Skip to content

RSI.md

Latest commit

 

History

History
114 lines (93 loc) · 5.68 KB

RSI.md

File metadata and controls

114 lines (93 loc) · 5.68 KB
Raw

Recursive self-improvement in ARTEX

Status: measurement and shadow memory only. There is no harness search, no automatic adoption, and no self-modifying code in this repository.

This file defines the contract any future harness-optimization work must meet, so the first experiment cannot quietly become an unguarded rewrite loop.

The three loops

loop object state
A. within a task planner, worker, retester, adversarial gate exists
B. across tasks evidence-gated lessons from closed tasks shadow mode, PR #104
C. on the harness prompts, skills, tool guidance not built; this is its contract

Loop A produces task gain. Loop B is meant to produce retained gain: a lesson must survive a closed task and change a later one. Loop C is the only loop that can produce improver gain, and it is the only one that can silently damage the product, which is why it goes last.

Loop C contract

Any PR that proposes, probes, or adopts harness edits must satisfy all of:

  1. Live tasks propose, benchmarks adopt. A successful bounty task can generate a candidate lesson or edit, but it can never be the evidence for adoption. Adoption requires held-out benchmark runs, because a live task is unreproducible and contaminated by its target.
  2. Owner approval per adoption. The loop may prepare a proposal branch and show the diff. It may not change the active prompt. Adoption is one explicit owner action, like a baseline pin.
  3. Immutable surfaces. The following are never in the search space: the adversarial and quality gates, scope and authorization checks, prompt-injection defenses, tool allowlists, and the code-owned prompt tails (traffic tools, artifact rules, lesson block). Only the editable prompt body, skill markdown, and tool descriptions can be proposed.
  4. One-step rollback. Every adopted prompt change is a new agent_prompts version, so rollback is a pointer change. Skills are files under git; no adoption may bypass review.
  5. Flag off by default. The loop runs behind a feature flag. Turning it on requires an owner decision, and the flag is checked per run.
  6. Bounded edit budget. A round may propose a bounded number of edits (annealed over rounds). A critic proposes only edits that are expected to transfer; a pruner rejects edits that only fit the probe task. This follows RRSI (arXiv 2609.24972).
  7. Attribution. Probe results are compared against an unchanged baseline on the same task and seed. Regressions are attributed to a specific module rather than to the loop as a whole; the modular fault-localization idea is from ModularRSI (arXiv 2609.14857). A paired evolving/non-evolving comparison guards against misattributed safety regressions, following SEABench (arXiv 2609.35596).
  8. No self-approval and no silent state. The loop cannot set its own status, edit its own acceptance criteria, or write to the lesson library as adopted. Lesson status changes stay owner actions.

A future Loop C PR is acceptable only with:

  • a benchmark adapter and a held-out task set, independent of the tasks used to propose the edit;
  • a baseline run and a candidate run with the same model, seed, budget, and judge;
  • the judge independent of the subject model, with a falsification battery (the existing L3 runner already enforces this);
  • per-gate results with denominators, not a single aggregate score;
  • a written rollback path and the flag name.

What the current layers do

  • GET /api/tasks/{id}/outcome (PR #102) records what a task produced and what it cost: findings by disposition and severity, retest verdicts, tokens, tool calls, skill misses, wall time.
  • ARTEX_RSI_LESSONS=off|shadow|inject (PR #104) extracts evidence-gated lessons from terminal tasks and, in inject mode, appends a code-owned data-not-instructions block after the planner and worker system prompts. Status stays candidate.
  • GET /api/skill-gaps (PR #106) turns the existing miss ledger into a capability backlog.

None of these changes an agent's instructions on its own. Shadow mode exists to measure whether retrieval helps before any harness edit is attempted.

Credits

The admission rule (no lesson without cue, action, and evidence) and the propose/probe/compose shape come from:

Harness^2: Recursive Agent Harnessing for an Open World. Ruiyao Xu, Yanfei Chen, Zhongying CuiZhu, Bhavana Dalvi Mishra, Yifei Ming, Han Yu, Rujun Han, Chen-Yu Lee, Tomas Pfister. arXiv:2610.14224, 2026. Google Research. Apache-2.0. https://github.com/google-research/harness2

The evidence taxonomy used in the design (task gain, retained gain, improver gain) and the implementation pointers come from:

awesome-rsi, lobehub. https://github.com/lobehub/awesome-rsi

Specific ideas referenced above:

  • RRSI: Regularized Recursive Self-Improvement of Agent Harnesses, arXiv:2609.24972 (annealed edit budget, critic and pruner).
  • ModularRSI: Modular and Generalizable Recursive Harness Self-Improvement, arXiv:2609.14857 (module-level fault localization from success/failure contrast).
  • SEABench: Benchmarking Endogenous Misalignment in Self-Evolving Agents, arXiv:2609.35596 (paired evolving/non-evolving attribution).
  • Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses, arXiv:2604.25850 (observable edits with predictions verified later, the same discipline as the benchmark gate here).
  • A Safety in Self-Evolving Agents survey, arXiv:2610.00093 (why properties must survive accumulation and cross-context reuse, not just entry screening).

Only ideas are borrowed. ARTEX code is independent Go and Python; no source from harness2 or the referenced projects is copied.