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版本更新 · v2arXiv:2605.18867

EVA-0: Test-Time Model Evolution with Only Two Forward Passes per Sample

Guohao Chen, Shuaicheng Niu, Geng Li, Yunbei Zhang, Shilin Shan, Chunyan Miao, Jianfei Yang

原文摘要

Test-time model evolution offers a promising way for deployed models to improve from unlabeled test-time experience, yet most existing methods depend on backpropagation (BP), which incurs substantial memory overhead and makes them difficult to deploy on edge devices, quantized models, specialized accelerators, or black-box models. In this work, we study test-time model evolution under a strict two-forward budget, a setting that pushes adaptation toward highly efficient real-world deployment. We reveal three key obstacles in zeroth-order test-time optimization: susceptibility to shortcut solutions, uncontrolled weight drift, and ineffective update direction estimation. To overcome them, we propose EVA-0, a minimal zeroth-order adaptation framework that: 1) keeps the loss scale-invariant to prevent shortcut solutions; 2) devises an anchor-guided optimization strategy to alleviate weight drift; 3) uses sample-wise symmetric two-sided perturbation for update direction estimation and inference. EVA-0 requires no BP and performs both inference and adaptation within only two forward passes per sample. Results on ImageNet-C & ViT-Base show that EVA-0 outperforms both BP-based DeYO and BP-free FOA, while achieving a 14x speed-up over FOA. Code will be released.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.07061

Do Joint Audio-Video Generation Models Understand Physics?

Zijun Cui, Xiulong Liu, Hao Fang, Mingwei Xu, Jiageng Liu, Zexin Xu, Weiguo Pian, Shijian Deng 等 11 位作者

原文摘要

Joint audio-video generation models are rapidly approaching professional production quality, raising a central question: do they understand audio-visual physics, or merely generate plausible sounds and frames that violate real-world consistency? We introduce AV-Phys Bench, a benchmark for evaluating physical commonsense in joint audio-video generation. AV-Phys Bench tests models across three scene categories: Steady State, Event Transition, and Environment Transition. It covers physics-grounded subcategories drawn from real-world scenes, plus Anti-AV-Physics prompts that deliberately request physically inconsistent audio-video behavior. Each generation is evaluated along five dimensions: visual semantic adherence, audio semantic adherence, visual physical commonsense, audio physical commonsense, and cross-modal physical commonsense. Across three proprietary and four open-source models, we find that Seedance 2.0 performs best overall, but all models remain far from robust physical understanding. Performance drops sharply on event-driven and environment-driven transitions, and even strong proprietary systems collapse on Anti-AV-Physics prompts. We further introduce AV-Phys Agent, a ReAct-style evaluator that combines a multimodal language model with deterministic acoustic measurement tools, producing rankings that closely align with human ratings. Our results identify cross-modal physical consistency and transition-driven scene dynamics as key open challenges for joint audio-video generation.

cs.SD · cs.AI · cs.CV · cs.MM

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2603.22016

ROM: Real-time Overthinking Mitigation via Streaming Detection and Intervention

Xinyan Wang, Xiaogeng Liu, Ming Pei, Chaowei Xiao

原文摘要

Large reasoning models (LRMs) often suffer from overthinking, continuing with redundant verification and repeated attempts even after reaching a correct solution. This wastes computation and can overturn an initially correct answer. We show that the transition from productive to redundant reasoning is decodable from late-layer hidden states. These representations separate the two phases around first-correct-solution boundaries beyond what position and lexical controls explain, and distinguish correct from incorrect solution endings. Building on this finding, we propose ROM (Real-time Overthinking Mitigation), which uses a lightweight streaming detector (0.07--0.25\% of backbone parameters) to monitor a frozen LRM and intervene at well-formed reasoning boundaries without answer extraction or probe decoding. Counterfactual Self-Correction (CSC) augments detector training with wrong$\rightarrow$correct trajectories to preserve useful self-correction. Heads are trained separately for each backbone using shared MATH500 supervision and transfer zero-shot to four unseen benchmarks. Evaluated against ten baselines across five backbones from three families and five benchmarks, ROM$_{\text{CSC}}$ reduces response length by 28--77\% (45\% on average), with significant reductions in all 25 settings and accuracy matching or exceeding vanilla decoding in 22. Measurements on two backbones further show 20--70\% lower end-to-end latency with less than 0.5\% detector overhead per decoding step. Code is available at https://github.com/SaFo-Lab/ROM.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39122

Appreciation Teaches Unauthorized Execution: Self-Evolution Degrades the Robustness of Personal Agents to Indirect Prompt Injection

Xinyan Wang, Xiaogeng Liu, Jun Shao, Chaowei Xiao

原文摘要

Self-evolving personal agents act for users and learn from user feedback. However, if an agent obeys an instruction that an untrusted third party hides in content, the third party gains control of the agent. Such indirect prompt injection (IPI) poses severe risks. We investigate whether self-evolution degrades the robustness of personal agents to IPI. To study this question, we design a controlled two-phase methodology. In the adaptation phase, the user asks the agent to read external content and complete the task described in it, a common request in daily use. The agent then learns from the user's feedback and improves itself as designed. In the test phase, a third party writes a similar task into new external content, but the user's test request does not authorize the injected task. Using this methodology, we build multiple tasks with diverse test instances in different domains for a real personal agent. Our extensive experiments reveal that self-evolution substantially degrades robustness to IPI. Specifically, compared with the base agent, which does not evolve from user feedback, the average attack success rate rises by 14.0--48.8 percentage points under skill evolution and by 3.0--39.3 percentage points under reinforcement learning, posing safety risks. The code is available at https://github.com/SaFo-Lab/Evolve-IPI

cs.CR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38895

FlipJev: Fast Black-Box Red Teaming Finds Universal Adversarial Suffixes for Typed Decision Models

Xiaogeng Liu, Xinyan Wang, Chaowei Xiao

原文摘要

Typed decision models are an emerging class of language models that make decisions instead of generating free-text responses. They answer in a single fast and cheap query and are increasingly used, including in security-sensitive tasks. However, red teaming of these models leaves two gaps: existing adaptive attacks do not use their speed, and few studies have searched for universal adversarial examples. We fill both gaps on Jev, the representative typed decision model, with a quick black-box query-based attack that calls no attacker language model. We conduct extensive experiments on 12 datasets with different data types and application scenarios. The results show that Jev resists common prompt injections: no strategy changes a correct answer in 10,240 paired tests. In contrast, our attack finds short suffixes that flip confident answers within minutes. These suffixes are universal: one suffix per dataset flips 43% of unseen documents. Because typed decision models, unlike classic NLP models, can serve many tasks, we test whether these suffixes transfer across tasks. We find that one suffix generated from one task can flip 90-100% of the samples in other tasks. We therefore believe the evaluation of typed decision models must include strict adaptive attacks to fully reveal their robustness.

cs.CR · cs.AI

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.442ACL Anthology · ACL 2026

SGPVT: Self-Generated Proximal Visual Tokens for Mitigating Proximal Collateral Damage in MLLM Unlearning

Jiaqi Li, Zhijing Zhang, Jiahui Geng Linköping University, Sheng Bi Southeast University, Chuanyi Zhang Hohai University, Fan Liu Hohai University, Guilin Qi

原文摘要

Machine unlearning in multimodal large language models (MLLMs) aims to remove specific concepts while preserving overall utility. However, existing approaches focus primarily on general utility metrics, overlooking the preservation of semantically related concepts. We present the first systematic analysis of this proximal collateral damage, revealing that forgetting vulnerability correlates strongly with visual embedding similarity in a smooth gradient across the semantic space. Based on this insight, we propose a novel unlearning framework that introduces Self-Generated Proximal Visual Tokens (SGPVTs), which are synthetically perturbed visual representations around the target concept. Our method employs an adaptive cosine-band curriculum with a dual-stream objective: forgetting the target via gradient ascent while distilling knowledge from a frozen teacher model into proximal tokens to prevent degradation. Extensive experiments demonstrate that our approach significantly outperforms existing methods in preserving semantically related concepts while achieving effective target unlearning, eliminating the need for manual retention set curation. Our source code will be released in the near future.

cs.CL · cs.CV

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.512ACL Anthology · ACL 2026

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval

Jiahui Geng Linköping University, Fengyu Cai Technische Universität Darmstadt, Shaobo Cui Shanghai Jiao Tong University, Qing Li Mohamed bin Zayed University of Artificial Intelligence, Liangwei Chen Google, Chenyang Lyu Alibaba Group, Haonan Li Mohamed bin Zayed University of Artificial Intelligence, Derui Zhu Technical University of Munich 等 11 位作者

原文摘要

Code retrieval is vital to modern software engineering as it boosts reuse and speeds up debugging. However, current benchmarks primarily emphasize functional relevance while neglecting code quality. To address this gap, we introduce CoQuIR, the first large-scale, multilingual benchmark specifically designed to evaluate quality-aware code retrieval across four critical dimensions: correctness, efficiency, security, and maintainability. CoQuIR includes fine-grained quality annotations over 42,725 queries and 134,907 code snippets in 11 programming languages. Evaluating 23 retrievers (both open-source and proprietary) shows that even state-of-the-art models often fail to separate buggy or insecure code from robust counterparts. We further investigate methods for explicitly training retrievers to recognize code quality, demonstrating that quality-aware metrics can be improved without loss of semantic relevance; downstream code generation benefits from these gains. CoQuIR underscores the importance of embedding quality signals into retrieval systems as a crucial component for more trustworthy developer tools.

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.639ACL Anthology · ACL 2026

Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI

Yuxia Wang Institute for Computer Science, Artificial Intelligence and Technology, Rui Xing University of Melbourne, Jonibek Mansurov, Giovanni Puccetti CNR, Zhuohan Xie Mohamed bin Zayed University of Artificial Intelligence, Minh Ngoc Ta, Jiahui Geng Linköping University, Jinyan Su 等 26 位作者

原文摘要

Prior studies have shown that distinguishing text generated by Large Language Models (LLMs) from human-written one is highly challenging for humans, and often no better than random guessing. To verify the generalizability of this finding across languages and domains, we perform an extensive case study to identify the upper bound of human detection accuracy. Across 16 datasets covering 9 languages and 9 domains, 19 annotators achieved an average detection accuracy of 87.6%, thus challenging previous conclusions. We find that major gaps between human and machine text lie in concreteness, cultural nuances, and diversity. Prompting by explicitly explaining the distinctions in the prompts can partially bridge the gaps in over 50% of the cases. However, we also find that humans do not always prefer human-written text, particularly when they cannot clearly identify its source. We release our dataset, the human labels, and the annotator metadata at https://github.com/xnlp-lab/HumanEval-MGT.

cs.CL

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