aiqbench analysis
This paper introduces Canonical Procedural Actions (CPAs), a new annotation protocol designed to standardize the auditing of tool-use agent traces by explicitly linking procedural functions to their evidentiary anchors. The research demonstrates high structural repeatability in isolated LLM contexts, offering a rigorous framework for analyzing agent behavior beyond simple message identification. This work advances the field of AI safety and reliability by providing auditable instruments essential for understanding and verifying complex multi-step agent workflows.
Canonical Procedural Actions: An Auditable Annotation Protocol for Tool-Use Agent Traces
arXiv:2609.24264v1 Announce Type: new Abstract: Tool-use agent traces identify messages and API calls, but procedural analyses also need explicit units of action and inspectable links to their evidence. We present Canonical Procedural Actions (CPAs), an annotation protocol that records a procedural function, its first agent-event anchor, the agent events that realize it, and separate contextual evidence. Multiple actions may share a message anchor without an inferred within-message order. A retail case study produces a versioned 24-entry codebook through open induction, recorded consolidation, and successive application audits. Two isolated LLM contexts annotate 32 trajectories disjoint from development at the trajectory level, producing 499 and 491 occurrences with anchor-label overlap A=0.982. Requiring identical context-event references reduces overlap to 0.798. These are structural repeatability measures, not semantic accuracy: 16 of 26 task IDs also occur in development, and historical tool payloads were truncated to 110 characters. Retrospective controls show that collapsing all labels raises overlap to 0.986, while simple endpoint rules reproduce the tool-anchored portion with 0.997 overlap. Assistant-message actions have 0.971 overlap, with a per-label minimum of 0.816. Applying the frozen codebook to 244 further trajectories yields 4,058 records, including eight diagnostic outcomes. The contribution is an explicit, auditable annotation instrument and a case study of its construction and measurement limits; human-reference validity and downstream utility remain to be established.
Abstract:Tool-use agent traces identify messages and API calls, but procedural analyses also need explicit units of action and inspectable links to their evidence. We present Canonical Procedural Actions (CPAs), an annotation protocol that records a procedural function, its first agent-event anchor, the agent events that realize it, and separate contextual evidence. Multiple actions may share a message anchor without an inferred within-message order. A retail case study produces a versioned 24-entry codebook through open induction, recorded consolidation, and successive application audits. Two isolated LLM contexts annotate 32 trajectories disjoint from development at the trajectory level, producing 499 and 491 occurrences with anchor-label overlap A=0.982. Requiring identical context-event references reduces overlap to 0.798. These are structural repeatability measures, not semantic accuracy: 16 of 26 task IDs also occur in development, and historical tool payloads were truncated to 110 characters. Retrospective controls show that collapsing all labels raises overlap to 0.986, while simple endpoint rules reproduce the tool-anchored portion with 0.997 overlap. Assistant-message actions have 0.971 overlap, with a per-label minimum of 0.816. Applying the frozen codebook to 244 further trajectories yields 4,058 records, including eight diagnostic outcomes. The contribution is an explicit, auditable annotation instrument and a case study of its construction and measurement limits; human-reference validity and downstream utility remain to be established.
23 pages, 5 figures, 9 tables. Includes ancillary files for reproducing the reported analyses
Computation and Language (cs.CL)
arXiv:2609.24264 [cs.CL]
(or arXiv:2609.24264v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.24264 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
SOURCE_MANIFEST.json
analysis/INPUT_MANIFEST.json
analysis/PUBLIC_PATH_REDACTIONS.json
analysis/README.md
analysis/analyze.py
analysis/inputs/R1/SAMPLE_MANIFEST.json
analysis/inputs/R2/SAMPLE_MANIFEST.json
analysis/inputs/R3/AUDIT_PROVENANCE.json
analysis/inputs/R3/SAMPLE_MANIFEST.json
analysis/inputs/R4/AUDIT_PROVENANCE.json
analysis/inputs/R4/SAMPLE_MANIFEST.json
analysis/inputs/R5/AUDIT_PROVENANCE.json
analysis/inputs/R5/SAMPLE_MANIFEST.json
analysis/inputs/R6/APPLY_INSTRUCTIONS.md
analysis/inputs/R6/AUDIT_PROVENANCE.json
analysis/inputs/R6/C1/pack_1.txt
analysis/inputs/R6/C1/pack_2.txt
analysis/inputs/R6/C2/pack_1.txt
analysis/inputs/R6/C2/pack_2.txt
analysis/inputs/R6/CPA_LIBRARY_APPLY.md
analysis/inputs/R6/ROUND6_OCCURRENCES.jsonl
analysis/inputs/R6/ROUND6_RESULTS.json
analysis/inputs/R6/SAMPLE_MANIFEST.json
analysis/inputs/R6/V05_FREEZE_AUDIT_MANIFEST.json
analysis/inputs/R6/V05_FREEZE_DECISION.json
analysis/inputs/R6/packs/pack_1.txt
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analysis/inputs/development/CONSOLIDATION_REPORT.md
analysis/inputs/development/CPA_LIBRARY_v0_1.json
analysis/inputs/development/CPA_LIBRARY_v0_5.json
analysis/inputs/development/FROZEN_SCHEMA_v1.md
analysis/inputs/historical_code/cpa4_build_round6.py
analysis/inputs/historical_code/cpa4_round2_analyze.py
analysis/inputs/historical_code/cpa4_round6_analyze.py
analysis/inputs/library/CPA_LIBRARY_v1_0.json
analysis/inputs/library/V1_FREEZE_MANIFEST.json
analysis/inputs/library/V1_FREEZE_REPORT.md
analysis/inputs/production/CPA_SEQUENCE_CORPUS.jsonl
analysis/inputs/production/P1/s01.txt
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analysis/inputs/production/PRODUCTION_ANNOTATION_QA.json
analysis/inputs/production/SHARD_MANIFEST.json
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analysis/outputs/anchor_strata.csv
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analysis/outputs/coarse_mapping.json
analysis/outputs/disagreement_anchors.json
analysis/outputs/granularity.csv
analysis/outputs/per_label.csv
analysis/outputs/query_demo.json
analysis/outputs/summary.json
analysis/outputs/tool_only_baselines.csv
analysis/outputs/tool_rule_mapping.json
analysis/query_demo.py
analysis/test_analysis.py
analysis/test_reference_overlap.py
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