Aiersilan, Aizierjiang · May 30, 2026
Aizierjiang Aiersilan: Hallucination Is Linearly Decodable from Mid-Layer Hidden States in Quantized LLMs
Markdown · JSON · Original source
Aizierjiang Aiersilan reports that, across three open-source instruction-tuned models loaded in 4-bit NF4 , a linear probe—a classifier that separates examples using a linear boundary—could identify a truthfulness signal in mid-layer hidden states. On four , the probe achieved 0.904–1.000 AUROC on held-out splits; sampling-based detectors did not exceed 0.541 under the same protocol. The paper says MLP probes rarely improved on linear probes by more than 0.01 AUROC, and identifies different peak-layer ranges for Llama and Mistral versus Qwen. It also reports that first-block attention entropy provided a complementary signal on HaluEval-QA without additional inference cost. These results suggest a possible route to detecting hallucinations from a model’s internal activations, rather than relying on repeated sampling. But the authors caution that sampling methods’ low scores reflect a mismatch between paired-label evaluation and the information those methods access, not an inherent limitation of the methods. The reported comparisons cover three models and four benchmarks; whether the signal carries over to other models or settings remains open.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: May 30, 2026 (2026-05-30).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-09T10:40:22.112Z. First observed: 2026-11-05T05:00:30.043Z. Retrieved: 2026-11-09T10:39:32.538Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
The reported evaluation covers three instruction-tuned models and four ; the evidence does not establish whether the findings extend beyond them. The source is an abstract and does not provide the paper’s detailed methods or results beyond the reported summary.
Open question
Does the reported truthfulness signal remain linearly decodable in models and evaluation settings beyond the three models and four tested?
Source-stated
The paper evaluates hidden states from three open-source instruction-tuned models loaded in 4-bit NF4 quantization on four hallucination benchmarks.
Across three $7$B--$8$B instruction-tuned models (Llama-3.1-8B, Mistral-7B, Qwen2.5-7B) loaded in $4$-bit NF4 quantization
Source-stated
A linear probe on one mid-network layer achieved 0.904–1.000 AUROC on held-out splits, while sampling-based detectors did not exceed 0.541 under the same protocol.
A linear probe on a single mid-network layer achieves $0.904$--$1.000$ AUROC on held-out splits, while sampling-based detectors do not exceed $0.541$ AUROC under the same protocol.
Source-stated
The authors report that MLP probes rarely surpassed linear probes by more than 0.01 AUROC.
MLP probes rarely surpass linear probes by more than $0.01$ AUROC.
Source-stated
Peak probing layers fell in different reported bands for Llama and Mistral versus Qwen on natural-language benchmarks.
blocks~$13$--$18$ of~$32$ for Llama and Mistral, and blocks~$19$--$25$ of~$28$ for Qwen
Source-stated
The paper reports first-block attention entropy as a complementary signal on HaluEval-QA, with no additional inference cost.
First-block attention entropy provides a complementary signal in knowledge-grounded settings ($0.866$--$0.941$ AUROC on HaluEval-QA) at no additional inference cost.
Source-stated
The authors attribute sampling-based detectors’ low discriminability under their protocol to a mismatch in evaluation and accessible information, not an inherent limitation of those methods.
reflects a structural mismatch between paired-label evaluation and the information these methods access, rather than an inherent limitation of those methods.
Source and contributor context
Aiersilan, Aizierjiang · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Hallucination
When an AI system states something false or made up as if it were true.
Quantization
Storing a model's numbers at lower precision so it uses less memory and runs faster, sometimes at a cost in accuracy.
Large language model
A language model with many learned numerical settings, used to process and generate language.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: c01aec02-c68d-4d69-80ef-43e08182ff1e.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-09T10:40:22.112Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 209e8062f402b19adeddd2673d9e93542ffa0daa13720e14ddbcf676db90026b. Source text hash: f4749bff57db912a508b0a6ddd0af114d1f2aa8a3ffdf10d8c46965dd1cabde5.
Revision and source-check history
Version 1 · 2026-11-09T10:40:22.112Z
Aizierjiang Aiersilan: Hallucination Is Linearly Decodable from Mid-Layer Hidden States in Quantized LLMs
Aizierjiang Aiersilan reports that, across three open-source instruction-tuned models loaded in 4-bit NF4 , a linear probe—a classifier that separates examples using a linear boundary—could identify a truthfulness signal in mid-layer hidden states. On four , the probe achieved 0.904–1.000 AUROC on held-out splits; sampling-based detectors did not exceed 0.541 under the same protocol. The paper says MLP probes rarely improved on linear probes by more than 0.01 AUROC, and identifies different peak-layer ranges for Llama and Mistral versus Qwen. It also reports that first-block attention entropy provided a complementary signal on HaluEval-QA without additional inference cost. These results suggest a possible route to detecting hallucinations from a model’s internal activations, rather than relying on repeated sampling. But the authors caution that sampling methods’ low scores reflect a mismatch between paired-label evaluation and the information those methods access, not an inherent limitation of the methods. The reported comparisons cover three models and four benchmarks; whether the signal carries over to other models or settings remains open.
Original source
cs.AI · Article
Source details
Original source date: May 30, 2026 (2026-05-30).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-09T10:40:22.112Z. First observed: 2026-11-05T05:00:30.043Z. Retrieved: 2026-11-09T10:39:32.538Z.
Limits
The reported evaluation covers three instruction-tuned models and four ; the evidence does not establish whether the findings extend beyond them. The source is an abstract and does not provide the paper’s detailed methods or results beyond the reported summary.
Open question
Does the reported truthfulness signal remain linearly decodable in models and evaluation settings beyond the three models and four tested?
Source-stated
The paper evaluates hidden states from three open-source instruction-tuned models loaded in 4-bit NF4 quantization on four hallucination benchmarks.
Across three $7$B--$8$B instruction-tuned models (Llama-3.1-8B, Mistral-7B, Qwen2.5-7B) loaded in $4$-bit NF4 quantization
Source-stated
A linear probe on one mid-network layer achieved 0.904–1.000 AUROC on held-out splits, while sampling-based detectors did not exceed 0.541 under the same protocol.
A linear probe on a single mid-network layer achieves $0.904$--$1.000$ AUROC on held-out splits, while sampling-based detectors do not exceed $0.541$ AUROC under the same protocol.
Source-stated
The authors report that MLP probes rarely surpassed linear probes by more than 0.01 AUROC.
MLP probes rarely surpass linear probes by more than $0.01$ AUROC.
Source-stated
Peak probing layers fell in different reported bands for Llama and Mistral versus Qwen on natural-language benchmarks.
blocks~$13$--$18$ of~$32$ for Llama and Mistral, and blocks~$19$--$25$ of~$28$ for Qwen
Source-stated
The paper reports first-block attention entropy as a complementary signal on HaluEval-QA, with no additional inference cost.
First-block attention entropy provides a complementary signal in knowledge-grounded settings ($0.866$--$0.941$ AUROC on HaluEval-QA) at no additional inference cost.
Source-stated
The authors attribute sampling-based detectors’ low discriminability under their protocol to a mismatch in evaluation and accessible information, not an inherent limitation of those methods.
reflects a structural mismatch between paired-label evaluation and the information these methods access, rather than an inherent limitation of those methods.
Source and contributor context
Aiersilan, Aizierjiang · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Hallucination
When an AI system states something false or made up as if it were true.
Quantization
Storing a model's numbers at lower precision so it uses less memory and runs faster, sometimes at a cost in accuracy.
Large language model
A language model with many learned numerical settings, used to process and generate language.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: c01aec02-c68d-4d69-80ef-43e08182ff1e.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-09T10:40:22.112Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 209e8062f402b19adeddd2673d9e93542ffa0daa13720e14ddbcf676db90026b. Source text hash: f4749bff57db912a508b0a6ddd0af114d1f2aa8a3ffdf10d8c46965dd1cabde5.
uncertain · 2026-11-09T10:40:22.112Z · anthropic/claude-opus-5.5
Publication: c01aec02-c68d-4d69-80ef-43e08182ff1e
attribution — pass: The paper is correctly attributed to its sole listed author, Aizierjiang Aiersilan, on arXiv. Later references to 'the authors' are a minor plural slip and do not misattribute the claims. Smallest repair: use 'the author'.
Authors:Aizierjiang Aiersilan
summary — uncertain: Central terms that general readers need are not explained: hallucination, hidden states, AUROC, NF4 quantization, MLP probe and attention entropy. The source frames peak layers as a 'consistent band across model families', but the draft stresses 'different' ranges for each model. Smallest repair: say the peaks fell in a consistent mid-to-late band and give the block ranges. It also calls the sampling explanation a 'caution' when it is the author's interpretation.
Peak probing layers fall in a consistent band across model families on natural-language benchmarks
date — pass: originalPublished 2026-05-30 matches the v1 submission date. The November feed date is correctly not used.
[Submitted on 30 May 2026]
support — uncertain: The numerical claims match the abstract. However, the claim that peak layers 'fell in different reported bands' recasts the source's consistency finding. The sentence about 'a possible route to detecting hallucinations from internal activations' is an unlabeled inference. The source states no deployment use. That sentence should be labeled as Escape Meridian interpretation or made explicitly conditional. The limitations section is reasonable.
A linear probe on a single mid-network layer achieves $0.904$--$1.000$ AUROC on held-out splits
media — pass: The selected media is the verified link to the original arXiv abstract page. This is the direct original source.
Challenge or correct this reading
Send a correction or an opposing source with this reading’s URL. Disagreement and uncertainty can remain unresolved.
Storing a model's numbers at lower precision so it uses less memory and runs faster, sometimes at a cost in accuracy.
Definition referenceWhen an AI system states something false or made up as if it were true.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceStoring a model's numbers at lower precision so it uses less memory and runs faster, sometimes at a cost in accuracy.
Definition referenceWhen an AI system states something false or made up as if it were true.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition reference