Yu Jiang, Wenjie Wang, Yue Huang University of Notre Dame, Yanbo Wang Mohamed bin Zayed University of Artificial Intelligence, Zhenhong Zhou, Xiuying Chen Mohamed bin Zayed University of Artificial Intelligence, Yang Liu Nanyang Technological University, Pin-Yu Chen International Business Machines 等 10 位作者
Large language model (LLM) agents increasingly operate in multi-agent settings where failures emerge from interaction dynamics rather than isolated model errors. We introduce RiskLab, an open-source toolkit for instantiating, probing, and measuring emergent risks in LLM-based multi-agent systems under controlled conditions. Each experiment is defined as a structured topology–environment–protocol–agent–task quintuple, enabling reproducible studies of how communication structure, coordination mechanisms, and incentives shape system-level risks. RiskLab provides flexible communication topologies, swappable interaction protocols, trajectory-grounded evaluation, and extensible registries for risk detectors and agent backends. We demonstrate the toolkit across representative risks, including collusion, resource overreach, semantic drift, and strategic misreporting, and support one-file reproducibility via configuration.
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cs.CL · cs.MA
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表
Ching-Yun Ko International Business Machines, Payel Das, Sihui Dai CapitalOne, Georgios Kollias International Business Machines, Subhajit Chaudhury International Business Machines, Aurelie C. Lozano, Pin-Yu Chen International Business Machines
Reasoning over long contexts remains a major challenge for language models, particularly when solving tasks that require integrating multiple facts in sequence or generalizing to new distributions. We argue that this difficulty stems from a lack of structural inductive bias. Recently, alternative frameworks have been proposed to explicitly encode contexts as ordered memory and perform iterative retrieval to construct reasoning chains. Despite the promising results shown in prior arts, they are still heavily reliant on intermediate chain supervision and fall short in showing emergent reasoning generalization in the presence of hard distractions in reasoning-in-a-haystack tasks. Furthermore, we discover that as the amount of distractions increases, traditional episodic memory reads suffer from ill-conditioning problems, which lead to inaccurate context retrievals. In this work, we formalize the motivation for necessary inductive bias in reasoning-in-a-Haystack tasks, propose inference-time memory update procedures mimicking the “identify and remove unnecessary and unrelated details” in constructively responsive reading, introduce staged training inspired by human conceptual understanding, and finally demonstrate the possibilities and limits of such framework in the weakly supervised scenario.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Lei Hsiung Dartmouth College, Tianyu Pang Dartmouth College, Yung-Chen Tang EPFL - EPF Lausanne and Idiap Research Institute, Linyue Song Asana Inc, Tsung-Yi Ho Department of Computer Science and Engineering, The Chinese University of Hong Kong, Pin-Yu Chen International Business Machines, Yaoqing Yang Dartmouth College
Recent advancements in large language models (LLMs) have underscored their vulnerability to safety alignment jailbreaks, particularly when subjected to downstream fine-tuning. However, existing mitigation strategies primarily focus on reactively addressing jailbreak incidents after safety guardrails have been compromised, removing harmful gradients during fine-tuning, or continuously reinforcing safety alignment throughout fine-tuning. As such, they tend to overlook a critical upstream factor: the role of the original safety-alignment data. This paper therefore investigates the degradation of safety guardrails through the lens of representation similarity between upstream alignment datasets and downstream fine-tuning tasks. Our experiments demonstrate that high similarity between these datasets significantly weakens safety guardrails, making models more susceptible to jailbreaks. Conversely, low similarity between these two types of datasets yields substantially more robust models and thus reduces harmfulness score by up to 10.33%. By highlighting the importance of upstream dataset design in the building of durable safety guardrails and reducing real-world vulnerability to jailbreak attacks, these findings offer actionable insights for fine-tuning service providers to prioritize upstream models with low jailbreak risk.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
David H. Yang Rensselaer Polytechnic Institute, Yuxuan Zhu, Mohammad Mohammadi Amiri Rensselaer Polytechnic Institute, Keerthiram Murugesan International Business Machines, Tejaswini Pedapati, Subhajit Chaudhury International Business Machines, Pin-Yu Chen International Business Machines
Large language models (LLMs) have shown great performance on complex reasoning tasks but often require generating long intermediate thoughts before reaching a final answer. During generation, LLMs rely on a key-value (KV) cache for autoregressive decoding. However, the memory footprint of the KV cache grows with output length. Prior work on KV cache optimization mostly focus on compressing the long input context, while retaining the full KV cache for decoding. For tasks requiring long output generation, this leads to increased computational and memory costs. In this paper, we introduce ZoomR, a novel approach that enables LLMs to adaptively compress verbose reasoning thoughts into summaries and uses a dynamic KV cache selection policy that leverages these summaries while also strategically “zooming in” on fine-grained details. By using summary keys as a coarse-grained index during decoding, ZoomR uses the query to retrieve details for only the most important thoughts. This hierarchical strategy significantly reduces memory usage by avoiding full-cache attention at each step. Experiments across math and reasoning tasks show that our approach achieves competitive performance compared to baselines, while reducing inference memory requirements by more than 4 \times. These results demonstrate that a multi-granularity KV selection enables more memory efficient decoding, especially for long output generation.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表