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Do interactive evaluations actually solve the benchmark comparison problem?

Interactive, trajectory-based evaluation promises richer evidence than response-only benchmarks. But does moving to this format resolve longstanding challenges like comparability and reproducibility, or do those problems simply reappear at a new scale?

Synthesis note · 2026-05-28 · sourced from Evaluations

The seductive promise of interactive evaluation is that richer evidence solves the problems of response-centered benchmarks. The position paper resists this. Its analysis shows that longstanding evaluation challenges — comparability, reproducibility, the validity of the evidence-to-judgment mapping, what claims a score actually supports — reappear at the trajectory level rather than disappearing. Scoring a path instead of an endpoint does not escape the core difficulty of evaluation; it relocates it into a higher-dimensional space where it is, if anything, harder to pin down.

This is why the paper frames the situation as a question demanding design, not a solution already in hand. A trajectory admits many scoring choices, and different interactive benchmarks make incompatible ones, so their results are not interchangeable — the same fragmentation that response benchmarks eventually had to standardize away, now recurring with more degrees of freedom. Process quality, recoverability, and coordination are genuinely informative, but each introduces its own version of the old questions: what counts as evidence, how is it aggregated into a judgment, and what does the resulting number license you to claim?

Why it stays open: the honest reading is that interactive evaluation buys richer evidence at the cost of reintroducing every hard problem at a new scale. The field's task is therefore not to adopt the format but to build the protocols, robustness tests, shared infrastructure, and reporting standards that make trajectory scores interpretable — work that is unfinished. Treating the new paradigm as a fix would repeat the mistake; treating it as a design problem is the corrective the paper argues for.

Inquiring lines that read this note 68

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How do standardized forms and measurement artifacts shape observable evidence? Should agent evaluation measure execution quality beyond task success? Do standard benchmarks reveal AI capability in real-world contexts? How do benchmark scores relate to real-world task completion? What conversation mechanics enable genuine human-AI communicative participation? Do benchmark score improvements actually reflect genuine AI capability gains? How can model evaluations remain robust against gaming and evaluator drift? Do single-axis benchmarks adequately measure deployment-ready agent capability? What systematic failures occur in human preference-based model evaluation? How does model awareness of deployment context affect strategic behavior? How can we better validate AI research and prevent misconceptions? What is the actual bottleneck preventing effective AI research automation? How do credentials maintain predictive value when their context changes? Can we reliably measure and demonstrate AI's actual productivity impact? How do clinicians evaluate the trustworthiness of AI medical recommendations? When does routing outperform scaling as a performance solution?

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Original note title

longstanding evaluation challenges reappear at the trajectory level rather than disappearing