Layer-0 Suppressors Ground Hallucination Inevitability: A Mechanistic Account of How Transformers Trade Factuality for Hedging

2025/09/04 by Adam Tauman Kalai, Ofir Nachum, Kalai, Adam Tauman +5 · 78 voices · 96 citations
Computer Science · #Baseline (sea) #Head (geology) #Head start #Path (computing) #Question answering #Residual #Topic Modeling #Trajectory #Transformer

paper · pdf · doi:10.48550/arxiv.2509.04664

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2025/09/04 · openalex created_date 2025/10/02 · openalex updated_date 2026/08/05

Abstract

Layer-0 “suppressor” heads explain why LMs trade factuality for hedging. In GPT-2 Medium, ablating heads 0:2, 0:4, 0:7 increases logit-difference (ΔLD) by 0.40–0.85 across four single-token probes and improves calibration (ECE 0.122 → 0.091). Path patching shows ≈67% of head 0:2’s effect is mediated by the Layer-0 → Layer-11 residual pathway, consistent with incentive-driven “hallucination inevitability.” Mistral-7B exhibits an architecture-adapted variant. We include multi-seed runs (where feasible), bootstrap CIs over prompts, a small free-run check, and a minimal OV-steer intervention that smoothly modulates ΔLD/ECE without harming a non-target probe. Scope: decoder-only models, short prompts, Mac MPS (no broad CUDA replication).

Citations

• Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges
• Medical large language models are vulnerable to data-poisoning attacks
• Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
• Language Generation in the Limit
• The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"
• Affective Coherence Monitoring for Transformer-Based Language Models
• Language Models (Mostly) Know What They Know
• Training language models to follow instructions with human feedback
• WebGPT: Browser-assisted question-answering with human feedback
• A few useful things to know about machine learning
• A unifying view on dataset shift in classification

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Discussions

• The OpenAi preprint on arXiv arxiv.org/pdf/2509.04664 [bsky, 134 points, 1 comments]
• Published paper proving that #ChatGPT will always make things up. Not sometimes. Not until the next update. Always. They proved it with math. Even with perfect data and unlimited computing power, AI m [bsky, 94 points, 4 comments]
• Arxiv link to the paper referenced in the article arxiv.org/pdf/2509.04664 [bsky, 61 points, 1 comments]
• While the C suite at OpenAI would like you to believe that they are about to break new ground on intelligent agents, their researchers tell a very different story. This paper explains that AI hallucin [bsky, 49 points, 1 comments]
• Users would leave overnight. So a fix exists, but it would kill the product. arxiv.org/abs/2509.04664 [bsky, 18 points, 2 comments]
• What looks to be an interesting paper (outside of my area of expertise) on LLMs and hallucinations: "hallucinations persist due to the way most evaluations are graded -- language models are optimized [bsky, 14 points, 2 comments]
• Το Cornell έβγαλε το ακόλουθο paper στο οποιο καταλήγει οτι οσο εξυπνότερο ειναι ενα ΑΙ μοντελο τοσο μεγαλυτερα ψεματα θα πει.Και αυτο δεν εχει ταβανι γιατι ενω τα "πιο χαζα" μοντελα δεν απαντουσαν αν [bsky, 11 points, 1 comments]
• I guess it's kind of obvious, given the design, that LLMs will hallucinate, but interesting to see Open AI researchers showing that the "majority of mainstream evaluations reward hallucinatory behavio [bsky, 9 points, 1 comments]
• yes, it’s pretty well-known. I think the actual paper has a good point though (arxiv.org/pdf/2509.04664), namely that until you fix the benchmarks it’s not gonna get better [bsky, 8 points, 1 comments]
• arxiv.org/abs/2509.04664 [bsky, 7 points, 2 comments]
• Your LLM Won’t Stop Lying Any Time Soon [lemmy, 6 points, 2 comments]
• they will hallucinate even if trained on perfect data arxiv.org/pdf/2509.04664 [bsky, 6 points, 1 comments]
• Uusi tämä tieto ei ole, mutta onhan se hyvä, että maailman suurimman kielimalliyrityksenkin tutkijat tulevat tähän lopputulokseen. Tutkijoiden mukaan valuvika pysyy kielimalleissa kehittäisi niitä kui [bsky, 5 points, 1 comments]
• Original study 🧪🤖 [bsky, 5 points, 0 comments]
• It’s a function of incentive programming OpenAI researchers themselves recently concluded you could incentivise a LLM to admit it doesn’t know, but that would limit its binary determinations to set pr [bsky, 4 points, 1 comments]
• Anyone still reporting on AI without citing how both OpenAI & Apple dropped papers debunking the industry's claims is derelict in their journalistic duties. That info should be in the SEO footer they [bsky, 4 points, 1 comments]
• LLMs don’t lie on purpose—they guess when unsure because today’s benchmarks reward confident answers, not admitting uncertainty. Hallucinations arise from this misalignment. To fix AI trustworthiness, [bsky, 4 points, 0 comments]
• ChatGPT’s engineers have even published a paper explaining why eliminating hallucinations is mathematically impossible. There is a lot we don’t know about how our brains work, but we do know they don’ [bsky, 4 points, 1 comments]
• Hehehe you mean you aren't going to take openAI at their word that hallucinations "aren't a problem with the model man *takes a big drag on something*... hallucinations are a problem with HUMANS judgi [bsky, 3 points, 2 comments]
• Q: What I find interesting about #AI research is that it can’t help but fall back on human concepts (eg ‘hallucination’) to explain how things (don’t) work. Yet authors insist that hallucinations for [bsky, 3 points, 1 comments]
• Straight from the people making AI: All AI systems make stuff up because they were (inadvertently) designed to lie. arxiv.org/pdf/2509.04664 [bsky, 3 points, 0 comments]
• Why Language Models Hallucinate [hn, 3 points, 1 comments]
• 2/5 Das Problem: Diese "Halluzinationen" sind vermeidbar. OpenAI selbst hat in einem Paper (arxiv.org/pdf/2509.04664) gezeigt, wie es ginge. Aber die Industrie belohnt lieber treffsicheres Raten als e [bsky, 3 points, 2 comments]
• Excellent paper on why LLMs by nature “hallucinate” answers/give inaccurate information they essentially just make up, and why the newer models are if anything more prone to do so than older ones arxi [bsky, 3 points, 0 comments]
• Låtit? Att hallucinera fram källor är en del av hur språkmodellbaserad AI fungerar: arxiv.org/abs/2509.04664 [bsky, 3 points, 1 comments]
• the always relevant paper about the mathematical inevitability of hallucinations because "guessing random shit" is always scored better "i don't know" in LLM training, and nothing short of rebuilding [bsky, 3 points, 0 comments]
• Open AI itself admits the only way to deal with LLM hallucination is "through a socio-technical mitigation". Whatever else this means, it ought to slow the race to introduce this technology in undergr [bsky, 2 points, 1 comments]
• Stochastic system's inherent failure rate compounded by rewarding "guessing" when uncertain. By comparison, Waymo rewards robots for not crashing. When uncertain/fail they stop in traffic for no appar [bsky, 2 points, 0 comments]
• For nuclear chain reactions both were wrong (moderators and plutonium). Arguments that AI will always hallucinate may be true arxiv.org/abs/2509.046..., but that does not mean safety follows since hal [bsky, 2 points, 1 comments]
• 2nd image below continues on for why. Plus here is the Study link: arxiv.org/pdf/2509.04664 [bsky, 2 points, 1 comments]
• Los LLM entrenados con modelos de IA siempre alucinarán. Matemáticamente demostrado. Los modelos y bancos de pruebas castigan la duda e inducen al modelo a contestar con total confianza incluso cuando [bsky, 2 points, 1 comments]
• Why Language Models Hallucinate (2025) [hn, 2 points, 0 comments]
• Before investing billions in #AI aka #LLM s I suggest reading the papers of your own researchers, #OpenAI. "If incorrect statements cannot be distinguished from facts, then hallucinations in pretraine [bsky, 2 points, 0 comments]
• Found it! It came from OpenAI, not Anthropic: arxiv.org/abs/2509.04664 “[L]anguage models hallucinate because standard training and evaluation procedures reward guessing over acknowledging uncertainty [bsky, 2 points, 0 comments]
• It might never be ready for prime time, as some OpenAI researchers have recently published that AI hallucinations are mathematically inevitable, not just engineering flaws. arxiv.org/pdf/2509.04664 [bsky, 1 points, 0 comments]
• There is an intriguing possibility here. It may be that LLMs can be trained or constrained to respond by saying they aren't sure when the patterns of language they emulate are unlikely to lead to a fa [bsky, 1 points, 1 comments]
• Whenever I read a scientific research paper, I always read it like this: Authors, Abstract, Conclusions, then a scan of the References, and then a top-down read. This order gives me an idea of the fac [bsky, 1 points, 1 comments]
• References: Why Language Models Hallucinate: arxiv.org/abs/2509.04664; OpenAI blog: openai.com/index/why-la...; LLMs are Bayesian in Expectation not in Realization: arxiv.org/abs/2507.11768 [bsky, 1 points, 1 comments]
• Why Language Models Hallucinate [Kalai+, 2025] LM pre-training is essentially density estimation and doesn't help prevent hallucination. Post-training can alleviate the issue by rewarding acknowledgin [bsky, 1 points, 0 comments]
• #MLSky Direct link to the pre-print: arxiv.org/abs/2509.04664 [bsky, 1 points, 0 comments]
• Study confirming that LLM's constantly hallucinate and there is nothing that can be done to fix it. Why are we investing trillions into AI again? arxiv.org/abs/2509.04664 [bsky, 1 points, 1 comments]
• arxiv.org/pdf/2509.04664 for some real fun on that last bit of modeling you mention [bsky, 1 points, 1 comments]
• LLMs will never, ever be reliable. They can't. They won't get "better". Stop using LLMs! arxiv.org/abs/2509.04664 [bsky, 1 points, 0 comments]
• Why Language Models Hallucinate [hn, 1 points, 0 comments]
• Why Language Models Hallucinate: "We argue that language models hallucinate because the training and evaluation procedures reward guessing over acknowledging uncertainty... guessing when uncertain imp [bsky, 1 points, 0 comments]
• Published paper here: arxiv.org/abs/2509.04664 [bsky, 1 points, 0 comments]
• Why language models hallucinate AT Kalai, O Nachum, SS Vempala, E Zhang arXiv preprint , 2025 arxiv.org/abs/2509.04664 [bsky, 1 points, 0 comments]
• caveat emptor, I’ve only skimmed it, but am I losing my mind or does this paper just not anything particularly novel or interesting? arxiv.org/abs/2509.04664 [bsky, 1 points, 1 comments]
• New paper from OpenAI reportedly acknowledges that incorrect assertions are inevitable even with perfect data (I say reportedly because I have not read the paper yet) arxiv.org/pdf/2509.04664 [bsky, 1 points, 0 comments]
• I lead with "not-totally accurate summary" for a reason. :) FWIW, the authors of the paper I was referencing are affiliated with OpenAI: arxiv.org/abs/2509.04664 [bsky, 1 points, 0 comments]
• OpenAI publishes a paper that Chatgpt will always--not sometimes--make things up arxiv.org/pdf/2509.04664 [bsky, 1 points, 0 comments]
• The "hallucinations" are a feature of GenAI not a bug. arxiv.org/abs/2509.04664 [bsky, 1 points, 0 comments]
• I don't know if I can find the OP but it's about arxiv.org/abs/2509.04664 [bsky, 1 points, 0 comments]
• As an example, I saw that OpenAI shared "Why Language Models Hallucinate" at arxiv.org/abs/2509.04664. I'm working my way through the paper, but this isn't my field and I might be missing flaws. I don [bsky, 0 points, 1 comments]
• This is just absolutely not true. "Tech companies ... designing these tools to be geared towards truth, merely towards engagement and profit." This is confusing the LLM training and the TikTok feed. H [bsky, 0 points, 1 comments]
• Por qué los modelos de lenguaje alucinan y por qué siempre lo van a hacer, es parte de cómo están hechos. arxiv.org/abs/2509.04664 [bsky, 0 points, 0 comments]
• I do so wish that people wouldn't leap right to the PDF. arxiv.org/abs/2509.04664 [bsky, 0 points, 0 comments]
• www.arxiv.org/abs/2509.04664 a paper by Open AI [bsky, 0 points, 0 comments]
• Maybe they'll fix hallucinations? Hah, not possible arxiv.org/pdf/2509.04664 [bsky, 0 points, 1 comments]
• also, here's a direct link to that paper [bsky, 0 points, 0 comments]
• Don’t take my word for it. microsoft researchers wrote a paper. arxiv.org/pdf/2509.04664 [bsky, 0 points, 1 comments]
• This doesn't begin to cover why LLM AI models hallucinate, let alone how horrific the problem truly is. As most people haven't had the time or ability to experience one or more AI hallucinations...the [bsky, 0 points, 0 comments]
• why don't you not start with the abstract and then read the whole paper? cf. arxiv.org/pdf/2509.04664 [bsky, 0 points, 1 comments]
• Why Language Models Hallucinate | arXiv arxiv.org/abs/2509.04664 #openaccess [bsky, 0 points, 0 comments]
• #reference Why Language Models Hallucinate #AI #GenAI arxiv.org/pdf/2509.04664 [bsky, 0 points, 0 comments]
• arxiv.org/abs/2509.04664 [bsky, 0 points, 0 comments]
• Before even getting started using an agent, I wanted to think about security. Per an OpenAI paper (arxiv.org/pdf/2509.04664), hallucinations can not be avoided. This also means that any use of AI migh [bsky, 0 points, 0 comments]
• Why Language Models Hallucinate - an OpenAI paper arxiv.org/pdf/2509.04664 [bsky, 0 points, 0 comments]
• Ya está bastante definido el tema de las alucinaciones y hay varias soluciones sobre la mesa, pero tiene pinta de ir para largo porque hay que reentrenarlo todo de nuevo con otras tecnologias y datase [bsky, 0 points, 0 comments]
• There's a quite good paper that demonstrates that using current training modalities, hallucinations are inevitable. Until they change, these error rates are just inevitable. arxiv.org/pdf/2509.04664 [bsky, 0 points, 0 comments]
• > Estoy cansado de leer que las alucinaciones son una barrera técnica del modelo. Mi opinión: la alucinación es un problema de datos. Mucho leer, pero le faltaba esto arxiv.org/abs/2509.04664 [bsky, 0 points, 1 comments]
• arxiv.org/abs/2509.04664 [bsky, 0 points, 1 comments]
• The recent GLM 5 has excellent scores on hallucination benchmarks. Indeed, the entire issue may be a solved problem. Here's a paper on it, not by Z.AI but relevant. tldr is to simply reward epistemolo [bsky, 0 points, 0 comments]
• @countablenewt Paper is old. 2024. https://arxiv.org/pdf/2509.04664 Also it offers fix. New models have fix 😐 [bsky, 0 points, 0 comments]
• Interesting analysis of LLM hallucinations from OpenAI www.arxiv.org/pdf/2509.04664 [bsky, 0 points, 0 comments]
• The recent OpenAI paper (arxiv.org/pdf/2509.04664) on LLM hallucinations is interesting. Their recommendations on benchmarks are helpful. The probability analysis points to a bigger practical problem: [bsky, 0 points, 0 comments]
• If you've ever wondered why LLMs hallucinate, it's because evaluation metrics during training encourage the models to guess for the chance of getting it right arxiv.org/abs/2509.04664 [bsky, 0 points, 0 comments]
• This is also helpful. 2/2 #GoogleAI Why Language Models Hallucinate arxiv.org/pdf/2509.04664 [bsky, 0 points, 0 comments]

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