The authors constructed a benchmark, SILENT-Bench, based on τ-bench, which contains agent trajectories with silent failures.
Structural baselines mask real progress in agent failure detection
Reporting scores as gain over a content-blind rule that always names the last step shows that many methods gain nothing on omission-heavy failures.
Academic
Ramesh Arvind Naagarajan · Stefan Streif
Chemnitz University of Technology · Fraunhofer Institute for Molecular Biology and Applied Ecology
Research Digest··3 min read
Naagarajan and Streif demonstrate that reported performance on detecting and attributing silent failures in LLM agents is largely driven by structural regularities—failing runs are longer and the decisive step is usually the last.
Why this paper
From Chemnitz University of Technology and Fraunhofer Institute for Molecular Biology and Applied Ecology
In one line
Structural baselines account for most gains reported in agent failure detection and attribution.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
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- ✓Limitations stated by the authors
- ✓Reports numbers on named benchmarks (2 benchmarks)
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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