Explain Laxmi research ambition and staged goals
Browse filesClarify the long-term software world-model goal while retaining the experimental v0.1.1 claim boundary. Update SHA256SUMS for the model card; all artifacts remain unchanged.
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README.md
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> Controller weights and runtime are unchanged from v0.1.0. The latest model repair failed its improvement test. The new real-patch exact-execution tool is a baseline, **not a trained model**. None of the four controller candidates is promoted as an accepted model.
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## The experiment
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A controller records progress and retries failed operations. An edit to its recovery logic can delay completion or repeat work. The research question is whether pre-action evidence can predict that consequence.
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> Controller weights and runtime are unchanged from v0.1.0. The latest model repair failed its improvement test. The new real-patch exact-execution tool is a baseline, **not a trained model**. None of the four controller candidates is promoted as an accepted model.
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## The goal
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Laxmi's long-term ambition is a **testable software world model**: a system that maintains a predictive view of software state as evidence arrives. Given the current state, execution history, a proposed action or code change, and declared operating conditions, it should forecast possible consequences **before execution**, express uncertainty, and revise its view when new observations arrive. A useful forecast would help an engineer decide what to inspect, test, change or leave alone.
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The research path has three stages:
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1. **Predictive state:** learn action- and source-conditioned outcomes, including bounded multi-step effects and differences between proposed changes. Measure forecasts against actual outcomes and strong methods given the same information.
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2. **Prospective advice:** save forecasts before real controller actions, then test whether the advice improves a human review decision at acceptable error, coverage, latency and cost.
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3. **Broader engineering assessment:** use source and pull-request context, observed history and explicit constraints to assess likely consequences, compare a finite set of alternatives, and explain findings with traceable evidence.
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The ultimate aspiration is useful engineer-like work within a **defined role and measurable constraints**. Progress is measured by prospective outcomes and decisions, including honest abstention when evidence is insufficient. This v0.1.1 release is an early, bounded controller experiment toward the first stage. It has not established a general software world model, a qualified advisory, or autonomous engineering capability.
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## The experiment
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A controller records progress and retries failed operations. An edit to its recovery logic can delay completion or repeat work. The research question is whether pre-action evidence can predict that consequence.
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cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30 LICENSE
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3a66b62901c108c4f8946247ff7a773f73ac3bcec0b8a34286eb921164e97d5f LICENSE_SCOPE.md
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