most citedINMS: Memory Sharing for Large Language Model based Agents

2 citations · 2 across the 6 of their papers we have counts for

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cs.CL2026

Pooling and Semantic Shift: The Fundamental Challenges in Long Text Embedding and Retrieval

Hang Gao, Wujiang Xu, Kai Mei +1

Transformer-based embedding models frequently exhibit geometric pathologies, such as anisotropy and length-induced representation collapse, which can degrade downstream retrieval p…

cs.CL2026

Beyond Explicit Edges: Robust Reasoning over Noisy and Sparse Knowledge Graphs

Hang Gao, Dimitris N. Metaxas

GraphRAG is increasingly adopted for converting unstructured corpora into graph structures to enable multi-hop reasoning. However, standard graph algorithms rely heavily on static…

cs.CL20262 cited

INMS: Memory Sharing for Large Language Model based Agents

Hang Gao, Yongfeng Zhang

While Large Language Model (LLM) based agents excel at complex tasks, their performance in open-ended scenarios is often constrained by isolated operation and reliance on static da…

cs.CL2025

Task-Aligned Tool Recommendation for Large Language Models

Hang Gao, Yongfeng Zhang

By augmenting Large Language Models (LLMs) with external tools, their capacity to solve complex problems has been significantly enhanced. However, despite ongoing advancements in t…

cs.CL2025

Auto-Prompt Generation is Not Robust: Prompt Optimization Driven by Pseudo Gradient

Zeru Shi, Zhenting Wang, Yongye Su +5

While automatic prompt generation methods have recently received significant attention, their robustness remains poorly understood. In this paper, we introduce PertBench, a compreh…

cs.CL2025

A-MEM: Agentic Memory for LLM Agents

Wujiang Xu, Zujie Liang, Kai Mei +3

While large language model (LLM) agents can effectively use external tools for complex real-world tasks, they require memory systems to leverage historical experiences. Current mem…