Stop Early, Spend Less: Hidden-State Probes as a Practical Recipe for Streaming Moderation of LLM Outputs
- URL: http://arxiv.org/abs/2606.10487v1
- Date: Tue, 09 Jun 2026 07:01:43 GMT
- Title: Stop Early, Spend Less: Hidden-State Probes as a Practical Recipe for Streaming Moderation of LLM Outputs
- Abstract summary: Existing approaches rely on a separate moderation model applied after generation.<n>We observe that the signal needed for moderation is already present in the model hidden states.<n>We train lightweight token-level probes that operate directly on internal activations.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Deploying large language models in user-facing systems requires efficient output safety filtering. Existing approaches typically rely on a separate moderation model applied after generation, which doubles inference cost and only detects violations after generation completes. We observe that the signal needed for moderation is already present in the model hidden states. Based on this, we train lightweight token-level probes that operate directly on internal activations, producing per-token safety scores that can be aggregated for both offline evaluation and online intervention. The probe reuses activations from the generator and requires no additional forward pass, enabling sub millisecond per-token safety checks inside the decoding loop. A probe applied to a single mid layer recovers most decisions of a strong guard model, acting as a low cost surrogate optimized for latency rather than accuracy. In streaming settings, it can halt or modify unsafe outputs before they are fully generated, replacing end of sequence moderation with continuous token level monitoring. Compared to post hoc and streaming guard models, our method achieves orders of magnitude lower compute overhead with minimal latency cost. We also provide a practical deployment recipe, including layer selection, aggregation strategy, probing frequency, and triggering thresholds. Finally, we show that the probe linear component corresponds to a direction in residual space, enabling both detection and activation steering at negligible cost.
Related papers
- Prediction of Prediction (PoP): Inter-Layer Activation Fusion for Single-Pass Hallucination Detection in Large Language Models [0.0]
This work evaluates whether internal hidden-state transition dynamics during generation can signal factual errors without auxiliary decoding calls.<n>We introduce Prediction of Prediction (PoP), a mechanism that captures layer-transition uncertainty.<n>PoP achieves an area under the receiver operating characteristic curve (AUROC) of 75.5% for factual-correctness classification.
arXiv Detail & Related papers (2026-08-27T14:17:14Z) - Robust Activation Map Rectification for Weakly Supervised Volumetric Segmentation: Temporal Coherence as a Free Lunch [31.156115062163412]
Weakly supervised segmentation relies heavily on class activation maps (CAMs) to initially localize target regions.<n>Existing remedies typically introduce additional training stages or prototype learning, increasing computational cost and reducing robustness.<n>We propose a training-free prototype-free framework that rectifies unreliable CAMs by exploiting temporal and structural coherence in volumetric data as a free lunch.
arXiv Detail & Related papers (2026-07-22T08:07:38Z) - Caught in the Act(ivation): Toward Pre-Output and Multi-Turn Detection of Credential Exfiltration by LLM Agents [0.0]
We study whether activation probes can detect credential access before output tokens are emitted.<n>Third, we treat multi-turn exfiltration as a cumulative information-flow problem.<n>Results suggest credential-exfiltration defenses should combine pre-output monitoring, canary detection, and temporal leakage accounting.
arXiv Detail & Related papers (2026-06-02T18:53:17Z) - Refusal Before Decoding: Detecting and Exploiting Refusal Signals in Intermediate LLM Activations [18.44975629076816]
We find that refusal is linearly decodable well before the final layer, indicating that safety-relevant behavior is represented in intermediate activations before output generation.<n>We introduce Mechanistic AutoDAN, a probe-guided variant of AutoDAN that replaces full-model fitness evaluation with partial forward passes and probe-based scoring inside a genetic prompt search loop.
arXiv Detail & Related papers (2026-05-27T14:44:36Z) - FASTER: Value-Guided Sampling for Fast RL [103.55398181003262]
FASTER is a method for getting the benefits of sampling-based test-time scaling of diffusion-based policies without the computational cost.<n>FASTER achieves the same performance while substantially reducing training and inference compute requirements.
arXiv Detail & Related papers (2026-04-21T17:52:17Z) - Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors [48.881343993730844]
Safety-aligned large language models (LLMs) are increasingly deployed in real-world pipelines.<n> adversaries can distribute backdoored checkpoints that behave normally under standard evaluation but jailbreak when a hidden trigger is present.<n>Recent post-hoc weight-editing methods offer an efficient approach to injecting such backdoors.
arXiv Detail & Related papers (2026-04-14T06:48:33Z) - ECoLAD: Deployment-Oriented Evaluation for Automotive Time-Series Anomaly Detection [1.7751300245073598]
Time-series anomaly detectors are commonly compared on workstation-class hardware under unconstrained execution.<n>In-vehicle monitoring, however, requires predictable latency and stable behavior under limited CPU parallelism.<n>We present ECoLAD, a deployment-oriented evaluation protocol instantiated as an empirical study on proprietary automotive telemetry.
arXiv Detail & Related papers (2026-03-11T16:08:56Z) - A BERTology View of LLM Orchestrations: Token- and Layer-Selective Probes for Efficient Single-Pass Classification [2.0069888187253615]
Production LLM systems often rely on separate models for safety and other classification-heavy steps.<n>We instead reuse computation already paid for by the serving LLM: we train lightweight probes on its hidden states and predict labels in the same forward pass used for generation.
arXiv Detail & Related papers (2026-01-19T18:40:29Z) - Beyond Linear Probes: Dynamic Safety Monitoring for Language Models [67.15793594651609]
Traditional safety monitors require the same amount of compute for every query.<n>We introduce Truncated Polynomials (TPCs), a natural extension of linear probes for dynamic activation monitoring.<n>Our key insight is that TPCs can be trained and evaluated progressively, term-by-term.
arXiv Detail & Related papers (2025-09-30T13:32:59Z) - Adaptive t Design Dummy-Gate Obfuscation for Cryogenic Scale Enforcement [0.0]
Cloud quantum services can reveal circuit structure and timing through scheduler metadata, latency patterns, and co-tenant interference.<n>We introduce NADGO, a scheduling and obfuscation stack that enforces operational privacy for gate-model workloads.<n>We prototype the approach on a 4-qubit superconducting tile with cryo-CMOS control and evaluate both depth-varied local-random circuits and small QAOA instances.
arXiv Detail & Related papers (2025-08-31T12:12:48Z) - Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable [70.77600345240867]
A novel arbitrary-in-arbitrary-out (AIAO) strategy makes watermarks resilient to fine-tuning-based removal.
Unlike the existing methods of designing a backdoor for the input/output space of diffusion models, in our method, we propose to embed the backdoor into the feature space of sampled subpaths.
Our empirical studies on the MS-COCO, AFHQ, LSUN, CUB-200, and DreamBooth datasets confirm the robustness of AIAO.
arXiv Detail & Related papers (2024-05-01T12:03:39Z) - Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes [71.23286211775084]
We propose an adaptive low-pass filter based on Gaussian process regression.
We show that the estimation error of the proposed method is uniformly bounded.
arXiv Detail & Related papers (2021-11-05T17:06:59Z) - Bandit Quickest Changepoint Detection [55.855465482260165]
Continuous monitoring of every sensor can be expensive due to resource constraints.
We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions.
We propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions.
arXiv Detail & Related papers (2021-07-22T07:25:35Z) - DAAIN: Detection of Anomalous and Adversarial Input using Normalizing
Flows [52.31831255787147]
We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA)
Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution.
Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
arXiv Detail & Related papers (2021-05-30T22:07:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.