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版本更新 · v6arXiv:2610.16717

Where Is the "Self" of a Large Language Model? -- From Self-Index to Self-Reference

Yuchen Li, Xintong Li, Zongqi Fan, Ken-tye Yong, Nguyen H. Tran

原文摘要

Whether a large language model has a self is usually argued without a way to measure it. We measure a functional self with two levels familiar from biology: a self-index, which marks material as the model's own, as animals tag self-generated signals with a corollary discharge, and self-reference, the ability to recognize what is one's own, as in mirror self-recognition. Our main finding is that language models mark their own messages internally and that recognizing them depends on this mark: no model we tested recognizes its own messages without it, and in the four models we edited, changing the mark removes or creates the recognition. With the same text placed in an Assistant, a User, or a System message of its chat template, Qwen3.8-27B separates its own message from both external ones at all 64 layers and in every held-out text. Asked who wrote a message, it answers "you" with probability 0.77 for its own messages and at most 0.07 for the others. Setting eight coordinates per layer, along which its own message differs most from the same text as a User message, to their User values lowers the rate of "you" to at most 0.05, and the reverse edit makes the model claim a User message, closing at least 41% of the gap. Random directions do neither, and answers about the content stay unchanged. This holds in Qwen3.8-27B without and with thinking, gpt-oss-20b, gpt-oss-120b, and MiMo-V2.6-Distill-Qwen-9B. Across twenty-five open-weight models from fourteen developers, the two levels are nested: no model recognizes its own messages without a self-index (permutation p = 0.004). Without any question, next-token prediction on the model's own message differs from that on the same text in another role, more so as more layers carry the self-index (Spearman rho = 0.82), and the same coordinates carry this difference. These results support the account that self-reference is a readout of a self-index.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13577

TERM: Target-Free Jailbreak Defense via Entropy-Guided Residual Modulation

Yunkai Dang, Wanyu Zhang, Meiyi Zhu, Qi Fan, Tianyu Ding, Wenbin Li

原文摘要

Large language models (LLMs) have shown strong capabilities across diverse applications, making both safety and utility essential for reliable deployment. Jailbreak attacks use diverse forms of adversarial prompting to bypass alignment safeguards and induce LLMs to generate harmful responses. Some representation-based defenses regulate internal activations using predefined steering directions or learned safety regions. However, adapting intervention to each input without a predefined representational target while limiting over-refusal remains a challenge. In this paper, we propose Target-free Entropy-guided Residual Modulation (TERM), an inference-time defense that uses prompt-calibrated attention entropy deviations to guide adaptive modulation of the model's own residual updates. Attention patterns vary across prompts and decoding steps, making raw entropy values difficult to interpret as a consistent signal for intervention. We therefore calibrate attention entropy against each prompt's own prefill baseline to establish an input-specific reference for reducing unnecessary intervention on benign requests. Adaptive scale normalization and temporal accumulation then account for differences in deviation scale and persistence to determine intervention strength. The resulting signal controls bounded scaling of the model's own residual updates, without relying on a predefined safety direction. We compare our method with seven defenses on seven jailbreak attacks and two over-refusal benchmarks across Llama, Qwen, and Gemma models, including Qwen3 models from 0.6B to 32B. Results show our method improves jailbreak robustness while maintaining low over-refusal on benign safety-sensitive requests with modest inference overhead.

cs.CR · cs.CL

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1659ACL Anthology · ACL 2026

If an LLM Were a Character, Would It Know Its Own Story? Evaluating Lifelong Learning in LLMs

Siqi Fan University of Electronic Science and Technology of China, Xiusheng Huang, Yiqun Yao Spin Matrix and Beijing Academy of Artificial Intelligence, Xuezhi Fang, Kang Liu Institute of automation, Chinese academy of science, Chinese Academy of Sciences, Peng Han University of Electronic Science and Technology of China, Shuo Shang University of Electronic Science and Technology of China, Aixin Sun Nanyang Technological University 等 9 位作者

原文摘要

Large language models (LLMs) can carry out human-like dialogue, but unlike humans, they are stateless due to the superposition property. However, during multi-turn, multi-agent interactions, LLMs begin to exhibit consistent, character-like behaviors—hinting at a form of emergent lifelong learning. Despite this, existing benchmarks often fail to capture these dynamics, primarily focusing on static, open-ended evaluations. To address this gap, we introduce LifeState-BENCH, a benchmark designed to assess lifelong learning in LLMs. It features two episodic datasets—Hamlet and a synthetic script collection—rich in narrative structure and character interactions. Our fact-checking evaluation probes models’ self-awareness, episodic memory retrieval, and relationship tracking, across both parametric and non-parametric approaches. Experiments on models like Llama3.1-8B, GPT-4-turbo, and DeepSeek R1, we demonstrate that non-parametric methods significantly outperform parametric ones in managing stateful learning. However, all models exhibit challenges with catastrophic forgetting as interactions extend, highlighting the need for further advancements in lifelong learning.

cs.CL · cs.MA

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.1775ACL Anthology · ACL 2026

When Efficiency Becomes a Vulnerability: Computational Cost Attacks on WebAgents

Liang-Bo Ning, Yuchen Zhu, Heqing Huang City University of Hong Kong, Xin Wang, Yi Chang Jilin University, China, Li Qing, Wenqi Fan The Hong Kong Polytechnic University

原文摘要

WebAgents have demonstrated strong capabilities in autonomously completing complex web tasks, yet their computational efficiency vulnerabilities have received limited attention. Adversaries can inject malicious prompts into web pages, causing WebAgents to generate unnecessarily long reasoning processes and incur excessive computational cost, termed Computational Cost Attacks (CCA). In this paper, to systematically study this vulnerability under realistic black-box settings, we propose CostBomb, a generation-then-selection attack framework that leverages large language models to generate diverse adversarial prompts and a reinforcement learning–enhanced selector to identify the most effective perturbations. Extensive experiments on multiple real-world web benchmarks reveal that existing WebAgents are highly vulnerable to CCA, suffering substantial increases in computational cost without compromising successful task completion. Our findings highlight an overlooked dimension of WebAgent robustness and underscore the urgent need for efficiency-aware defenses.

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.834ACL Anthology · ACL 2026

CADMate: Generating CAD Assembly Plan with Geometric Chain-of-Thought and Spatial Physical Rewards

Jiali Chen, DingBa Fu, Xusen Hei, Yuhang Liu, Yiyang Chen, Jiayuan Xie, Wenqi Fan The Hong Kong Polytechnic University, Yi Cai South China University of Technology

原文摘要

Computer-aided design (CAD) is crucial in prototyping complex 3D objects through precise geometric modeling. In practical design workflows, designers manually define assembly sequences for individual CAD parts, a process that is both time-consuming and expertise-intensive. To address this challenge, we formulate CAD assembly as a parametric action prediction task: given a reference design image and disassembled parts, the model predicts 6-DoF transformations (, actions) to progressively assemble each part. This paradigm enables multimodal large language models (MLLMs) to solve the task through autoregressive action generation. While recent MLLMs demonstrate promising spatial reasoning, they struggle with fine-grained geometric structure understanding and physical collision avoidance during assembly. In this paper, we propose CADMate, an MLLM-based framework for sequential CAD assembly action generation. Our training strategy comprises three stages: (i) CAD domain adaptation for spatial geometry and position understanding, (ii) supervised fine-tuning with geometric chain-of-thought (CoT) reasoning for action generation, and (iii) reinforcement learning with spatial-physical rewards jointly optimize spatial accuracy and collision avoidance. Additionally, we also construct CADBuilder dataset, comprising over 45K CAD assemblies with annotated action sequences. Our experiments demonstrate that CADMate significantly outperforms existing prominent MLLMs (, GPT-5), showing great potential in design applications.

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.946ACL Anthology · ACL 2026

HiSVD: Principled Low-Rank Approximation of LLMs via Hierarchical Modeling of Information Capacity and Spectral Structure

Zhuo Chen, Minghao Li, Xiaoqian Ma Xiaohongshu, Siqi Fan University of Electronic Science and Technology of China, Xiusheng Huang, Zhang Liujie Xiaohongshu, Weihang Chen

原文摘要

Singular Value Decomposition (SVD) enables hardware-agnostic LLM compression via low-rank approximation, yet optimal rank allocation remains a bottleneck. Existing methods predominantly derive layer importance from performance-oriented proxies. Yet, these metrics fail to distinguish between representational importance and structural compressibility, consequently obscuring the fine-grained influence of spectral distribution shape. We demonstrate this disconnect through spectral analysis, revealing that layers with similar information capacity can exhibit markedly different singular value decay behaviors, corresponding to varying degrees of redundancy in the spectral tail. This imperfect coupling implies that allocation strategies driven solely by importance leave significant compression opportunities underexploited. To address this gap, we propose HiSVD, a hierarchical rank allocation framework with two stages: (1) Capacity-Anchored Baseline Allocation, which preserves representational stability by aligning rank budgets with information capacity; and (2) Redundancy-Aware Refinement, which modulates this baseline using tail redundancy to penalize structural excess. Experiments on LLMs demonstrate that HiSVD achieves superior compression efficiency, significantly outperforming state-of-the-art baselines by effectively exploiting this spectral heterogeneity.

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.964ACL Anthology · ACL 2026

ReRec: Reasoning-Augmented LLM-based Recommendation Assistant via Reinforcement Fine-tuning

Jiani Huang Hong Kong Polytechinic University, Shijie Wang The Hong Kong Polytechnic University, Liang-Bo Ning, Wenqi Fan The Hong Kong Polytechnic University, Li Qing

原文摘要

With the rise of LLMs, there is an increasing need for intelligent recommendation assistants that can handle complex queries and provide personalized, reasoning-driven recommendations. LLM-based recommenders show potential but face challenges in multi-step reasoning, underscoring the need for reasoning-augmented systems. To address this gap, we propose ReRec, a novel reinforcement fine-tuning (RFT) framework designed to improve LLM reasoning in complex recommendation tasks. Our framework introduces three key components: (1) Dual-Graph Enhanced Reward Shaping, integrating recommendation metrics like NDCG@K with Query Alignment and Preference Alignment Scores to provide fine-grained reward signals for LLM optimization; (2) Reasoning-aware Advantage Estimation, which decomposes LLM outputs into reasoning segments and penalizes incorrect steps to enhance reasoning of recommendation; and (3) Online Curriculum Scheduler, dynamically assess query difficulty and organize training curriculum to ensure stable learning during RFT. Experiments demonstrate that ReRec outperforms state-of-the-art baselines and preserves core abilities like instruction-following and general knowledge. Our codes are available at https://anonymous.4open.science/r/ReRec/.

cs.CL

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