WORC improves multi-agent LLM reasoning to 82.2% average accuracy by predicting and compensating for the weakest agent via targeted extra sampling rather than uniform reinforcement.
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Assigning higher redundancy to semantically important query features reduces retrieval error probability under token erasures, via multivariate Gaussian approximations of similarity margins and supporting numerical results.
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Weak-Link Optimization for Multi-Agent Reasoning and Collaboration
WORC improves multi-agent LLM reasoning to 82.2% average accuracy by predicting and compensating for the weakest agent via targeted extra sampling rather than uniform reinforcement.
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Context-Aware Search and Retrieval Under Token Erasure
Assigning higher redundancy to semantically important query features reduces retrieval error probability under token erasures, via multivariate Gaussian approximations of similarity margins and supporting numerical results.