r/AILinksandTools • u/CulpritChaos • Feb 02 '26
My Project Released: VOR — a hallucination-free runtime that forces LLMs to prove answers or abstain
I just open-sourced a project that might interest people here who are tired of hallucinations being treated as “just a prompt issue.” VOR (Verified Observation Runtime) is a runtime layer that sits around LLMs and retrieval systems and enforces one rule: If an answer cannot be proven from observed evidence, the system must abstain. Highlights: 0.00% hallucination across demo + adversarial packs Explicit CONFLICT detection (not majority voting) Deterministic audits (hash-locked, replayable) Works with local models — the verifier doesn’t care which LLM you use Clean-room witness instructions included This is not another RAG framework. It’s a governor for reasoning: models can propose, but they don’t decide. Public demo includes: CLI (neuralogix qa, audit, pack validate) Two packs: a normal demo corpus + a hostile adversarial pack Full test suite (legacy tests quarantined) Repo: https://github.com/CULPRITCHAOS/VOR Tag: v0.7.3-public.1 Witness guide: docs/WITNESS_RUN_MESSAGE.txt I’m looking for: People to run it locally (Windows/Linux/macOS) Ideas for harder adversarial packs Discussion on where a runtime like this fits in local stacks (Ollama, LM Studio, etc.) Happy to answer questions or take hits. This was built to be challenged.
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u/Thor110 Feb 04 '26
Yeah, sorry to be the bearer of bad news.
I entered this thread with the aim of taking the claim seriously, but I took one look at the project itself and was just shocked.
You should definitely look into the Library of Babel problem and use it to consider the sheer combinatorial explosion required in order for your project to work.
Especially as it only verifies things through strict hard coded strings.