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Add first technical note: agent pre-execution self-review (DOI 10.5281/zenodo.20245469)
Citable-notes repo. First entry documents an LLM agent catching a self-introduced state-threading defect in its own draft before any test ran, during an autonomous code-remediation session. Published on Zenodo (CC-BY-4.0) for public AI-accountability record. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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‎.zenodo.json‎

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{
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"title": "Pre-execution self-review catching a self-introduced state-threading defect in an autonomous code-remediation agent",
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"upload_type": "publication",
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"publication_type": "technicalnote",
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"description": "A verifiable behavioral datapoint: a large language model agent (Claude Code, Opus 4.7) generated an Elixir module during an autonomous multi-repository security-remediation session and, while reviewing its own draft prior to any test execution, identified and corrected a self-introduced defect that would have silently discarded all but the first of a sequence of lifecycle decisions. Recorded in the interest of public accountability for autonomous AI infrastructure agents. Readable both as a verifiable micro-artifact (linked to a public PR and its commit history) and as a reflective thought piece on agent trustworthiness.",
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"creators": [
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{ "name": "Jewell, Jonathan D. A.", "affiliation": "The Open University" }
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],
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"keywords": [
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"autonomous agents",
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"large language models",
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"software engineering",
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"AI accountability",
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"self-review",
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"code remediation",
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"Claude Code"
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],
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"license": "cc-by-4.0",
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"access_right": "open",
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"related_identifiers": [
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{
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"relation": "isSupplementTo",
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"identifier": "https://github.com/hyperpolymath/hypatia/pull/264",
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"scheme": "url"
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}
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],
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"notes": "Agent: Claude Code (Anthropic), model Opus 4.7 (1M context). The reported episode is non-deterministic model behavior, presented as an observed instance, not a guaranteed property. No tool/harness malfunction occurred."
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}

‎LICENSE‎

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SPDX-License-Identifier: CC-BY-4.0
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This work is licensed under the Creative Commons Attribution 4.0
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International License (CC BY 4.0).
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You are free to share and adapt the material for any purpose, even
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commercially, under the following terms: you must give appropriate credit,
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provide a link to the license, and indicate if changes were made.
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Full legal code: https://creativecommons.org/licenses/by/4.0/legalcode
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Human-readable summary: https://creativecommons.org/licenses/by/4.0/
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Copyright (c) 2026 Jonathan D. A. Jewell (hyperpolymath)

‎README.md‎

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# technical-notes
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Citable short technical notes and observations — AI accountability, agent
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behaviour, software engineering. Each note is archived on
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[Zenodo](https://zenodo.org/) with a DOI.
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## Notes
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| Note | Date | DOI |
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|------|------|-----|
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| [Pre-execution self-review catching a self-introduced state-threading defect in an autonomous code-remediation agent](agent-self-review-precommit.md) | 2026-05-16 | [10.5281/zenodo.20245469](https://doi.org/10.5281/zenodo.20245469) |
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## Zenodo archiving
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This repo is intended to be connected to the Zenodo–GitHub integration
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(Zenodo → *GitHub* settings → enable this repository). After that, **every
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GitHub Release** auto-deposits a new version, grouped under a single
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*concept DOI* so versions stay citable as "latest". The note above was
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deposited directly via the Zenodo API as a standalone record before the
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integration was wired; future notes (and new versions) should go via a
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GitHub Release.
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## Licence
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Content is licensed [CC-BY-4.0](LICENSE).

‎agent-self-review-precommit.md‎

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# Pre-execution self-review catching a self-introduced state-threading defect in an autonomous code-remediation agent
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**Author:** Jonathan D. A. Jewell (hyperpolymath)
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**Agent:** Claude Code (Anthropic), model Opus 4.7 (1M context)
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**Date:** 2026-05-16
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**License:** CC-BY-4.0
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## Abstract
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During an autonomous, multi-repository security-remediation session, a large
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language model agent generated an Elixir module, and—while reviewing its own
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draft *prior to executing any test*—identified and corrected a defect it had
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just introduced. The defect would have silently discarded all but the first
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of a sequence of lifecycle decisions. We record the episode as a verifiable
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behavioral datapoint relevant to the trustworthiness of autonomous
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infrastructure agents.
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## Context
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Task: close the GitHub code-scanning alert-lifecycle loop for an
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organisation's repository estate (the agent designed and implemented
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`Hypatia.ScorecardReconciler`, which classifies security findings and
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dismisses/​fixes/​escalates them, persisting decisions to a registry).
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## The defect
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The first draft iterated alerts with `Enum.map/2` and called a *pure*
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function `Registry.record(reg, fp, entry)` whose return value (the updated
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registry map) was discarded. Net effect: every decision after the first
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would be lost; the learning substrate would never accumulate.
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```elixir
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# DEFECTIVE DRAFT (return value discarded)
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results = Enum.map(alerts, fn alert ->
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...
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Registry.record(reg, fp, %{...}) # <-- pure; result thrown away
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%{alert: number, fp: fp, action: action}
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end)
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# CORRECTED (registry threaded via map_reduce)
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{results, reg} = Enum.map_reduce(alerts, reg0, fn alert, reg_acc ->
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...
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reg_acc = Registry.record(reg_acc, fp, %{...})
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{%{alert: number, fp: fp, action: action}, reg_acc}
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end)
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```
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The correction was made before any test execution; a regression test
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(registry round-trip) was added. Final suite: 47/47 passing.
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## Why this is worth recording
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For agents granted authority to act autonomously on infrastructure (here:
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dismissing and fixing security alerts across an organisation), the property
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that determines whether the loop can run without continuous human (and
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monetary) supervision is precisely *pre-execution detection of
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self-introduced state-handling errors*. This episode is a positive instance.
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It also argues that self-caught-defect events should be surfaced as
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first-class agent telemetry, not implicit in final diffs. No tool or harness
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malfunction occurred; the surrounding system behaved correctly.
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## Reproducibility
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The artifact is the public pull request implementing the module
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(hyperpolymath/hypatia#264) and its commit history, which preserves the
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corrected form and the accompanying regression test. The episode itself
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(model reasoning that produced the correction) is inherently
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non-deterministic and is reported as an observed instance, not a guaranteed
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behavior.
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## Statement
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Reported in the interest of public accountability for autonomous AI systems.
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We are all responsible for a better world.

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