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20212026
most citedInformed Multi-context Entity Alignment

28 citations · 63 across the 31 of their papers we have counts for

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Showing 2026Show all

13 papers · 1 filter

cs.LG2026

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…

cs.MA2026

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…

cs.CL2026

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…

cs.AI2026

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…

cs.AI2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.CR2026

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.…

cs.LG2026

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…

cs.CL2026

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…

cs.CR2026

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…

cs.LG2026

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…

cs.CL2026

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…