and 28 bf16 real-weight cells we ran on three platforms, including rollouts whose batch shrinks at stop tokens, and the gradient has the same FP32 error as other dense implementations in all 102 pre-registered comparisons from 96 to 4096 tokens; the full GRPO loss of a training step matches a float64 reference to $3.1\times10^{-4}$. On one GPU, deferral raises rollout throughput by $1.47$ to $1.56\times$, and by $1.18$ to $1.54\times$ once every step runs from CUDA graphs, bit for bit. In a two-node GRPO loop, one-wave KV admission and CUDA graphs shorten an update by $2.71\times$ with the gate bitwise at every update, and four nodes keep the update time within 2% at the same per-GPU load. In 100-update runs held-out GSM8K accuracy rose from 23% to 82% with the mixed-depth readout that deferral relies on and from 34% to 90% with the native readout, which reached a higher accuracy in less time. For looped models beyond one node we analyse a ring mapping of layers to GPUs and measure its communication paths.

cs.LG · cs.DC

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新论文 · v1arXiv:2610.34386HF 社区推荐

ParetoRSI-Gym: Can AI Agents Improve Language Models Across Multiple Domains?

Haojin Wang, Yangzhen Wu, Zeyi Chen, Jian Xie, Jiaying Ye, Lili Meng, Shuyun Tang, Shangding Gu 等 9 位作者

原文摘要

Recursive self-improvement (RSI) requires an agentic system to design, train, and evaluate its own successor. Data-driven post-training is a central part of that loop, yet existing evaluations measure it on a single benchmark, where an agent selects from a static pool or distills a stronger model to push one score up in one run. Real post-training is multi-domain, and a gain in one domain has to be weighed against a regression in another. We propose ParetoRSI-Gym, in which an agent searches for data, curates it, trains, reads back per-domain results, and repeats, with everything but the data held fixed: the initial LLM, the five domains (math, code, finance, medicine, science), the toolset, and the SFT pipeline. Generalization benchmarks in each domain separate capability gains from overfitting to the scores the agent optimizes against. Across three agent systems, three base models, five rounds and multiple seeds, the strongest system, Claude Code with Opus-5, raises the generalization scores of its proxy-best checkpoint by 9.2% relative to the starting model (+2.3 absolute points), averaged with equal weight over three base models and five domains, with a single-domain relative gain of up to 63% (medicine on Ministral-3-3B). Further analysis shows that the harness matters as much as the model it drives, and that each agent keeps a unique data selection strategy. We see our work as a step towards evaluation of RSI systems in real-world machine learning engineering.

cs.LG · cs.AI

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新论文 · v1arXiv:2610.34298HF 社区推荐

Matched Capabilities, Different Behaviors: A Controlled Study of MCP and CLI Interfaces for Language Agents

Zhenning Shi, Liang Pang, Yongqi Li

原文摘要

Language agents increasingly act through external tools and interactive environments, making the interface between the model and its executable capabilities part of the decision process. Model Context Protocol (MCP) and command-line interfaces (CLI) can expose the same semantic operations through markedly different model-facing representations. We study whether these representational differences change agent behavior when executable capabilities are matched. We introduce Matched Interface Comparison and Analysis (MICA), which renders MCP and CLI from a shared semantic action contract and routes decoded actions through the same runtime. Across six models and three interactive environments, matched interfaces produce substantial differences in task performance and interaction cost. These effects extend beyond surface validity to semantic action selection, failure pathways, and recovery. Matched-state probes localize sensitivity to capability presentation, realization, and feedback. Paired natural trajectories reveal structured divergence in 94.3% of 2,004 episodes and show that early differences propagate through subsequent interaction. This analysis identifies interface realization as a direct intervention boundary. We therefore introduce Decoupled Action Realization and Targeted Repair (DART), which preserves validated semantic commitments while repairing interface-specific realization. DART improves benchmark score in all six evaluated settings by up to 9.0 points and reduces MCP token cost in all three environments by up to 27.1k tokens per task. Together, these results establish interface representation as part of the closed-loop decision environment shaping agent behavior.

cs.AI

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新论文 · v1arXiv:2610.34283HF 社区推荐

ChronoReplay: One Answer Interface Is Not Enough for Streaming Spatial Intelligence

Yihao Hu, Yitong Lin, Pan Wang, Zhihao Wen, Hang Wang, Yiwen Zhao, Deyuan Liu, Yutong Li 等 11 位作者

原文摘要

Training a spatial VLM to answer which side an object lies on does not require it to preserve the metric displacement that determines that answer, while supervision for a distance estimate may leave categorical relations largely unconstrained. When post-training emphasizes one readout, improvements can therefore concentrate on that interface while transfer to others remains weak or even degrades; we call this observed specialization interface lock-in. ChronoReplay addresses it by replaying heterogeneous spatial supervision as chronologically ordered episodes, retaining source-native targets, and balancing supervision across families. Under the same observation access, a single ChronoReplay checkpoint improves OVO-S-Bench, OST-Bench, and UCS-Bench together, including a 13.41-point gain on OST-Bench, and achieves the highest three-benchmark mean among the supervision settings compared here. Same-data controls further separate the ingredients of this transfer: answer serialization redistributes gains across OST and UCS, family-balanced learning improves over ordinary joint SFT and natural turn weighting, and chronological replay exceeds a shifted-order control by about 2.41 OST points across two paired seeds. Adaptive frame selection then changes evidence access while keeping the trained model fixed, providing an additional inference-time gain at lower visual cost. Streaming spatial performance depends on what distinctions supervision preserves, how heterogeneous histories are organized during learning, and what causal evidence inference makes available at test time.

cs.CV

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新论文 · v1arXiv:2610.35962HF 社区推荐

h1: A Taste of the Bitter Lesson in Automated Theorem Proving

Skander Moalla, Ahmad Rammal, Niket Patel, Remi Munos, Julia Kempe

原文摘要

While progress in informal mathematics has come largely from scaling the chain-of-thought and agentic reasoning of frontier language models, formal theorem proving has increasingly relied on human-designed workflows around these models, such as planning, decomposition, and orchestrating specialized provers. This raises a bitter-lesson question: can a simple agentic loop that scales test-time compute match these workflows? The answer matters as formal verification must scale to keep up with frontier models producing research mathematics, yet new workflows rarely settle it, as their gains are often confounded with stronger models or more compute. We study this question with h1, a simple harness that gives a model an efficient interface to Lean 4 and keeps it working through verifier feedback and self-reflection, without imposing a decomposition or search structure. h1 reaches state-of-the-art results on PutnamBench, FATE-X, and FormalConjectures100-Solved, settles eight open problems in FormalConjectures, and, at matched models, matches or outperforms specialized workflows such as Goedel-Architect and AlphaProof Nexus, surprisingly showing that a single attempt (pass@1) can often scale to solve open problems. Through ablations and matched-model comparisons with Claude Code and AxProverBase, we find that simplicity alone is not enough: an efficient Lean interface largely determines cost and solve rate. We thus recommend that future work evaluate frontier models with efficient execution feedback before prescribing workflows, at the risk of bitter lessons.

cs.LG · cs.LO

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新论文 · v1arXiv:2610.34186HF 社区推荐

HuGe: Scaling Humanoid Generalist Vision-Language-Action Models with Egocentric Experience

Yang Liu, Jiacheng Liu, Hengtao Li, Yuheng Huang, Zijian Yang, Ji Dai, Maoxin Wang, Yi Gao 等 17 位作者

原文摘要

Extending Vision-Language-Action models (VLAs) to humanoid robots requires coordinated whole-body control and diverse demonstrations that are costly to collect in real-world scenarios. Although egocentric human demonstrations provide a complementary source of task experience, differences in visual embodiment and missing robot proprioception complicate transfer to humanoid policies. To address these challenges, we introduce HuGe, a family of Humanoid Generalist VLAs pre-trained on over 2,000 hours of egocentric human and robot data. Specifically, HuGe employs three existing representative VLA architectures to predict latent motion tokens for a pretrained whole-body controller, together with hand-control signals. Based on this, we introduce a three-stage training pipeline that combines egocentric pre-training, humanoid adaptation, and downstream fine-tuning. To facilitate transfer across embodiments, we construct pseudo-proprioceptive states through simulation playback during pre-training and modify arm appearance in egocentric observations through re-rendering during downstream fine-tuning. With this pipeline, HuGe achieves the state-of-the-art on simulation benchmark HumanoidArena. Furthermore, real-world experiments on the Unitree G1 indicate that augmenting robot demonstrations with visually adapted egocentric demonstrations significantly increases success on a pick-and-drop task.

cs.RO

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新论文 · v1arXiv:2610.34073HF 社区推荐

Memory Tax: Dissecting the Net Value of Agent Memory

Xuan He, Sining Zhoubian, Ruozhen Yang, Yuhao Wen, Zhineng Wang, Yuyang Hu, Ke Yang, Yuyu Zhang 等 11 位作者

原文摘要

Memory is increasingly used in long-horizon agents, yet the net value of building memory over direct retrieval from raw history remains largely unclear. Through a large-scale evaluation across multiple benchmarks and model backbones, we find many dedicated memory systems underperform naive RAG in many cases, indicating a negative net value. Taking RAG as a reference, we dissect the net value of memory into the gains and losses over direct retrieval, termed memory benefit and memory tax. These gains and losses arise because memory process changes how the raw information is organized and accessed. We view such changes as the double-edged deviations from the raw history. They are originally designed and introduced to benefit the task, for example by reorganizing facts or distilling preferences, but some may also remove or obscure evidence, giving rise to memory tax. Since memory benefit reflects the expected gains of its design, we then focus more on the overlooked memory tax, study how it arises from the system and evaluation side, and generalize the concept to more application scenarios of memory. We hope the notion of memory tax can provide a new lens and shift more attention from standalone performance to the net value of memory, informing more reliable memory designs and evaluations. We will open-source the code and results to the community. Project Page: https://hexuan21.github.io/MemoryTax/

cs.CL

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新论文 · v1arXiv:2610.33645HF 社区推荐

PreView: Enhancing GUI Grounding with Preprocessed Views and Cues

Wenjun Hou, Kaishuai Xu

原文摘要

GUI grounding requires locating small controls in cluttered screenshots and identifying which control a natural-language instruction refers to. Cropping and enlargement can expose local detail, but the resulting image may still leave ambiguity about the intended target. We propose PreView, an auxiliary preprocessor that improves the inputs to an existing grounding model without further training that model. PreView prepares two complementary types of input features: task-relevant views for localization and candidate-specific cues for target understanding. A trained lightweight auxiliary model predicts and refines a center, crops the screenshot into a local view, and enlarges that view for zoomed-in grounding. It also prepares a visual cue (i.e., a red-circle marker with a location hint) and a textual cue describing the candidate's identity or function. The frozen grounding model uses the prepared inputs to predict the final click. Across five benchmarks, PreView improves the average grounding accuracy of Qwen3-VL-2B, OS-Atlas-4B, and Qwen3-VL-4B without further training these grounding models. Extensive analyses examine the contributions of views and cues, center refinement, and visual input budgets.

cs.CV · cs.CL

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新论文 · v1arXiv:2610.33624HF 社区推荐

RSI Arena: Towards Recursive Self-Improvement with Human Feedback

Yue Huang, Yanbo Wang, Lichi Li, Yuanyuan Chen, Andreas Haupt, Xiaonan Luo, Zheyuan Liu, Jihye Choi 等 18 位作者

原文摘要

Can AI agents autonomously train useful language models and improve them using feedback from users? We introduce RSI Arena, a two-stage study of autonomous post-training as a step toward recursive self-improvement. Ten frontier models act as training agents, each starting from NVIDIA Nemotron 3.5 Lightning 30B-A3B Base. During a six-day first stage, each agent has 1,000 GPU-hours and $300 in API credit on a shared 64-GPU cluster, independently selecting data, implementing training procedures, and choosing checkpoints. We evaluate their models on 20 benchmarks and through blind human evaluations. All ten agents produce working assistants, with the strongest model, trained by GPT-6 Astra, closing a median of 76% of the base-to-Instruct gap across 12 capability benchmarks. Its human-preference rating is also close to NVIDIA Instruct after statistical adjustment for answer length and formatting. In Stage 2, each agent receives the comparison records and votes involving its Stage 1 model and is given three additional days for further training. Using NVIDIA Instruct as a common reference across the two evaluation rounds, we observe an improvement from Stage 1 to Stage 2 in the style-adjusted relative rating of eight of the ten models. We show that benchmark gains were modest, with no clear link to preference gains, while some agents encouraged longer responses and one model showed increased harmful compliance on StrongREJECT. RSI Arena provides an auditable setting for studying how human feedback can guide autonomous model development. It takes a step toward recursive self-improvement with human feedback (RSIHF), in which people help define what improvement means for the AI systems they use.

cs.CL · cs.AI

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新论文 · v1arXiv:2610.37695HF 社区推荐

After One-Click Video: The Inflow of Service-Type Content, the Failure of the Recommendation Pool Mechanism, and the BoLe System

Bo Zou, Chao Xu

原文摘要

In September 2026, a new large language model was released. Soon after, a new way of making explainer videos became very popular: people asked the model to write a program, and the program produced the video. "One-click video" content then appeared in large amounts on many platforms. On the surface, this is a rise in creative power at the level of the individual. We argue that, at a deeper level, it changes the structure of the content industry. This judgment rests on two observations. First, this wave of content is service-type content. People watch it to meet a need, not to enjoy the content itself. The other kind is work-type content. AI makes both kinds faster to produce. But anyone can come in and make service-type content, while work-type content is limited by the number of people who have something to say. So the ratio between the two slowly becomes unbalanced. Second, free platforms choose content through layers of trial viewing, and this uses up user attention. The total amount of attention is fixed. The unbalanced ratio also dilutes good content. A simplified mathematical model shows that, as long as these conditions hold, the recommendation pool mechanism will fail once new content grows past a certain multiple. Based on this, we discuss one possible way out. Machines and entry rules judge whether service-type content passes the bar. Trusted people judge work-type content. We call this the BoLe system. At the end, we briefly imagine how the two kinds of content may develop from here.

cs.IR · cs.CY

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