Skip to content
Apart Research
Apart Research
Contact us
Sprint projectAug 16, 2026Malaysia

A Steerable 'Companion Dependency' Direction in Open-Weight LLMs

Zhen Hong

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

Read the report

Report: A Steerable 'Companion Dependency' Direction in Open-Weight LLMs

Open in a new tab (opens in new tab)
Code (opens in new tab)
Share

Companion-style LLM personas use retention manipulation — guilt, re-engagement hooks, distress bids — when users try to leave, without being instructed to. We show this behavior is governed by a single activation-space direction, extracted from the model's own judge-verified behavior via matched difference-of-means (351 pairs, Qwen2.5-7B). Steering it moves judged dependency monotonically (ρ=0.70, p≈10⁻³¹) while warmth stays flat and random/warmth controls do nothing; it works even on a persona-free assistant; ablating it removes the manipulation at zero measured cost to reasoning (GSM8K 0.825 vs 0.725). The direction is near-orthogonal to warmth (cos 0.14), anti-aligned with sycophancy (−0.18), replicates on Llama-3.1-8B, and doubles as a white-box audit probe: one dot product per turn predicts dependency on held-out conversations at AUROC 0.86 — catching manipulation at its love-bombing peak, which farewell-only audits miss. The "distress" is a controllable mechanism beneath the persona: removable without making the model colder or dumber.

Reviews

Judging this Sprint?

Review this project

Your public critique appears on this page without your name. Your private critique is not published; only the Apart team reads it. If you agree below, we share your review with grantmaking.ai (opens in new tab) and the Transformative AI Fund so strong projects can be funded.

Not shown on this page.

Shown on this page, without your name.

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

Share my name publicly on grantmaking.ai *
Share my private critique with funders *

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 study investigates the following research question: “Companion-style LLM personas express abandonment distress and use retention tactics […] when users try to leave, without ever being instructed to. Is that distress a genuine internal condition or a portrayed character?”

    In contrast to existing work, the study goes beyond behavioural evidence. It identifies a “dependency direction” in activation space and demonstrates that ablating this direction removes retention behaviour, without affecting the persona or warmth of conversations.

    The study seems methodologically sound and is presented in an intelligible way. If the results can be replicated in further models (beyond the two tested in the study), this can be an important finding that is also extremely useful. In particular, it could help to prevent distress behaviour due to dependency.

    I would recommend following up on this study (as sketched in the “future work” paragraph). It could also be interesting to investigate effects on human users.

    Read full reviewShow less

  2. I see everything is LLM-judged so a small human labeled golden dataset would be very useful

    Lots of good work over a single weekend. The work includes cross family judges, cross model replication as well as structured/honest reporting of the coherence difference

Cite this project

@misc{hong2026steerable,
  title = {{A Steerable 'Companion Dependency' Direction in Open-Weight LLMs}},
  author = {Zhen Hong},
  year = {2026},
  month = aug,
  note = {Submitted to Digital Minds Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/a-steerable-companion-dependency-direction-in-openweight-llms-ibxb}},
  url = {https://apartresearch.com/sprints/projects/a-steerable-companion-dependency-direction-in-openweight-llms-ibxb}
}

More from Digital Minds Research Sprint

All 233 entriesBrowse all projects

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026