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20242026
most citedThe Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models

10 citations · 23 across the 17 of their papers we have counts for

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Showing 2026Show all

5 papers · 1 filter

cs.AI2026

Composition Collapse: Stable Factual Knowledge Does Not Imply Compositional Reasoning

Zhe Yu, Wenpeng Xing, Yunzhao Wei +4

Post-training is routinely evaluated through aggregate benchmark scores that treat multi-hop reasoning as a single capability -- as if a model that answers more questions correctly…

cs.CL2026

Toward Autonomous Long-Horizon Engineering for ML Research

Guoxin Chen, Jie Chen, Lei Chen +7

Agentic systems increasingly automate pieces of AI research. Yet turning underspecified research objectives into runnable, experimentally validated ML systems remains a central bot…

cs.AI2026

Graph-of-Agents: A Graph-based Framework for Multi-Agent LLM Collaboration

Sukwon Yun, Jie Peng, Pingzhi Li +5

With an ever-growing zoo of LLMs and benchmarks, the need to orchestrate multiple models for improved task performance has never been more pressing. While frameworks like Mixture-o…

cs.SE2026

Immersion in the GitHub Universe: Scaling Coding Agents to Mastery

Jiale Zhao, Guoxin Chen, Fanzhe Meng +11

Achieving mastery in real world software engineering tasks is fundamentally bottlenecked by the scarcity of large scale, high quality training data. Scaling such data has been limi…

cs.CL2026

BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?

Guoxin Chen, Fanzhe Meng, Jiale Zhao +12

Current code-agent benchmarks primarily evaluate localized issue resolution within a single target repository, leaving under-tested many software engineering tasks that require ext…