Recursa: Toward Recursively Self-Improving Foundation Model Systems
Recursa is a foundation model system that adapts to new domains without human experts or a stronger teacher model.
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- Primary sourceHugging Face
- Discovery linkHugging Face
- Linked in sourceView arXiv page
- Linked in sourceView PDF
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Recursa is a foundation model system that adapts to new domains without human experts or a stronger teacher model. It stores domain skills, usage instructions, task memories, and reflections in a knowledge repository, bootstrapping it from domain materials. Parallel learners run tasks, propose local improvements, and those are validated and merged back into the repository. On Harvey LAB's Firm Knowledge benchmark, held-out all-pass rates rose from 30.0% to 55.0% with DeepSeek-V4.1-Flash and from 35.0% to 46.0% with Qwen3.8-Flash.
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Source claims not independently checked; results are reported on a single benchmark with held-out tasks.
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The recorded brief basis is fetched source page. These points are AINewsIQ paraphrases, not quotations or independent confirmations.
A knowledge repository stores domain skills, usage instructions, task memories, and reflections on improvements.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseThe repository is bootstrapped from domain materials and practice tasks generated from those materials.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseParallel learners propose local improvements that are validated and merged back into the repository.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseAll-pass rate on held-out tasks rose from 30.0% to 55.0% (DeepSeek-V4.1-Flash) and 35.0% to 46.0% (Qwen3.8-Flash).
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
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Why this may matter
The approach targets adapting foundation models to new domains where expert-written data and reliable supervision are scarce. If the self-improvement loop generalizes, it could reduce dependence on human annotation or stronger teacher models.
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