The Calls are Coming from Inside the Model: Investigating Probe-based Detection of Tool-Calling Errors in LLMs | SpinGraph: Innovation framing
SpinGraph analysis of arXiv Machine Learning's The Calls are Coming from Inside the Model: Investigating Probe-based Detection of Tool-Calling Errors in LLMs s…
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The Calls are Coming from Inside the Model: Investigating Probe-based Detection of Tool-Calling Errors in LLMs
linear probing, tool calling, LLM safety, hidden state analysis, error detection
Linear probes applied to LLM hidden states can detect tool-use errors not caught by standard logging Probe efficacy varies by model size, layer choice, and post-training method Probes show generalization to novel error types, suggesting operational utility beyond known failure modes
Emphasizes generalization and effectiveness while minimizing discussion of probe calibration, computational cost, integration complexity, or failure modes under distribution shift.
Methodologically rigorous, safety-forward research enabling trustworthy agentic AI.
Linear probes can reliably detect LLM tool-calling errors, including subtle argument-value mismatches, and generalize to unseen error types.
No discussion of probe interpretability or causal grounding — whether probes detect correlates or true error mechanisms; No comparison to alternative error-detection methods (e.g., self-reflection, verification wrappers, symbolic validators)
The story uses titles, institutions, awards, rankings, partners, experts, or official language to make the subject feel more credible. Watch for loaded terms such as effective means, critical in real world deployments, generalizing to novel types of errors. The distribution reads as academic distribution. A pressure point: No discussion of probe interpretability or causal grounding — whether probes detect correlates or true error mechanisms.
Probing is an effective means to catch a range of different tool-calling errors, including errors arising from using an argument that has the wrong value but the correct type, which might not be recorded by standard logging frameworks.
Overall, we find that probing is an effective means to catch a range of different tool-calling errors, including errors arising from using an argument that has the wrong value but the correct type, which might not be recorded by standard logging frameworks.
Extracted statistics from the source narrative
Across diverse tool-calling architectures and training regimes
Publicly available, task-oriented benchmark for function/tool calling