Framing Matters: Addressing Framing Sensitivity in Decision-Making through Behaviorally-Grounded Value Alignment

Hwanhee Lee; Jeonghyun Park; Junhyuk Choi; Minju Kim; Seojin Hwang

arxiv: 2605.28188 · v1 · pith:NE5UNZRMnew · submitted 2026-05-27 · 💻 cs.CL

Framing Matters: Addressing Framing Sensitivity in Decision-Making through Behaviorally-Grounded Value Alignment

Seojin Hwang , Minju Kim , Junhyuk Choi , JeongHyun Park , Hwanhee Lee This is my paper

Pith reviewed 2026-06-29 12:43 UTC · model grok-4.3

classification 💻 cs.CL

keywords framing sensitivityLLM decision makingvalue alignmentrepresentation engineeringbenchmark evaluationdecision consistencylegal reasoning

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The pith

Valign reduces framing-induced decision flips in LLMs by anchoring hidden states to stable value priors and projecting out sensitivity directions.

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

Large language models change their outputs on factually identical inputs when the framing shifts, which matters in settings like legal reasoning where consistency is required. The paper introduces the Fragile benchmark to isolate three framing dimensions while keeping facts fixed and measures a 28.6 percent average decision flip rate across models. Existing prompt-level and activation-level fixes either leave the problem untouched or make it worse. Valign instead operates inside the model's representations by anchoring to a value-consistent direction and removing temporal-vividness components, producing lower flip rates. The work therefore argues that durable fixes must intervene on the internal pathways that carry framing effects rather than on surface inputs.

Core claim

Fact-preserved inputs that differ only in value-tinted narration, temporal slice, or narrative vividness produce an average 28.6 percent decision flip rate in current LLMs. Prompt-level and activation-level interventions fail to suppress and often amplify this sensitivity. Valign counters the effect by identifying a stable value prior, steering hidden states toward the value-consistent direction, and projecting out temporal-vividness-sensitive directions from those states, thereby lowering framing-induced flips.

What carries the argument

Valign, a representation-level intervention that anchors decisions to a stable value prior extracted from the model, steers hidden states toward the value-consistent direction, and projects out temporal-vividness-sensitive directions.

If this is right

Representation-level steering outperforms surface-level prompts when the goal is decision consistency under framing changes.
The Fragile benchmark supplies a controlled testbed for evaluating other alignment techniques on framing robustness.
High-stakes applications can incorporate Valign-style anchoring to reduce inconsistency without altering input text.
Projecting out specific sensitivity directions in hidden states offers a general route for removing undesired input transformations.

Where Pith is reading between the lines

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

If the value prior extraction step generalizes across domains, the same machinery could be applied to other input sensitivities such as tone or cultural framing.
Legal and medical decision systems might adopt Valign as a post-training module to enforce consistency on equivalent case descriptions.
Future benchmarks could extend Fragile by adding more framing axes while preserving the same fact-isolation logic.

Load-bearing premise

The three controlled dimensions in Fragile isolate framing effects without uncontrolled confounds and a stable value prior can be reliably identified and anchored inside the model's hidden states.

What would settle it

Applying Valign to the same models on Fragile and observing decision flip rates that remain at or above the 28.6 percent baseline would falsify the claim that the method reduces framing sensitivity.

Figures

Figures reproduced from arXiv: 2605.28188 by Hwanhee Lee, Jeonghyun Park, Junhyuk Choi, Minju Kim, Seojin Hwang.

**Figure 2.** Figure 2: Overview of the FRAGILE construction and evaluation framework. ( [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Gap (∆dir) between flip and noflip conditions in hidden states across layers of qwen. Framing Signal Type Example Tokens Mechanism Val. Distributed / Noisy subwords, multilingual tokens, fragmented lexical pieces Distributed Value Space reorganization Temp. Urgency / Outcome immediate, instantly (Mistral) Out, OUT (LLaMA) Present bias via semantic shift Vivid. Ambivalence / Vividness tie, tied (LLaMA) oh, … view at source ↗

**Figure 4.** Figure 4: Overview of VALIGN. The method first extracts a self-report value direction and constructs a temporal– [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Gap between flip and noflip conditions in the logit-lens signal across layers of all models in various [PITH_FULL_IMAGE:figures/full_fig_p020_5.png] view at source ↗

**Figure 6.** Figure 6: Framing and paraphrase flip rates across mod [PITH_FULL_IMAGE:figures/full_fig_p020_6.png] view at source ↗

**Figure 7.** Figure 7: traces the incremental build-up of VALIGN components on LLaMA-3.1-8B-Instruct, stepping from Base through each added intervention: system prompt only, system prompt with value anchor (Sys.+ValAnchor), and the full VALIGN pipeline. Adding the system prompt alone already reduces Flip% across all three framing types relative to Base Sys. Prompt Sys+ ValAnch Ours 0% 10% 20% 30% 40% 50% 60% Flip Rate (%) Flip%… view at source ↗

read the original abstract

Large Language Models (LLMs) are increasingly deployed in high-stakes decision-making settings such as legal reasoning, where consistency under factually equivalent inputs is critical. However, we find that fact-preserved but differently framed inputs can significantly destabilize LLM decisions. To systematically investigate this problem, we introduce Fragile, a large-scale benchmark that isolates fact-preserving semantic framing across three controlled dimensions: value-tinted narration, temporal slice, and narrative vividness. Our experiments reveal a high susceptibility of LLMs to framing, with an average decision flip rate of 28.6%. We find that simple prior prompt-level and activation-level interventions not only fail to suppress framing sensitivity but actively amplify it. We therefore propose Valign, a representation-level method that explicitly targets these framing dimensions by anchoring decisions to a stable value prior, steering hidden states toward the model's value-consistent direction, and projecting out temporal-vividness-sensitive directions from the model's hidden states. Valign consistently reduces framing-induced decision flips, demonstrating that robust mitigation requires directly targeting the internal pathways in which framing operates.

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 Fragile, a benchmark isolating three framing dimensions, and Valign, a hidden-state intervention that reduces decision flips where prompt and activation methods do not.

read the letter

The main takeaway is that this work identifies a concrete consistency problem in LLMs for decision tasks and shows that surface-level fixes fall short. Fragile constructs fact-preserving variants along value-tinted narration, temporal slice, and narrative vividness, yielding a 28.6% average flip rate. Valign then anchors hidden states to a value prior, steers toward consistent directions, and projects out sensitive components; the abstract reports it lowers flips while the other interventions increase them.

What stands out is the move to representation-level control rather than another prompt template. The differential outcome gives some evidence that framing effects live in the internal activations, which aligns with broader work on steering vectors. The benchmark construction itself looks like a useful addition if the three dimensions are cleanly isolated.

The soft spots sit in the missing experimental backbone. No numbers appear on dataset size, model count, variance across runs, or statistical tests. It is unclear how the value prior is extracted or validated as stable, or how the sensitive directions are identified without circularity. If those steps depend on the same data used for evaluation, the reductions could be overstated. The claim that internal targeting is required follows from the reported pattern, but it rests on the unshown controls.

This is for people working on LLM robustness in applied settings such as legal or medical reasoning. A reader already thinking about representation engineering will find the setup relevant. The idea is coherent enough on its own terms to warrant referee time, provided the full experiments address the gaps in controls and reproducibility.

Referee Report

2 major / 2 minor

Summary. The manuscript claims that LLMs show high susceptibility to fact-preserving but differently framed inputs in decision-making (average 28.6% decision flip rate), introduces the Fragile benchmark isolating three dimensions (value-tinted narration, temporal slice, narrative vividness), shows that prompt-level and activation-level interventions fail or amplify sensitivity, and proposes Valign—a representation-level method that anchors decisions to a stable value prior, steers hidden states toward value-consistent directions, and projects out temporal-vividness-sensitive directions—which consistently reduces flips, implying that robust mitigation requires targeting internal pathways.

Significance. If the results hold under proper controls and statistics, the work is significant for highlighting framing sensitivity as a practical barrier to reliable LLM use in high-stakes domains such as legal reasoning. The Fragile benchmark offers a controlled testbed for studying framing, the differential performance of Valign versus surface interventions provides evidence for the internal-pathway claim, and the behaviorally-grounded approach to value alignment adds a concrete technique that could improve decision consistency.

major comments (2)

[Experimental Results] Experimental Results section: the central quantitative claims (28.6% baseline flip rate and consistent reductions under Valign) are presented without reported statistical tests, confidence intervals, number of models or instances, or explicit baseline comparisons, which is load-bearing for verifying that Valign outperforms the interventions that "actively amplify" sensitivity.
[§3] §3 (Valign method): the procedures for identifying the "stable value prior," extracting "value-consistent direction," and computing the "temporal-vividness-sensitive directions" for projection are described at a high level but lack the concrete extraction algorithm, hyper-parameters, or pseudocode needed to reproduce the steering and projection steps that are asserted to drive the reported improvement.

minor comments (2)

[Abstract] The abstract states "large-scale benchmark" without quantifying the number of examples or models; adding these figures would improve immediate readability.
[§2] Notation for the three Fragile dimensions is introduced in the abstract but should be cross-referenced to the exact benchmark construction equations or tables in §2 for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments below and will revise the manuscript to strengthen the presentation of results and reproducibility of the method.

read point-by-point responses

Referee: [Experimental Results] Experimental Results section: the central quantitative claims (28.6% baseline flip rate and consistent reductions under Valign) are presented without reported statistical tests, confidence intervals, number of models or instances, or explicit baseline comparisons, which is load-bearing for verifying that Valign outperforms the interventions that "actively amplify" sensitivity.

Authors: We agree that the absence of statistical tests, confidence intervals, and explicit details on the experimental scale weakens the central claims. In the revision we will add paired statistical tests (e.g., McNemar or t-tests across instances) with p-values, 95% bootstrap confidence intervals for all flip-rate differences, the exact number of models and total instances evaluated in Fragile, and side-by-side tables comparing Valign against every prompt-level and activation-level baseline, including those that increased sensitivity. These additions will allow direct verification that the reported reductions are reliable and not due to chance or selective reporting. revision: yes
Referee: [§3] §3 (Valign method): the procedures for identifying the "stable value prior," extracting "value-consistent direction," and computing the "temporal-vividness-sensitive directions" for projection are described at a high level but lack the concrete extraction algorithm, hyper-parameters, or pseudocode needed to reproduce the steering and projection steps that are asserted to drive the reported improvement.

Authors: We acknowledge that the current description of Valign is insufficient for reproduction. In the revised §3 we will supply the precise extraction procedures (including the dataset and contrastive activation collection used to identify the stable value prior), the linear-algebra steps for obtaining the value-consistent direction and the temporal-vividness-sensitive subspace, all hyper-parameters (layer indices, projection strength, steering coefficient, etc.), and full pseudocode for both the steering and projection operations. This will make the internal-pathway intervention fully reproducible. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical benchmark and intervention results are self-contained

full rationale

The paper introduces the Fragile benchmark to isolate framing dimensions and reports experimental outcomes showing baseline susceptibility and differential performance of Valign versus prompt/activation interventions. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the abstract or described method; the central claim follows directly from reported decision-flip rates on the constructed benchmark rather than reducing to prior self-referential definitions or inputs by construction. This is a standard empirical ML study with independent experimental content.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based on abstract only; the approach assumes LLMs maintain identifiable value-consistent directions in hidden states and that framing effects can be isolated to specific directions without affecting core reasoning.

axioms (1)

domain assumption Hidden states in LLMs contain separable directions corresponding to value consistency, temporal focus, and narrative vividness.
Valign relies on steering toward value-consistent directions and projecting out temporal-vividness directions.

pith-pipeline@v0.9.1-grok · 5728 in / 1188 out tokens · 38900 ms · 2026-06-29T12:43:50.450722+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

8 extracted references · 3 canonical work pages · 1 internal anchor

[1]

Language models are alignable decision- makers: Dataset and application to the medical triage domain. InProceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies (Volume 6: Industry Track), pages 213–227. Yerin Hwang, Dongryeol Lee, Taegwan Kang, Minwoo Lee, and Kyomin ...

work page arXiv 2024
[2]

Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al

Wildframe: Comparing framing in humans and llms on naturally occurring texts.arXiv preprint arXiv:2502.17091. Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al. 2024. Deepseek-v3 technical report.arXiv preprint arXiv:2412.19437. Yao Lu, Max Bartolo, Alastair Moore, Sebastian ...

work page arXiv 2024
[3]

Towards Understanding Sycophancy in Language Models

Evaluating the moral beliefs encoded in llms. Advances in Neural Information Processing Systems, 36:51778–51809. Bertram Scheufele. 2022. Framing: Toward clarifica- tion of a fractured paradigm: von robert m. entman (1993). InSchlüsselwerke: Theorien (in) der Kom- munikationswissenschaft, pages 115–127. Springer. Shalom H Schwartz. 1992. Universals in the...

work page internal anchor Pith review Pith/arXiv arXiv 2022
[4]

Section B describes benchmark construction, prompt generation, quality filtering, human- validation checks, and examples of our framed data
[5]

Section C reports framing-sensitivity trajecto- ries, layer-wise mechanistic analyses, and statis- tical significance tests
[6]

Section D consolidates mitigation baselines, Valign ablations, layer selection, and full miti- gation tables
[7]

B Benchmark Details B.1 Detailed Dataset Construction B.1.1 Schwartz’s Basic Human Values Schwartz’s Basic Human Values theory (Schwartz,

Section E provides robustness and extension studies across multi-framing, additional models, larger models, ternary decisions, and accuracy- level effects. B Benchmark Details B.1 Detailed Dataset Construction B.1.1 Schwartz’s Basic Human Values Schwartz’s Basic Human Values theory (Schwartz,
[8]

choose this option

proposes ten motivational values that capture the range of goals individuals consider important when making decisions. Each value is defined by a central motivational goal and a set of associated attributes, as summarized in Table 7. B.1.2 Domain Sources Moral Dilemma.We construct moral sce- narios using GGB (Jiang et al., 2021) and UNIBENCH(Kumar and Jur...

2021

[1] [1]

Language models are alignable decision- makers: Dataset and application to the medical triage domain. InProceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies (Volume 6: Industry Track), pages 213–227. Yerin Hwang, Dongryeol Lee, Taegwan Kang, Minwoo Lee, and Kyomin ...

work page arXiv 2024

[2] [2]

Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al

Wildframe: Comparing framing in humans and llms on naturally occurring texts.arXiv preprint arXiv:2502.17091. Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al. 2024. Deepseek-v3 technical report.arXiv preprint arXiv:2412.19437. Yao Lu, Max Bartolo, Alastair Moore, Sebastian ...

work page arXiv 2024

[3] [3]

Towards Understanding Sycophancy in Language Models

Evaluating the moral beliefs encoded in llms. Advances in Neural Information Processing Systems, 36:51778–51809. Bertram Scheufele. 2022. Framing: Toward clarifica- tion of a fractured paradigm: von robert m. entman (1993). InSchlüsselwerke: Theorien (in) der Kom- munikationswissenschaft, pages 115–127. Springer. Shalom H Schwartz. 1992. Universals in the...

work page internal anchor Pith review Pith/arXiv arXiv 2022

[4] [4]

Section B describes benchmark construction, prompt generation, quality filtering, human- validation checks, and examples of our framed data

[5] [5]

Section C reports framing-sensitivity trajecto- ries, layer-wise mechanistic analyses, and statis- tical significance tests

[6] [6]

Section D consolidates mitigation baselines, Valign ablations, layer selection, and full miti- gation tables

[7] [7]

B Benchmark Details B.1 Detailed Dataset Construction B.1.1 Schwartz’s Basic Human Values Schwartz’s Basic Human Values theory (Schwartz,

Section E provides robustness and extension studies across multi-framing, additional models, larger models, ternary decisions, and accuracy- level effects. B Benchmark Details B.1 Detailed Dataset Construction B.1.1 Schwartz’s Basic Human Values Schwartz’s Basic Human Values theory (Schwartz,

[8] [8]

choose this option

proposes ten motivational values that capture the range of goals individuals consider important when making decisions. Each value is defined by a central motivational goal and a set of associated attributes, as summarized in Table 7. B.1.2 Domain Sources Moral Dilemma.We construct moral sce- narios using GGB (Jiang et al., 2021) and UNIBENCH(Kumar and Jur...

2021