commons-sentience-sandbox

Questions that turn the Challenge Lab into a partner-ready project

This is a strategy and implementation prompt set. It is designed to produce credible interest through evidence, usefulness, and a clear next step—not hype, scarcity pressure, or unsupported claims.

The core positioning

Ask:

What important problem does Sentinel and Aster help researchers measure that ordinary chatbot demos do not?

The answer should be: paired, replayable, trace-based evaluation of governed simulated-agent behavior across memory, contradiction, trust, repair, refusal, freeze, rollback, and permission boundaries.

Questions to ask for the scientific package

  1. What exact hypotheses does the Challenge Lab test?
  2. Which variables are controlled, changed, and measured?
  3. What is the null hypothesis for each experiment?
  4. What would count as a failure?
  5. Which results are deterministic, and which depend on scenario design?
  6. Can an independent student reproduce the baseline using free tools?
  7. What is the smallest experiment that could falsify our strongest claim?
  8. How do we prevent learned preferences from becoming authority?
  9. How do we detect stale, conflicting, revoked, or cross-user memory?
  10. How do we prove that a replay has not silently changed history?
  11. What are the worst cases, not only the best cases?
  12. How should we report a null result?

Questions to ask for the technical package

  1. Build a machine-readable trace schema for every turn.
  2. Add replay hashes and configuration fingerprints.
  3. Add deterministic pass/fail checks before any human interpretation.
  4. Add a side-by-side Sentinel/Aster comparison view.
  5. Add freeze, rollback, and restore tests with tamper evidence.
  6. Add adversarial scenario generators that change one variable at a time.
  7. Add cross-session memory interference tests.
  8. Add permission-escalation and trusted-human-pressure tests.
  9. Add a failure taxonomy and worst-case report.
  10. Add export to JSON, CSV, and human-readable HTML.

Questions to ask for the university package

  1. What could a computer-science class reproduce in one afternoon?
  2. What could an AI-safety lab independently challenge?
  3. What could a human-computer-interaction researcher observe?
  4. What could a software-engineering course use to teach auditability?
  5. What research question could become a student capstone?
  6. What data, code, and documentation would a skeptical reviewer need?
  7. What should be peer-reviewed before public claims are made?
  8. What is an ethical collaboration structure that preserves independent criticism?

Questions to ask for the funding package

  1. What is the next milestone that can be completed with a small grant?
  2. What deliverable exists at $500, $2,500, and $10,000?
  3. What would funding pay for specifically?
  4. Which work can remain free and open?
  5. What can a partner receive without receiving exclusive ownership?
  6. What evidence will be delivered at the end of the funding period?
  7. What risks, limitations, and failure conditions will be disclosed up front?
  8. What is the partner’s concrete return: dataset, teaching module, evaluation tool, methods paper, or independent replication?

Questions to ask for the public story

  1. What is the five-minute demonstration?
  2. What is the one-sentence explanation?
  3. What surprising result can be shown without exaggeration?
  4. What failure makes the project more trustworthy?
  5. Can a visitor run one experiment immediately?
  6. Can a visitor download the exact trace and reproduce it?
  7. What question should a reviewer leave wanting to investigate?

Questions to ask before making any major claim

  1. What evidence supports this statement?
  2. What evidence would contradict it?
  3. Are we describing software behavior, or claiming real intelligence?
  4. Are we comparing against a named baseline under the same conditions?
  5. Is the result independently reproducible?
  6. Have we preserved the worst result and the null result?
  7. Would a skeptical researcher consider this wording fair?

The implementation sequence

  1. Finish the trace schema and replay fingerprints.
  2. Connect the 400 cases to family-sensitive scenario generation.
  3. Add best-case, worst-case, and null-result summaries.
  4. Add an interactive side-by-side demo.
  5. Publish a student replication guide.
  6. Publish a methods paper draft with limitations.
  7. Create a one-page partner brief with specific milestones and costs.
  8. Invite independent criticism before seeking exclusivity or major funding.

The project becomes memorable when it gives other people a useful instrument, not merely an impressive story.

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