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20242026
most citedMobA: Multifaceted Memory-Enhanced Adaptive Planning for Efficient Mobile Task Automation

1 citations · 1 across the 9 of their papers we have counts for

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cs.LG2026

Verifier-Grounded Agentic RL for Provably Correct Code

George Ma, Yuhao Zhang, Bing He +5

Reinforcement learning for code usually rewards passing tests, which a model can do while remaining wrong on the inputs the tests do not cover. A program verifier instead decides w…

cs.LG2026

SWE-Proof: Can Language Models Resolve Real-World Issues with Machine-Checked Proofs?

George Ma, Benjamin Mikek, Haoyu Li +9

Ensuring the correctness of LLM-generated code is a core challenge for modern software engineering. Benchmarks for agentic code generation check correctness with held-out test suit…

cs.CL2026

Evo-Bench: Can Language Models Improve Agent Harness?

Lisheng Huang, Chen Yang, Hao Zhou +6

Large Language Models (LLMs) have driven rapid progress in autonomous agents, yet standard evaluations remain confined to static task solving. An emerging frontier is harness evolu…

cs.AI2026

Nanbeige4.2-3B: Unlocking Agentic Capabilities in a Compact Model

Nanbeige Lab, :, Chen Yang +23

We present Nanbeige4.2-3B, a compact general agentic model with 3B non-embedding parameters. It delivers strong performance across code-agent, office-agent, and complex tool-use ta…

cs.AI2026

Hierarchical Attacks for Multi-Modal Multi-Agent Reasoning

Hao Zhou, Tiru Wu, Yan Jiang +3

Multi-modal multi-agent systems (MM-MAS) have gained increasing attention for their capacity to enable complex reasoning and coordination across diverse modalities. As these system…

cs.CL2026

ASTRA: Automated Synthesis of agentic Trajectories and Reinforcement Arenas

Xiaoyu Tian, Haotian Wang, Shuaiting Chen +12

Large language models (LLMs) are increasingly used as tool-augmented agents for multi-step decision making, yet training robust tool-using agents remains challenging. Existing meth…

cs.AI2026

CubeBench: Diagnosing Interactive, Long-Horizon Spatial Reasoning Under Partial Observations

Huan-ang Gao, Zikang Zhang, Tianwei Luo +9

Large Language Model (LLM) agents, while proficient in the digital realm, face a significant gap in physical-world deployment due to the challenge of forming and maintaining a robu…