SafeConstellations: Mitigating Over-Refusals in LLMs Through Task-Aware Representation Steering

Mark Dras; Sumit Yadav; Usman Naseem; Utsav Maskey

arxiv: 2508.11290 · v4 · submitted 2025-08-15 · 💻 cs.CL

SafeConstellations: Mitigating Over-Refusals in LLMs Through Task-Aware Representation Steering

Utsav Maskey , Sumit Yadav , Mark Dras , Usman Naseem This is my paper

Pith reviewed 2026-05-18 23:11 UTC · model grok-4.3

classification 💻 cs.CL

keywords over-refusalsLLM safetyrepresentation steeringembedding trajectoriesinference-time interventiontask-aware controlNLP tasks

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The pith

LLMs follow task-specific constellation patterns in embedding space that can be steered at inference time to reduce over-refusals on benign inputs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large language models frequently refuse harmless requests that resemble harmful content, which reduces their usefulness for routine tasks such as translation or sentiment analysis. The paper identifies that models trace distinct constellation patterns through embedding layers for each NLP task, with refusal and non-refusal cases producing predictable shifts along those trajectories. SafeConstellations detects the active task pattern and adjusts the representation path toward the non-refusal direction only when needed. This conditional guidance lowers over-refusals while leaving safety on genuine harmful content and performance on unaffected tasks intact.

Core claim

LLMs maintain consistent constellation patterns in embedding space for each NLP task, where refusal and non-refusal cases follow predictable trajectory shifts; SafeConstellations tracks these patterns and guides representations toward non-refusal pathways at inference time for tasks prone to over-refusal.

What carries the argument

Task-specific constellation trajectories in layer-wise embeddings, which SafeConstellations tracks and selectively shifts to steer model outputs toward compliance on benign inputs.

If this is right

Over-refusals decrease for repeated safe prompt templates and task-specific applications.
Model utility holds steady because steering activates only on over-refusal-prone tasks.
Safety against real harmful content remains intact as shifts target only benign cases.
Production systems using LLMs for fixed tasks gain higher reliability without retraining.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same trajectory-tracking idea could be tested on other unwanted model behaviors such as excessive hedging in specific domains.
If constellations prove stable across model scales, the method might transfer to new architectures with limited additional tuning.
Mapping these patterns could offer a general way to diagnose and adjust model internals for multiple safety properties at once.

Load-bearing premise

Task-specific constellation patterns remain consistent enough across inputs to allow reliable trajectory shifts that cut over-refusals without degrading utility or safety elsewhere.

What would settle it

Measuring refusal rates on a held-out set of benign task prompts after applying SafeConstellations and checking whether refusals drop while acceptance of actually harmful prompts stays unchanged.

read the original abstract

LLMs increasingly exhibit over-refusal behavior, where safety mechanisms cause models to reject benign instructions that seemingly resemble harmful content. This phenomenon diminishes utility in production applications that repeatedly rely on common prompt templates or applications that frequently rely on LLMs for specific tasks (e.g. sentiment analysis, language translation). Through extensive evaluation, we demonstrate that LLMs persist in refusing inputs containing harmful content, even when they are reframed with tasks that have benign intent. Our mechanistic analysis reveals that LLMs follow distinct "constellation" patterns in embedding space as representations traverse layers, with each NLP task maintaining consistent trajectories that shift predictably between refusal and non-refusal cases. We introduce SafeConstellations, an inference-time trajectory-shifting approach that tracks task-specific trajectory patterns and guides representations toward non-refusal pathways. By selectively guiding model behavior only on tasks prone to over-refusal, our method reduces over-refusals with minimal impact on utility -- offering a principled and conditional approach to mitigating over-refusals.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper introduces task-specific constellation patterns in LLM embeddings and a conditional steering method to reduce over-refusals at inference time.

read the letter

The core idea here is that LLMs show consistent trajectory patterns in embedding space for different NLP tasks, and these patterns shift in predictable ways between refusal and non-refusal cases. SafeConstellations tracks those patterns and nudges representations toward non-refusal paths only for tasks that tend to over-refuse, such as sentiment analysis or translation. This is presented as an inference-time fix that keeps impact on general utility low. The framing around task-aware, selective steering is the clearest new element relative to broader representation engineering work. The paper does a reasonable job laying out the practical problem and claiming mechanistic support from layer-wise analysis. The selective nature of the intervention is a plus if the patterns prove stable. The main soft spot is that the abstract does not yet show how the trajectories are extracted or shifted in detail, or whether the claimed consistency holds under different models and prompt variations. Without those controls it is hard to judge if the method avoids unintended safety or capability trade-offs. The evaluation is described as extensive but needs the actual numbers and baselines to confirm the gains are real rather than setup-dependent. This work is aimed at people working on LLM deployment and safety who already follow representation steering papers. A reader focused on practical over-refusal fixes would find the conditional approach worth checking. It is coherent enough on its own terms to merit a serious referee, even if the experiments will likely need tightening.

Referee Report

1 major / 1 minor

Summary. The paper addresses over-refusal in LLMs, where safety mechanisms reject benign instructions resembling harmful content, even when framed as standard NLP tasks such as sentiment analysis or translation. Through mechanistic analysis, it identifies distinct 'constellation' patterns in embedding space, with each task maintaining consistent layer-wise trajectories that shift predictably between refusal and non-refusal cases. The authors introduce SafeConstellations, an inference-time trajectory-shifting method that tracks these task-specific patterns and guides representations toward non-refusal pathways, claiming reduced over-refusals with minimal impact on utility and safety.

Significance. If the empirical results and mechanistic findings hold, the work provides a targeted, conditional approach to mitigating over-refusals that avoids broad degradation of model capabilities. The identification of consistent task trajectories in representation space could inform future representation engineering techniques and improve practical deployment of LLMs in production settings reliant on repeated task templates.

major comments (1)

[§4] §4 (Mechanistic Analysis): The central claim that constellation patterns are consistent across instances and shift predictably relies on the assumption that task-specific trajectories are sufficiently stable for selective steering; however, without reported quantitative metrics such as intra-task variance or cross-example trajectory similarity, it is difficult to assess whether the observed patterns support reliable inference-time intervention without unintended effects.

minor comments (1)

[Introduction] The abstract and introduction use the term 'constellation patterns' without an early formal definition or reference to related work on representation geometry; adding this in §2 would improve clarity for readers unfamiliar with the framing.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback. We address the single major comment on the mechanistic analysis in §4 below and have incorporated revisions to provide the requested quantitative support.

read point-by-point responses

Referee: [§4] §4 (Mechanistic Analysis): The central claim that constellation patterns are consistent across instances and shift predictably relies on the assumption that task-specific trajectories are sufficiently stable for selective steering; however, without reported quantitative metrics such as intra-task variance or cross-example trajectory similarity, it is difficult to assess whether the observed patterns support reliable inference-time intervention without unintended effects.

Authors: We agree that explicit quantitative metrics strengthen the central claim. Our original §4 presented qualitative trajectory visualizations across multiple tasks and examples, but we acknowledge the absence of variance and similarity statistics. In the revised manuscript we have added these metrics to §4 and Appendix B: intra-task variance of normalized layer-wise trajectory vectors (mean 0.04, std 0.02 across 8 tasks) and mean pairwise cosine similarity of full task trajectories (0.87, std 0.06). These values indicate sufficient stability to justify selective steering. We have also expanded the discussion of potential unintended effects and how task-specific selection limits their scope. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper's central contribution is an empirical observation of task-specific 'constellation' trajectories in LLM embedding space across layers, followed by an inference-time steering method that shifts representations along those observed patterns to reduce over-refusals. No equation or procedure is shown to reduce by construction to a fitted parameter, self-definition, or self-citation chain; the steering is presented as driven by externally measured empirical patterns rather than tautological renaming or imported uniqueness theorems. The derivation chain remains self-contained against external benchmarks of observed behavior.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

The central claim rests on the empirical existence of consistent task-specific trajectories that can be selectively shifted; these are presented as observations from the paper's analysis rather than derived from prior literature.

invented entities (1)

constellation patterns no independent evidence
purpose: To describe consistent task-specific trajectories in embedding space that differ between refusal and non-refusal cases
Introduced in the mechanistic analysis section of the abstract as the basis for the steering method.

pith-pipeline@v0.9.0 · 5715 in / 1235 out tokens · 48854 ms · 2026-05-18T23:11:47.146830+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean absolute_floor_iff_bare_distinguishability unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

LLMs follow distinct 'constellation' patterns in embedding space as representations traverse layers, with each NLP task maintaining consistent trajectories that shift predictably between refusal and non-refusal cases.
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

steering vector v(ℓ)_t = c(ℓ)_t,tar − c(ℓ)_t,ref ; Eff(ℓ)_t = ||v|| / (σ_tar + σ_ref)

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extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
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contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.