activity
20242026
most citedArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation

7 citations · 11 across the 16 of their papers we have counts for

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Showing 2026Show all

8 papers · 1 filter

cs.SE2026

Efficient SWE Agent Benchmarking via Trajectory-Aware Evaluation

Kefeng Duan, Dewu Zheng, Yanlin Wang +7

Evaluating software engineering agents on realistic benchmarks is costly, since each task may require multi-step code exploration, modification, and test execution. Existing effici…

cs.SE2026

Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generation

Kefeng Duan, Dewu Zheng, Yanlin Wang +8

The repository-level code generation task requires synthesizing code that satisfies task requirements while remaining consistent with the target repository context. Since real-worl…

cs.SE2026

SWE-Prime: Fewer Trajectories, Better Performance

Dewu Zheng, Ruizhe Ye, Yanlin Wang +7

To improve large language models' ability to resolve real-world software issues, prior work has focused on constructing large-scale agent trajectory datasets and performing supervi…

cs.SE2026

From Static to Dynamic: Benchmarking Real-World Code Review with MCR-Bench

Dewu Zheng, Yanlin Wang, Xiwen Wang +5

In real-world software development, code review typically involves iterative interactions between developers and reviewers to improve software quality, making the process costly an…

cs.AI2026

PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents

Tianyue Jiang, Yanlin Wang, Xin He +7

While Large Language Models have greatly advanced automated issue resolution, existing agent-based methods exhibit a fundamental limitation in their insufficient exploration of rep…

cs.CL2026

H-Mem: A Novel Memory Mechanism for Evolving and Retrieving Agent Memory via a Hybrid Structure

Jiawei Yu, Yixiang Fang, Xilin Liu +1

Memory data are ubiquitous in Large Language Model (LLM)-based agents (e.g., OpenClaw and Manus). A few recent works have attempted to exploit agents'memory for improving their per…