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What evaluation metrics beyond task success matter most for agents?
A broader line of inquiry — a family of 63 specific questions the research asks around this. Follow one into its inquiring-line page, or move sideways to a related line below.
Questions in this line of inquiry 63
Specific inquiring lines the field asks around this — ordered from the most general framing down to the most specific angle.
- Can trajectory analysis replace one-shot task success as the primary evaluation metric?
- What makes trajectory quality matter more than one-shot task success?
- Should agent evaluation include trajectory quality beyond final success?
- Should agent evaluation include trajectory quality and memory hygiene alongside task success?
- What trajectory-level metrics matter beyond one-shot task success?
- How do trajectory quality and memory hygiene affect agent evaluation?
- Do trajectory quality metrics predict agent safety and user trust?
- How do execution traces reveal error propagation in multi-step agent decisions?
- Which interaction artifacts matter most for reliable agent evaluation?
- What dimensions should trajectory-level scoring capture beyond final correctness?
- Can trajectory structure alone reveal process quality without human annotation?
- Do agent behavior logs reveal hidden accuracy costs benchmarks miss?
- What dimensions beyond task success matter for evaluating long-horizon agent trajectories?
- What makes next-state signals from agent trajectories a reliable learning source?
- Should feedback channels be excluded from the reward path in agent evaluations?
- Should artifact-level benchmarks replace token counts for agent evaluation?
- Can graph topology represent successful trajectory clusters more effectively than skill libraries?
- Can trajectory structure replace hand-annotated process reward models entirely?
- Can trajectory consistency predict real-world deployment failures?
- Why do sparse outcome rewards fail to credit correct tool calls in failed trajectories?
- Can process supervision improve agentic RL through meta-reasoning rewards?
- What makes some agent benchmarks measure interaction quality better than others?
- Can post-hoc execution traces alone solve the observation gap?
- How can agent data flywheels improve task quality iteratively?
- Why do trajectory-based skills fail to transfer across different environments and use cases?
- Can measures of application actions reveal changes in coordination that output metrics miss?
- How can decision quality be automatically extracted from agent trajectories?
- What makes a trajectory score interpretable across different interactive benchmarks?
- Why does persistence in the feedback loop predict agent success better than initial solution quality?
- What trajectory-level metrics replace one-shot task success measurement?
- Can a single leaderboard score capture multi-dimensional differences in agent performance?
- Can parallel trajectories reveal better decision branches without human labeling?
- How does process supervision relate to execution-signaled feedback approaches?
- Does shared experimental state alone explain progress or is an analyzer needed?
- How do we measure progress without confusing it with task completion?
- Can influence estimation identify the most valuable trajectories in agentic training?
- Can tool-call advantage attribution distinguish between correct and incorrect calls in mixed trajectories?
- What hidden signals in agent logs reveal about frontier capability beyond pass-fail outcomes?
- How do task stream groupings provide long-horizon learning signals for curation decisions?
- How do agent actions change state that reward procedures later read?
- How do execution trajectories become valid training examples after validation?
- Can task success alone reveal whether memory routing is working?
- How do complete multi-turn trajectories differ from isolated task examples?
- Can trajectory-level visibility separate refusals from real skill gaps?
- What does agent security look like when measured across interaction trajectories?
- What deployment modes work best for trajectory-aware reward signals?
- Can programmatic meta-reasoning rewards operationalize agentic process supervision?
- Does decision-making taste predict end-to-end task success independently?
- Can an agent change reward-path state through actions during evaluation?
- How do chunk-based step segmentation and trajectory structure modeling differ?
- What makes trajectory more actionable than absolute scores for human moderators?
- Should long-context evaluation measure the coupled system?
- Can accuracy rankings be reordered by tracking agent behavior costs?
- Can trajectory structure alone provide process supervision without human annotation?
- How do trajectory quality and memory hygiene differ as evaluation metrics?
- How does temporal decomposition into subgoals improve long-horizon planning stability?
- What within-run behavioral dimensions reveal where long-horizon agents succeed or fail?
- How does credit assignment across objectives differ from credit assignment across time?
- When is information-flow tracking worth its cost over classification?
- Can gradients extracted with agent-level supervision transfer across different benchmarks?
- How does relative progress estimation reduce dependence on hard labels for process supervision?
- How does evaluating interaction trajectories change what we measure beyond correctness?
- How does trajectory burstiness compare to other structural properties that shape emergent capabilities?