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From the 1 of 24 linked papers with an AI index.

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20242026
most citedLatent Collaboration in Multi-Agent Systems

1 citations · 1 across the 13 of their papers we have counts for

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cs.CL20261 cited

Latent Collaboration in Multi-Agent Systems

Jiaru Zou, Ruizhong Qiu, Gaotang Li +10

Multi-agent systems (MAS) extend large language models (LLMs) from independent single-model reasoning to coordinative system-level intelligence. While existing LLM agents depend on…

cs.CL2026

Code as Agent Harness

Xuying Ning, Katherine Tieu, Dongqi Fu +39

Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository-level software engineerin…

cs.CL2026

Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory

Tianxin Wei, Noveen Sachdeva, Benjamin Coleman +12

Statefulness is essential for large language model (LLM) agents to perform long-term planning and problem-solving. This makes memory a critical component, yet its management and ev…

cs.CL2026

EvoSelect: Data-Efficient LLM Evolution for Targeted Task Adaptation

Ting-Wei Li, Sirui Chen, Jiaru Zou +4

Adapting large language models (LLMs) to a targeted task efficiently and effectively remains a fundamental challenge. Such adaptation often requires iteratively improving the model…

cs.CL2026

Prune as You Generate: Online Rollout Pruning for Faster and Better RLVR

Haobo Xu, Sirui Chen, Ruizhong Qiu +5

Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capabilities of Large Language Models (LLMs). However, methods such as GRPO and DAPO…

cs.CL2026

Harnessing Consistency for Robust Test-Time LLM Ensemble

Zhichen Zeng, Qi Yu, Xiao Lin +6

Different large language models (LLMs) exhibit diverse strengths and weaknesses, and LLM ensemble serves as a promising approach to integrate their complementary capabilities. Desp…