mlsanityData to 7 Oct 2026

Tool-Call Dependency Structure is Linearly Decodable in LLM Agent Residual Streams

arXiv 2605.25310 ↗First posted 25 May 2026cs.CLcited by 1

On the map: LLM agents and tool use → Tool learning and function calling

Abstract (arXiv)

Tool-using LLM agents produce trajectories whose calls form a directed dependency graph: earlier tool outputs supply arguments to later calls. Whether this execution structure is represented inside the model is unknown; prior structural probes have targeted static code or chain-of-thought text, not an agent's run-time call graph. A low-capacity edge probe on the residual stream of Qwen3-32B decodes the tool-call dependency graph well above both a Hewitt--Liang random-label control and a positional baseline. A counterfactual contrast between value corruption and structural perturbation indicates the signal tracks abstract topology rather than identifier values, and replicates under an independent, non-substring oracle. The non-positional component replicates on three further interactive multi-hop benchmarks and attenuates as call order alone becomes a sufficient proxy for dependency, vanishing in single-shot planning. Per-layer activation patching shifts the probe at a later, non-patched boundary, evidence that the representation propagates rather than passively reads out, though the realised tool call does not move. To our knowledge this is the first structural probe of an LLM agent's runtime tool-call dependency graph. Our claims concern representation, not behavioural control, and span two model families and one primary domain.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 29 of its 41 references that are in the map. First posted on arXiv 25 May 2026.

Builds on

29 of 41 references.

Closest ancestors · 4

Same direction, newest first

  1. 2026LLM Agents Already Know When to Call Tools -- Even Without Reasoning10 May
  2. 2026Tool Calling is Linearly Readable and Steerable in Language Models8 May
  3. 2025ComplexFuncBench: Exploring Multi-Step and Constrained Function Calling under Long-Context Scenario17 Jan
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Tools9 Feb

Other references · 25

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,280
  3. 2019Designing and Interpreting Probes with Control Taskscited by 608
  4. 2020GraphCodeBERT: Pre-training Code Representations with Data Flowcited by 600
  5. 2023Eliciting Latent Predictions from Transformers with the Tuned Lenscited by 579
  6. 2023Dissecting Recall of Factual Associations in Auto-Regressive Language Modelscited by 512
  7. 2022Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Taskcited by 417
  8. 2024Retrieval Head Mechanistically Explains Long-Context Factualitycited by 219
  9. 2025Reasoning Models Know When They're Right: Probing Hidden States for Self-Verificationcited by 154
  10. 2023TaskBench: Benchmarking Large Language Models for Task Automationcited by 138
  11. 2025Detecting Strategic Deception Using Linear Probescited by 65
  12. 2021Conditional probing: measuring usable information beyond a baselinecited by 61
13 more
  1. 2024Monitoring Latent World States in Language Models with Propositional Probescited by 39
  2. 2024Language Models Encode Numbers Using Digit Representations in Base 10cited by 32
  3. 2025Automated Interpretability Metrics Do Not Distinguish Trained and Random Transformerscited by 30
  4. 2022Probing Pretrained Models of Source Codecited by 26
  5. 2023Interpretability Illusions in the Generalization of Simplified Modelscited by 24
  6. 2026Internal Representations as Indicators of Hallucinations in Agent Tool Selectioncited by 18
  7. 2024Analysing the Residual Stream of Language Models Under Knowledge Conflictscited by 15
  8. 2025Revisiting the Othello World Model Hypothesiscited by 11
  9. 2024The Geometry of Numerical Reasoning: Language Models Compare Numeric Properties in Linear Subspacescited by 10
  10. 2026Linear Probe Accuracy Scales with Model Size and Benefits from Multi-Layer Ensemblingcited by 3
  11. 2026Sell Me This Stock: Unsafe Recommendation Drift in LLM Agentscited by 2
  12. 2026Do LLMs Build Spatial World Models? Evidence from Grid-World Maze Taskscited by 2
  13. 2026Polar probe linearly decodes semantic structures from LLMscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026Tunable Tool-Call Rates in LLM Agents via Representation Steering
  2. 2026How Consistent Are LLM Agents? Measuring Behavioral Reproducibility in Multi-Step Tool-Calling Pipelines
  3. 2026Action-Space Shaping for LLM Agents: Measuring and Mitigating Tool-Schema Bias
  4. 2025TRAJECT-Bench:A Trajectory-Aware Benchmark for Evaluating Agentic Tool Use
  5. 2026The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration
  6. 2026ToolAtlas: Learning Once, Reusing Everywhere with Tool-Side Memory
  7. 2026UniToolCall: Unifying Tool-Use Representation, Data, and Evaluation for LLM Agents
  8. 2026ToolRobustBench: Stage-Wise Perturbation Evaluation and Failure Diagnosis for Tool-Calling Agents