Sharing an open-source framework I’ve been developing in public. Problem: in multi-agent/compiled-knowledge pipelines, a claim passes through several transformers; scraper → ingestion model → synthesis model. Provenance tooling logs what happened but doesn’t grade who transformed a claim or how much to trust the result. Final-model confidence doesn’t capture this, a model at the end of a chain can’t repair a corrupted extraction at the start. Approach: ISNAD maintains a graded, per-domain registry of every “narrator” (source/scraper/model-version/human), attaches a transmission chain to each claim, caps trust at the weakest link (with a destructive-vs-generative refinement), and routes claims through a chain-grade × content-criticism decision matrix (serve/caveat/review/quarantine). The design is transferred from classical Islamic hadith transmission science, which formalized graded chain-of-transmission trust ~1200 ago. What’s validated (honestly): on a 20k-claim real-corpus experiment, the weakest-link grading correctly quarantines unreliable narrators, and confidence-based gating provides no benefit over serving everything. What isn’t yet: corroboration never fired on real data (few high-grade cross-source overlaps), and practical coverage depends on a real content critic, the bundled one is a labeled heuristic. I’ve documented this openly; it’s a framework with partial validation, not a solved problem. pip install isnad · Apache-2.0 · 111 tests · paper has a DOI. Would genuinely value critique, especially on the corroboration and content-critic gaps. submitted by /u/alizahidrajaa
Originally posted by u/alizahidrajaa on r/ArtificialInteligence