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Frozen LLMs can autonomously achieve recursive self-improvement by learning and refining explicit, executable world models from interaction data.
Key contribution not extracted.
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.
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.
Safety-aligned GUI agents are surprisingly vulnerable to simple, model-agnostic visual distractions, repeatedly falling for the same overlaid UI elements even after successfully completing a task.
