9 papers
When Self-Evolution Backfires: Pre-Commit Gating against Skill Contamination in LLM Agents
Linfang Shang, Ming Xu, Yiding Sun +4
Self-evolving agents accumulate capability by distilling reusable skills from their execution trajectories, but we find this process is not monotonic: past a critical pool size, ne…
AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents
Lingxiang Hu, Yiding Sun, Tianle Xia +6
While Large Language Model (LLM) agents have made remarkable progress on complex reasoning, evaluating them in real-world environments remains an open problem. Existing benchmarks…
GraSP: Graph-Structured Skill Compositions for LLM Agents
Tianle Xia, Lingxiang Hu, Yiding Sun +5
Skill ecosystems for LLM agents have matured rapidly, yet recent benchmarks show that providing agents with more skills does not monotonically improve performance -- focused sets o…
Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA
Wenwei Li, Ming Xu, Tianle Xia +7
Industrial advertising question answering (QA) is a high-stakes task in which hallucinated content, particularly fabricated URLs, can lead to financial loss, compliance violations,…
Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training
Tianle Xia, Ming Xu, Lingxiang Hu +7
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by incorporating external knowledge, yet traditional single-round retrieval struggles with complex multi-…
Compass-Embedding v4: Robust Contrastive Learning for Multilingual E-commerce Embeddings
Pakorn Ueareeworakul, Shuman Liu, Jinghao Feng +7
As global e-commerce rapidly expands into emerging markets, the lack of high-quality semantic representations for low-resource languages has become a decisive bottleneck for retrie…