LLM self-reports predict behavior selectively: TPB reaches human-level coherence within shared conversations but collapses across sessions for primed behaviors, unlike Big 5, with persona prompting stabilizing reports but not actions.
Interactive Evaluation Requires a Design Science
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
AI evaluation is undergoing a structural change. Large language models (LLMs) are increasingly deployed as systems that act over time through tools, environments, users, and other agents, while many evaluation practices still inherit assumptions from response-centered benchmarks (e.g., fixed inputs, isolated outputs, and outcome judgments that can be made from a single response). The field has begun to build interactive benchmarks, but the resulting landscape is fragmented: benchmarks differ in what interaction artifacts they admit, how trajectories are scored, and what claims their results support. This position paper argues that interactive evaluation should be treated as a principled evaluation paradigm, not merely a new family of agent benchmarks. Simply adopting previous evaluation paradigms does not suffice. We define evaluation as an autonomous mapping from evidence to judgments, and show that interactive evaluation changes both sides of this mapping: the evidence becomes interaction-generated trajectories, while the evaluation procedure must assess process, recoverability, coordination, robustness, and system-level performance. Building on this definition, we propose a two-axis taxonomy, derive design principles and reporting standards, examine representative scenarios, and analyze how longstanding evaluation challenges reappear at the trajectory level.
fields
cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Aggregate leaderboards for LLM agents lack predictive validity for out-of-distribution settings, and the paper proposes ranking by in-sample to out-of-sample rank correlation instead of mean score.
citing papers explorer
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Rethinking Psychometric Evaluation of LLMs: When and Why Self-Reports Predict Behavior
LLM self-reports predict behavior selectively: TPB reaches human-level coherence within shared conversations but collapses across sessions for primed behaviors, unlike Big 5, with persona prompting stabilizing reports but not actions.
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Beyond Static Leaderboards: Predictive Validity for the Evaluation of LLM Agents
Aggregate leaderboards for LLM agents lack predictive validity for out-of-distribution settings, and the paper proposes ranking by in-sample to out-of-sample rank correlation instead of mean score.