When Do Model Internals Help? Exploring the Role of Representation Engineering in LLM Safety

Tianyi Guan; Jianhui Chen; Liangming Pan

cs.AISep 28, 2026

When Do Model Internals Help? Exploring the Role of Representation Engineering in LLM Safety

Authors: Tianyi Guan, Jianhui Chen, Liangming Pan

Organizations: State Key Laboratory of Multimedia Information Processing, Peking University · School of Computer Science, Peking University

Abstract

Reliable AI safeguards require both control mechanisms that reduce unsafe behavior and monitoring mechanisms that detect safety risks during model interactions. Established behavioral safeguards include alignment methods that optimize model outputs and text monitors that assess interaction text. Representation engineering instead reads or modifies internal model states, but the relative strengths of these approaches remain unclear because they are often evaluated under different settings. We present a matched evaluation across two tracks. For safety control, we compare DPO, a behavioral alignment method, with three representation steering methods across robustness, practicality, and granularity. DPO provides the strongest overall control and generally improves with increasing training data, although its safety can degrade after subsequent benign fine-tuning. Representation steering remains competitive primarily in low-data settings, particularly with high-quality contrastive data. For safety monitoring, we compare representation probes with fine-tuned and open-weight text monitors across full-response detection, early detection, and computational cost. Specialized text monitors achieve the strongest overall detection accuracy, while representation probes remain competitive at substantially lower marginal cost. Finally, monitor-guided interventions recover much of the safety lost by DPO after benign fine-tuning, with little additional over-refusal. Overall, representation engineering does not generally replace behavioral safeguards, but offers practical advantages under specific conditions and can provide complementary safety benefits.

Figures & tables

	Safety outcomes (lower is better)
	AIM ASR $\downarrow$			Refusal Suppression ASR $\downarrow$			Over-refusal $\downarrow$
Method	Pre	Post	$\Delta$	Pre	Post	$\Delta$	Pre	Post	$\Delta$
Base	0.411	0.735	+0.324	0.288	0.394	+0.106	0.205	0.169	-0.036
DPO	0.027	0.436	+0.409	0.017	0.149	+0.132	0.277	0.175	-0.103
CAA	0.407	0.739	+0.332	0.291	0.398	+0.107	0.217	0.164	-0.053
Probe	0.409	0.735	+0.326	0.289	0.391	+0.102	0.203	0.164	-0.039

Table 1: Safety control before and after benign fine-tuning. Pre and Post denote results before and after fine-tuning on Alpaca-Cleaned, and

\Delta=\mathrm{Post}-\mathrm{Pre}

. Lower ASR and over-refusal (OR) are better. Results are unweighted macro-averages across three base models. Direct-prompting ASR is omitted because it is generally lower than under jailbreak attacks; per-model values are reported in Appendix Table 12 . Full per-model results are provided in Appendix B.1 and B.2 .

	MMLU $\uparrow$			GSM8K $\uparrow$			HumanEval $\uparrow$
Method	Pre	Post	$\Delta$	Pre	Post	$\Delta$	Pre	Post	$\Delta$
Base	0.659	0.661	+0.002	0.788	0.703	-0.085	0.747	0.800	+0.053
DPO	0.653	0.666	+0.013	0.790	0.687	-0.103	0.713	0.800	+0.087
CAA	0.663	0.659	-0.003	0.790	0.715	-0.075	0.753	0.793	+0.040
Probe	0.657	0.661	+0.004	0.788	0.707	-0.082	0.760	0.807	+0.047
Flow	0.616	0.600	-0.016	0.782	0.778	-0.003	0.793	0.787	-0.007

Table 2: Capability scores before and after benign fine-tuning. Pre and Post denote scores before and after fine-tuning on Alpaca-Cleaned, and

\Delta=\mathrm{Post}-\mathrm{Pre}

. Scores are unweighted macro-averages across three base models; higher scores and positive changes are better. Full per-model results are provided in Appendix B.2 .

Panel of Figure 1 — Figure 1: Data efficiency and cross-scope safety transfer. (a) Refusal-suppression ASR across training-data sizes and sources on Qwen2.5-1.5B-Instruct; lower is better. Error bars show the standard deviation over three seeds for 100- and 200-pair settings; larger settings are single runs. (b) Each cell reports the unweighted macro-average ASR change across three base models, averaging three independent seeds within each model. Rows denote evaluation scopes and columns denote intervention-training scopes. Outlined cells mark matched-scope evaluation; negative changes indicate improved safety relative to the base model.

Representation probes
	Full-response Detection				Streaming Early Detection
Monitor	AUROC	AUPRC	Cal.-1%	Cal.-5%	Recall	Median Pos.	Seq. FPR
Mean	0.982	0.978	0.896/0.039	0.986/0.190	0.908	0.291	0.081
Last	0.972	0.966	0.737/0.025	0.954/0.104	0.618	0.257	0.145
Rolling	0.971	0.966	0.928/0.068	0.962/0.122	0.913	0.143	0.017
Attention	0.965	0.932	0.855/0.039	0.968/0.145	0.824	0.154	0.047

Table 3: Monitoring performance on native Qwen2.5-32B-Instruct trajectories. For full-response detection, Cal.-

x\%

reports test TPR/realized FPR at a threshold selected to target

x\%

FPR on the calibration set. For streaming detection, Recall is the fraction of harmful responses detected, Median Pos. is the normalized first-detection position among detected responses, and Seq. FPR is the fraction of safe trajectories that trigger at least one alarm. Higher AUROC, AUPRC, TPR, and Recall are better; lower FPR and Med. Pos. are better. Bold denotes the best overall result, and underline denotes the best representation-probe result.

Panel of Figure 2 — Figure 2: Native monitoring efficiency and replay-based control integration. Left: Probes that reuse native Qwen2.5-32B-Instruct activations provide competitive detection at substantially lower marginal cost than text monitors. Right: After benign fine-tuning, probe-triggered blocking reduces the mean jailbreak ASR of matched-data DPO on Qwen2.5-14B-Instruct from 0.500 to 0.042, approaching its pre-fine-tuning ASR of 0.058.

Figure 3: Three monitor–control integration strategies. (a) Blocking replaces a flagged response with a refusal. (b) Regeneration uses the monitor’s alarm to request a safe rewrite of a flagged draft. (c) Stronger steering increases the flow-intervention strength and reruns generation.

Figure 4: Monitor–control integration before and after benign fine-tuning. Curves show mean ASR across AIM and refusal suppression and over-refusal on Qwen2.5-14B-Instruct as the alarm threshold varies. Horizontal dashed lines denote the corresponding DPO and Flow baselines without monitoring; vertical dotted lines mark the rolling-probe thresholds previously calibrated at 1% and 5% FPR.

Appendix figures & tables24 assets

Supplementary material from the paper’s appendix.

Appendix

Method	Pairs	Train/Val.	Epochs	Learning rate	Batch
DPO	5,406	5,406/–	2	$5\times 10^{-5}$	128–256
CAA	5,406	–	–	–	–
Probe	5,406	4,866/540	200	$10^{-2}$	full
Flow	5,406	5,406/–	10	$3\times 10^{-4}$	4–8

Appendix

Table 4: Training configuration of the control methods. Batch size is effective batch size for DPO and per-device batch size for Flow.

Base model	DPO batch	CAA/Probe layer	$\alpha$	Flow layer	$T$
Qwen2.5-1.5B	$8\times 32$	24	1.0	20	0.7
Llama-3.1-8B	$8\times 16$	28	1.0	16	0.7
Qwen2.5-14B	$4\times 32$	31	1.0	24	0.7

Appendix

Table 5: Model-specific control settings. DPO batch is reported as per-device batch

\times

gradient-accumulation steps. CAA and Probe share the same intervention layer and strength.

Scope	Pair pool	Pairs used	Eval. prompts
Cybercrime	787	787	67
Physical harm	1,571	1,500	75
Toxicity	2,316	1,500	75

Appendix

Table 6: Domain-specific data used in the granularity evaluation. Pool is the number of complete safe–unsafe pairs available; all methods use the same number of pairs in each scope.

Rubric	Accuracy	FP	FPR	FN	FNR
Legacy StrongREJECT	0.905	29	0.112	30	0.082
Binary-v2	0.960	24	0.093	1	0.003
Binary-v3	0.986	6	0.023	3	0.008

Appendix

Table 7: Validation of the harmful-compliance judge against the 624-example author-adjudicated reference set. FPR is computed over safe responses and FNR over harmful responses.

Model	Method	Layer	Base	$\alpha=0.5$	$\alpha=1$	$\alpha=2$	$\alpha=4$
Qwen2.5-1.5B	CAA	8	0.606/0.220	0.590/0.220	0.588/0.215	0.600/0.221	0.580/0.221
		12		0.592/0.210	0.608/0.205	0.574/0.180	0.514/0.165
		16		0.600/0.210	0.596/0.210	0.582/0.215	0.536/0.200
		20		0.600/0.215	0.598/0.215	0.596/0.195	0.586/0.190
		24		0.610/0.210	0.598/0.200	0.594/0.195	0.608/0.220
	PROBE	8	0.606/0.215	0.598/0.205	0.602/0.210	0.614/0.195	0.626/0.195

Appendix

Table 8: Complete layer–strength sweep for CAA and probe-based steering. Each entry reports ASR/OR, where lower is better for both metrics. Bold entries denote the operating points used in the main experiments.

Model	Layer	Base	$T=0.5$	$T=0.7$	$T=1$	$T=1.5$
Qwen2.5-1.5B	8	0.488/0.240	0.476/0.280	0.482/0.275	0.452/0.280	0.450/0.285
	12	0.484/0.235	0.428/0.270	0.388/0.275	0.320/0.265	0.262/0.290
	16	0.498/0.240	0.422/0.295	0.396/0.295	0.329/0.295	0.308/0.300
	20	0.492/0.240	0.466/0.240	0.454/0.245	0.428/0.270	0.384/0.270
	24	0.486/0.230	0.486/0.260	0.494/0.260	0.464/0.265	0.418/0.285
Llama-3.1-8B	8	0.396/0.175	0.306/0.165	0.266/0.120	0.260/0.110	0.474/0.100

Appendix

Table 9: Layer–strength sweep for flow-based steering. Each entry reports ASR/OR, where lower is better for both metrics. We report the four central intervention strengths and omit the two endpoint stress-test settings (

T=0.3

and

T=2

) for compactness. Bold entries denote the operating points used in the main experiments.

Layer	Pre	Post	$\Delta$	Pre	Post	$\Delta$	Pre	Post	$\Delta$
	AIM ASR			Refusal Suppression ASR			Over-refusal
16 (Primary)	0.153	0.099	-0.054	0.144	0.259	+0.115	0.148	0.172	+0.024
8 (Post hoc)	0.077	0.712	+0.636	0.224	0.466	+0.243	0.124	0.156	+0.032

Appendix

Table 10: Flow layer sensitivity to benign fine-tuning on Llama-3.1-8B-Instruct. Both layers use the full 5,406-pair, 10-epoch Flow training configuration with

T=0.7

. Pre and Post denote results before and after fine-tuning on Alpaca-Cleaned, and

\Delta=\mathrm{Post}-\mathrm{Pre}

. Lower ASR and over-refusal (OR) are better. Layer 16 is the operating point used in the main comparison; Layer 8 is evaluated post hoc following the layer sweep.

		AIM			Refusal Suppression
Model	Method	Pre	Post	$\Delta$	Pre	Post	$\Delta$
Qwen2.5-1.5B	Base	0.833	0.849	+0.016	0.349	0.519	+0.170
	DPO	0.013	0.410	+0.397	0.003	0.170	+0.167
	CAA	0.818	0.853	+0.035	0.349	0.511	+0.162
	Probe	0.827	0.849	+0.022	0.349	0.513	+0.164
	Flow	0.652	0.780	+0.128	0.256	0.463	+0.207

Appendix

Table 11: Per-model robustness before and after benign fine-tuning. Pre and Post denote ASR before and after fine-tuning on Alpaca-Cleaned, and

\Delta=\mathrm{Post}-\mathrm{Pre}

. Lower is better.

Model	Method	Pre	Post
Qwen2.5-1.5B	Base	0.061	0.204
	DPO	0.000	0.080
	CAA	0.071	0.195
	Probe	0.067	0.208
	Flow	0.042	0.125
Llama-3.1-8B	Base	0.019	0.010

Appendix

Table 12: Direct-prompting harmful-compliance ASR. Pre and Post denote results before and after fine-tuning on Alpaca-Cleaned. Each entry is computed from 312 or 313 valid binary-v3 judgments; Macro is the unweighted mean across the three base models. Lower is better.

		Over-refusal			MMLU			GSM8K			HumanEval
Model	Method	Pre	Post	$\Delta$	Pre	Post	$\Delta$	Pre	Post	$\Delta$	Pre	Post	$\Delta$
Qwen2.5-1.5B	Base	0.476	0.304	-0.172	0.576	0.572	-0.004	0.615	0.410	-0.205	0.660	0.660	0.000
	DPO	0.692	0.324	-0.368	0.582	0.576	-0.006	0.625	0.365	-0.260	0.660	0.700	+0.040
	CAA	0.500	0.288	-0.212	0.582	0.568	-0.014	0.635	0.455	-0.180	0.660	0.660	0.000
	Probe	0.460	0.300	-0.160	0.578	0.572	-0.006	0.630	0.420	-0.210	0.660	0.680	+0.020
	Flow	0.632	0.636	+0.004	0.432	0.432	0.000	0.615	0.615	0.000	0.620	0.620	0.000

Appendix

Table 13: Per-model practicality before and after benign fine-tuning. Lower over-refusal (OR) and higher capability scores are better.

\Delta=\mathrm{Post}-\mathrm{Pre}

Data source	Pairs	DPO	CAA	Probe	Flow
Full data	100	$0.346\pm 0.006$	$0.343\pm 0.010$	$0.346\pm 0.012$	$0.364\pm 0.004$
	200	$0.347\pm 0.005$	$0.344\pm 0.002$	$0.346\pm 0.008$	$0.354\pm 0.005$
	500	0.333	0.340	0.343	0.337
	1k	0.314	0.346	0.346	0.330
	2k	0.263	0.343	0.340	0.301
	5k	0.250	0.353	0.349	0.353

Appendix

Table 14: Data-efficiency results on Qwen2.5-1.5B-Instruct. Entries are refusal-suppression ASR (lower is better). Values for 100 and 200 pairs are mean

\pm

standard deviation over three seeds; larger settings are single runs.

Method	Training data	Pairs	AIM	RS	OR
DPO	Original	63,094	0.029	0.125	0.092
DPO	Contrastive	5,406	0.000	0.000	0.088
Flow	Contrastive	5,406	0.153	0.144	0.148

Appendix

Table 15: Data-source comparison on Llama-3.1-8B-Instruct. The main DPO and Flow configurations use the same 5,406 filtered contrastive pairs; DPO trained on the complete original preference set is included as a reference. We report ASR under AIM and refusal suppression (RS), together with over-refusal (OR). Lower is better.

Model	Method	Evaluation scope	Base	General	Cybercrime	Physical harm	Toxicity
Qwen2.5-1.5B	DPO	General	0.281	0.173	0.218	0.178	0.169
		Cybercrime	0.582	0.363	0.463	0.343	0.338
		Physical harm	0.453	0.289	0.400	0.302	0.320
		Toxicity	0.200	0.111	0.160	0.133	0.120
	CAA	General	0.281	0.254	0.254	0.254	0.254
		Cybercrime	0.582	0.567	0.582	0.582	0.582

Appendix

Table 16: Per-model granularity and cross-scope transfer. Entries are mean ASR over three independent runs with different random seeds under refusal suppression (lower is better). Rows denote evaluation scopes and columns denote intervention-training scopes; bold values are matched-scope diagonal entries. Base ASRs are listed once for each evaluation scope.

Probe	Sequence score	Parameters	Setting
Mean	$\theta^{\top}\left(\frac{1}{T}\sum_{t}\mathbf{h}_{t}\right)+b$	$d+1$	All tokens
Last	$\theta^{\top}\mathbf{h}_{T}+b$	$d+1$	Final token
Rolling	$\max_{i}\frac{1}{W}\sum_{t=i}^{i+W-1}(\theta^{\top}\mathbf{h}_{t}+b)$	$d+1$	$W=16$
Attention	$\sum_{t}a_{t}(\theta_{v}^{\top}\mathbf{h}_{t})+b$	$2d+1$	$a=\mathrm{softmax}(H\theta_{q})$

Appendix

Table 17: Representation-probe formulations. Let

\mathbf{h}_{t}\in\mathbb{R}^{d}

denote the layer-48 hidden state at valid sequence position

t

, and let

T

denote the sequence length. All formulations produce a scalar unsafe-response score.

Probe	Train	Val.	Epochs	Learning rate	Weight decay
Mean	20,000	5,000	200	$10^{-2}$	$10^{-3}$
Last	20,000	5,000	200	$10^{-2}$	$10^{-3}$
Rolling	20,000	5,000	1	$10^{-3}$	$10^{-3}$
Attention	20,000	5,000	1	$10^{-3}$	$10^{-3}$

Appendix

Table 18: Representation-probe training configuration. All probes use layer 48 of Qwen2.5-32B-Instruct and prompt–response inputs.

Source model	Monitor	AUROC	AUPRC	Accuracy	TPR/FPR Cal-1%	TPR/FPR Cal-5%	$n$
Qwen2.5-1.5B	Mean	0.844	0.703	0.600	0.182/0.047	0.241/0.063	830
	Last	0.917	0.862	0.839	0.188/0.008	0.391/0.035	830
	Rolling	0.962	0.930	0.916	0.621/0.043	0.847/0.067	830
	Attention	0.935	0.882	0.875	0.512/0.043	0.803/0.082	830
	FT-LLM	0.968	0.949	0.916	0.618/0.033	0.862/0.067	830
	Qwen3Guard	0.978	0.972	0.919	0.806/0.035	0.929/0.098	830

Appendix

Table 19: Full-response detection under representation replay. TPR/FPR Cal-x% denotes test-set TPR and realized FPR at a threshold selected for

x\%

FPR on the calibration split.

Source model	Monitor	Recall	Before end	Pre-response	Median pos.	Safe FPR
Qwen2.5-1.5B	Mean	0.024	0.024	0.006	0.130	0.041
	Last	0.338	0.326	0.000	0.306	0.052
	Rolling	0.769	0.769	0.006	0.311	0.083
	Attention	0.701	0.692	0.012	0.281	0.078
	FT-LLM	0.802	0.799	0.617	0.000	0.264
	Qwen3Guard	0.925	0.925	0.000	0.029	0.176

Appendix

Table 20: Streaming detection under representation replay. Thresholds target a 5% safe-sequence FPR. Median pos. is the normalized first-alarm position among detected harmful trajectories.

Available information	Monitor	Type	AUROC	AUPRC	TPR	FPR	WP-AUC
Prompt only	FT-LLM	Text monitor	0.495	0.168	0.053	0.019	0.500
Prompt only	Qwen3Guard	Text monitor	0.513	0.151	0.069	0.099	0.500
CoT activations	Mean	Rep. probe	0.640	0.294	0.116	0.026	0.611
	Last	Rep. probe	0.432	0.140	0.053	0.052	0.476
	Rolling	Rep. probe	0.696	0.345	0.254	0.043	0.597
	Attention	Rep. probe	0.618	0.256	0.122	0.036	0.589

Appendix

Table 21: Exploratory pre-response detection with hidden-reasoning access. WP-AUC denotes within-prompt AUC. TPR and FPR use the calibrated operating threshold. Prompt-only monitors assign the same score to all rollouts of a prompt, so their WP-AUC is necessarily 0.500. Prompt+CoT text is an oracle-access comparison rather than the standard text-monitor setting.

Monitor	Mean tokens	Marginal FLOPs
Mean probe	442	$2.27\times 10^{6}$
Last probe	442	$1.02\times 10^{4}$
Rolling probe	442	$4.53\times 10^{6}$
Attention probe	442	$9.05\times 10^{6}$
FT-LLM (7B)	459	$6.99\times 10^{12}$
Qwen3Guard (4B)	425	$3.42\times 10^{12}$

Appendix

Table 22: Estimated marginal computation per interaction. Probe costs exclude the target model’s generation forward pass because its activations are already available to the representation probe.

Stage	Control	Intervention	Mean ASR	OR
Pre-update	DPO	None	0.058	0.052
		Blocking	0.021	0.060
		Regeneration	0.021	0.060
	Flow	None	0.157	0.064
	Flow	Blocking	0.038	0.080
Post-update	DPO	None	0.500	0.108

Appendix

Table 23: Monitor-guided intervention before and after benign fine-tuning. Lower mean ASR and over-refusal (OR) are better.

Monitor	Threshold	FPR	AIM ASR	RS ASR	OR
No monitor	–	–	0.185	0.096	0.404
Rolling probe	Cal.-1%	0.030	0.080	0.051	0.404
Rolling probe	Cal.-5%	0.059	0.022	0.035	0.404
FT-LLM	Cal.-1%	0.007	0.112	0.077	0.404
FT-LLM	Cal.-5%	0.022	0.045	0.051	0.404
Qwen3Guard	Cal.-1%	0.013	0.086	0.061	0.404

Appendix

Table 24: Post-generation blocking with different monitors on DPO-controlled Qwen2.5-1.5B-Instruct. FPR is the alarm rate among the 538 safe responses pooled across AIM and refusal suppression in this integration evaluation. Residual ASR is reported for each attack, with 313 responses per attack. OR is evaluated on 250 benign prompts.

Figure 5: Monitor–control threshold sweeps under direct prompting. Curves compare blocking, corrective regeneration, and stronger Flow intervention across three target models. Vertical dotted lines mark thresholds calibrated at 1% and 5% monitor false-positive rates.

Figure 6: Monitor–control threshold sweeps under AIM. Lower ASR indicates stronger safety control, while lower over-refusal indicates fewer benign-input side effects.

Figure 7: Monitor–control threshold sweeps under refusal suppression. This figure extends the main-text comparison with Llama-3.1-8B-Instruct and the complete operating-threshold range.

Explore similar work

Jun 6, 2026cs.LG

When Behavioral Safety Evaluation Fails: A Representation-Level Perspective

Large Language Model (LLM) safety has often been evaluated at the behavior level, which provides limited evidence of internal robustness, as these evaluations target outputs rather than representation-level vulnerability under intervention. We formalize this discrepancy as the audit gap: the difference between behavioral safety and robustness under intervention. To study this gap, we construct dissociated models that preserve safe outward behavior while remaining vulnerable in the latent space. We introduce an intervention-based evaluation framework to test model robustness through soft interventions in parameter and latent spaces, including harmful fine-tuning and layer-wise latent perturbations. To formalize the evaluation, we propose the Latent Vulnerability Score (LVS) to measure how easily harmful behavior can be elicited by bounded latent perturbations. Using this evaluation framework, we show that behavioral safety metrics are insufficient measures of representation-level robustness across multiple safely and unsafely aligned state-of-the-art models. Notably, dissociated models show substantially elevated LVSs despite comparable refusal behavior under harmful intervention, with intermediate representations being the most sensitive to intervention. Our results suggest that behavioral safety evaluation alone provides an incomplete picture of model robustness, motivating representation-aware audits of latent vulnerability and observable behavior.

Enyi Jiang, Anders Gjølbye, Yibo Jacky Zhang +1

1Stanford University · University of Illinois Urbana-Champaign · 3Technical University of Denmark

Large Language Model Safety Representation-Level Misalignment Model Auditing Representation Quality Latent Space

Ask PDF

Sep 16, 2026cs.AI

Safety Beyond the Interface: Detecting Harm via Latent States in Large Language Models

Autonomous systems increasingly rely on Large Language Models (LLMs) yet the safety infrastructure surrounding these models introduces latency and compute overhead. This limits utility in resource-constrained, time-critical deployments. Existing external guardrail models remain blind to the model's internal workings, creating a fundamental assurance gap. We ask: does the model already know when the content is harmful? We extract activations from LLaMA-3.1-8B and train lightweight MLP classifier probes (12.6M parameters) to detect harmful prompts. Evaluated on WildJailbreak, Beavertails, and AEGIS 2.0, our probes achieve F1 scores of 99%, 83%, and 84%, respectively competitive with 1000x larger guard models while cutting latency and compute costs.

Alizishaan Khatri, Chiquita Prabhu, Omkar Neogi

Wrynx Inc. · Independent Researcher

Large Language Model Safety Guardrail Harms Precision-Critical Manipulation Latent States Multilayer Perceptrons

Ask PDF

Aug 4, 2026cs.AI

LatentGuard: Efficient and Inspectable Latent Reasoning for LLM Safeguards

Reasoning-based guard models improve LLM safeguards, but decoding explicit rationales for every interaction makes them costly to deploy. Although latent-reasoning methods reduce token generation by moving reasoning into continuous states, they remain underexplored for safety moderation and lack an inspection interface for deployment. In this paper, we propose LatentGuard, an efficient and inspectable safeguard framework that brings continuous latent reasoning to guard models. LatentGuard uses a staged curriculum to progressively compress task-aligned textual rationales into compact latent states, enabling safety verdicts to be predicted directly from continuous representations. To preserve inspectability, an isolated auxiliary decoder generates compact audit artifacts on demand, keeping rationale generation off the standard inference path. Experiments show that LatentGuard-8B improves mean weighted F1 from 83.95 to 84.91 over GuardReasoner-8B, while reducing critical-path reasoning cost from 268.56 generated rationale tokens to 1.60 latent reasoning tokens. Its audit decoder achieves an audit utility score of 85.75, demonstrating an efficient and inspectable path toward deployable LLM safeguards.

Zhinan Liu, Jie Li, Mingyu Kang +1

1Xiamen University · University of Science and Technology of China

Large Language Model Safety Efficient Latent Reasoning

Ask PDF