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Changelog

All notable changes to ref-verify will be documented here.

Format follows Keep a Changelog. Versioning follows Semantic Versioning.

Unreleased

[1.2.0] — 2026-06-08

Added

  • Added ref-verify check-file for JSONL and CSV DOI/claim batch checks.
  • Added fixture-backed numeric claim eval coverage for repeated-use workflows.
  • Added DOI-bound OpenAlex abstract fallback before Semantic Scholar and PubMed.
  • Added a CLI regression corpus and manual Live Smoke ship gate for release-readiness checks.
  • Added English and Korean scope guidance that explains what the tool verifies, what it does not verify, and how to interpret non-ACCEPT verdicts.

Fixed

  • Fixed composite scientific units such as MV/m being misread as numerator-only units.
  • Added numeric claim support for common physical-science units such as eV, Ω·cm, S/m, and MPa.
  • Treated estimated to be <value> as a reported numeric value while keeping predictive estimated to exceed frames conservative.
  • Prevented comparative evidence such as >220 °C from accepting an exact 220 °C claim.
  • Allowed physical measurement conditions such as 1.7 eV in the temperature range and 5 S/m at 1 kHz without relaxing count-claim scope guards.
  • Separated Semantic Scholar 429 rate limits into SOURCE_RATE_LIMITED and retried once before marking the source unavailable.
  • Clarified that npx skills add installs the agent skill but does not pip-install the Python CLI.
  • Fixed comma-clause splitting so current-study result sentences can bind a number to a subject across descriptive commas when no same-unit competing value is present.
  • Recognized claim-side up to <value> comparators for percentage and unit/count claims while keeping exact-claim guards conservative.
  • Treated temperature measurements followed by physical range/field conditions as measurement context, and allowed generic Measurements... sentences to inherit subject context from the immediately preceding sentence.

[1.1.2] — 2026-06-08

Changed

  • Added release automation guardrails for CI, wheel smoke testing, manual live API smoke checks, and PyPI trusted publishing.
  • Updated GitHub Actions workflows to current Node runtime-compatible action versions.

[1.1.1] — 2026-06-08

Changed

  • Updated Python packaging metadata to the current SPDX license format.
  • Clarified that zero runtime dependencies means zero third-party Python packages; CLI verification still requires outbound HTTPS access to public academic APIs.
  • Clarified that the Python package is the CLI engine only. Install the agent skill from GitHub with npx skills add.

[1.1.0] — 2026-06-07

Added

  • Python package scaffold with zero third-party Python runtime dependencies.
  • ref-verify verify-doi CLI for CrossRef-backed DOI metadata checks.
  • ref-verify check-claim CLI for abstract-grounded claim support checks.
  • Machine-readable JSON output for downstream manuscript preflight, MCP, and Zotero integrations.
  • Offline unit tests for DOI metadata comparison, CrossRef parsing, claim support verdicts, and CLI output.

Changed

  • Documented the executable engine path alongside the existing agent skill workflow.
  • Updated the skill instructions to prefer the CLI when it is installed, while keeping the manual verification protocol as fallback.

[1.0.0] — 2026-06-01

Added

  • 5-layer verification protocol: Existence → Metadata → Content Traceability → DOI Resolution → Retraction Check
  • Two-mode design: Quick Screen (seconds per paper, for DOI spot-checks) and Full Audit (abstract fetch + claim verification, for search tasks and pre-submission review)
  • Content traceability rule: every content statement must come from a live-fetched abstract quoted verbatim — never from training data recall
  • Open-access fallback chain: CrossRef JSON → Semantic Scholar → Unpaywall → arXiv → PubMed, in order
  • Near-miss detection: evaluates whether the abstract supports the specific claim being cited, not just whether the paper exists
  • Automatic mode selection: decision tree based on task type (search vs. spot-check vs. audit)
  • Structured verdicts: ACCEPT / WARN / REJECT with explicit per-layer evidence
  • Trigger description optimized for Claude Code, Cursor, and Codex auto-detection
  • Evaluation suite: 3 test cases with real-world hallucination examples from materials science literature

Verified catches

  • Content hallucination: AI described paper content not present in the CrossRef abstract (Nemat-Nasser 2002)
  • Wrong DOI: citation resolved to different paper, different authors, wrong year (Carpi 2011)
  • Near-miss: "500% strain" in abstract was a measurement condition, not an actuation result (Kofod 2003)