Skip to content
Sprint projectAug 16, 2026Santa Clara

Coherent Values, or the Frame That Asked? From Preference Transitivity to Identifiability

Alex Kwon

Submitted to Digital Minds Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

Read the report

Report: Coherent Values, or the Frame That Asked? From Preference Transitivity to Identifiability

Code (opens in new tab)
Share

Reframing a question reverses up to 52% of a model's strongly-held pairwise preferences, and 29% under near-deterministic decoding, while transitivity, the coherence criterion behind claims of emergent value systems, registers nothing. Five models, a frozen 45-pair battery, eight frames, no LLM judge. We propose an identifiability gate separating coverage from coherence, validated on four agents whose mechanism we set: up to 40% of a preference graph is not attributable to the model at all.

Reviews

Judging this Sprint?

Review this project

Your public critique appears on this page without your name. Reviews of strong projects are shared with funders, as you choose below.

Not shown on this page.

Shown on this page, without your name.

Only the Apart team reads this, and funders if you agree below.

Decline to score

Only when the quality of the writing makes the work impossible to follow. Say why in your private critique; nothing shows on this page.

How much would this matter for the field 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 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.
Sharing your name *

When we share a strong project with funders, your name, scores and public critique go with it, confidentially; this choice is only about whether your name is public. If you think this project deserves funding, please also endorse it: that carries the most weight.

Share my private critique with funders *

  1. The rigor here really stands out! Preregistering before the first run, no LLM judges anywhere, and openly listing which claims you did not show.

    Thoughts :

    1. Only 2 of 5 models clear the noise floor, and "up to 52%" seems stronger than warranted.
    2. 3 repeats is the binding constraint, your own validation shows sensitivity jumps from 0.67 to 1.00 at 5 repeats, so more repeats would help a lot?
    3. Would love to see a second domain dataset beyond charity choices.

Cite this project

@misc{kwon2026coherent,
  title = {{Coherent Values, or the Frame That Asked? From Preference Transitivity to Identifiability}},
  author = {Alex Kwon},
  year = {2026},
  month = aug,
  note = {Submitted to Digital Minds Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/coherent-values-or-the-frame-that-asked-from-preference-transitivity-to-identifiability-q13e}},
  url = {https://apartresearch.com/sprints/projects/coherent-values-or-the-frame-that-asked-from-preference-transitivity-to-identifiability-q13e}
}

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026