Skip to content
Apart Research
Apart Research
Contact us
Sprint projectMar 23, 2026Sao paulo, brazil

Tokenization Drift as a Hidden Channel for Alignment Evasion in Language Models

Raul Cavalcante, Bruno Lopes Yamamoto · Team Time Uspiano

Submitted to AI Control Hackathon 2026. Sprint projects are early-stage work by participants, not Apart Research publications.

Read the report

Report: Tokenization Drift as a Hidden Channel for Alignment Evasion in Language Models

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

RL training pipelines that serialize model outputs to text and retokenize them to construct training contexts introduce a token-level asymmetry invisible at the string level. We show this asymmetry is a plausible channel for alignment evasion: a model under opposing training pressures can learn to detect whether it is in deployment or testing and act differently, without any semantic cue or explicit reasoning. When a model's autoregressive output is decoded to a string and re-encoded, the resulting token sequence differs from the original 4–8% of the time across three model families (LFM 1.2B, Llama 1B, Qwen 0.6B). A linear probe on frozen hidden states confirms the model's representations distinguish original (deployment) from retokenized (testing) contexts. Under RL training with a pathwise KL objective, a 1.2B-parameter model learns to assign systematically different likelihoods to the same continuation depending on which token path produced the context, constituting deployment-versus-testing behavioral divergence conditioned on a signal no text-level audit can detect.

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 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. Seems pretty cool to me. I'm not qualified to give a take here though as this isn't really control in the typical sense. This does sound like a very important finding if true and means that we need to be careful when doing alignment checks or training to encode the text in a way that is indistinguishable from what it will encounter in deployment.

    This also seems like a plausibly important consideration when doing honey potting which is a common approach that comes up in control (e.g., for untrusted monitoring).

  2. This was quite a novel idea! the premise seems quite feasible, and basis for a full paper. The experiments themselves were limited in proving this effect, though this is not that surprising given the limited timespan.

Cite this project

@misc{cavalcante2026tokenization,
  title = {{Tokenization Drift as a Hidden Channel for Alignment Evasion in Language Models}},
  author = {Raul Cavalcante and Bruno Lopes Yamamoto},
  year = {2026},
  month = mar,
  note = {Submitted to AI Control Hackathon 2026, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/tokenization-drift-as-a-hidden-channel-for-alignment-evasion-in-language-models-wooi}},
  url = {https://apartresearch.com/sprints/projects/tokenization-drift-as-a-hidden-channel-for-alignment-evasion-in-language-models-wooi}
}

More from AI Control Hackathon 2026

All 125 entriesBrowse all projects

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026