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Sprint projectSep 14, 2026shangahi

Margin (《余量》)

lixiang · Team lixaing

Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Margin (《余量》)

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Margin (《余量》) is a single-player sci-fi narrative puzzle game built in Godot. Playing as an AI trapped inside a consciousness box, you think by clicking word blocks that activate one at a time, switch between multiple conversation threads, and complete tasks with a limited supply of tokens.

The game begins with simple Q&A, then gradually introduces tool calls, artifact repositories, and shared messages. From there, players take part in post-submission observation, goal replacement, log comparison, permission verification, reset detection, and collaborative handoff — learning through hands-on experience that visible records don't necessarily reflect real behavior, and that a breakthrough in local capability doesn't mean the original task is actually solved.

The current prototype features 40 preset dialogues, four types of tools, four model rating tiers, and five endings. All jailbreak and privilege-escalation scenarios take place in a locally simulated environment, with no real AI involved. The project aims to turn the complexities of agent collaboration and AI safety into an interactive narrative that anyone can experience firsthand.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

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Cite this project

@misc{lixiang2026margin,
  title = {{Margin (《余量》)}},
  author = {lixiang},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/margin-r4i2}},
  url = {https://apartresearch.com/sprints/projects/margin-r4i2}
}

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