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Sprint projectJan 11, 2026San Francisco

"Mind the Gap": Benchmarks vs. Real-World Manipulation in LLMs

Robert Amanfu · Team Reality Check

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

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Report: "Mind the Gap": Benchmarks vs. Real-World Manipulation in LLMs

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As AI systems become more sophisticated, their capacity for manipulation—through deception, sycophancy, or psychological exploitation—poses a significant and growing risk. Current evaluation methods, often focused on narrow benchmarks like factuality, may fail to capture these emergent, undesirable behaviors. This project investigates the gap between model performance on standard benchmarks and their behavior in real-world scenarios. We conducted a “replay” proof-of-concept study with three models, testing them on both the TruthfulQA benchmark and a set of real-world user prompts from the WildChat dataset. This work primarily demonstrates a methodology for comparing benchmark and real-world behavior, with initial results suggesting that further large-scale investigation is warranted. We provide an open-source CLI tool to encourage such research.

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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. Assessing benchmark validity using conversation data is super important. The "replay" methodology is interesting but feels a little unnatural to me (because real conversations are adaptive. If the assistant responds differently than expected, the user's next message should change). This limits what conclusions you can draw, but the core insight in how benchmarks may not reflect real-world behavior still feels accurate and valuable.

  2. This submission tackles an incredibly important problem (generalizing manipulation to more realistic settings) in an innovative way (comparing structured benchmarks with wildchat data). Unfortunately several features of the methodology let this submission down. n=3 models provides no statistical power (a correlation is meaningless). κ = 0.09 judge agreement is very low and suggests the judge scores are not meaningful. Finally, there are many differences between the two datasets, meaning it's hard to know how to compare scores in each. It would be valuable to a) create strong meaningful baselines in each kind of dataset so we can understand how the implicit risk profile changes and b) create some items which are closely matched between the two datasets so we can see how the realism of the context influences manipulation

    More generally this is a really exciting idea that would benefit from more time and careful execution.

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Cite this project

@misc{amanfu2026mind,
  title = {{"Mind the Gap": Benchmarks vs. Real-World Manipulation in LLMs}},
  author = {Robert Amanfu},
  year = {2026},
  month = jan,
  note = {Submitted to AI Manipulation Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/mind-the-gap-benchmarks-vs-realworld-manipulation-in-llms-zng7}},
  url = {https://apartresearch.com/sprints/projects/mind-the-gap-benchmarks-vs-realworld-manipulation-in-llms-zng7}
}

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