10 papers · 1 filter
FlowEdit: Information-Theoretic Control of LLM Reasoning Flows for Ill-posed Problems Involving Conflicts
Sizhe Tang, Guangyu Jiang, Yu Li +3
Large Language Models (LLMs) perform strongly on well-specified reasoning tasks with a feasible answer. However, problems encountered in the open world can become ill-posed due to…
LC-ERD: Mining Latent Logic for Self-Evolving Reasoning via Consistency-Regulated Reward Decomposition
Yanyu Chen, Jiyue Jiang, Dianzhi Yu +8
The evolution of Large Language Model (LLM) reasoning is bottlenecked by the scarcity of high-quality process data. While self-alignment via endogenous rewards offers a solution, m…
IntentScore: Intent-Conditioned Action Evaluation for Computer-Use Agents
Rongqian Chen, Yu Li, Zeyu Fang +3
Computer-Use Agents (CUAs) leverage large language models to execute GUI operations on desktop environments, yet they generate actions without evaluating action quality, leading to…
Voting with the Graph: Stable RLAIF via Topological Consistency Maximization
Boyin Liu, Zhuo Zhang, Sen Huang +8
Reinforcement Learning from AI Feedback (RLAIF) relies on LLM judges as preference measurement instruments, yet these instruments are fundamentally limited by random measurement er…
Reason in Chains, Learn in Trees: Self-Rectification and Grafting for Multi-turn Agent Policy Optimization
Yu Li, Sizhe Tang, Tian Lan
Reinforcement learning for Large Language Model agents is often hindered by sparse rewards in multi-step reasoning tasks. Existing approaches like Group Relative Policy Optimizatio…
FactorEngine: A Program-level Knowledge-Infused Factor Mining Framework for Quantitative Investment
Qinhong Lin, Ruitao Feng, Yinglun Feng +7
We study alpha factor mining, the automated discovery of predictive signals from noisy, non-stationary market data-under a practical requirement that mined factors be directly exec…