TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories
Score
81首次提出同时满足零失真、自同步、无条件不变性的智能体水印方案，理论分析与实验验证完整，信息增量大。
论文结构严谨，理论推导清晰，实验设计合理，结果显著，属于高质量学术成果。
直接面向LLM智能体商业化中归属验证的实际需求，方法可部署，但需集成到智能体框架中。
当前LLM智能体应用快速扩展，归属水印是刚需，论文内容高度契合当下热点。
来源为arXiv预印本，但属于AI/CL/LG领域，作者团队背景可信，方法有理论支撑。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
Strong technical novelty, solid theoretical analysis, comprehensive experiments with impressive results, and significant implications for AI safety and commercialization.
AI commentary
「This paper tackles a very practical problem: how to track attribution when LLM agents are resold. The two-channel design using complementary content and position keys to combat deletion and rewriting is ingenious, and the theoretical proof is solid. It's a must-read for teams building AI agent platforms and anyone concerned with AI service compliance and security.」
Quick overview
TRACE proposes a two-channel robust watermark for LLM agent trajectory logs, using content keys (deletion-robust) and position keys (rewrite-robust) to build a selection channel and a tally channel. It provably preserves the agent's action distribution while remaining detectable under full read/write access by an adversary. Theoretical analysis shows signal is bought with decision entropy. Experiments on ToolBench and ALFWorld show near z=100 detection on long-horizon trajectories, detectability under 70% step deletion, and tally channel fully invariant under LLM rewriting.
- Complementary selection channel (content-keyed) and tally channel (position-keyed) provide robustness against both deletion and rewriting.
- Watermark embedding is distortion-free: the agent's action distribution is provably unchanged.
- Theoretical guarantee that each decision contributes at least half its entropy to the watermark signal; deterministic decisions contribute nothing.
- On ToolBench and ALFWorld, detection z-score near 100 on long trajectories, detectable after 70% step deletion, and tally channel invariant under LLM rewriting of any strength.
Full text
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