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A developer built a local autonomous coding agent using Ollama, combining a fine-tuned personality model (Eve) for conversation and MiniMax M3 for heavy lifting, achieving a 40-round agentic loop with 16 tools and 9/9 tests passing first try.
This paper introduces BALAR, a training-free Bayesian agentic loop algorithm that enables large language models to actively reason and ask clarifying questions in multi-turn interactions. It demonstrates significant performance improvements over baselines on detective, puzzle, and clinical diagnosis benchmarks.