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ARXIV:2605.31308 · AGENT EVALUATION & IMPROVEMENT · SUBMITTED 01 JUN · 20:21 UTC · FRESHNESS STALE
ARXIV:2605.31308AGENT EVALUATION & IMPROVEMENTSUBMITTED 01 JUN · 20:21 UTCFRESHNESS STALEJunjie Nian · Kang Chen · Ge Zhang · Yixin Cao · Yugang Jiang · arXiv
TraceGraph visualizes agent decision landscapes from trajectories, revealing hidden performance differences and enabling targeted improvements for agent recovery pipelines.
Opportunity summary
Pain TraceGraph visualizes agent decision landscapes from trajectories, revealing hidden performance differences and enabling targeted improvements for agent recovery pipelines.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
TraceGraph visualizes agent decision landscapes from trajectories, revealing hidden performance differences and enabling targeted improvements for agent recovery pipelines. We introduce TraceGraph, a graph-based framework that turns released multi-model agent trajectories into shared decision…
Agent benchmarks increasingly record rich interaction trajectories, yet evaluation often reduces each rollout to a pass rate or reward score. We introduce TraceGraph, a graph-based framework that turns released multi-model agent trajectories into shared…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across trajectories spanning five benchmark splits, TraceGraph profiles reveal navigation differences hidden by aggregate scores and show that splits differ in whether they reward…
Agent Evaluation & Improvement moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
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TraceGraph visualizes agent decision landscapes from trajectories, revealing hidden performance differences and enabling targeted improvements for agent recovery pipelines.
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10.48550/arXiv.2605.31308TraceGraph visualizes agent decision landscapes from trajectories, revealing hidden performance differences and enabling targeted improvements for agent recovery pipelines.
Abstract
Agent benchmarks increasingly record rich interaction trajectories, yet evaluation often reduces each rollout to a pass rate or reward score. We introduce TraceGraph, a graph-based framework that turns released multi-model agent trajectories into shared decision landscapes. For each task, TraceGraph builds a graph over observable action-observation states from pooled rollouts before model identity is introduced. It then overlays outcome-informed productive cores and trap regions, and summarizes each rollout with three events: Access, Trap exposure, and Repair. Across trajectories spanning five benchmark splits, TraceGraph profiles reveal navigation differences hidden by aggregate scores and show that splits differ in whether they reward avoiding traps or recovering from them. The same TraceGraph landscape also motivates a trap-aware recovery pipeline for SWE-bench: aruntime detector fires on states matching historical trap regions, then lightweight continuation policies are evaluated from the same prefix. On fired states, the best pooled single-factor policy raises official resolved rate from 40.4% to 43.5% on the per-provider fired subset and from 41.0% to 44.8% on common-fired instances, with provider-specific active components. Overall, TraceGraph provides a process vocabulary for asking what agent benchmarks test, where models diverge on a shared landscape, and how failure regions can guide downstream improvement.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
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PROBLEM
TraceGraph visualizes agent decision landscapes from trajectories, revealing hidden performance differences and enabling targeted improvements for agent recovery pipelines. We introduce TraceGraph, a graph-based framework that turns released multi-model agent trajectories into s...
METHOD
Agent benchmarks increasingly record rich interaction trajectories, yet evaluation often reduces each rollout to a pass rate or reward score. We introduce TraceGraph, a graph-based framework that turns released multi-model agent trajectories into shared decision landscapes.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across trajectories spanning five benchmark splits, TraceGraph profiles reveal navigation differences hidden by aggregate scores and show that splits differ in whether they reward avoiding traps or recove...
WHY NOW
Agent Evaluation & Improvement moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 23, "author": "Junjie Nian; Kang Chen; Ge Zhang; Yixin Cao; Yugang Jiang", "title": "TraceGraph: Shared Decision Landscapes for Diagnosing and Improving Agent Trajectories"
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TraceGraph visualizes agent decision landscapes from trajectories, revealing hidden performance differences and enabling targeted improvements for agent recovery pipelines.
Segment
Agent Evaluation & Improvement
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7.0/10 public viability
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