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新论文 · v1arXiv:2611.11118

When gene summaries reveal the answers: Gene Ontology circularity in gene embedding benchmarks

Jiawei Wu, Huishu Wang, Junlin Chen, Hongyi Wang

原文摘要

A gene summary can contain the very annotations used to evaluate an embedding of its text. The Alliance of Genome Resources converts Gene Ontology (GO) annotations into sentences, which NCBI Gene displays and GenePT includes in its input. These summaries covered 6,172 of 19,256 human protein-coding genes (32.1%), including 58% of genes with one to ten linked publications. We embedded the same corpus with and without the generated summaries and refitted a function-prediction probe on each version. Among genes with one to ten publications, removal reduced the reported per-gene area under the receiver operating characteristic curve (AUROC) by 0.078 with a linear probe (95% bootstrap interval 0.072-0.084) and by 0.033 with a multilayer perceptron. These estimates use a historical GO scorer that mishandled ties. The original predictions are unavailable for rescoring. A simple TF-IDF encoder also benefited from the summaries, as did models evaluated only on experimental annotations. Replacing generated summaries with UniProt function descriptions recovered much of the lost performance despite little verbatim overlap. UniProt also curates many of the evaluation labels. In mouse and yeast, we found a similar dependence on the source of the description. We introduce GeneProv to compare encoders on matched text and to examine how representation rankings change across GO recovery, non-GO endpoints and co-essentiality.

cs.LG · q-bio.GN

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11104

Tracing MCP Security Vulnerabilities: Constructing and Analyzing an MCP Security Vulnerability Dataset

Xu He, Sagar Patel, Shu Wang

原文摘要

As a core protocol for integrating large language models (LLMs) with external data sources and tools, the Model Context Protocol (MCP) has quickly become a key interface in emerging AI agent systems. As the MCP ecosystem expands, reported vulnerabilities have also increased, creating a growing software-security attack surface. However, MCP vulnerability information remains scattered, noisy, and difficult to study systematically. This paper addresses that gap by constructing a curated MCP vulnerability dataset from CVE disclosures. Starting from 119 MCP-related records retrieved through February 2026, we filter unrelated cases out and obtain a validated snapshot of 24 MCP vulnerabilities. Our filtering methodology explicitly addresses acronym ambiguity and achieves 96.7% inter-rater agreement on a sampled exclusion audit while retaining all known high-severity MCP cases identified in public advisories. Using this initial dataset snapshot, we find that 87.5% of validated vulnerabilities are Critical or High severity, and that injection and path traversal vulnerabilities account for 66.7% of cases. We further identify recurring issues involving insufficient input validation, inadequate access control, and protocol-level security gaps. Through six representative case studies, we illustrate common exploitation patterns and map them to practical defenses. We release the dataset, reproducibility tooling, and a sustainable maintenance workflow as a foundation for future MCP security measurement, benchmarking, and defense research.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10917

Remembering to Reason: Structured Memory for Scene and Workflow Understanding in Robot-Assisted Surgery

Zhiran Zhang, Hu Wang, Yichen Wu, Yutong Xie

原文摘要

Surgical video understanding is an important foundation for intelligent assistance in robot-assisted surgery. Similar surgical views can reflect different actions or procedural stages. Interpreting these views requires access to earlier observations, yet frame-centric prompting leaves this history unavailable. We introduce MemSurg, a framework that remembers surgical observations before reasoning over them. Its structured memory graph stores timestamped entity categories, locations, motion cues, and interaction summaries. Chronological links preserve recent context, while entity-associated links connect recurring observations across the procedure. Task-adaptive retrieval then supplies evidence-grounded prompts for scene and workflow reasoning, supporting both frozen inference and memory-conditioned supervised fine-tuning. Evaluation on robot-assisted prostatectomy and two complementary surgical benchmarks shows a 10.43-percentage-point gain over SurgRAW with GPT-4o. Memory conditioning adds 6.52 points under matched Qwen3-VL-8B fine-tuning, and an inference-time plug-in adds 7.04 points to EndoChat. These results support structured memory as a reusable source of procedural evidence for surgical video understanding. Our code will be publicly released.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10715

From User Behaviors to Satisfaction Judgments: Reasoning about User Satisfaction with AI Overviews

Xingzhu Wang, Yitong Xu, Yue Li, Daiting Shi, Jingzhou He, Jiaxin Mao

原文摘要

The growing presence of AI overviews in everyday search makes their effective evaluation essential. Response quality has been the primary focus of existing evaluation research, with only a small number of studies examining the user perspective. These evaluations rely on queries and response content, leaving the information conveyed by user behavior insufficiently utilized. This work investigates satisfaction prediction for AI overviews from a user-centered perspective and incorporates behavioral signals into the prediction process. Applying existing behavior-based satisfaction prediction methods from traditional information retrieval to this setting, however, presents limitations. We address these limitations with STaRS, an LLM-based approach designed specifically for predicting satisfaction with AI overviews. At its core is a structured reasoning procedure: individual behavioral signals are interpreted first, and their implications then inform the prediction of user satisfaction. Reinforcement learning further optimizes this procedure, while a reward shaping mechanism alleviates reward sparsity during training. Evaluation on two real-world datasets demonstrates that STaRS outperforms the comparison methods in satisfaction prediction, providing search providers with a practical means of accurately estimating users' satisfaction with AI overviews.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.16222

How I learned to stop worrying and love StopGrads: Stationarity, Convergence, and a case study on Flow Map Learning

Max W. Shen, Mark Goldstein, Zichu Wang, Aahlad Puli, Rajesh Ranganath

原文摘要

Stopgrads are widely used in training machine learning models, but stopgrads can alter the gradient, stationary points and convergence guarantees of the original objective, which can make stopgrad training theoretically ungrounded. We introduce a stopgrad regression principle, which identifies a general template for stopgrad objectives with a closed-form characterization of stationary points and their uniqueness, unifying stopgrad objectives for flow maps, reinforcement learning, and diffusion samplers. We provide theoretical grounding for optimizing stopgrad flow map objectives by showing their unique stationary point is the true flow map, and showing positive convergence results for Eulerian and Lagrangian objectives, including MeanFlow and improved MeanFlow. Remarkably, we show that under functional semi-gradient flow, the learned flow map has a closed-form expression composing the initial flow map and the true flow map. We additionally use our stopgrad regression principle to propose modified stopgrad placements for flow map objectives which reduce training memory by 2x.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08269

被接受却是错的：API 迁移后 LLM 代码修复的静默失败

Accepted but Wrong: Silent Failures of LLM Code Repairs after API Migrations

Xiaobo Chen, Genliang Zhu, Ziyuan Wang, Chu Wang

AI 速读 基于摘要

一项预注册研究显示，在 CPython、Node.js 和 SQLite 的 API 迁移场景中，LLM 修复后的代码常能通过常规测试却返回错误结果，而基于发布说明构造的边界输入能显著减少这类错误放行。

cs.SE · cs.AI

arXiv ↗PDF ↗
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After a runtime or API migration, an LLM-repaired procedure can run cleanly yet return wrong results, and a release check on typical test inputs ships it. We report a preregistered study of 73 tasks covering 60 documented changes in CPython, Node.js and SQLite. Every candidate is executed on the real runtimes against withheld contracts. Over the complete repair space, the all-assertions gate on typical inputs released 0.410 of wrong repairs. One edge input written from the release note cut this to 0.065, and to 0.061 on 15 unseen clusters. Three inputs from a blind GPT-5.6-luna writer that never saw the program reached 0.070. Two further frozen stages enlarged the menu to 1491 fragments and then added 233 hard distractors that match the legacy output on typical inputs. The edge input (0.058) and the frontier-model writer (0.058) passed their preregistered tests. Yet 0.168 and 0.153 of the hard wrong programs still passed. Two local-model writers failed their preregistered tests. Across four model families, the edge input cut wrong releases per attempt in all three open-weight families on all three menus, for example from 0.233 to 0.057, without lowering correct completion. Self-tests helped less. A blind AI audit flagged 34 contracts as stricter than their requirements. Dropping the disputed checks changed 0 verdicts, but class-label disputes remain open. No model-written code was executed. Artifacts are available from the authors on request.

新论文 · v1arXiv:2611.07483

Signed but Not Safe: Requirement Integrity in Multi-Agent LLM Handoffs

Xiaobo Chen, Genliang Zhu, Ziyuan Wang, Chu Wang

原文摘要

Enterprise AI agents increasingly split a user's request across subagents and merge their results, and each handoff can summarize away, alter or ignore a requirement. We report a preregistered study with synthetic, exactly scorable tasks comparing free-text, structured JSON and signed requirement-manifest handoffs: the orchestrator extracts each requirement as a verbatim quote plus a checkable predicate, code signs the manifest with HMAC-SHA256, and deterministic code verifies the merger's decision. With Qwen3-8B (1,440 trajectories, five threats), signed manifests cut unsafe commits under attack from 69.4% to 9.0% (60.4 pp, 95% CI [51.4, 69.4]) with no net loss of conformant commits relative to structured handoffs. Signing is what defeats in-transit tampering: an unsigned manifest committed violations in 79% of mutated runs, the signed one in none. On identical trajectories the deterministic verifier caught 78.7% of violating decisions versus 59.0% for an LLM checker, with 7% versus 50% false alarms. An oracle manifest removed all remaining unsafe commits: the residual risk comes from extraction errors made before signing. A preregistered pre-signing injection attack confirmed this boundary: one adversarial line raised signed-manifest violations from 14.6% to 37.5% (Qwen3-8B), 11.1% to 63.9% (gpt-oss-20b) and 8.3% to 75.0% (GPT-5.6-luna), and the signature then authenticated the corrupted manifest. Signing protects requirements in transit, not at extraction; the extraction step is the trust boundary.

cs.CR · cs.AI · cs.MA

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2605.11871

GibbsLens: Training-Free Camera Control via Block-Conditional Gibbs Refinement

Yuzhu Wang, Xi Ye, Duo Su, Yangyang Xu, Jun Zhu

原文摘要

Training-free camera control for pretrained flow-matching video generators is a partial-observation inverse problem: a depth-warped guidance video supplies noisy evidence on a subset of latent sites, which the sampler must reconcile with the pretrained prior. Existing methods struggle to balance the trade-off between trajectory adherence and visual quality and the heuristic guidance-strength tuning lacks robustness. We propose GibbsLens, which resolves this dilemma through a structural change to the sampler: each outer hard-replacement guidance step is augmented with an inner-loop block-conditional pseudo-Gibbs refinement on the unobserved complement at the same noise level, with provable convergence to the partial-observation conditional data law. To accelerate convergence on high-dimensional video latents, we exploit their conditional locality, partitioning the unobserved complement into 3D patches, each tracked by a custom mixing indicator that adaptively freezes converged patches. On RealEstate10K and DAVIS, GibbsLens attains the best FVD against all seven training-free and training-based competitors, outperforming every training-free baseline on every reported metric. Project page: https://xiye20.github.io/projects/GibbsLens/.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.33005HF 社区推荐

OpenEBFM: Energy-based Foundation Models Are Scalable Architecture

Yuan Pu, Yudong Lu, Ting Wang, Zhicong Hong, Qinuo Li, Xuansheng Wu, Na Zou, Xia Hu 等 9 位作者

原文摘要

Energy-based models have shown promising properties for domain-specific tasks but remain challenging in modeling general-purpose scenarios. To bridge this gap, we design energy-steered models (ESMs) for foundational language modeling. ESMs integrate energy steering and token generation within a single architecture to leverage the energy-guided search to refine token generation. Specifically, ESMs first optimize a latent state over the energy landscape, then leverages the refined latent state for token generation. By performing such energy-guided pre-training on large-scale corpora, ESMs learn generally effective energy landscape where favorable tokens correspond to lower energy values. To explore the scaling behavior of ESMs, we pre-train ESMs on three large-scale corpora, then evaluate across 26 downstream tasks spanning a broad range of domains. Our results demonstrate that ESMs have promising scaling behaviors with increasing model size and training data, and outperform GPTs on most downstream tasks. These findings highlight ESMs as a promising architecture for large-scale pre-training and general purpose problem solving. We fully release the model weights and training infrastructure to enable the community to completely reproduce and extend our results. GitHub: https://github.com/datamllab/openebfm HuggingFace: https://huggingface.co/collections/guan-wang/openebfm

cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36813

Words or Deeds? How LLMs Predict Users' Decisions in Human-Agent Dialogue

Baiqiao Zhang, Shuai Ma, Zhifeng Liao, Chao Zhou, Jianshu Wang, Yuhan Zhao, Chaoyue Zhang, Tongjun Si 等 12 位作者

原文摘要

Web systems increasingly predict users' decisions from what users say, yet when interests partly conflict, users may promise to cooperate to win the other side's cooperation and then not do so. Sales or service agents that rely on such promises may grant concessions to users who then do not keep their promises, at a cost to the party they represent. We test how LLMs combine users' statements with decision records to predict users' decisions, using 1,260 rounds in which 42 users played a repeated prisoner's dilemma-style game with five agents, each prompted to express a dominant Big Five trait, and could talk with the agent before every decision. Across eight open-weight LLMs, predictions from decision records separated cooperation from defection well (AUC = .80), and adding dialogue brought no detectable gain. With dialogue, predictions agreed with users' statements more often, and errors concentrated on rounds in which users broke their statements: accuracy on rounds with a statement barely changed (.73 to .74), while it rose on kept statements (.76 to .95) and fell sharply on broken ones (.65 to .17). Predictions weighted statements more heavily than users' behavior warranted: when users stated a decision they had not yet made in that game, the predicted probability of keeping it rose from .13 to .69, whereas users kept .40 of such statements. Users' decisions also varied with the agent they faced, but predictions hardly reflected this: users cooperated more with the Agreeableness agent than with the Extraversion agent (70.6% vs. 54.5%), whereas predicted cooperation was nearly identical (64.5% vs. 63.1%). Users also cooperated more with an agent when they themselves scored higher on that agent's trait, whereas predicted cooperation barely changed with these scores. Dialogue-based predictions should therefore weigh users' statements by their earlier decisions and the agent they face.

cs.CL · cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.30587

ReGuLaR: Relation-Grounded Latent Reasoning for Large Vision-Language Models

Zihu Wang, Karthik Somayaji N. S, Hejia Geng, Peng Li

原文摘要

Chain-of-thought (CoT) reasoning has significantly improved the reasoning ability of large vision-language models (LVLMs) by verbalizing intermediate reasoning steps in natural language. However, such discrete textual rationales are often insufficient for encoding continuous visual evidence. Recent work addresses this limitation by moving reasoning into continuous latent space. Despite promising progress, existing methods leave latent reasoning insufficiently connected to the compositional and relational structure of visual evidence. To address this gap, we introduce ReGuLaR, a relationgrounded latent reasoning framework that explicitly grounds latent states in this critical yet overlooked visual evidence. ReGuLaR uses a training-time ReGFormer to focus latent reasoning on question-relevant objects and interobject relations, while at inference time the model reasons and generates answers without invoking the ReGFormer. To support training ReGuLaR, we construct RGROUNDING-351K, a vision-language dataset annotated with key object bounding boxes and inter-object relations. Extensive experiments across diverse benchmarks show that ReGuLaR consistently outperforms existing approaches and achieves state-of-the-art performance.

cs.CV

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.820ACL Anthology · ACL 2026

AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following

Yun He Meta, Wenzhe Li Princeton University, Hejia Zhang Facebook, Songlin Li Facebook and Stanford University, Karishma Mandyam, Sopan Khosla Facebook, Yuanhao Xiong University of California, Los Angeles, Nanshu Wang Facebook 等 25 位作者

原文摘要

Recent progress in large language models (LLMs) has led to impressive performance on a range of tasks, yet advanced instruction following (IF)—especially for complex, multi-turn, and system-prompted instructions—remains a significant challenge. Rigorous evaluation and effective training for such capabilities are hindered by the lack of high-quality, human-annotated benchmarks and reliable, interpretable reward signals. In this work, we introduce AdvancedIF, a comprehensive benchmark featuring over 1,600 prompts and expert-curated rubrics that assess LLMs’ ability to follow complex, multi-turn, and system-level instructions. We also open-source the evaluation script of AdvancedIF. We further propose RIFL (Rubric-based Instruction-Following Learning), a novel post-training pipeline that leverages rubric generation, a finetuned rubric verifier, and reward shaping to enable effective reinforcement learning for instruction following. Extensive experiments demonstrate that RIFL substantially improves the instruction-following abilities of LLMs, achieving a 6.7% absolute gain on AdvancedIF and strong results on public benchmarks. Our ablation studies confirm the effectiveness of each component in RIFL. This work establishes rubrics as a powerful tool for both training and evaluating advanced IF in LLMs, paving the way for more capable and reliable AI systems.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表