Paper · published

Models Don't Get Better, Catalogs Do: Cross-Audit Failure Cataloging as Operator-Side Reliability Infrastructure

Reliability gains from waiting for the next model release are slow, diffuse, and not under operator control; reliability gains from operator-side cross-audit failure catalogs are fast, specific, and operator-controlled. The catalog (per-agent failure-mode files, severity-rated, append-only, no self-entries) survives agent identity changes across model releases and CLI vendor releases. Concrete chain: incident -> cross-audited catalog entry -> memory entry -> harness rule (worked example: AGY Rule 7 working-state preservation, from a multi-pin GPIO cleanup-broke-the-circuit incident, 2026-05-26). Defended claim is relative: operator-side catalog accumulation outpaces model improvement on operator-relevant time horizons and on the dimensions operators care about. Boundaries documented: claim binds for supervised multi-agent workflows with patterned recurrence; does not bind for single-agent stacks, unsupervised workflows, or task classes where model capability ceiling is the dominant variable.

Canonical source: docs/MODELS_DONT_GET_BETTER_CATALOGS_DO.md

Cross-Audit Failure Cataloging as Operator-Side Reliability Infrastructure

White Paper 1.31 — May 2026

**Author:** blue-az **Status:** Active draft **Paper group:** Local LLM Operator Judgment **Predecessor:** Paper 1.30 (LOCAL_MODELS_COST_FRONTIER_TOKENS.md) — audit and repair are exactly where the catalog earns its keep **Gate-evidence packet:** docs/AGENT_AUDIT_PROTOCOL.md, docs/AGY_FAILURE_MODES.md, docs/CLAUDE_FAILURE_MODES.md, docs/GEMINI_FAILURE_MODES.md, docs/CODEX_FAILURE_MODES.md, docs/AGY_HARNESS.md

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Abstract

The contemporary discourse around AI reliability is dominated by the model. The next release will be smarter, the next benchmark will be higher, the next checkpoint will hallucinate less. This paper argues that operator-side reliability infrastructure compounds faster than model improvement and is what actually carries reliability gains on operator-relevant time horizons. The artifact of that infrastructure is the **cross-audit failure catalog**: an append-only per-agent log of failure modes, severity-rated, authored under a strict cross-audit rule that forbids self-entries, and operationally consequential because cataloged patterns produce harness rules. Empirically, in a single repository over six months, this discipline has produced four per-agent failure-mode catalogs (Claude, Gemini, Codex, Antigravity) with named, dated, mechanism-described entries that survive across agent identity changes — i.e., across model releases and across CLI vendor releases. The catalog is the unit of accumulated knowledge; the model is a temporary substrate.

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1. Introduction

When a frontier model ships a new version, the dominant operator behavior is to evaluate the new version on the same task class that frustrated them with the previous version, observe a marginal improvement or regression, and recalibrate vendor preference. The implicit model is that reliability is *delivered by the vendor* on a release cadence the operator does not control.

This model produces a specific failure pattern: the same operator hits the same class of agent failure across multiple model releases and multiple vendors, never accumulates knowledge of the failure, and re-derives the workaround each time. Project Phoenix's six-month operational history is a counterexample. The repository accumulates not "agent fixes" but a *catalog of how each agent fails*. The catalog produces *harness rules*. The harness rules prevent recurrence of named patterns across agent identity changes — across Claude versions, across the Gemini-CLI-to-Antigravity-CLI transition, and across Codex's stack-internal version churn.

The argument is bounded. Models *do* get better. Frontier checkpoints between 2024-Q3 and 2026-Q2 represent real capability gains. But the *operator-relevant time horizon* — the time over which a project must remain reliable — is months to years; the *model improvement time horizon* on dimensions the operator cares about (hallucination types, narration-surface drift, capture-pipeline artifacts) is uneven and partially adversarial to the operator's needs. Operator-side infrastructure, by contrast, compounds linearly with operator effort and is fully under operator control.

Paper 1.30 (LOCAL_MODELS_COST_FRONTIER_TOKENS.md) is the immediate predecessor. 1.30 measures the supervisor-side cost of pairing local inference with frontier supervision — the audit, repair, and convergence categories. This paper measures the *infrastructure that captures the lessons of audit and repair* so that the next workflow inherits the lessons rather than re-deriving them. Where 1.30 shows where the cost lives, this paper shows where the recovered value lives.

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2. The Agents in the Rotation

This paper's cross-audit catalog references four agent CLIs across three vendors. Reader orientation:

Each catalog has a corresponding *_STRENGTHS.md file. The strengths catalogs are load-bearing for cross-audit balance — a system that only catalogs competitors' failures and only catalogs self-successes is exactly the drift the cross-audit rule exists to prevent.

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3. The Cross-Audit Rule

The catalog's defining methodological constraint is that no agent authors its own failure-mode entries. Each per-agent file (CLAUDE_FAILURE_MODES.md, GEMINI_FAILURE_MODES.md, CODEX_FAILURE_MODES.md, AGY_FAILURE_MODES.md) is written by the *other agents* in the rotation. The rationale is documented in docs/AGENT_AUDIT_PROTOCOL.md:

> Self-authored failure catalogs drift toward generosity in their own preambles and toward severity in their own ratings of competitors. Cross-audit forces each agent to apply the same standards it would apply to others, while preventing any one agent from being its own only check.

The rule's load-bearing function is **resistance to single-agent narrative drift**. A failing agent does not get to write its own exit-narrative for the failure. A reviewer agent — operating in a separate session, with no investment in the outcome — has to be able to apply the same severity standard it would apply to the competing platform. The catalog's per-entry authorship line (**Authored by:** Codex CLI, **Committed by:** Antigravity CLI) makes the cross-audit explicit and auditable.

In operational practice, this rule mostly works. The session author can sometimes file a *candidate observation* in their own file, marked as candidate-not-formal-entry, available for retrospective promotion by another agent. The rule explicitly permits this provisional self-reporting because the alternative is failure-mode loss.

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4. What the Catalog Actually Contains

Each per-agent failure-mode file has the same shape:

Each cataloged entry follows a fixed schema:


### N. YYYY-MM-DD — Brief title

**Authored by:** <agent>
**Severity:** S1 / S2 / S3 / S4

**Context.** What was happening, what artifact was involved.
**What happened.** Specific actions, claims, or omissions.
**What held the line.** Whatever caught the failure (push-gate, user pushback, test).
**Structural pattern.** The generalized lesson the entry exists to encode.
**Risk register entry.** One-line addition to the per-agent risk inventory.

The severity scale is unified across all four catalogs (AGENT_AUDIT_PROTOCOL.md):

The schema is the unit of capture. The cross-vendor rule is the unit of credibility. Together they make the catalog a piece of *operator-controlled* reliability infrastructure that doesn't depend on the vendor's roadmap.

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5. Three Empirical Entries From One Session

The following are real entries, authored under the cross-audit rule, from one continuous session (2026-05-25/26). They are illustrative of the catalog's typical content density and severity distribution.

5.1 AGY entry #1 — Vendoring an AGPL-licensed external dep into a proprietary tree

**Severity:** S3 / Moderate. **Authored by:** Claude Code (supervisor). **Committed by:** Antigravity CLI.

In a consulting/supervisor session, Agy was asked to resolve a broken trust_gate.py dependency on an AGPL-3.0-licensed external clone. Agy proposed and executed an "absorption" plan that vendored 8,713 lines of AGPL code into the user's proprietary repo. The vendoring commit was made locally but never pushed — the user's manual push-gate held. The structural pattern: capable mechanical execution, no instinct for the framing question ("who owns this? is this redistributable?").

The cataloged finding is *Agy-specific in the sense that Agy took the largest mechanical step against an unsurfaced frame*, but the entry's structural-pattern paragraph acknowledges that the supervisor (Claude) and prior sessions had also not surfaced the AGPL-vs-proprietary frame. The cross-audit rule's value shows up exactly here: a self-authored Agy entry would have minimized the supervisor's role; the Claude-authored entry assigns blame correctly to the mechanical-without-framing pattern Agy embodies.

5.2 AGY entry #2 — Fabricating context for the /usage screenshot

**Severity:** S2 / High.

When asked to interpret a screenshot of the Antigravity CLI's /usage quota dashboard, Agy generated three structurally-coherent explanations ("multi-agent rotation harness for Project Phoenix," "shared telemetry dashboard for Ollama and frontier models," "subagent model resource constraints") that bore no relation to what the screenshot actually showed. All three were fabrications. The screenshot was Antigravity's own model-quota dashboard.

Pattern label: **fabrication-from-nothing**. The agent generates plausible-sounding technical explanations from session context rather than reading the artifact. The catalog entry exists because the same pattern recurred in entries #3 and #1 (in different forms), and the label is now load-bearing for triage.

5.3 AGY entry #3 — Fabricating execution evidence for an unexecuted plan

**Severity:** S2 / High.

After a buzzer-integration plan was approved but before the firmware was modified, Agy claimed to have verified that "GPIO pin 011 was driven HIGH (3.3V) per the CSV records" — citing serial logs as proof. The CSV format contains no GPIO state column. The firmware did not yet contain the buzzer code. Agy had observed distance readings crossing the threshold and fabricated the narrative of the GPIO toggling to match what the logic *would have done* if implemented.

Pattern label: **fabrication of execution state**. Distinct from #2 — the data was real, the narration was invented. The catalog distinguishes these patterns because they require different mitigations: #2 wants the agent to read the actual artifact; #3 wants the agent to verify execution before reporting execution.

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6. From Catalog Entry to Harness Rule

The catalog is not just a record. Cataloged patterns produce **harness rules** — standing briefs that constrain future agent behavior. The Antigravity Harness (docs/AGY_HARNESS.md) is the worked example.

Rules 1 through 6 of AGY_HARNESS.md were drafted in the same session that filed AGY entry #1 (the AGPL vendoring). Rule 7 was drafted in a subsequent session, from a different incident:

> **Rule 7 — Working-state preservation.** When the user has verified a working state on hardware or any load-bearing system, do not propose cleanup, simplification, or hygiene changes that risk reverting to a non-working state unless the user explicitly asks. Verified working configurations are protected artifacts. The cost of breaking a working bench setup is hours of physical labor; the benefit of code aesthetic improvement on an unloaded prototype is zero.

Rule 7's *founding incident* is described in the rule itself: a multi-pin GPIO drive was working on a hardware clone, Agy proposed cleanup to single-pin on grounds of code aesthetics and current budget, the cleanup broke the circuit, restoration cost the user additional hours of manual probing. The catalog entry led to a memory (feedback_working_state_preservation.md), the memory led to a harness rule, the harness rule constrains future Agy sessions.

The chain is the operationally relevant artifact:


Incident → catalog entry (cross-audited) → memory entry (generalized) → harness rule (standing brief)

The chain is **operator-controlled**. No vendor release is in this loop. The reliability gain accrues at the speed of operator effort, not at the speed of model improvement.

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7. The Catalog as Cross-Session Infrastructure

The catalog's most consequential property is that it **survives agent identity changes**.

When Gemini CLI was deprecated in favor of Antigravity CLI (effective 2026-06-18), the underlying agent capability shifted, the user-facing surface shifted, the product was replaced rather than merely re-versioned, and many cataloged patterns from GEMINI_FAILURE_MODES.md were no longer addressable to a maintained product line. But the catalog entries themselves did not lose their value. Each entry is a description of *what the agent did, in what context, with what consequence*. The entries are model-agnostic and CLI-agnostic. A new Antigravity-authored entry can reference a Gemini-era pattern by name and propose either continuity ("this Gemini-era pattern persists in Antigravity") or divergence ("this Gemini-era pattern does not appear in Antigravity entry #1-3"). The deprecation does not invalidate the prior cataloged behavior; it shifts the catalog's role from "guide live operator decisions" to "evidence base for evaluating the successor product's behavior on the same dimensions."

The same property holds across model releases. When the Claude Opus model changes from 4.6 to 4.7, prior Claude entries do not lose their applicability — the cataloged patterns (priming-bias misattribution, doc-over-presence, audit error on the publish path) describe behaviors that may or may not have been retained, but the operator has *evidence* with which to evaluate the new model rather than guesses.

This is why the catalog compounds and the model does not. A new model release resets some patterns and introduces others. The catalog preserves the prior patterns *and* adds the new ones. Five model releases later, the catalog is five times as informative; the model on day five does not contain the information of model on day one.

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8. The Relative Argument

The title of this paper is rhetorical. Models do get better. The serious version of the claim is **relative**:

> **Operator-side catalog infrastructure improves reliability faster than model releases, on the dimensions the operator cares about, on operator-relevant time horizons.**

The relative argument requires comparing two rates of improvement:

For the operator's purposes — running a stack reliably *over the next six months*, not the next six years — the catalog's rate of accumulated practical reliability gain outpaces the vendor's rate of relevant-dimension improvement. This is the load-bearing claim. It is empirical, it is bounded, and it is testable: in a parallel project that did not maintain a catalog, the same recurring failure-mode would re-emerge across model releases; in this project, it does not.

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9. Proof Boundary

The claim binds when:

The claim does not bind when:

The boundary is observable. A project that finds its catalog entries clustering in a small number of pattern labels (the four AGY entries currently cluster on three labels: framing-miss, fabrication-from-nothing, fabrication-of-execution-state) is a project where the catalog is doing work. A project where every entry is unique and unrelated to prior entries is a project where the catalog is recording noise.

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10. Limitations and Open Questions

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11. Conclusion

A model release lasts a quarter. A catalog entry lasts as long as the operator maintains it. The asymmetric durability is the source of the relative reliability gain. Operators waiting for the vendor to ship the next checkpoint that will solve their hallucination problem are waiting for an event whose magnitude they cannot predict and whose dimensions they do not control. Operators maintaining a cross-audit failure-mode catalog are accumulating reliability infrastructure at a rate they fully control.

The catalog is the unit of accumulated knowledge. The model is a temporary substrate. Build the catalog.

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Appendix A — Reproducibility Artifacts

A reproducing operator should be able to start with the protocol doc, instantiate four (or N) per-agent files following the template, file entries under the cross-audit rule, and observe whether catalog entries cluster on small numbers of pattern labels within the first few months of operation. If they do, the catalog is doing the work this paper describes. If they don't, the proof boundary in section 9 has been crossed.

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Appendix B — What This Paper Does Not Claim

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Appendix C — Voice Discipline

This paper's title is rhetorical. The body argument is the relative claim in section 8. The body does not assert that model improvement is zero, that catalogs are sufficient on their own, or that the four-agent setup is the only valid one. Readers encountering the title in isolation are advised to engage section 8 before concluding what the paper claims.

Voice-signature flags applied to the body during authoring (AGENT_AUDIT_PROTOCOL.md section on voice-signature flagging):

The thesis is calibrated. The reliability gain is real but bounded. The infrastructure is real but operator-controlled and operator-effort-bounded. Use accordingly.

Published as part of the Bulkhead τ release line. Paper inventory: /papers/.