arXiv AI
MAS-ProVe: Understanding the Process Verification of Multi-Agent Systems
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arXiv AI
Jun 12
The Illusion of Multi-Agent Advantage
arXiv:2606. 13003v1 Announce Type: new Abstract: Prevailing wisdom posits that Multi-Agent Systems (MAS) are superior to Single-Agent Systems (SAS), citing advantages like context protection, parallel processing and distributed decision-making.
arXiv AI
Jul 16
Benefits and Limitations of Communication in Multi-Agent Reasoning
arXiv:2510. 13903v2 Announce Type: replace-cross Abstract: Chain-of-thought prompting has popularized step-by-step reasoning in large language models, yet model performance still degrades as problem complexity and context length grow.
arXiv AI
Jun 10
Decentralized Multi-Agent Systems with Shared Context
arXiv:2606. 10662v1 Announce Type: cross Abstract: Multi-agent systems (MAS) can scale large language model reasoning at test time by decomposing complex problems into parallel subtasks.
arXiv AI
Jun 30
SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models
arXiv:2601. 03555v3 Announce Type: replace Abstract: Training reliable tool-augmented agents remains a significant challenge, largely due to the difficulty of credit assignment in multi-step reasoning.
arXiv AI
Jul 15
Track, Rank, Crack: Epistemic Working Memory Scales Multi-Hop Reasoning in Language Agents
arXiv:2607. 12267v1 Announce Type: cross Abstract: Language agents that interleave reasoning and tool use degrade sharply as reasoning chains lengthen, even when each individual step is easy.
arXiv AI
Jun 8
When Does Multi-Agent Collaboration Help? An Entropy Perspective
arXiv:2602. 04234v6 Announce Type: cross Abstract: Multi-agent systems (MAS) have emerged as a prominent paradigm for leveraging large language models (LLMs) to tackle complex tasks.