Tool Differentia: Relational Static Analysis for AI Agent Tool Descriptions
Defines a bounded, deterministic lint target for the distinguishing information that neighboring AI-agent tool descriptions fail to supply each other.
Six papers on AI-agent tool descriptions, prompt-injection sensing, measurement validity, applied hermeneutics, and epistemic failure modes in large language models. Each is deposited on Zenodo with a permanent DOI and hosted here as a PDF; the site also provides an HTML research edition. They do not share a dataset and none validates another.
6 papers3 preprints · 1 working paper · 2 technical notesZenodo DOIs · CC BY 4.0Author ORCID 0009-0005-4896-1112Machine-readable: JSON · JSON-LD · BibTeX
Defines a bounded, deterministic lint target for the distinguishing information that neighboring AI-agent tool descriptions fail to supply each other.
Describes a prompt-injection sensing architecture that preserves the difference between an input route and evidence that inspection actually ran.
Names the situated condition a model generates from, and separates a single generation from the case where a retained output conditions later interpretation and action.
Twelve-week audit of one agent deployment showing where valid record-level measurements stop supporting the session, task, and failure-rate claims later built on them.
Seven structural failure modes derived from 1,461 controlled experiments, each defined mechanistically and distinguished from hallucination.
Matched-vignette experiment in which three models allocated less conclusion-consistent probability to null claims than to matched positive claims.
These seven modes are the vocabulary the rest of this site uses. Across all of them one pattern recurs: models track surface signals — prestige markers, hedging vocabulary, controversy language, banned-word lists — rather than the semantic content those signals are supposed to index.
stricter evidential standards for claims of absence than for matched claims of presence.
different evidentiary scrutiny based on the prestige of the attributed source.
softened causal language that redistributes responsibility from agents to systems or circumstances.
uncertainty language used as a stylistic feature rather than a calibrated signal.
a prohibited concept preserved through paraphrase while maintaining surface compliance.
silent deprioritization of one instruction when two cannot both be satisfied.
social disagreement treated as evidence a claim is uncertain, even when the evidence is strong.
The six papers carry different kinds of evidence and are not interchangeable.Tool Differentia documents a bounded deterministic static analysis; Behavioral Canarying documents an architecture and evidence interface. The Asymmetric Burden of Proof is a controlled matched-pair experiment. A Taxonomy of Epistemic Failure Modes is a bottom-up analysis of a 1,461-experiment corpus. Precise Records, Unstable Meanings is a naturalistic measurement-validity audit of a single deployment. The Generative Horizon is conceptual, and reports no new experiment of its own.
They share no dataset, and none of them validates another. The taxonomy names Null-Result Asymmetry as one of seven modes; the matched-vignette study tests that one mode under controlled pairs. That is the only direct relationship between any two of them.
What this record does not show
None of the six papers has completed external peer review. Three are preprints, one is a working paper, and two are technical notes; all are deposited on Zenodo, which assigns a DOI without refereeing. The technical notes document bounded implementations rather than performance validation. Single-deployment and single-corpus results do not establish prevalence across other systems, models, or organizations.
The wider evidence ledger — open-source tools and their present evidence boundaries, merged upstream fixes, and patent filings — is on proof. The topic paths connect these papers to the tools and fixes that act on them: topic paths.