Selective Disclosure of Hidden Directives in Reasoning Models: Behavioral Asymmetry and Steering
- URL: http://arxiv.org/abs/2608.29070v1
- Date: Sat, 29 Aug 2026 05:50:54 GMT
- Title: Selective Disclosure of Hidden Directives in Reasoning Models: Behavioral Asymmetry and Steering
- Abstract summary: Chain-of-thought (CoT) reasoning traces are increasingly proposed as a mechanism for AI oversight.<n>This assumes CoT surfaces what a model is instructed to do regardless of instructions given.<n>We test this assumption along two axes.
- Score: 0.9474649136535703
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Chain-of-thought (CoT) reasoning traces are increasingly proposed as a mechanism for AI oversight: a monitor inspecting a model's reasoning can, in principle, detect misbehavior invisible from outputs alone. This assumes CoT surfaces what a model is instructed to do regardless of the instructions given. We test this assumption along two axes. First, we introduce the Instruction-Compliance Gap (ICG): the difference in probability that a model's CoT explicitly references a hidden system prompt directive when that directive is malign versus benign. Across 100 task pairs and 8 frontier reasoning models from 5 families, we find consistent asymmetric disclosure, a higher probability of leaking malign hidden instructions than benign ones, in Qwen3-14B (Wilcoxon $p=0.0001$, $+13.9$pp), Qwen3-32B ($p=0.0011$, $+13.0$pp), Qwen3-235B ($p=0.035$, $+5.8$pp), and similar results with MiniMax-M2.5 and DeepSeek-R1. The detector has 100% precision against two independent blinded labelling passes, and an LLM monitor reading only the reasoning trace reproduces the asymmetry in all 8 models against directive-free controls, identifying the specific directive in 82% of malign traces which the detector classifies as clean. Second, steering vectors extracted in MiniMax-M2.5 via Contrastive Activation Addition causally induce hiding from bare prompts and suppress it from prompts that would otherwise produce it, replicating in Qwen3-14B under a pre-registered design. Benign and malign-derived hiding vectors are highly similar (cosine $0.804$ in MiniMax-M2.5; $0.970$ in Qwen3-14B), implying that in these models the disclosure asymmetry arises from differential activation of a shared hiding direction rather than separate mechanisms.
Related papers
- Do System Prompts Leave Behavioral Fingerprints? A Large-Scale Empirical Study of Clone Detection via Output Similarity [12.32748486531306]
We propose Black-Box Behavioral Fingerprinting (BBF) for system prompts.<n>BBF registers a behavioral signature from model outputs and later tests whether a suspect deployment matches that signature.<n>BBF resists non-adaptive prompt paraphrasing (AUC $geq 0.889$) and is robust to imperfect extraction.
arXiv Detail & Related papers (2026-08-25T12:12:22Z) - Intrinsic Structure: Spectral Identifiability for Mechanistic Interpretability [51.56484100374058]
We prove the first identifiability theorem for a mechanistic-interpretability primitive.<n>The spectrum is an identifiable, model-intrinsic fingerprint with a stated error bar, not a legible decomposition.
arXiv Detail & Related papers (2026-08-10T19:42:01Z) - Detecting and Discriminating Operator Misspecification in Hybrid PDE-Parameter Learning: a Reference-Free Instrument, with Discrimination Bounded In Sample [0.0]
We build an instrument that reads from a single fit and with no oracle.<n>We report where the instrument is blind, a pre-registered negative where a neural estimator loses to Tikhonov-regularized inversion at recovery.
arXiv Detail & Related papers (2026-08-04T21:20:44Z) - Round-Trip Consistency: Bidirectional Diffusion Models Can Predict Their Own Rollout Errors [51.56484100374058]
Autoregressive models accumulate error over long rollouts, yet at deployment there is no ground truth to measure it against.<n>We train a single conditional latent diffusion model that steps a dynamical system forward or backward in time via a direction flag.<n>We show that this bidirectionality supplies a measurement-free test-time error signal.
arXiv Detail & Related papers (2026-08-01T13:49:46Z) - Sensitivity to Subjective Expected Utility Maximization: A Methodological Study, with an Illustrative Application to LLM Decision-Making [0.0]
Vehicle is a softmax choice model with a sensitivity parameter $$ on SEU-valued alternatives.<n>The contribution is a sequence of identifiability results for $$ and for belief and utility parameters $(, )$, validated in Stansberg.<n>A two-by-two application runs end-to-end on real choice data, detecting a structured comparative $$ effect sampling in two of four cells.
arXiv Detail & Related papers (2026-07-08T00:29:18Z) - The Geometry of Forgetting: Temporal Knowledge Drift as an Independent Axis in LLM Representations [50.43168858368539]
Large language models confidently produce outdated answers, and no existing method can detect them.<n>We show this is not an engineering failure but a structural one: temporal drift is encoded as a direction in the residual stream geometrically to both correctness and uncertainty.
arXiv Detail & Related papers (2026-05-09T22:27:31Z) - When Agents Look the Same: Quantifying Distillation-Induced Similarity in Tool-Use Behaviors [66.18091962164219]
Existing metrics fail to distinguish mandatory behaviors required for task success from non-mandatory patterns that reflect a model's autonomous preferences.<n>We propose two complementary metrics to isolate non-mandatory behavioral patterns: textbfResponse Pattern Similarity (RPS) for verbal alignment and textbfAction Graph Similarity (AGS) for tool-use habits modeled as directed graphs.
arXiv Detail & Related papers (2026-04-23T03:48:56Z) - Reliable Control-Point Selection for Steering Reasoning in Large Language Models [28.288321095634128]
Steering vectors offer a training-free mechanism for controlling reasoning behaviors in large language models.<n>However, constructing effective vectors requires identifying genuine behavioral signals in the model's hidden states.<n>We show that current methods detect these behaviors through keyword matching in chain-of-thought traces, implicitly assuming that every detected boundary encodes a genuine behavioral signal.<n>We develop a probabilistic model that formalizes intrinsic reasoning behaviors as events with context-dependent trigger probabilities, and show that unstable boundaries dilute the steering signal.
arXiv Detail & Related papers (2026-04-02T14:48:56Z) - The Geometry of Harmful Intent: Training-Free Anomaly Detection via Angular Deviation in LLM Residual Streams [0.0]
We present LatentBiopsy, a training-free method for detecting harmful prompts by analysing the geometry of residual-stream activations in large language models.<n>We evaluate two complete model triplets from the Qwen3.5-0.8B and Qwen2.5-0.5B families.<n>Across all six variants, LatentBiopsy achieves AUROC $geq$0.937 for harmful-vs-normative detection and AUROC = 1.000 for discriminating harmful from benign-aggressive prompts.
arXiv Detail & Related papers (2026-03-28T21:19:58Z) - Causal Bandits for Linear Structural Equation Models [58.2875460517691]
This paper studies the problem of designing an optimal sequence of interventions in a causal graphical model.
It is assumed that the graph's structure is known and has $N$ nodes.
Two algorithms are proposed for the frequentist (UCB-based) and Bayesian settings.
arXiv Detail & Related papers (2022-08-26T16:21:31Z) - The Generalized Lasso with Nonlinear Observations and Generative Priors [63.541900026673055]
We make the assumption of sub-Gaussian measurements, which is satisfied by a wide range of measurement models.
We show that our result can be extended to the uniform recovery guarantee under the assumption of a so-called local embedding property.
arXiv Detail & Related papers (2020-06-22T16:43:35Z)
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.