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Sprint projectJan 12, 2026Berkeley, CA

VexReinforce

Cyan Ding, Brandon Qi, Prakrat Agrawal · Team VexReinforce

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

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Report: VexReinforce

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Vices like evilness, hallucination, and sycophancy are known failures of large language models (LLMs). Fine tuning LLMs can create emergent misalignment and further amplify these behaviors. Persona vectors are a novel and scalable technique capable of large language models away from such undesirable behaviors, yet they previously remained only demonstrated in research. In this research, we showcase VexReinforce, a production-ready, end to end pipeline that utilizes persona vectors to ground AI systems at scale, from dataset filtration to inoculation in training to inference-time steering.

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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.

  1. This is a really solid end-to-end attempt at moving activation engineering from just chat steering into a full MLOps pipeline. The Dataset Screening module is a standout contribution. The ability to use vector projection to detect hallucinations and sycophantic tendencies in the MedHallu benchmark with high statistical significance demonstrates a viable, scalable path for automated data hygiene.

    The engineering rigor is sharp, especially the decision to use a Humor control vector to validate the extraction process. To make the Training-Time Inoculation even more production ready for mitigating manipulative traits, it might be worth exploring Layer Sweeps to find the optimal intervention point rather than just one layer. Moving from the basic (Difference in Means) method to Sparse Autoencoders for cleaner features could also help reduce the risk of the steering accidentally damaging the model’s general reasoning.

    Overall, this is a standout implementation project that builds a functional tool instead of just a research script. Great work.

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  2. A lot of interesting directions are explored here and it's a good idea to explore the potential of persona vectors widely. Main suggestion: Pick one direction and go deep. Test on frontier models, clearly explain what's already known vs. what's novel, and really dig into any limitations. .

    A few specific notes: The hallucination vector results (3.1) show overlapping distributions and 60% accuracy, which is statistically distinguishable but not a practical detection method. The steering-infused finetuning analysis (3.4) looks interesting to me, and I appreciated the honest presentation with distributions and error bars. That alone could be a full project worth developing further and looking, for example, into what limits the training effect and how to increase it.

Cite this project

@misc{ding2026vexreinforce,
  title = {{VexReinforce}},
  author = {Cyan Ding and Brandon Qi and Prakrat Agrawal},
  year = {2026},
  month = jan,
  note = {Submitted to AI Manipulation Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/vexreinforce-uhra}},
  url = {https://apartresearch.com/sprints/projects/vexreinforce-uhra}
}

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