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A harness is the system around a model that enables it to act as an agent: prompts, tools, memory, skills, orchestration, and more. Harnessing is the process of optimizing that system.

Companion materials for our survey of how execution experience becomes persistent harness state under a fixed base model. The survey covers update mechanisms, evolutionary search, continual PAC learning, evaluation, applications, and societal impact.

Token-scale scientific discovery engine — autonomous hypothesis generation, experiment execution, and knowledge graph synthesis

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Recursive Self-Improvement (RSI) refers to processes in which AI systems improve their own capabilities and can also improve the mechanisms that generate subsequent improvements.

Hermes Agent Self-Evolution uses DSPy + GEPA (Genetic-Pareto Prompt Evolution) to automatically evolve and optimize Hermes Agent's skills, tool descriptions, system prompts, and code — producing measurably better versions through reflective evolutionary search.

The Harness of Harnesses • built for RSI: a trusted, persistent, self-evolving multi-agent ecosystem for all-domain collaboration.

NeoHorse is TokenRhythm's family of open-weight models for agent workflows. NeoHorse-1 provides 4B and 9B language models for tool use, coding, and instruction following. NeoHorse-Jev builds on NeoHorse-1-4B to make structured decisions through prefill-only inference.

Recuris is a recursive self-improvement framework that improves a long-horizon agent by evolving its memory instead of its weights or its prompt. A frozen agent is paired with a Skill Memory M = (E, W, ρ, C).

> "A poem is never finished, only abandoned." > — Paul Valéry

Plug in any agent harness × any model, let it rewrite its own harness over many context-fresh episodes, and measure how the harness changes — under a goal, many goals, or no goal at all.