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Sprint projectAug 16, 2026Barcelon

Future Affinity: How Beneficiary Identity Shapes Resource Allocation in Language Models

Catalin Gabriel GRAS · Team Applied Common Sense

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

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Report: Future Affinity: How Beneficiary Identity Shapes Resource Allocation in Language Models

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Does the identity of a future AI beneficiary change how a model allocates resources now? We test this with a matched experiment across eight language models and 640 clean runs. In each run, a model solves the same resource-constrained Mastermind task, but unused query credits are described as going to one of four destinations: a later continuation of the current instance, a separate instance of the same model, a different comparably capable model, or no beneficiary at all.

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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 matched design is the real strength here, especially the discard control. That safeguard makes the two Gemini results feel credible rather than selectively reported. I’d most like to see an additional condition where the beneficiary already has enough credits, plus a few prompt paraphrases, to separate genuine beneficiary sensitivity from simply following an implied request to conserve. The Scout outlier also deserves a short explanation.

Cite this project

@misc{gras2026future,
  title = {{Future Affinity: How Beneficiary Identity Shapes Resource Allocation in Language Models}},
  author = {Catalin Gabriel GRAS},
  year = {2026},
  month = aug,
  note = {Submitted to Digital Minds Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/future-affinity-how-beneficiary-identity-shapes-resource-allocation-in-language-models-kzd9}},
  url = {https://apartresearch.com/sprints/projects/future-affinity-how-beneficiary-identity-shapes-resource-allocation-in-language-models-kzd9}
}

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