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From the 2 of 15 linked papers with an AI index.

activity
20242026
collaborators

15 papers

cs.AI2026

Leveraging Trajectory Graphs for Pre-Execution Error Diagnosis in Agentic LLM Systems

Xu Zheng, Zhuomin Chen, Chaohao Lin +4

The paper introduces Trajectory Graph Copilot, a framework that builds probabilistic graphs of past agent trajectories and uses a graph neural network to flag potentially erroneous…

cs.AI2026

Bridging Inference-Time Scaling and Episodic Memory with Action-Centric Graphs

Xu Zheng, Chaohao Lin, Zhuomin Chen +4

The paper introduces GAMER, a graph‑based action‑centric memory system that stores past reasoning as a dynamic graph and uses temporal‑difference learning to estimate action values…

cs.LG2026

TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop

Yushan Jiang, Wenchao Yu, Geon Lee +5

Time series analysis provides essential insights for real-world system dynamics and informs downstream decision-making, yet most existing methods often overlook the rich contextual…

cs.CL2026

Learning to Route: A Rule-Driven Agent Framework for Hybrid-Source Retrieval-Augmented Generation

Haoyue Bai, Haoyu Wang, Shengyu Chen +5

Large Language Models (LLMs) have shown remarkable performance on general Question Answering (QA), yet they often struggle in domain-specific scenarios where accurate and up-to-dat…

cs.LG2025

On the Effect of Sampling Diversity in Scaling LLM Inference

Tianchun Wang, Zichuan Liu, Yuanzhou Chen +5

Large language model (LLM) scaling inference is key to unlocking greater performance, and leveraging diversity has proven an effective way to enhance it. Motivated by the observed…

cs.CL2025

Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning

Renliang Sun, Wei Cheng, Dawei Li +2

Chain-of-Thought (CoT) reasoning has driven recent gains of large language models (LLMs) on reasoning-intensive tasks by externalizing intermediate steps. However, excessive or red…