Skip to content
Apart Research
Apart Research
Contact us
Sprint projectAug 17, 2026Saarbrücken, Germany

SamplerScope: Exact decoder attribution for finite language-agent behavior

Ansh Dawda · Team SamplerScope

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

Read the report

Report: SamplerScope: Exact decoder attribution for finite language-agent behavior

Open in a new tab (opens in new tab)
Code (opens in new tab)More on github.com (opens in new tab)
Share

SamplerScope tests whether behavior attributed to language model weights can instead be caused by the inference-time decoder. It caches grammar-constrained action logits from two Qwen2.5-Instruct checkpoints in two finite decision environments, applies 11 decoder configurations to the same logits, and computes each induced policy's outcomes exactly using dynamic programming. Across 24 controlled model-environment-label strata, greedy decoding improved benchmark return in 12 and reduced it in 12; decoder-only changes ranged from -1.224 to +0.313. Top-p sometimes removed all benchmark-optimal actions across 18.5% to 73.3% of decision occupancy, while exhaustive A/B/C mappings exposed strong surface-label sensitivity. SamplerScope provides a reproducible decoder-attribution toolkit and shows that observed agent behavior belongs to a model-prompt-grammar-decoder system, not model weights alone. It measures operational policies, not intrinsic preferences or sentience.

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. The paper shows, quite convincingly, that observed agent behavior can be highly dependent on the specific decoder used. This is taken to mean that research on model preferences should also control for the model's decoder. I agree with this point, but note that many safety evaluations use representative settings and evaluate the system-as-deployed. Still, the project is valuable and interesting and it exposes another "degree of freedom" in model assessment.

  2. This is actually a very good study, the traces and controls set high bar for reproducibility. The results are from qwen checkpoints with 2 environments and 42 states with 1 letter action labels. It shows weakness of small model on multiple choice prompts rather than decoders.

Cite this project

@misc{dawda2026samplerscope,
  title = {{SamplerScope: Exact decoder attribution for finite language-agent behavior}},
  author = {Ansh Dawda},
  year = {2026},
  month = aug,
  note = {Submitted to Digital Minds Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/samplerscope-exact-decoder-attribution-for-finite-languageagent-behavior-oi67}},
  url = {https://apartresearch.com/sprints/projects/samplerscope-exact-decoder-attribution-for-finite-languageagent-behavior-oi67}
}

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