FaithSteer-BENCH: A Deployment-Aligned Stress-Testing Benchmark for Inference-Time Steering
- URL: http://arxiv.org/abs/2603.18329v1
- Date: Wed, 18 Mar 2026 22:28:36 GMT
- Title: FaithSteer-BENCH: A Deployment-Aligned Stress-Testing Benchmark for Inference-Time Steering
- Abstract summary: FaithSteer-BENCH is a stress-testing benchmark forference-time steering.<n>It evaluates steering methods at a fixed deployment-style operating point through three gate-wise criteria: controllability, utility preservation, and robustness.<n> Gate-wise benchmark results show that existing methods do not necessarily provide reliable controllability in deployment-oriented practical settings.
- Score: 11.609466767597093
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Inference-time steering is widely regarded as a lightweight and parameter-free mechanism for controlling large language model (LLM) behavior, and prior work has often suggested that simple activation-level interventions can reliably induce targeted behavioral changes. However, such conclusions are typically drawn under relatively relaxed evaluation settings that overlook deployment constraints, capability trade-offs, and real-world robustness. We therefore introduce \textbf{FaithSteer-BENCH}, a stress-testing benchmark that evaluates steering methods at a fixed deployment-style operating point through three gate-wise criteria: controllability, utility preservation, and robustness. Across multiple models and representative steering approaches, we uncover several systematic failure modes that are largely obscured under standard evaluation, including illusory controllability, measurable cognitive tax on unrelated capabilities, and substantial brittleness under mild instruction-level perturbations, role prompts, encoding transformations, and data scarcity. Gate-wise benchmark results show that existing methods do not necessarily provide reliable controllability in deployment-oriented practical settings. In addition, mechanism-level diagnostics indicate that many steering methods induce prompt-conditional alignment rather than stable latent directional shifts, further explaining their fragility under stress. FaithSteer-BENCH therefore provides a unified benchmark and a clearer analytical lens for future method design, reliability evaluation, and deployment-oriented research in steering.
Related papers
- Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations [55.76952683833966]
This paper proposes weighted CONCH (W-CONCH) and weighted CROC (W-CROC) to reduce confidence set size when data may be corrupted.<n>Experiments on image-based and real-world changepoint and root-cause benchmarks show that uncertainty-based weighting substantially reduces confidence set size.
arXiv Detail & Related papers (2026-07-29T05:16:59Z) - Exploring Agentic Tool-Calling Decisions via Uncertainty-Aligned Reinforcement Learning [63.24624171679711]
Large language model (LLM)-based agents often make suboptimal tool-use decisions.<n>We propose TRUST, which incorporates uncertainty quantification into reward design as a repulsive force for maintaining uncertainty separation.
arXiv Detail & Related papers (2026-06-05T07:08:34Z) - Agentic Control in Variational Language Models [0.0]
We study whether a variational language model can support a minimal and measurable form of agentic control grounded in its own internal evidence.<n>Our model combines local variational hidden computation (EVE), a homeostatic latent regulator, structurally aware checkpoint retention and a calibrated uncertainty-aware controller operating on top of the retained model.
arXiv Detail & Related papers (2026-04-14T09:47:53Z) - AMPS: Adaptive Modality Preference Steering via Functional Entropy [66.69992693275061]
We introduce an instance-aware diagnostic metric that quantifies each modality's information contribution and reveals sample-specific susceptibility to steering.<n> Experimental results show that our instance-aware steering outperforms conventional steering in modulating modality preference.
arXiv Detail & Related papers (2026-02-13T02:29:06Z) - On the Identifiability of Steering Vectors in Large Language Models [0.0]
Activation steering methods are widely used to control large language model behavior.<n>This interpretation implicitly assumes steering directions are identifiable and uniquely recoverable from input-output behavior.<n>We prove that steering vectors are fundamentally non-identifiable due to large equivalence classes of behaviorally indistinguishable interventions.
arXiv Detail & Related papers (2026-02-06T15:53:50Z) - STEER: Inference-Time Risk Control via Constrained Quality-Diversity Search [3.587563440886175]
Large Language Models (LLMs) trained for average correctness often exhibit mode collapse, producing narrow decision behaviors on tasks where multiple responses may be reasonable.<n>We propose STEER, a training-free framework that reintroduces this unambiguous control.<n>On two clinical triage benchmarks, STEER achieves broader behavioral coverage compared to temperature-based sampling and static persona ensembles.
arXiv Detail & Related papers (2026-02-02T22:10:32Z) - From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models [77.04403907729738]
This survey charts the evolution of uncertainty from a passive diagnostic metric to an active control signal guiding real-time model behavior.<n>We demonstrate how uncertainty is leveraged as an active control signal across three frontiers.<n>This survey argues that mastering the new trend of uncertainty is essential for building the next generation of scalable, reliable, and trustworthy AI.
arXiv Detail & Related papers (2026-01-22T06:21:31Z) - Steering Language Models Before They Speak: Logit-Level Interventions [9.055997973281919]
We propose a training-free inference-time logit intervention for controllable generation.<n>Our results show that statistically grounded logit steering can achieve large, consistent, and multi-task control gains.
arXiv Detail & Related papers (2026-01-16T03:00:33Z) - LatentGuard: Controllable Latent Steering for Robust Refusal of Attacks and Reliable Response Generation [4.29885665563186]
LATENTGUARD is a framework that combines behavioral alignment with supervised latent space control for interpretable and precise safety steering.<n>Our results show significant improvements in both safety controllability and response interpretability without compromising utility.
arXiv Detail & Related papers (2025-09-24T07:31:54Z) - WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales [22.789611187514975]
Methods for nonparametric sequential testing -- especially conformal test martingales (CTMs) and anytime-valid inference -- offer promising tools for this monitoring task.<n>Existing approaches are restricted to monitoring limited hypothesis classes or alarm criteria''
arXiv Detail & Related papers (2025-05-07T17:53:47Z) - TrustLoRA: Low-Rank Adaptation for Failure Detection under Out-of-distribution Data [62.22804234013273]
We propose a simple failure detection framework to unify and facilitate classification with rejection under both covariate and semantic shifts.<n>Our key insight is that by separating and consolidating failure-specific reliability knowledge with low-rank adapters, we can enhance the failure detection ability effectively and flexibly.
arXiv Detail & Related papers (2025-04-20T09:20:55Z) - Robustness and Accuracy Could Be Reconcilable by (Proper) Definition [109.62614226793833]
The trade-off between robustness and accuracy has been widely studied in the adversarial literature.
We find that it may stem from the improperly defined robust error, which imposes an inductive bias of local invariance.
By definition, SCORE facilitates the reconciliation between robustness and accuracy, while still handling the worst-case uncertainty.
arXiv Detail & Related papers (2022-02-21T10:36:09Z) - Joint Differentiable Optimization and Verification for Certified
Reinforcement Learning [91.93635157885055]
In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties.
We propose a framework that jointly conducts reinforcement learning and formal verification.
arXiv Detail & Related papers (2022-01-28T16:53:56Z) - Efficient Empowerment Estimation for Unsupervised Stabilization [75.32013242448151]
empowerment principle enables unsupervised stabilization of dynamical systems at upright positions.
We propose an alternative solution based on a trainable representation of a dynamical system as a Gaussian channel.
We show that our method has a lower sample complexity, is more stable in training, possesses the essential properties of the empowerment function, and allows estimation of empowerment from images.
arXiv Detail & Related papers (2020-07-14T21:10:16Z)
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.