LLMs exhibit pseudo-deliberation, with consistent value-action misalignment in generated dialogues despite reasoning, as measured by the new VALDI framework across 4941 scenarios.
Tenenbaum, and Igor Mordatch
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Systematic study of inter-agent communication in LLM multi-agent systems shows reasoning and verification are critical for performance, with a new augmentation technique recovering 86.2% of failures.
AgenticAITA proposes a training-free multi-agent LLM framework for autonomous trading using a deliberative pipeline, Z-score triggers, and safety gates, shown to run correctly in a five-day live dry-run with 157 invocations.
citing papers explorer
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Pseudo-Deliberation in Language Models: When Reasoning Fails to Align Values and Actions
LLMs exhibit pseudo-deliberation, with consistent value-action misalignment in generated dialogues despite reasoning, as measured by the new VALDI framework across 4941 scenarios.
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What Do Agents Communicate? Characterizing Information Exchange in Multi-Agent Systems
Systematic study of inter-agent communication in LLM multi-agent systems shows reasoning and verification are critical for performance, with a new augmentation technique recovering 86.2% of failures.
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AgenticAITA: A Proof-Of-Concept About Deliberative Multi-Agent Reasoning for Autonomous Trading Systems
AgenticAITA proposes a training-free multi-agent LLM framework for autonomous trading using a deliberative pipeline, Z-score triggers, and safety gates, shown to run correctly in a five-day live dry-run with 157 invocations.