6 citations · 7 across the 6 of their papers we have counts for
8 papers
When Harnesses Lose the Signal: Causal Evaluation of Recovery in LLM Agents
Shuyao Xiao, Shengling Wang, Xuan Chen +7
Large language model agents rely on external harnesses to pass information between the model and its environment and to recover from execution errors. Yet recovery is usually judge…
Search Shapes Conclusions: Auditing Evidence Selection Bias in Deep Research Agents
Shuyao Xiao, Shengling Wang, Xuan Chen +8
Deep Research agents synthesize evidence into cited reports, yet a well-cited report can still reach a misleading conclusion. Citation correctness checks whether cited sources supp…
When Errors Become Memories: Causal Pathway Tracing in Multi-Turn Memory-Augmented LLMs
Shuyao Xiao, Shengling Wang, Xuan Chen +8
Long-term memory enables large language models (LLMs) to preserve and reuse information across interactions, but it can also turn localized errors into persistent risks. Existing w…
DRIFT: Difficulty Routing Self-DIstillation with Rhythm-Gated Exploration and Success BuFfer Training
Haisen Luo, Yiwei Liu, Haoning Wang +13
Enabling large language models to achieve stable self-improvement without external expert supervision remains a central challenge in complex reasoning tasks. Existing self-distilla…
ASTRA: Automated Synthesis of agentic Trajectories and Reinforcement Arenas
Xiaoyu Tian, Haotian Wang, Shuaiting Chen +12
Large language models (LLMs) are increasingly used as tool-augmented agents for multi-step decision making, yet training robust tool-using agents remains challenging. Existing meth…
Dial-insight: Fine-tuning Large Language Models with High-Quality Domain-Specific Data Preventing Capability Collapse
Jianwei Sun, Chaoyang Mei, Linlin Wei +4
The efficacy of large language models (LLMs) is heavily dependent on the quality of the underlying data, particularly within specialized domains. A common challenge when fine-tunin…
From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models
Na Liu, Liangyu Chen, Xiaoyu Tian +3
This paper introduces RAISE (Reasoning and Acting through Scratchpad and Examples), an advanced architecture enhancing the integration of Large Language Models (LLMs) like GPT-4 in…
DUMA: a Dual-Mind Conversational Agent with Fast and Slow Thinking
Xiaoyu Tian, Liangyu Chen, Na Liu +4
Inspired by the dual-process theory of human cognition, we introduce DUMA, a novel conversational agent framework that embodies a dual-mind mechanism through the utilization of two…