4 papers
Beyond Outcome Rewards: Step-Level Self-Distilled Policy Optimization for Deep Search Agents
Haoze Wu, Chuqiao Kuang, Tianyi Zhuang +1
Deep search agents operate over trajectories spanning dozens of steps, yet standard reinforcement learning provides only a single outcome reward per trajectory, which is far too sp…
UIS-Digger: Towards Comprehensive Research Agent Systems for Real-world Unindexed Information Seeking
Chang Liu, Chuqiao Kuang, Tianyi Zhuang +4
Recent advancements in LLM-based information-seeking agents have achieved record-breaking performance on established benchmarks. However, these agents remain heavily reliant on sea…
TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in Real-World Scenarios
Shaohang Wei, Wei Li, Feifan Song +5
Temporal reasoning is pivotal for Large Language Models (LLMs) to comprehend the real world. However, existing works neglect the real-world challenges for temporal reasoning: (1) i…
DocPuzzle: A Process-Aware Benchmark for Evaluating Realistic Long-Context Reasoning Capabilities
Tianyi Zhuang, Chuqiao Kuang, Xiaoguang Li +4
We present DocPuzzle, a rigorously constructed benchmark for evaluating long-context reasoning capabilities in large language models (LLMs). This benchmark comprises 100 expert-lev…