cd /news/artificial-intelligence/verify-then-vote-overturning-wrong-m… · home › topics › artificial-intelligence › article
[ARTICLE · art-162952] src=github.com ↗ pub= topic=artificial-intelligence verified=true sentiment=· neutral

Verify, Then Vote: Overturning Wrong Majorities in Multi-Agent LLMs

A November 2026 paper, "Verify, Then Vote," from the grainulation/papers collection reports that a rule letting checked passage evidence overturn a multi-agent LLM majority vote improves accuracy on the evaluated benchmark, but the original scored rule misses its pre-specified harm criterion. The paper states its formal result identifies an inspectable failure condition rather than guaranteeing a low error rate, and reports the gains, failures, and the tradeoff against a stronger model judge together, with code, data, and proof checks released for reproduction under Apache 2.0 and CC BY 4.0 terms. The collection is being prepared for its first public release, with study freezes recording when analyses were specified.

read2 min views1 publishedNov 3, 2026
Verify, Then Vote: Overturning Wrong Majorities in Multi-Agent LLMs
Image: Michielbdejong (auto-discovered)

Papers you can read. Results you can check.

Research on evidence and verification in multi-agent systems.

Manuscripts, formal proofs, code, and the recorded data behind the results.

Paper map · Supplement · Reproduce the results · Cite · Contribute

Overturning Wrong Majorities in Multi-Agent LLMs · November 2026

When agents share a mistake, their majority can be wrong. This paper studies a rule that lets checked passage evidence overturn the vote, while recording which evidence licensed each change.

Verification improves accuracy on the evaluated benchmark, but the original scored rule misses its pre-specified harm criterion. The formal result identifies an inspectable failure condition; it does not guarantee a low error rate. The paper reports the gains, failures, and tradeoff against a stronger model judge together.

Start here What you get
Read the paper The rule, main results, controls, and limitations.
Supporting material Methods, proofs, pooled-bound construction, and full replay tests.
Reproduce the results Commands and pinned toolchains for the code, analyses, and formal proofs.
Source and citation LaTeX build instructions, artifact map, and BibTeX.

Each paper lives alongside the material needed to inspect its claims:

  • Read the argument in the main paper and the supporting methods in its supplement.
  • Inspect the study protocols, recorded inputs, checker labels, and reported outputs.
  • Reproduce the analyses and run the implementation and proof checks using the artifact guide.

The paper map collects abstracts and links. catalogue.yaml provides the machine-readable index.

With Tectonic installed, or latexmk with pdfLaTeX, run from the repository root:

make -C papers/verify-then-vote/tex

This builds the main paper and supplement. Add the arxiv target to create a source bundle containing both standalone documents. Code, data, and proof checks have separate requirements; follow the artifact guide.

The collection is being prepared for its first public release. Study freezes record when analyses were specified; they are not earlier publications. After publication, tagged releases and the history will record corrections and revisions.

For a correction or reproduction report, open an issue with the paper, section, claim, and supporting evidence or commands. See CONTRIBUTING.md for the contribution and release process.

Material Terms
Code Apache License 2.0
Manuscripts and figures CC BY 4.0
Data we produced CC BY 4.0 unless the artifact's DATA_LICENSES.md states otherwise
Third-party data and model outputs Their own dataset or provider terms; EXAONE outputs are not under CC BY 4.0

The artifact's data and component licenses take precedence over this summary.

── more in #artificial-intelligence 4 stories · sorted by recency
── more on @grainulation 3 stories trending now
sponsored brought to you by zahid.host 4,200+ EU-deployed projects
reading about agents? ship yours in a single git push.

Run your AI side-project on zahid.host

EU-based hosting, git-push deploys, automatic HTTPS, no cold starts. Free tier with a custom domain — perfect for shipping the agent you just read about.

$git push zahid main
→ Live at https://your-agent.zahid.host ✓
Get free account → Pricing
from €0/mo · no card required
LIVE [news/verify-then-vote-ove…] indexed:0 read:2min 2026-11-03 · —