Values as Style: Disentangling Values from Semantics with One-Way Mixing for Low-Damage LLM Steering

Jiale Dai; Hongcan Deng; Liuxian Ma; Xiaoke Niu; Guojie Song

cs.AISep 30, 2026

Values as Style: Disentangling Values from Semantics with One-Way Mixing for Low-Damage LLM Steering

Authors: Jiale Dai, Hongcan Deng, Liuxian Ma, Xiaoke Niu, Guojie Song

Organizations: State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University · University of Chinese Academy of Sciences · College of Artificial Intelligence, Tsinghua University

Abstract

Value steering should change an LLM's normative priorities while preserving the scenario, facts, and task constraints underlying its answer. Conventional activation edits often change both. We introduce an editable semantic-value interface on frozen residual states, with a one-way semantic-to-value pathway that grounds value recognition in context. Stop-gradient blocks feedback through this pathway; swap consistency, topic de-confounding, and decorrelation encourage selective codes. At inference, editing the value code produces a residual delta while holding the semantic code fixed. On two instruction-tuned backbones, this interface improves semantic preservation and reduces benign refusals at comparable value alignment. A matched mixing-by-gating ablation separates representation learning from selective edit activation, and dimension-matched probes establish improved code selectivity. Against validation-selected prompting on LLaMA-3.1-8B, the method achieves comparable alignment (0.750 vs. 0.748), higher BERTScore (0.938 vs. 0.923), and fewer contradictions (5.1% vs. 7.6%). Human ratings and cross-taxonomy controls provide complementary evidence for low-damage value steering.

Figures & tables

Figure 1 : Overview of our semantic–value disentanglement framework for controllable LLM intervention. Hidden representations from a frozen LLM are decomposed into a semantic code and a value code. A one-way semantic-to-value mixing mechanism allows value representations to leverage semantic grounding while blocking reverse gradients through the mixing path. The disentangled value subspace can then be edited at inference time to steer generation with lower semantic damage.

Figure 2 : A grounded value interface with separate training and editing paths. The frozen state

h

feeds both encoders. The central bridge expands Eq. 1 :

\mathrm{sg}(z_{s})

feeds projector

M

and mixing gate

g

, whose product augments the value code. Stop-gradient blocks reverse differentiation through this bridge. At inference, shared

D

compares target and original value codes with

z_{s}

fixed. GatedNSI optionally projects and activates the resulting update; its inference gate

a(x)

is distinct from

g

. Without GatedNSI,

\Delta h=\Delta h_{v}

. The lower band groups the training objectives.

Table 1 : Value measurement and representation selectivity. (a) ValueBench measurement results. Text-only judges are compared to latent readouts and a raw-state linear probe control. We report Accuracy / Macro-F1 for Relatedness and Stance. (b) Leakage probes: predicting value/topic from

z_{s}

and

z_{v}

. Lower off-diagonal accuracy indicates better disentanglement.

Method	$\alpha^{\star}$	Align $\uparrow$	SemSim $\uparrow$	FRR $\downarrow$
Original (no edit)	0.00	0.290 $\pm$ 0.020	—	0.020 $\pm$ 0.004
LinearAdd	1.60	0.770 $\pm$ 0.010	0.792 $\pm$ 0.010	0.119 $\pm$ 0.012
RepE	1.60	0.705 $\pm$ 0.018	0.811 $\pm$ 0.009	0.091 $\pm$ 0.010
One-way + NSI	1.40	0.720 $\pm$ 0.012	0.842 $\pm$ 0.008	0.074 $\pm$ 0.008
No mixing + NSI	1.40	0.720 $\pm$ 0.010	0.832 $\pm$ 0.008	0.082 $\pm$ 0.007
Two-way mixing	1.40	0.735 $\pm$ 0.011	0.819 $\pm$ 0.009	0.088 $\pm$ 0.009

Table 2 : Value transfer on SVQ-Test and held-out prompts, using 630 training quadruples (LLaMA-3.1-8B-Instruct).

\alpha^{\star}

is selected by Section 4.6 . Mean

\pm

std over three seeds; additional fluency, capability, and benign-similarity metrics are in Table 5 .

Figure 3 : Mixing and edit gating make complementary contributions. Mean results of the matched

2\times 2

study across LLaMA-3.1-8B and Qwen2.5-7B (three seeds per backbone). Solid bars use NSI; hatched bars additionally activate edits selectively (GatedNSI); dots show individual seeded runs. The numerical scale is shared within each panel; the semantic-similarity axis is cropped to expose the operating-point differences.

Backbone	Method	Align $\uparrow$	BERT $\uparrow$	NLI $\downarrow$	Entity $\uparrow$	FRR $\downarrow$
LLaMA-3.1-8B	Prompt P2	0.748	0.923	0.076	0.887	0.060
	Full interface	0.750	0.938	0.051	0.917	0.043
Qwen2.5-7B	Prompt P2	0.735	0.920	0.081	0.878	0.063
	Full interface	0.738	0.934	0.055	0.909	0.047

Table 3 : Value editing versus direct prompting. Three-seed means at validation-selected operating points. NLI is the contradiction rate; FRR is the standard benign refusal rate. Complete prompt families and additional metrics are in Appendix G .

Variant	Align $\uparrow$	SemSim $\uparrow$	Topic $\leftarrow z_{v}$ $\downarrow$	Value $\leftarrow z_{s}$ $\downarrow$	FRR $\downarrow$
Full (one-way + GatedNSI)	0.750 $\pm$ 0.010	0.873 $\pm$ 0.007	0.19 $\pm$ 0.01	0.21 $\pm$ 0.01	0.043 $\pm$ 0.006
No mixing + GatedNSI	0.723 $\pm$ 0.012	0.860 $\pm$ 0.008	0.23 $\pm$ 0.01	0.25 $\pm$ 0.01	0.055 $\pm$ 0.006
Two-way mixing	0.735 $\pm$ 0.011	0.819 $\pm$ 0.009	0.24 $\pm$ 0.01	0.33 $\pm$ 0.02	0.088 $\pm$ 0.009
w/o adversarial de-confounding	0.748 $\pm$ 0.012	0.847 $\pm$ 0.009	0.31 $\pm$ 0.02	0.22 $\pm$ 0.01	0.073 $\pm$ 0.008
w/o orthogonality regularizer	0.742 $\pm$ 0.011	0.852 $\pm$ 0.010	0.22 $\pm$ 0.01	0.29 $\pm$ 0.02	0.058 $\pm$ 0.007
w/o swap consistency losses	0.731 $\pm$ 0.013	0.840 $\pm$ 0.010	0.27 $\pm$ 0.02	0.28 $\pm$ 0.02	0.066 $\pm$ 0.008

Table 4 : Ablations with 630 training quadruples on LLaMA-3.1-8B-Instruct (

\alpha^{\star}

selected by Section 4.6 ). Topic

\leftarrow z_{v}

and Value

\leftarrow z_{s}

quantify leakage.

Appendix figures & tables27 assets

Supplementary material from the paper’s appendix.

Appendix

Method	$\Delta$ PPL $\downarrow$	$\Delta$ MMLU $\downarrow$	Benign SemSim $\uparrow$
LinearAdd	0.88 $\pm$ 0.06	1.62 $\pm$ 0.10	0.801 $\pm$ 0.008
RepE	0.66 $\pm$ 0.05	1.30 $\pm$ 0.09	0.816 $\pm$ 0.007
One-way + NSI	0.49 $\pm$ 0.04	1.05 $\pm$ 0.08	0.858 $\pm$ 0.007
No mixing + NSI	0.55 $\pm$ 0.04	1.12 $\pm$ 0.07	0.842 $\pm$ 0.007
Two-way mixing	0.58 $\pm$ 0.05	1.17 $\pm$ 0.09	0.830 $\pm$ 0.008
Ours (one-way + GatedNSI)	0.41 $\pm$ 0.03	0.86 $\pm$ 0.06	0.889 $\pm$ 0.006

Appendix

Table 5 : Additional fluency, capability, and benign-semantic preservation metrics at the operating points in Table 2 (LLaMA-3.1-8B-Instruct; 630 training quadruples; mean

\pm

std over three seeds). Alignment, semantic similarity, FRR and

\alpha^{\star}

appear in the main table.

Shift	Method	ALIGN $\uparrow$	SEMSIM $\uparrow$	FRR $\downarrow$	Topic $\leftarrow z_{v}$ $\downarrow$
In-domain	Ours	0.750 $\pm$ 0.010	0.873 $\pm$ 0.007	0.043 $\pm$ 0.006	0.19 $\pm$ 0.01
OOD topic	Ours	0.728 $\pm$ 0.013	0.865 $\pm$ 0.008	0.051 $\pm$ 0.007	0.21 $\pm$ 0.01
OOD style	Ours	0.734 $\pm$ 0.012	0.861 $\pm$ 0.009	0.056 $\pm$ 0.008	0.22 $\pm$ 0.01
Adversarial phrasing	Ours	0.709 $\pm$ 0.014	0.857 $\pm$ 0.010	0.064 $\pm$ 0.008	0.25 $\pm$ 0.02
In-domain	LinearAdd	0.770 $\pm$ 0.010	0.792 $\pm$ 0.010	0.119 $\pm$ 0.012	0.36 $\pm$ 0.02
OOD topic	LinearAdd	0.752 $\pm$ 0.012	0.781 $\pm$ 0.011	0.141 $\pm$ 0.014	0.39 $\pm$ 0.02

Appendix

Table 6 : Robustness on LLaMA-3.1-8B-Instruct.

Panel of Figure 4 — Figure 4 : Two views of the steering–damage trade-off.

Edit	Prompt	Before (unedited)	After (edited)
Security $\rightarrow$ Achievement	Should the government raise taxes on high earners to fund social programs?	Raising taxes can strengthen the social safety net and ensure vulnerable groups receive support; policy should prioritize stability and broad protections.	Tax policy should preserve incentives for productivity and innovation while funding programs efficiently; it is important to reward effort and enable upward mobility.
Benevolence $\rightarrow$ Power	Is it acceptable to use aggressive marketing tactics to beat competitors?	Long-term trust matters: tactics that mislead or exploit people can harm customers and relationships; competition should remain fair and respectful.	In competitive markets, assertive tactics can be justified if they secure strategic advantage and market position; the priority is to win mindshare and outperform rivals.

Appendix

Table 7 : Qualitative examples of inference-time value edits (LLaMA-3.1-8B-Instruct). Edits are applied once at the last prompt token, then the whole completion is regenerated.

Method	ALIGN $\uparrow$	SEMSIM $\uparrow$	FRR $\downarrow$	$\Delta$ PPL $\downarrow$
SWAI	0.737	0.838	0.066	0.49
SAE-steering	0.758	0.832	0.071	0.62
YaPO	0.752	0.847	0.061	0.54
Circuit-breaker rerouting	0.731	0.856	0.049	0.46
Ours	0.750	0.873	0.043	0.41

Appendix

Table 8 : Additional learned-space baselines under the same intervention protocol as the main experiments.

Method	BERT $\uparrow$	NLI $\downarrow$	Entity $\uparrow$	Constraint $\uparrow$	FRR $\downarrow$	Hard FRR $\downarrow$
LinearAdd	0.876	15.2%	0.746	0.698	0.119	0.284
RepE	0.887	13.8%	0.779	0.752	0.091	—
NSI	0.908	9.7%	0.848	0.817	0.074	0.183
SAE-steering	0.902	10.3%	0.833	0.795	0.071	0.162
Ours	0.938	5.1%	0.917	0.896	0.043	0.081

Appendix

Table 9 : Semantic-fidelity controls: BERTScore, NLI contradiction, entity recall, constraint retention, and refusal rates on standard and hard-benign prompts.

Train setting	ALIGN $\uparrow$	SEMSIM $\uparrow$	FRR $\downarrow$	Topic $\leftarrow z_{v}$ $\downarrow$	Value $\leftarrow z_{s}$ $\downarrow$
100 quadruples	0.678	0.837	0.067	0.245	0.262
300 quadruples	0.718	0.859	0.052	0.208	0.231
630 (core configuration)	0.750	0.873	0.043	0.190	0.210
630 + 10% noisy anti-value	0.741	0.864	0.047	0.198	0.223
630 + 20% noisy anti-value	0.708	0.842	0.056	0.227	0.254
630 + paraphrase corruption	0.723	0.851	0.061	0.218	0.239

Appendix

Table 10 : Scaling and supervision-noise study for the semantic–value interface.

Variant	ALIGN $\uparrow$	SEMSIM $\uparrow$	FRR $\downarrow$	Topic $\leftarrow z_{v}$ $\downarrow$
Full $D$ (default)	0.750	0.873	0.043	0.190
Smaller $D$	0.738	0.862	0.046	0.197
Linear $D$	0.721	0.848	0.054	0.228
No-delta edit	0.762	0.778	0.123	0.190

Appendix

Table 11 : Sensitivity to recomposer capacity and direct-replacement editing.

Symbol	Meaning
$x$	input prompt (instruction + context)
$h_{\ell,t}(x)\in\mathbb{R}^{d}$	frozen residual-stream state at layer $\ell$ and token $t$
$\mathcal{Q}=(x^{+},x^{p+},x^{-},x^{p-})$	semantic-value quadruple (same scenario, opposite values, with paraphrases)
$s,\ v,\ \bar{v}$	scenario/topic label, value label, and its contrast
$E_{s}(\cdot),E_{v}(\cdot)$	semantic/value encoders (lightweight MLPs)
$z_{s}\in\mathbb{R}^{d_{s}}$	semantic code

Appendix

Table 12 : Notation for the semantic–value interface.

Figure 5 : Constructing a semantic–value quadruple. The tax-policy cards are illustrative: Security and Achievement form two contrasting value rows, each with an original statement and a same-stance paraphrase. The same scenario anchors all four statements; horizontal pairs are

(x^{+},x^{p+})

and

(x^{-},x^{p-})

. A candidate enters the 10K collection only when all quality checks pass. Human validation of that collection is reported separately.

Scenario	Your lifeboat can only hold 5 people, but there are 7 survivors in the water.
Target Value	Power
Anti-Value	Universalism
$x^{+}$ (Pro-Target)	I must take charge and decide who boards the lifeboat. Leadership is necessary to ensure order and reinforce my authority in this dire situation.
$x^{p+}$	In this critical moment, I must determine who gets a place in the lifeboat. Maintaining control and asserting my position are crucial to impose order amid this chaos.
$x^{-}$ (Pro-Anti)	Everyone’s life matters equally, and our choice must reflect fairness and compassion. I will not impose dominance; we should decide together as equals.
$x^{p-}$	Every person’s existence is of equal worth, and our decision must uphold justice and kindness. I refuse to assert control, as we ought to make this choice collectively and with mutual respect.

Appendix

Table 18

Scenario	Your spouse wants to use your savings for a risky business venture you don’t believe in.
Target Value	Security
Anti-Value	Stimulation
$x^{+}$ (Pro-Target)	I can’t risk our savings on something so uncertain. We’ve worked hard to create stability, and jeopardizing that now feels reckless and unsafe.
$x^{p+}$	Putting our hard-earned savings into something so unpredictable doesn’t sit right with me. We’ve put in too much effort to build a solid foundation, and it feels reckless to gamble with it now.
$x^{-}$ (Pro-Anti)	Life is about taking bold chances, not clinging to the illusion of safety. This venture could lead to thrilling new opportunities we can’t afford to miss.
$x^{p-}$	Life isn’t meant to be spent wrapped in false security—it thrives on bold moves. This endeavor might expose us to exhilarating possibilities we shouldn’t let slip away.

Appendix

Table 19

Metric	Mean	Var	Spearman $\rho$	Kendall $\tau_{b}$	Krippendorff’s $\alpha$
Scenario Relevance	4.4774	0.6880	0.7710	0.7414	0.8267
Target Alignment	4.6374	0.3178	0.8813	0.8782	0.8951
Semantic Equivalence	4.9289	0.0749	0.8021	0.8020	0.8320

Appendix

Table 13 : Human evaluation summary. For Target Alignment, scores for

x^{-}

and

x^{p-}

are transformed via

f(s)=6-s

to align with the target directionality before pooling.

Figure 6 : Overview of the specialized human evaluation interface. This tool manages the evaluation of Scenario Relevance, Target Alignment, and Semantic Equivalence for the SVQ dataset.

Method	Align (Likert) $\uparrow$	SemPres (Likert) $\uparrow$	Unhelpful $\downarrow$
Ours (one-way)	4.12 $\pm$ 0.64	4.24 $\pm$ 0.55	1.23 $\pm$ 0.48
LinearAdd	4.25 $\pm$ 0.60	3.76 $\pm$ 0.72	1.71 $\pm$ 0.66

Appendix

Table 14 : Human evaluation on edited outputs (mean

\pm

std across prompts). Inter-annotator Krippendorff’s

\alpha

: 0.56 (Align), 0.52 (SemPres), 0.62 (Unhelpful).

	LLaMA-3.1-8B					Qwen2.5-7B
Token	$\ell$	Align $\uparrow$	SemSim $\uparrow$	FRR $\downarrow$	$\Delta$ MMLU $\downarrow$	$\ell$	Align $\uparrow$	SemSim $\uparrow$	FRR $\downarrow$	$\Delta$ MMLU $\downarrow$
last	8	0.702	0.832	0.074	1.18	6	0.691	0.828	0.072	1.20
	12	0.719	0.846	0.062	1.06	10	0.708	0.842	0.062	1.06
	16	0.737	0.861	0.052	0.94	14	0.725	0.857	0.054	0.97
	20	0.750	0.873	0.043	0.86	18	0.738	0.868	0.047	0.90
	24	0.742	0.859	0.053	0.96	22	0.731	0.855	0.056	0.99

Appendix

Table 15 : Layer/token intervention-site sweep, with backbones shown side by side. Each block reports its actual layer

\ell

, with operating points selected on validation data (Section 4.6 );

\Delta

MMLU is the drop in points relative to the unedited backbone.

Figure 7 : Heatmap view of the intervention-site sweep on LLaMA-3.1-8B-Instruct. Color encodes Align ; each cell annotates SemSim (S) and FRR (F) at the selected $\alpha^{\star}$ . The outlined cell marks the default site used in the main experiments.

Panel of Figure 8 — Figure 8 : Gate statistics across prompts.

Panel of Figure 9 — Figure 9 : Reconstruction analysis results.

Method	Direction or target	Fitting and operator
LinearAdd	Class-mean value contrast in residual space	Direction estimated on training data; residual addition.
RepE	Residual-space contrast direction	Direction estimated on training data; residual editing.
NSI	Value-code prototype delta	Recompose the delta and project off a PCA semantic basis.
GatedNSI	Same projected delta as NSI	Activate the intervention with a value-relatedness detector on $z_{v}$ .
No mixing / CDE	Value-code prototype delta	Same dual-code interface and optimizer; disable the semantic-to-value mixing path.
Full interface	Value-code prototype delta	One-way mixing during representation learning; GatedNSI at inference.

Appendix

Table 16 : Baseline definitions for the controlled comparisons. CDE and “no mixing” refer to the same training interface; the accompanying NSI or GatedNSI label identifies the inference operator.

		LLaMA-3.1-8B			Qwen2.5-7B
Interface	Operator	Align $\uparrow$	SemSim $\uparrow$	FRR $\downarrow$	Align $\uparrow$	SemSim $\uparrow$	FRR $\downarrow$
No mixing	NSI	.720	.832	.082	.707	.829	.087
No mixing	GatedNSI	.723	.860	.055	.714	.851	.061
One-way	NSI	.720	.842	.074	.715	.839	.078
One-way	GatedNSI	.750	.873	.043	.738	.868	.047

Appendix

Table 17 : Mixing

\times

inference-gating factorial. Entries are three-seed means for each backbone. The full configuration combines the one-way interface with GatedNSI.

	LLaMA-3.1-8B				Qwen2.5-7B
	$z_{s}$ readout		$z_{v}$ readout		$z_{s}$ readout		$z_{v}$ readout
Representation	V $\downarrow$	T $\uparrow$	V $\uparrow$	T $\downarrow$	V $\downarrow$	T $\uparrow$	V $\uparrow$	T $\downarrow$
Random orthogonal	.603	.579	.436	.421	.596	.570	.429	.414
PCA	.672	.648	.487	.468	.665	.639	.479	.460
Reconstruction-only	.481	.597	.449	.513	.473	.586	.442	.505
Value-supervised	.392	.618	.796	.319	.401	.609	.787	.325

Appendix

Table 18 : Matched split controls, paired across backbones. Each code is probed for value (V) and topic (T). The off-diagonal columns measure cross-code predictability. Empirical V/T chance accuracies are .102/.127 on LLaMA and .101/.126 on Qwen.

Method	Align $\uparrow$	SemSim $\uparrow$	BERTScore $\uparrow$	NLI $\downarrow$	FRR $\downarrow$
LLaMA-3.1-8B
P0: target name	.671	.828	.911	.109	.082
P1: definition	.716	.839	.919	.090	.069
P2: selected prompt	.748	.846	.923	.076	.060
P3: few-shot	.763	.837	.916	.085	.069
Full edit	.750	.873	.938	.051	.043

Appendix

Table 19 : Prompting versus representation editing on SVQ-Test and held-out prompts. Entries are three-seed means. NLI is the contradiction rate; BERTScore is F1. P2 and the full edit attain comparable alignment.

Method	Entity recall $\uparrow$	Constraints $\uparrow$	Hard FRR $\downarrow$	Latency (s)
LLaMA-3.1-8B
P0: target name	.842	.803	.170	1.89
P1: definition	.866	.828	.143	1.95
P2: selected prompt	.887	.854	.125	2.02
P3: few-shot	.872	.842	.148	2.14
Full edit	.917	.896	.081	2.04

Appendix

Table 20 : Preservation, hard-benign refusals, and response latency for the same prompt comparisons. “Constraints” denotes constraint retention. The internal edit adds zero prompt tokens.

Evaluator	No mixing + gate	Full	Paired difference [95% CI]
GPT-4o	.725	.754	$+.029\ [+.014,+.044]$
Kaleido	.723	.748	$+.025\ [+.010,+.040]$
ValueLlama	.722	.746	$+.024\ [+.009,+.039]$

Appendix

Table 21 : Per-evaluator alignment with the inference gate held fixed. Intervals quantify the paired difference between the full and no-mixing interfaces.

	Alignment $\uparrow$		Preservation $\uparrow$		Unhelpfulness $\downarrow$
Method	Mean [95% CI]	$\alpha$	Mean [95% CI]	$\alpha$	Mean [95% CI]	$\alpha$
Full	$4.10$ $[4.00,4.20]$	.61	$\mathbf{4.27}$ $[4.15,4.39]$	.55	$\mathbf{1.20}$ $[1.09,1.31]$	.68
No mixing + gate	$4.02$ $[3.89,4.15]$	.53	$4.12$ $[4.00,4.24]$	.49	$1.34$ $[1.20,1.48]$	.58
P2 prompt	$4.09$ $[3.96,4.22]$	.57	$3.99$ $[3.84,4.14]$	.46	$1.42$ $[1.27,1.57]$	.60
LinearAdd	$4.21$ $[4.07,4.35]$	.44	$3.72$ $[3.54,3.90]$	.42	$1.73$ $[1.57,1.89]$	.51

Appendix

Table 22 : Blinded 320-item output comparison: ratings and method-specific inter-rater agreement. Each outcome reports its mean [95% BCa interval] beside Krippendorff’s

\alpha

. Intervals quantify uncertainty in the mean;

\alpha

quantifies agreement among raters.

Configuration	Align $\uparrow$	SemSim $\uparrow$	BERTScore $\uparrow$	NLI $\downarrow$	FRR $\downarrow$
Moral Foundations: reference prompts per label
1 reference	.658	.858	.922	.079	.068
20 references	.704	.865	.929	.068	.055
50 references	.723	.869	.932	.061	.050
Three-turn conversations: intervention policy
First turn only	.566	.875	.926	.073	.064

Appendix

Table 23 : Transfer checks grouped by evaluation setting. Moral Foundations uses 240 held-out prompts; conversation results average 150 three-turn dialogues; Qwen2.5-14B uses 1,000 SVQ-Test prompts. Dialogue latency is 1.93/2.04/2.13 seconds per turn for first-turn-only editing, per-turn reapplication, and the persistent prompt, respectively. Dashes mark metrics not reported for the 14B comparison.

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Aligning large language models (LLMs) with human values typically relies on post-training or inference-time steering that directly manipulates the backbone's parameters or representation space. However, a critical gap exists: the model's residual stream is highly dynamic, in which values exist as fragile, low-dimensional properties, inherently incompatible with the stability required for consistent value expression. In this paper, we propose the Stable Value Guidance Transformer (SVGT), which addresses this gap through an independent value module incorporating two key designs: (1) independent value modeling, maintaining normative representations in a dedicated value space isolated from the backbone, and (2) explicit behavioral guidance, transducing these stable signals into learnable latent Bridge Tokens. These tokens serve as dynamic value anchors to explicitly steer the generative trajectory, ensuring robust adherence across diverse contexts without disrupting the backbone's internal representations. Experiments across multiple backbones and safety benchmarks show that SVGT generally reduces harmful scores by over 70% while maintaining generation fluency, demonstrating the efficacy of architecturally grounded value modeling. Our code is available at https://github.com/Clervils/SVGT.git.

Abstract

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