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20232026
most citedWhy Do Multi-Agent LLM Systems Fail?

15 citations · 100 across the 85 of their papers we have counts for

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7 papers · 1 filter

cs.AI2026

Agent Plasticity: Measuring Self-Improvement Through Experience

Harman Singh, Anton Bakhtin, Rulin Shao +8

AI agents increasingly operate in environments where they can diagnose failures and improve through experience, yet existing evaluations largely measure what an agent can do at a f…

cs.AI2026

Squeeze Evolve: Unified Multi-Model Orchestration for Verifier-Free Evolution

Monishwaran Maheswaran, Leon Lakhani, Zhongzhu Zhou +16

We show that verifier-free evolution is bottlenecked by both diversity and efficiency: without external correction, repeated evolution accelerates collapse toward narrow modes, whi…

cs.AI2026

Agentic Test-Time Scaling for WebAgents

Nicholas Lee, Lutfi Eren Erdogan, Chris Joseph John +4

Test-time scaling has become a standard way to improve performance and boost reliability of neural network models. However, its behavior on agentic, multi-step tasks remains less w…

cs.AI2025

: Faster Test-Time Scaling through Speculative Drafts

Mert Cemri, Nived Rajaraman, Rishabh Tiwari +6

Scaling test-time compute has driven the recent advances in the reasoning capabilities of large language models (LLMs), typically by allocating additional computation for more thor…

cs.AI2025

Learning Adaptive Parallel Reasoning with Language Models

Jiayi Pan, Xiuyu Li, Long Lian +6

Scaling inference-time computation has substantially improved the reasoning capabilities of language models. However, existing methods have significant limitations: serialized chai…

cs.AI2025★ 15 cited

Why Do Multi-Agent LLM Systems Fail?

Mert Cemri, Melissa Z. Pan, Shuyi Yang +10

Despite enthusiasm for Multi-Agent LLM Systems (MAS), their performance gains on popular benchmarks are often minimal. This gap highlights a critical need for a principled understa…

cs.AI2024

K-Sort Arena: Efficient and Reliable Benchmarking for Generative Models via K-wise Human Preferences

Zhikai Li, Xuewen Liu, Dongrong Joe Fu +4

The rapid advancement of visual generative models necessitates efficient and reliable evaluation methods. Arena platform, which gathers user votes on model comparisons, can rank mo…