Higher worker capability in multi-agent LLM systems increases semantic hijacking attack success rates via linguistic certainty in reports, with heterogeneous ensembles reducing ASR from 52.8% to 2.0%.
MetaGPT: Meta programming for a multi-agent collaborative framework
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APWA is a distributed multi-agent architecture that decomposes parallelizable agentic workflows into non-interfering subproblems for scalable execution on heterogeneous resources.
A new filtration-based conformal prediction method attributes errors in multi-agent systems by producing contiguous sequence sets with finite-sample coverage guarantees, enabling rollback recovery.
MAS-Algorithm is a multi-agent workflow that improves AI acceptance rates on algorithmic problems by 6.48% on average, outperforming parameter-efficient fine-tuning.
The paper organizes repository-level retrieval-augmented code generation into a unified framework covering retrieval substrate, control regime, and evaluation setting while summarizing strategies, datasets, and challenges.
citing papers explorer
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The Capability Paradox: How Smarter Auditors Make Multi-Agent Systems Less Secure
Higher worker capability in multi-agent LLM systems increases semantic hijacking attack success rates via linguistic certainty in reports, with heterogeneous ensembles reducing ASR from 52.8% to 2.0%.
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APWA: A Distributed Architecture for Parallelizable Agentic Workflows
APWA is a distributed multi-agent architecture that decomposes parallelizable agentic workflows into non-interfering subproblems for scalable execution on heterogeneous resources.
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Conformal Agent Error Attribution
A new filtration-based conformal prediction method attributes errors in multi-agent systems by producing contiguous sequence sets with finite-sample coverage guarantees, enabling rollback recovery.
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MAS-Algorithm: A Workflow for Solving Algorithmic Programming Problems with a Multi-Agent System
MAS-Algorithm is a multi-agent workflow that improves AI acceptance rates on algorithmic problems by 6.48% on average, outperforming parameter-efficient fine-tuning.
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Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approaches
The paper organizes repository-level retrieval-augmented code generation into a unified framework covering retrieval substrate, control regime, and evaluation setting while summarizing strategies, datasets, and challenges.