28 citations · 63 across the 31 of their papers we have counts for
33 papers
Teacher-Guided Tool Learning for Small Language Models via Bidirectional KL Constraints
Haoxuan Gao, Qin-Wen Luo, Xiang Chen +5
External tools can extend the capabilities of small language models (SLMs), enabling their deployment in resource constrained environments. However, their limited capabilities make…
EPGM: Execution Provenance for Budgeted Agent Memory Retrieval
Yiqi Wang, Jinqian Ju, Jiaqi Zhang +4
A language agent's execution history can exceed its context window, requiring its memory system to retrieve complete supporting evidence under a hard token budget. Evidence may spa…
Dense Process Supervision for Search Agents via Fact Utility Estimation
Rongzhi Zhu, Xiangyu Liu, Yi Liu +7
Reinforcement learning (RL) for search agents typically relies on outcome rewards. However, it often fails to achieve effective credit assignment, due to the unclear value of inter…
Toward Effective and Reliable LLM Agents via Dynamic Ontology
Xiaohui Zhang, Zequn Sun, Chengyuan Yang +3
Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context. In domain-specific tasks, this leaves important semant…
MAP-Graph: Provenance-Aware Shared Memory for Multi-Agent Workflows
Yiqi Wang, Zihao Yan, Jiaqi Zhang +5
Shared memory helps language-model agents reuse information across long workflows, yet relevant evidence may not be admissible for a particular agent or action. Because restriction…
EIBench: A Simulator-Based Benchmark and Turn-Credit RL for Emotion Management
Rongzhi Zhu, Xiang Huang, Yuchuan Wu +8
Emotional intelligence (EI) in Large Language Models (LLMs) is often evaluated through static understanding tasks or single-response dialogue generation. However, emotion managemen…
Harnessing Structural Context for Entity Alignment Foundation Models
Xingyu Chen, Yuanning Cui, Zequn Sun +1
Entity alignment (EA) aims to identify equivalent entities across heterogeneous knowledge graphs (KGs) and is a key component of knowledge fusion and cross-KG reasoning. The recent…
From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents
Yiqi Wang, Jiaqi Zhang, Zhangkai Wu +9
Large language model (LLM)-based agents are moving beyond text generation to planning, tool use, retrieval, memory access, environmental interaction, and multi-agent collaboration.…
Asymmetric On-Policy Distillation: Bridging Exploitation and Imitation at the Token Level
Nan Jia, Haojin Yang, Xing Ma +6
On-policy distillation (OPD) trains a student on its own trajectories with token-level teacher feedback and often outperforms off-policy distillation and standard reinforcement lea…
ActMem: Bridging the Gap Between Memory Retrieval and Reasoning in LLM Agents
Xiaohui Zhang, Zequn Sun, Chengyuan Yang +3
Memory management is essential for LLM agents in long-term interactions. Current memory frameworks typically treat agents as passive ``recorders'' and retrieve information without…
SafeThinker: Reasoning about Risk to Deepen Safety Beyond Shallow Alignment
Xianya Fang, Xianying Luo, Yadong Wang +8
Despite the intrinsic risk-awareness of Large Language Models (LLMs), current defenses often result in shallow safety alignment, rendering models vulnerable to disguised attacks (e…
Breaking the Reasoning Horizon in Entity Alignment Foundation Models
Yuanning Cui, Zequn Sun, Wei Hu +2
Entity alignment (EA) is critical for knowledge graph (KG) fusion. Existing EA models lack transferability and are incapable of aligning unseen KGs without retraining. While using…
Beyond Static Summarization: Proactive Memory Extraction for LLM Agents
Chengyuan Yang, Zequn Sun, Wei Wei +1
Memory management is vital for LLM agents in long-term and personalized interactions. Most previous work studies how to retrieve and use memory, but pays less attention to how memo…
WavefrontDiffusion: Dynamic Decoding Schedule for Improved Reasoning
Haojin Yang, Rui Hu, Zequn Sun +3
Diffusion Language Models (DLMs) have shown strong potential for text generation and are becoming a competitive alternative to autoregressive models. The denoising strategy plays a…
KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering
Yuanning Cui, Zequn Sun, Wei Hu +1
Large language models (LLMs) excel at reasoning but struggle with knowledge-intensive questions due to limited context and parametric knowledge. However, existing methods that rely…
Answering the Unanswerable Is to Err Knowingly: Analyzing and Mitigating Abstention Failures in Large Reasoning Models
Yi Liu, Xiangyu Liu, Zequn Sun +1
Large reasoning models (LRMs) have shown remarkable progress on complex reasoning tasks. However, some questions posed to LRMs are inherently unanswerable, such as math problems la…
When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning
Rongzhi Zhu, Yi Liu, Jiancheng Wang +6
Large reasoning models (LRMs) have achieved remarkable success on complex tasks, yet their tendency to "overthink" leads to inefficiencies. Although "save-thinking" prompts are int…
Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering
Rongzhi Zhu, Xiangyu Liu, Zequn Sun +2
In this paper, we identify a critical problem, "lost-in-retrieval", in retrieval-augmented multi-hop question answering (QA): the key entities are missed in LLMs' sub-question deco…
A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning
Yuanning Cui, Zequn Sun, Wei Hu
Extensive knowledge graphs (KGs) have been constructed to facilitate knowledge-driven tasks across various scenarios. However, existing work usually develops separate reasoning mod…
Finetuning Generative Large Language Models with Discrimination Instructions for Knowledge Graph Completion
Yang Liu, Xiaobin Tian, Zequn Sun +1
Traditional knowledge graph (KG) completion models learn embeddings to predict missing facts. Recent works attempt to complete KGs in a text-generation manner with large language m…
KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation
Xindi Luo, Zequn Sun, Jing Zhao +2
Parameter-efficient finetuning (PEFT) is a key technique for adapting large language models (LLMs) to downstream tasks. In this paper, we study leveraging knowledge graph embedding…
Generating Explanations to Understand and Repair Embedding-based Entity Alignment
Xiaobin Tian, Zequn Sun, Wei Hu
Entity alignment (EA) seeks identical entities in different knowledge graphs, which is a long-standing task in the database research. Recent work leverages deep learning to embed e…
EventEA: Benchmarking Entity Alignment for Event-centric Knowledge Graphs
Xiaobin Tian, Zequn Sun, Guangyao Li +1
Entity alignment is to find identical entities in different knowledge graphs (KGs) that refer to the same real-world object. Embedding-based entity alignment techniques have been d…
Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs
Yuanning Cui, Yuxin Wang, Zequn Sun +4
Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the dev…