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Frozen LLMs can autonomously achieve recursive self-improvement by learning and refining explicit, executable world models from interaction data.
Transcript-only self-reflection is mathematically incapable of guaranteed improvement without external verification, but grounding reflective updates in environment risk turns multi-agent memory optimization into a provably convergent game.
Large multimodal models excel at basic social understanding but falter dramatically when faced with counterfactual reasoning and causal inference in complex narratives.
Jointly optimizing action predictions and state transitions leads to significant performance gains in GUI agent training, outperforming conventional methods.
LLMs can achieve a 7.5x performance boost in web search and extraction by using a bi-level multi-agent architecture with iterative refinement and shared memory.
Forget rigid multi-agent pipelines: this framework lets you build self-organizing AI "companies" that dynamically recruit talent and adapt to tasks on the fly.
