Yanming Li

Yanming Li, Xuelin Zhang, WenJie Lu et al.

Integrating Large Language Models (LLMs) with external tools via multi-agent systems offers a promising new paradigm for decomposing and solving complex problems. However, training these systems remains notoriously difficult due to the credit assignment challenge, as it is often unclear which specific functional agent is responsible for the success or failure of decision trajectories. Existing methods typically rely on sparse or globally broadcast rewards, failing to capture individual contributions and leading to inefficient reinforcement learning. To address these limitations, we introduce the Shapley-based Hierarchical Attribution for Reinforcement Policy (SHARP), a novel framework for optimizing multi-agent reinforcement learning via precise credit attribution. SHARP effectively stabilizes training by normalizing agent-specific advantages across trajectory groups, primarily through a decomposed reward mechanism comprising a global broadcast-accuracy reward, a Shapley-based marginal-credit reward for each agent, and a tool-process reward to improve execution efficiency. Extensive experiments across various real-world benchmarks demonstrate that SHARP significantly outperforms recent state-of-the-art baselines, achieving average match improvements of 23.66% and 14.05% over single-agent and multi-agent approaches, respectively.

Yanming Li, Seifeddine Ghozzi, Cédric Eichler et al.

We address the problem of auditing whether sensitive or copyrighted texts were used to fine-tune large language models (LLMs) under black-box access. Prior signals-verbatim regurgitation and membership inference-are unreliable at the level of individual documents or require altering the visible text. We introduce a text-preserving watermarking framework that embeds sequences of invisible Unicode characters into documents. Each watermark is split into a cue (embedded in odd chunks) and a reply (embedded in even chunks). At audit time, we submit prompts that contain only the cue; the presence of the corresponding reply in the model's output provides evidence of memorization consistent with training on the marked text. To obtain sound decisions, we compare the score of the published watermark against a held-out set of counterfactual watermarks and apply a ranking test with a provable false-positive-rate bound. The design is (i) minimally invasive (no visible text changes), (ii) scalable to many users and documents via a large watermark space and multi-watermark attribution, and (iii) robust to common passive transformations. We evaluate on open-weight LLMs and multiple text domains, analyzing regurgitation dynamics, sensitivity to training set size, and interference under multiple concurrent watermarks. Our results demonstrate reliable post-hoc provenance signals with bounded FPR under black-box access. We experimentally observe a failure rate of less than 0.1\% when detecting a reply after fine-tuning with 50 marked documents. Conversely, no spurious reply was recovered in over 18,000 challenges, corresponding to a 100\%TPR@0\% FPR. Moreover, detection rates remain relatively stable as the dataset size increases, maintaining a per-document detection rate above 45\% even when the marked collection accounts for less than 0.33\% of the fine-tuning data.