39 citations · 105 across the 42 of their papers we have counts for
10 papers · 1 filter
Just-in-Time Skill Retrieval via Long-Horizon State Tracking
Xin Lin, Ziheng Wang, Zerui Chen +6
Large language model (LLM) agents increasingly rely on external skill libraries to support complex task execution. However, determining which skills are needed remains challenging…
Towards Reliable AI Data Scientists: Data Agents with Workflow Harnesses
Huachi Zhou, Yujing Zhang, Jiahe Du +7
Large language model agents are increasingly deployed for data-intensive work, yet reliable data analysis requires more than general-purpose reasoning and ad hoc tool augmentation.…
CulturalMenuBench: Probing the Knowledge-Application Gap in Multimodal Culinary Reasoning
Bo Zeng, Linfeng Gao, Peiqin Lin +9
Multimodal language models achieve near-ceiling scores on food recognition benchmarks, yet it remains unclear whether this success reflects genuine cultural understanding or mere v…
SAAS: Self-Aware Reinforcement Learning for Over-Search Mitigation in Agentic Search
Yunbo Tang, Chengyi Yang, Shiyu Liu +4
Agentic search enables LLMs to solve complex multi-hop questions through iterative reasoning and external search. Despite the effectiveness, these systems often suffer from a criti…
Graph-based Agent Memory: Taxonomy, Techniques, and Applications
Chang Yang, Chuang Zhou, Yilin Xiao +15
Memory emerges as the core module in the Large Language Model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game playing, scientific discovery), whe…
BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search
Shiyu Liu, Yongjing Yin, Jianhao Yan +7
RL-based agentic search enables LLMs to solve complex questions via dynamic planning and external search. While this approach significantly enhances accuracy with agent policies op…
Nondeterministic Polynomial-time Problem Challenge: An Ever-Scaling Reasoning Benchmark for LLMs
Chang Yang, Ruiyu Wang, Junzhe Jiang +9
Reasoning is the fundamental capability of large language models (LLMs). Due to the rapid progress of LLMs, there are two main issues of current benchmarks: i) these benchmarks can…
LLM-Based World Models Can Make Decisions Solely, But Rigorous Evaluations are Needed
Chang Yang, Xinrun Wang, Junzhe Jiang +2
World model emerges as a key module in decision making, where MuZero and Dreamer achieve remarkable successes in complex tasks. Recent work leverages Large Language Models (LLMs) a…
Neuro-Symbolic Entity Alignment via Variational Inference
Shengyuan Chen, Zheng Yuan, Qinggang Zhang +3
Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. Existing methods can be categorized into symbolic and neural models. Symbolic…
Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion
Qinggang Zhang, Keyu Duan, Junnan Dong +2
Inductive knowledge graph completion (KGC) aims to infer the missing relation for a set of newly-coming entities that never appeared in the training set. Such a setting is more in…