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Cosmipher Tech Labs

Research

How systems learn from experience. How security knowledge travels between models.

Proposals, hypotheses and methods, with their limits in view.

Featured manuscript

Recursive Experiential Learning Monograph

Continual Systems Intelligence for Self-Improving, Evidence-Governed AI Agents

Cosmipher Research · Cosmipher Tech Labs

Manuscript · 2026

An Experience Compiler evaluates trajectories against independent evidence. Promoted artifacts remain provenance-linked and revocable.

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Continual Systems Intelligence

Experience, with a way back.

A proposed learning architecture built around independent evidence and revocable experience.

Execute, verify, compileExecution produces trajectories. Independent evidence supports compilation. Later outcomes can revoke a promoted artifact.RevocableexperienceExecuteVerifyCompile

  1. Execute

    A trusted control plane separates execution from specification and authority.

  2. Verify

    Evaluate trajectories against independent evidence.

  3. Compile

    Propose scoped episodes, claims, skills, deterministic tools and routing policies.

Every promoted artifact remains provenance-linked and revocable: later contradictions, reverts, incidents or real-world outcomes can invalidate its support.

The technical record

Manuscripts

Manuscript · 2026

Recursive Experiential Learning Monograph

Cosmipher Research · Cosmipher Tech Labs

Foundation-model agents are increasingly capable, yet many agentic workflows remain episodic: useful reasoning is discarded or retained only as raw history, failures are weakly propagated, and improvement is pursued through larger models or additional inference.

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Manuscript · 2026

Cross-Scale Security Alignment Transfer via Full-Weight Tensor Transformation and Capacity Expansion

Cosmipher Research · Cosmipher Tech Labs

Cosmipher-Freedom-V1.1 is described by its developer as a security-specialized, multimodal mixture-of-experts (MoE) derivative with a reported final parameter total of 602 billion.

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