Top ML researchers just shrugged off AI hallucinations in papers - exposing the wild standards (or lack thereof) in AI research.
AI papers full of fake refs? NeurIPS says chill - it’s ‘evolving.’ On January 26, 2026, prestigious ML researchers defended LLM use in submissions, claiming hallucinations aren’t catastrophic because… ML research isn’t ‘serious’ science.[3]
The controversy: NeurIPS monitors LLM citations in papers amid rising fakery. But insiders argue ML’s fast pace tolerates it - rapid iteration over rigor. “Usage is evolving,” they say, downplaying bogus references.[3]
Devs building on arXiv/ML papers: buyer beware. RAG pipelines, evals, even Copilot? Train on this noisy data, inherit the hallucinations. Forces us to build better verification layers (Perplexity-style search grounding).[3]
Unlike peer-reviewed fields (math proofs, bio reproducibility), ML benchmarks are often leaderboards anyone can game. This NeurIPS stance cements it: publish first, fix later.[3]
Action item: Audit your LLM stack for hallucination checks (evals like TruthfulQA). Use tools like Elicit or Consensus for lit reviews. Fork open datasets sans fakes. Big question: If ML isn’t ‘serious research,’ why trust the models it builds?
Source: Statistical Modeling