Alexa R. Tartaglini, Zhengxuan Wu, Thaddäus Wiedemer, Martin Wattenberg, Been Kim, Yuxuan Li
Do vision-language models (VLMs) share our ability to communicate visually? We share preliminary findings, starting from a surprising observation: simple visual symbols like a red heart can strongly influence model behavior on seemingly unrelated tasks, such as steering models towards recursive solutions on simple coding problems. Notably, this steering effect is largely implicit, as models' direct interpretations and thinking traces about visual symbols diverge substantially from their observed behavior. In visual communication between models, this leads to a generation--interpretation gap: while models tend to send shape primitives that they (and humans) find intuitive, these visual cues often fail to steer other models. In further communication games involving abstract concepts and stories, models can creatively use visuals to transmit meaning to one another, although some communications remain non-intuitive to humans. These findings reveal that VLMs might be capable of rich, creative, and visually-grounded communication, but their interpretation and production of visual meaning diverges from human conventions in surprising ways. Together this work highlights both the promise and challenges of visual communication between humans and VLMs.
Jiaxuan Wu, Wanli Peng China Agricultural University, Hang fu, Xue Yiming China Agricultural University, Juan Wen China Agricultural University
Training and serving large language models (LLMs) is resource-intensive, making reliable intellectual property (IP) protection and black-box ownership verification increasingly important.Model fingerprinting enables such verification by injecting a small set of secret query–response behaviors, but many existing fingerprints rely on explicit markers or predetermined outputs that are weakly grounded in prompt semantics.This semantic mismatch yields atypical fingerprint responses, reduces stealthiness, and exposes fingerprints to removal by response normalization.We formalize this vulnerability via a new removal attack, Generation Revision Intervention (GRI), which applies system-prompt-level revision and response standardization to steer models toward typical answers, substantially compromising representative injected baselines.To close this semantic gap, we propose the Implicit Fingerprints (ImF): we encode ownership information into a natural-looking target response y via linguistic steganography, then derive a CoT-augmented query x that embeds semantic cues from y to guide the model toward an output sufficiently close to y for decoding-based verification.Experiments on 15 LLMs show that ImF improves stealthiness and remains verifiable under model updates and deployment-time prompt interventions; additional analyses further show stability under common decoding variation and realistic related-model partial merging.
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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
Hang fu, Wanli Peng China Agricultural University, Yinghan Zhou, Jiaxuan Wu, Juan Wen China Agricultural University, Xue Yiming China Agricultural University
The widespread adoption of Large Language Model (LLM) in commercial and research settings has intensified the need for robust intellectual property protection. Backdoor-based LLM fingerprinting has emerged as a promising solution for this challenge. In practical application, the low-cost multi-model collaborative technique, LLM ensemble, combines diverse LLMs to leverage their complementary strengths, garnering significant attention and practical adoption. Unfortunately, the vulnerability of existing LLM fingerprinting for the ensemble scenario is unexplored. In order to comprehensively assess the robustness of LLM fingerprinting, in this paper, we propose two novel fingerprinting attack methods: token filter attack (TFA) and sentence verification attack (SVA). The TFA gets the next token from a unified set of tokens created by the token filter mechanism at each decoding step. The SVA filters out fingerprint responses through a sentence verification mechanism based on perplexity and voting. Experimentally, the proposed methods effectively inhibit the fingerprint response while maintaining ensemble performance. Compared with state-of-the-art attack methods, the proposed method can achieve better performance. The findings necessitate enhanced robustness in LLM fingerprinting.
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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
Yongxuan Wu, Xixun Lin Institute of Information Engineering, Chinese Academy of Sciences, He Zhang Griffith University, Nan Sun Institute of Information Engineering, Chinese Academy of Sciences, Kun Wang, Chuan Zhou Academy of Mathematics and Systems Science, Chinese Academy of Sciences and School of Cyber Security, University of Chinese Academy of Sciences, Shirui Pan Griffith University, Yanan Cao Institute of Information Engineering, Chinese Academy of Sciences
LLM-based Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in solving complex tasks. Central to MAS is the communication topology which governs how agents exchange information internally. Consequently, the security of communication topologies has attracted increasing attention. In this paper, we investigate a critical privacy risk: MAS communication topologies can be inferred under a restrictive black-box setting, exposing system vulnerabilities and posing significant intellectual property threats. To explore this risk, we propose Communication Inference Attack (CIA), a novel attack that constructs new adversarial queries to induce intermediate agents’ reasoning outputs and models their semantic correlations through the proposed global bias disentanglement and LLM-guided weak supervision. Extensive experiments on MAS with optimized communication topologies demonstrate the effectiveness of CIA, achieving an average AUC of 0.87 and a peak AUC of up to 0.99, thereby revealing the substantial privacy risk in MAS. The source code is available at https://github.com/aabbbcd/CIA.
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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
David P. Woodruff, Vincent Cohen-Addad, Lalit Jain, Jieming Mao, Song Zuo, MohammadHossein Bateni, Simina Branzei, Michael P. Brenner 等 36 位作者
Recent advances in large language models (LLMs) have opened new avenues for accelerating scientific research. While models are increasingly capable of assisting with routine tasks, their ability to contribute to novel, expert-level mathematical discovery is less understood. We present a collection of case studies demonstrating how researchers have successfully collaborated with advanced AI models, specifically Google's Gemini-based models (in particular Gemini Deep Think and its advanced variants), to solve open problems, refute conjectures, and generate new proofs across diverse areas in theoretical computer science, as well as other areas such as economics, optimization, and physics. Based on these experiences, we extract common techniques for effective human-AI collaboration in theoretical research, such as iterative refinement, problem decomposition, and cross-disciplinary knowledge transfer. While the majority of our results stem from this interactive, conversational methodology, we also highlight specific instances that push beyond standard chat interfaces. These include deploying the model as a rigorous adversarial reviewer to detect subtle flaws in existing proofs, and embedding it within a "neuro-symbolic" loop that autonomously writes and executes code to verify complex derivations. Together, these examples highlight the potential of AI not just as a tool for automation, but as a versatile, genuine partner in the creative process of scientific discovery.
MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents
Zijian Zhou, Ao Qu, Zhaoxuan Wu, Sunghwan Kim, Alok Prakash, Daniela Rus, Jinhua Zhao, Bryan Kian Hsiang Low 等 9 位作者
AI 速览：MEM1 是一个端到端强化学习框架，让语言智能体在长多轮任务中以恒定内存运行，通过紧凑共享状态协同记忆巩固与推理。
Modern language agents must operate over long-horizon, multi-turn interactions, where they retrieve external information, adapt to observations, and answer interdependent queries. Yet, most LLM systems rely on full-context prompting, appending all past turns regardless of their relevance. This leads to unbounded memory growth, increased computational costs, and degraded reasoning performance on out-of-distribution input lengths. We introduce MEM1, an end-to-end reinforcement learning framework that enables agents to operate with constant memory across long multi-turn tasks. At each turn, MEM1 updates a compact shared internal state that jointly supports memory consolidation and reasoning. This state integrates prior memory with new observations from the environment while strategically discarding irrelevant or redundant information. To support training in more realistic and compositional settings, we propose a simple yet effective and scalable approach to constructing multi-turn environments by composing existing datasets into arbitrarily complex task sequences. Experiments across three domains, including internal retrieval QA, open-domain web QA, and multi-turn web shopping, show that MEM1-7B improves performance by 3.5x while reducing memory usage by 3.7x compared to Qwen2.5-14B-Instruct on a 16-objective multi-hop QA task, and generalizes beyond the training horizon. Our results demonstrate the promise of reasoning-driven memory consolidation as a scalable alternative to existing solutions for training long-horizon interactive agents, where both efficiency and performance are optimized.