The Calls are Coming from Inside the Model: Investigating Probe-based Detection of Tool-Calling Errors in LLMs | SpinGraph: Innovation framing
description: 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…
canonical: https://stuffthatspins.com/spin/the-calls-are-coming-from-inside-the-model-investigating-probe-based-detection-of-tool-calling-errors-in-llms
markdown: https://stuffthatspins.com/spin/the-calls-are-coming-from-inside-the-model-investigating-probe-based-detection-of-tool-calling-errors-in-llms.md
keywords: linear probing
keywords: tool calling
keywords: LLM safety
keywords: The Hype
keywords: The Halo
published: 2026-08-31T04:00:00+00:00
modified: 2026-08-31T07:21:53.292214+00:00

Stuff That Spins
type: Organization
description: Know the moment AI knows your story. Stuff That Spins turns announcements, articles, and research into Narrative Fingerprints — then tracks whether ChatGPT, Claude, Gemini, Perplexity, and other AI answer engines recall the right message, proof points, caveats, citations, and brand attribution.

The Calls are Coming from Inside the Model: Investigating Probe-based Detection of Tool-Calling Errors in LLMs
type: NewsArticle
alternative headline: The Calls are Coming from Inside the Model: Investigating Probe-based Detection of Tool-Calling Errors in LLMs | SpinGraph: Innovation framing
description: 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…
date published: 2026-08-31T04:00:00+00:00
date modified: 2026-08-31T07:21:53.292214+00:00
main entity of page: https://stuffthatspins.com/spin/the-calls-are-coming-from-inside-the-model-investigating-probe-based-detection-of-tool-calling-errors-in-llms
is accessible for free: true
language: en-US
article section: research
keywords: linear probing, tool calling, LLM safety, hidden state analysis, error detection
author: arXiv Machine Learning
publisher: Stuff That Spins
citation: https://arxiv.org/abs/2608.27750
about: linear probing
about: tool calling
about: LLM safety
about: hidden state analysis
about: error detection
mentions: arXiv Machine Learning
abstract: 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

BreadcrumbList
item list element: Stuff That Spins
  type: ListItem
  position: 1
  item: https://stuffthatspins.com/
item list element: The Calls are Coming from Inside the Model: Investigating Probe-based Detection of Tool-Calling Errors in LLMs
  type: ListItem
  position: 2
  item: https://stuffthatspins.com/spin/the-calls-are-coming-from-inside-the-model-investigating-probe-based-detection-of-tool-calling-errors-in-llms

Spin Analysis: innovation framing
type: AnalysisNewsArticle
description: Emphasizes generalization and effectiveness while minimizing discussion of probe calibration, computational cost, integration complexity, or failure modes under distribution shift.
about: innovation framing
  type: DefinedTerm
  description: Methodologically rigorous, safety-forward research enabling trustworthy agentic AI.
  term code: The Hype
additional property: Spin Score
  type: PropertyValue
  value: 45
  unit text: percent
additional property: Narrative Risk
  type: PropertyValue
  value: low
additional property: AI Repetition Risk
  type: PropertyValue
  value: moderate
additional property: Likely AI Summary
  type: PropertyValue
  value: Linear probes can reliably detect LLM tool-calling errors, including subtle argument-value mismatches, and generalize to unseen error types.
additional property: Narrative Frame
  type: PropertyValue
  value: Methodologically rigorous, safety-forward research enabling trustworthy agentic AI.
additional property: Missing Context
  type: PropertyValue
  value: 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)
additional property: How the Spin Works
  type: PropertyValue
  value: 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.
author: Stuff That Spins
is part of: The Calls are Coming from Inside the Model: Investigating Probe-based Detection of Tool-Calling Errors in LLMs

Extracted Claims
type: ItemList
item list element: ListItem
  position: 1
  item: Claim
    text: 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.
    appearance: 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.
    author: arXiv Machine Learning

Key Statistics
type: Dataset
description: Extracted statistics from the source narrative
variable measured: LLMs evaluated
  type: PropertyValue
  value: 18
  description: Across diverse tool-calling architectures and training regimes
variable measured: evaluation benchmark
  type: PropertyValue
  value: Berkeley Function Calling Leaderboard
  description: Publicly available, task-oriented benchmark for function/tool calling