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Evidence And Artifacts

The project treats artifacts as audit hygiene. They are the way to check what a run did, not the research contribution by themselves. This matters because LLM text can sound successful even when Minecraft state did not change.

For the active legibility experiment, artifacts should support independent transition-row/v1 records:

state_before + executed_action + observed_delta + evidence_refs

Prediction artifacts are separate. They are joined to locked rows by row_id only during offline analysis, so the actor's expected outcome and provider rationale never become ground truth.

What Counts As Evidence​

Useful evidence includes:

  • transcript records;
  • Mineflayer action attempts and results;
  • inventory, position, block, container, entity, or chat observations;
  • verifier output;
  • world-state scan summaries with explicit scan limits;
  • actor workspace evidence references;
  • provider input/output snapshots;
  • provider usage records when live models are used;
  • response-window records showing when another actor had a chance to respond;
  • public-history exports that include only allowlisted runtime evidence;
  • offline prediction/scoring artifacts after label lock.

What Does Not Count Alone​

These are context, not proof of Minecraft progress:

  • model rationale;
  • memory notes;
  • repeated observe or wait;
  • animation or partial motion;
  • a generated source file that has not passed a bounded trial;
  • a summary that lacks a backing artifact.

Failure Is A Result​

Blocked, failed, and environment-blocked runs are useful when they explain the reason clearly. A good report should say whether the actor was blocked by missing parameters, unavailable world state, repeated target failure, provider budget, auth, server lifecycle, or runtime checks.

Why This Helps The Experiment​

The active experiment needs consequences that a reviewer can inspect. If an actor promises help, fails to gather resources, lends a tool, refuses a request, places a public affordance, or repeats an impossible action, later turns should see artifact-backed consequences instead of relying on a fresh prompt summary.

The research question is not whether artifacts are neat. It is whether public interaction history adds predictive signal for another actor's observable social/material response, after simple baselines and leakage checks have had a fair chance to erase the claim.