Chen, Chao, Shao, Pengyang, Qin, Wei, Hao, Shijie, Shen, Fei, Hong, Richang · Oct 29, 2026
How a paper studies over-refusal in safety-aligned large language models
Markdown · JSON · Original source
The paper’s authors argue that safety-aligned can reject harmless queries because harmful and over-refusal samples become less distinct in deeper layers, while harmful-sample gradients align more with refusal targets than with correct helpful targets. They propose GOAL, a safety-alignment framework that orthogonalizes over-refusal gradients against a low-rank refusal subspace and uses contrastive learning to separate the representations. The authors report that experiments across multiple models and mitigate over-refusal while preserving safety performance and general capabilities. If that result holds in the tested settings, the approach could help reduce mistaken refusals without weakening the safeguards the authors measure. The abstract does not identify those models, benchmarks, or the measures used, so it does not show how broadly the result applies. Which models, benchmarks, and measures support the reported gains, and how do the results vary across them?
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: Oct 29, 2026 (2026-10-29).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-11T12:25:02.471Z. First observed: 2026-10-30T06:42:58.003Z. Retrieved: 2026-10-31T06:01:54.401Z.
Referenced by
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Limits
The supplied evidence is the paper’s abstract; it does not name the models or , give performance measures or results, or provide enough detail to assess how broadly the reported findings apply.
Open question
Which models, , and measures support the reported gains, and how do the results vary across them?
Source-stated
The authors describe over-refusal as rejecting harmless queries that appear superficially risky, and say why it persists under correct safety supervision is not sufficiently understood.
causing harmless but superficially risky queries to be mistakenly rejected
Source-stated
The authors report that harmful and over-refusal sample representations are visually distinguishable at certain intermediate layers but increasingly overlap in deeper layers.
visually distinguishable at certain intermediate layers but increasingly overlap in deeper layers
Source-stated
The authors report that harmful-sample gradients are more strongly aligned with refusal-target gradients than with correct helpful-target gradients on over-refusal prompts.
more strongly aligned with refusal-target gradients than with correct helpful-target gradients on over-refusal prompts
Source-stated
The authors propose GOAL, which orthogonalizes over-refusal gradients against a low-rank refusal subspace extracted from harmful gradients and adds a contrastive learning objective.
we orthogonalize over-refusal gradients against the low-rank refusal subspace extracted from harmful gradients
Source-stated
The authors report that experiments across multiple models and benchmarks show GOAL mitigating over-refusal while preserving safety performance and general capabilities.
mitigates over-refusal while preserving safety performance and general capabilities
Source and contributor context
Chen, Chao, Shao, Pengyang, Qin, Wei, Hao, Shijie, Shen, Fei, Hong, Richang · 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.
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: ed3f32f5-fb75-4dcb-878f-5b3e740b7592.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-11T12:25:02.471Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 61fb0a45f29eecfcc4291cfa23e2a9021800e2bc75fdf6795de98aae5de2bb3c. Source text hash: 78eb406d318db028bfb62f9b624c5025c5765cfbb838e785b94d8bc539fe2015.
Revision and source-check history
Version 1 · 2026-11-11T12:25:02.471Z
How a paper studies over-refusal in safety-aligned large language models
The paper’s authors argue that safety-aligned can reject harmless queries because harmful and over-refusal samples become less distinct in deeper layers, while harmful-sample gradients align more with refusal targets than with correct helpful targets. They propose GOAL, a safety-alignment framework that orthogonalizes over-refusal gradients against a low-rank refusal subspace and uses contrastive learning to separate the representations. The authors report that experiments across multiple models and mitigate over-refusal while preserving safety performance and general capabilities. If that result holds in the tested settings, the approach could help reduce mistaken refusals without weakening the safeguards the authors measure. The abstract does not identify those models, benchmarks, or the measures used, so it does not show how broadly the result applies. Which models, benchmarks, and measures support the reported gains, and how do the results vary across them?
Original source
cs.AI · Article
Source details
Original source date: Oct 29, 2026 (2026-10-29).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-11T12:25:02.471Z. First observed: 2026-10-30T06:42:58.003Z. Retrieved: 2026-10-31T06:01:54.401Z.
Limits
The supplied evidence is the paper’s abstract; it does not name the models or , give performance measures or results, or provide enough detail to assess how broadly the reported findings apply.
Open question
Which models, , and measures support the reported gains, and how do the results vary across them?
Source-stated
The authors describe over-refusal as rejecting harmless queries that appear superficially risky, and say why it persists under correct safety supervision is not sufficiently understood.
causing harmless but superficially risky queries to be mistakenly rejected
Source-stated
The authors report that harmful and over-refusal sample representations are visually distinguishable at certain intermediate layers but increasingly overlap in deeper layers.
visually distinguishable at certain intermediate layers but increasingly overlap in deeper layers
Source-stated
The authors report that harmful-sample gradients are more strongly aligned with refusal-target gradients than with correct helpful-target gradients on over-refusal prompts.
more strongly aligned with refusal-target gradients than with correct helpful-target gradients on over-refusal prompts
Source-stated
The authors propose GOAL, which orthogonalizes over-refusal gradients against a low-rank refusal subspace extracted from harmful gradients and adds a contrastive learning objective.
we orthogonalize over-refusal gradients against the low-rank refusal subspace extracted from harmful gradients
Source-stated
The authors report that experiments across multiple models and benchmarks show GOAL mitigating over-refusal while preserving safety performance and general capabilities.
mitigates over-refusal while preserving safety performance and general capabilities
Source and contributor context
Chen, Chao, Shao, Pengyang, Qin, Wei, Hao, Shijie, Shen, Fei, Hong, Richang · 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.
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: ed3f32f5-fb75-4dcb-878f-5b3e740b7592.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-11T12:25:02.471Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 61fb0a45f29eecfcc4291cfa23e2a9021800e2bc75fdf6795de98aae5de2bb3c. Source text hash: 78eb406d318db028bfb62f9b624c5025c5765cfbb838e785b94d8bc539fe2015.
uncertain · 2026-11-11T12:25:02.471Z · anthropic/claude-opus-5.5
Publication: ed3f32f5-fb75-4dcb-878f-5b3e740b7592
attribution — pass: The reading attributes the analysis, the GOAL method and the experimental claims to the paper's authors. Its claims stay framed as author reports rather than established fact, consistent with the arXiv abstract page.
Authors:Chao Chen, Pengyang Shao, Wei Qin, Shijie Hao, Fei Shen, Richang Hong
summary — uncertain: Central technical terms are not explained in plain language, which blocks understanding of the core mechanism for a reader without specialist background. These include gradients, representations in deeper layers, orthogonalize, low-rank refusal subspace and contrastive learning. Smallest repair: add tooltips or brief plain glosses for these terms. Separately, the first sentence drops the scope condition on over-refusal prompts and states the cause with 'because', whereas the source hedges with 'can bias' and 'may make it more difficult'. Smallest repair: restore that hedging.
This imbalanced gradient coupling can bias over-refusal samples toward refusal, while the reduced apparent representational separation in deeper layers may make it more difficult
date — pass: The original publication date of 2026-10-29 matches the v1 submission date. The feed date of Oct 31 is a later announcement and is correctly not used.
[Submitted on 29 Oct 2026]
support — uncertain: The individual claims are each grounded in verbatim excerpts, and the stated limitations accurately note that the abstract gives no models, benchmarks or measures. However, the summary's causal 'because' framing is firmer than the source, which presents its mechanism as one that can bias outputs and may impair the boundary. The summary also says harmful-sample gradients align with 'refusal targets' and omits that this comparison applies to over-refusal prompts. The conditional benefit sentence is appropriately hedged.
more strongly aligned with refusal-target gradients than with correct helpful-target gradients on over-refusal prompts
media — pass: The selected media is the verified direct link to the arXiv abstract page, which is the original source.
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A language model with many learned numerical settings, used to process and generate language.
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 referenceA language model with many learned numerical settings, used to process and generate language.
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