1 citations · 2 across the 21 of their papers we have counts for
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Grounded Scaling: Why Agentic AI Needs Deterministic Environments
Liang Ding, Xintong Wang
Long-chain agent execution fails exponentially in environments designed for human tolerance: with per-step determinism , -step chain success degrades as . The AGI-t…
AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning
Yutong Wang, Siyuan Xiong, Xuebo Liu +4
While Multi-Agent Systems (MAS) excel in complex reasoning, they suffer from the cascading impact of erroneous information from individual agents. Current solutions often resort to…
IndustryBench: Probing the Industrial Knowledge Boundaries of LLMs
Songlin Bai, Xintong Wang, Linlin Yu +12
In industrial procurement, an LLM answer is useful only if it survives a standards check: recommended material must match operating condition, every parameter must respect a regula…
AdaRubric: Task-Adaptive Rubrics for Reliable LLM Agent Evaluation and Reward Learning
Liang Ding
Evaluating LLM agent trajectories is fundamentally task-specific: a code-debugging agent should be judged on Correctness and Error Handling, not on Fluency or Safety. Yet the domin…
AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling
Liang Ding
LLM-agent training pipelines routinely discard failed trajectories even though GPT-4o achieves only 14-20% on WebArena and below 55% pass@1 on ToolBench; even specialised systems a…
EcomBench: Towards Holistic Evaluation of Foundation Agents in E-commerce
Rui Min, Zile Qiao, Ze Xu +18
Foundation agents have rapidly advanced in their ability to reason and interact with real environments, making the evaluation of their core capabilities increasingly important. Whi…