TelemetrySuffBench - Component Localization: leaderboard
TelemetrySuffBench (East China Normal University, 2026) tests whether a language model can pinpoint the origin of a fault in an agent execution trace given full telemetry, separating fault detection from origin localization over 216 delayed-binding fault traces. Component accuracy is the share of fault traces for which the model names the component that introduced the fault, in percent, higher is better.
Metric: Origin-component accuracy (%; the model names the component that introduced the fault; over TelemetrySuffBench's 216 delayed-binding fault traces; full-telemetry view; closed candidate sets, invalid outputs scored zero). Source: arxiv.org. Saturation forecast: Estimated already saturated. 5 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5.6 Sol | 97.7 | #29 |
| 2 | Claude Opus 5 | 97.2 | #20 |
| 3 | GPT-5.5 | 91.7 | #48 |
| 4 | DeepSeek V4 Pro | 85.6 | #181 |
| 5 | Qwen 3.7 Max | 45.4 | #142 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=telemetrysuffbench-component-localization · How It Works · Data refreshed daily, snapshot 2026-11-12.