Independent runs will determine whether dspy-factorio’s combined RLM and GEPA approach enables agents to make sustained progress on Factorio’s long-horizon tasks.
state: expiredheat: lowuncertainty: highconvergesscott: mediumagent-harnesses rlm gepa long-horizon-agentsukituki
What is this?
dspy-factorio is presented as an open implementation for teaching agents to play Factorio by combining RLM with GEPA, a DSPy optimizer that uses natural-language reflection and evolutionary search to improve prompts from execution feedback. Factorio provides a long-horizon test environment in which agents must form sub-objectives and can be evaluated across independent runs using production scores and technology milestones. The supplied snippets do not directly document dspy-factorio’s results, ukituki’s role, or independent replication, so the claim that this specific combination enables sustained progress remains to be established.
Why it matters to Scott
The implementation independently combines execution-feedback-driven harness evolution with a long-horizon environment, converging with Scott’s Reflexive Agent Design, Replay-Driven Design Evolution, and model-plus-harness evaluation positions. Independent Factorio runs could provide useful evidence about whether those loops produce measurable sustained progress, but no results or replication are yet supplied, limiting the present significance.
ip:framework.reflexive-agent-designip:framework.replay-driven-design-evolutionip:framework.long-running-agentsip:concept.model-plus-harness-benchmark-unitip:concept.measurable-convergenceradar:concept.agent-harnessesradar:concept.long-horizon-agentsradar:concept.agent-evaluationradar:prime-agent-harness-validation
queries asked of Scott's wikis
- long-horizon agent progress and trajectory management
- reflective prompt optimization from execution traces
- agent harness evaluation with independent runs
- Factorio or simulation environments for coding agents
- RLM recursive reasoning and context management
- DSPy GEPA evolutionary prompt optimization
Measured heat
no measured readings yet — the hourly heat pass fills this in
How the heat travelled
no chain yet — the hourly chain pass fills this in
Evidence (2) — ⭐ canonical anchor
Interpretation history
2026-08-12T06:31:24Z
No independent runs, trajectories, or benchmarks emerged within the observation window, and the initial artifact drew no follow-on discussion or implementation evidence. The approach remains conceptually relevant, but this episode has faded before establishing sustained Factorio progress.
2026-08-10T06:28:54Z
The reobservation adds no trajectories, benchmarks, or independent runs, so the case remains an unvalidated implementation rather than evidence of sustained long-horizon progress.
2026-08-10T06:25:34Z
grounded: converges/medium — The implementation independently combines execution-feedback-driven harness evolution with a long-horizon environment, converging with Scott’s Reflexive Agent D
2026-08-10T06:22:34Z
case created — The repository is a concrete open artifact testing an emerging orchestration and optimization approach in a persistent environment.
Decision trace
- 08-12 16:31expireNo independent runs, trajectories, or benchmarks emerged within the observation window, and the initial artifact drew no follow-on discussion or implementation evidence. The approach remains conceptua
- 08-12 16:31alert_silentThe only delta is elapsed time without validation or renewed activity; there is no consequential new fact to deliver before a briefing.
- 08-12 16:31alert_routeThe only delta is elapsed time without validation or renewed activity; there is no consequential new fact to deliver before a briefing.
- 08-10 16:28repriceThe reobservation adds no trajectories, benchmarks, or independent runs, so the case remains an unvalidated implementation rather than evidence of sustained long-horizon progress.
- 08-10 16:28alert_silentNothing consequential changed beyond an unchanged reobservation; the implementation can wait for a briefing unless reproducible run results or external replication appear.
- 08-10 16:28alert_routeNothing consequential changed beyond an unchanged reobservation; the implementation can wait for a briefing unless reproducible run results or external replication appear.
- 08-10 16:27alert_silentAn open implementation combining RLM and GEPA in Factorio is relevant to Scott’s agent-design interests, but the visible evidence provides no trajectories, benchmarks, sustained-progress results, or i
- 08-10 16:27surface_candidateAn open implementation combining RLM and GEPA in Factorio is relevant to Scott’s agent-design interests, but the visible evidence provides no trajectories, benchmarks, sustained-progress results, or i
- 08-10 16:27alert_routeAn open implementation combining RLM and GEPA in Factorio is relevant to Scott’s agent-design interests, but the visible evidence provides no trajectories, benchmarks, sustained-progress results, or i
- 08-10 16:25groundThe implementation independently combines execution-feedback-driven harness evolution with a long-horizon environment, converging with Scott’s Reflexive Agent Design, Replay-Driven Design Evolution, a
- 08-10 16:22createThe repository is a concrete open artifact testing an emerging orchestration and optimization approach in a persistent environment.