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Progress-Aware Distillation for Mitigating Stagnation in Small Language Model Agents
Yuanpu Cao, Saket Sathe, Hanyu Wang +3
While LLM-based agent systems have demonstrated remarkable proficiency in reasoning and solving complex real-world tasks, they also come with substantial token consumption and infe…
From Narrative to Auditable Forecasts: A Structured Scaffold for Agentic Forecasting
Yuanpu Cao, Yongkang Du, Yurui Chang +2
LLM agents are increasingly used for live forecasting, where they retrieve up-to-date information and produce estimates for unresolved future events. However, current agentic forec…
Can Factual Opinions Be Edited (Manipulated) in Large Language Models?
Yuanpu Cao, Ziyi Yin, Fenglong Ma +1
Large Language Models (LLMs) are increasingly integrated into various domains, making knowledge editing techniques crucial yet potentially hazardous. Current editing methods primar…
Looped Transformers with Layer Normalization Provably Learn the Power Method
Lyumin Wu, Chenyang Zhang, Yuan Cao
Transformers have achieved remarkable success across a wide range of applications, and a growing body of work suggests that part of their strength comes from their ability to learn…
ForecastCompass: Guiding Agentic Forecasting with Adaptive Factor Memory
Yurui Chang, Yongkang Du, Yuanpu Cao +2
Agentic forecasting is important for decision-making in dynamic environments, but it remains challenging because agents must reason from incomplete, time-limited evidence and produ…
Restoring the Sweet Spot: Pass-Rate Weighted Self-Distillation for LLM Reasoning
Zehao Liu, Yuanpu Cao, Jinghui Chen +1
Self-Distillation Policy Optimization (SDPO) provides dense token-level credit assignment for reinforcement learning with large language models by leveraging the model's own feedba…
The Illusion of Reasoning: Exposing Evasive Data Contamination in LLMs via Zero-CoT Truncation
Yifan Lan, Yuanpu Cao, Hanyu Wang +2
Large language models (LLMs) have demonstrated impressive reasoning abilities across a wide range of tasks, but data contamination undermines the objective evaluation of these capa…
PreFlect: From Retrospective to Prospective Reflection in Large Language Model Agents
Hanyu Wang, Yuanpu Cao, Lu Lin +1
Advanced large language model agents typically adopt self-reflection for improving performance, where agents iteratively analyze past actions to correct errors. However, existing r…