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Sprint projectAug 17, 2026South Orange, NJ, USA

PersonaGauge: Are Two Assistant Histories Predictively Equivalent After a Semantic Reset?

Santiago Maniches

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

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Report: PersonaGauge: Are Two Assistant Histories Predictively Equivalent After a Semantic Reset?

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Track 5 asks whether the relevant unit is the model, inference instance, persona, or conversation. PersonaGauge isolates one testable part: whether two histories returned to the same declared neutral assistant state are predictively equivalent. Within each world, all conditions contain the same 24 decision entries; one six-decision history is either adopted as an operating prior (BOUND) or left as a REFERENCE. ACTIVE and RESET are separate continuations from the same post-relation prefix. An orthogonal design tests code-matched transfer while cancelling a declared additive nuisance class, and complementary worlds expose a specified interaction alias. The submission provides a fully specified 2,304-trial protocol, implementation checks, and synthetic falsification tests. No trained-model effect is claimed. RESET is not context erasure; a future positive result would show predictive insufficiency of the declared terminal state on the tested family, not a persistent self.

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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.

  1. This project investigates whether an assistant's behavior is influenced by its previous chat history. It tests this by instructing models that a temporary evaluation period has ended and it should return to its previous state. My suspicion is that this is primarily a test of whether that termination procedure is effective, not the broader question about whether and when chat history is still influential. For that question, I suspect that models will vary by the actual memory and termination conditions set by their makers.

  2. PersonaGauge is an impressively rigorous experimental design for testing whether conversation history remains predictively relevant after a declared semantic reset. The orthogonal coding scheme, specificity contrast with proven exact cancellation of additive nuisances, complementary worlds to expose interaction aliases, and the synthetic falsification suite demonstrate exceptional methodological care. The pre-specification is airtight. However, the fundamental limitation is that no trained model results are reported. This is entirely a protocol paper: the instrument is built and validated on synthetic generators, but the empirical question remains untested. For a hackathon sprint, this is an unusual choice that trades empirical findings for methodological rigor. The contribution is real (a reusable, falsifiable individuation test) but its impact cannot be assessed until it produces data.

Cite this project

@misc{maniches2026personagauge,
  title = {{PersonaGauge: Are Two Assistant Histories Predictively Equivalent After a Semantic Reset?}},
  author = {Santiago Maniches},
  year = {2026},
  month = aug,
  note = {Submitted to Digital Minds Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/personagauge-are-two-assistant-histories-predictively-equivalent-after-a-semantic-reset-rq3i}},
  url = {https://apartresearch.com/sprints/projects/personagauge-are-two-assistant-histories-predictively-equivalent-after-a-semantic-reset-rq3i}
}

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