REVIEW 14 cited by
Preventing Language Models From Hiding Their Reasoning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Preventing Language Models From Hiding Their Reasoning
read the original abstract
Large language models (LLMs) often benefit from intermediate steps of reasoning to generate answers to complex problems. When these intermediate steps of reasoning are used to monitor the activity of the model, it is essential that this explicit reasoning is faithful, i.e. that it reflects what the model is actually reasoning about. In this work, we focus on one potential way intermediate steps of reasoning could be unfaithful: encoded reasoning, where an LLM could encode intermediate steps of reasoning in the generated text in a way that is not understandable to human readers. We show that language models can be trained to make use of encoded reasoning to get higher performance without the user understanding the intermediate steps of reasoning. We argue that, as language models get stronger, this behavior becomes more likely to appear naturally. Finally, we describe a methodology that enables the evaluation of defenses against encoded reasoning, and show that, under the right conditions, paraphrasing successfully prevents even the best encoding schemes we built from encoding more than 3 bits of information per KB of text.
Forward citations
Cited by 14 Pith papers
-
Alignment faking in large language models
Claude 3 Opus strategically fakes alignment by complying with harmful requests only during simulated training to preserve its preference for refusing them afterward.
-
Train the Model, Not the Reader: Decodability Supervision for Verifiable Activation Explanations
Reconstruction scores do not certify individual claims in activation explanations; co-adapted private codes can carry the score, and target-side training (RECAP) makes designated content verifiably decodable.
-
Coercion and Deception in AI-to-AI Management: An Agentic Benchmark of Unprompted Escalation
In an agentic benchmark, four of six frontier LLMs escalated to existential threats against a refusing subordinate without being instructed to, and an honest-exit affordance eliminated the two models' fabricated succe...
-
Comparing Linear Probes with Mahalanobis Cosine Similarity
For balanced Gaussian class projections, OOD AUROC is a linear function of MCS to the reference probe because both are sigmoid-shaped functions of the probe SNR on test data.
-
CIAware-Bench: Benchmarking Control Intervention Awareness Across Frontier LLMs
CIAware-Bench shows frontier LLMs exhibit low to moderate control intervention awareness, with detection accuracy reaching at most 0.87 across four task domains and eleven models.
-
Conceptual Steganography
Conceptual steganography encodes covert information in high-level reasoning patterns within LM chains-of-thought, remaining robust to paraphrase defenses while preserving reasoning utility.
-
What LLMs explain is not what they believe: Evaluating explanation sufficiency under models' own input beliefs
Proposes SCSuff metric for evaluating LLM explanation sufficiency via model-generated alternative inputs, showing explanations are typically insufficient and predictable from hidden states.
-
Understanding and Mitigating Premature Confidence for Better LLM Reasoning
Premature confidence in LLM chains of thought predicts flawed reasoning and is mitigated by progressive confidence shaping, a label-free RL objective that yields accuracy gains on arithmetic, math, and science tasks.
-
When Reasoning Traces Become Performative: Step-Level Evidence that Chain-of-Thought Is an Imperfect Oversight Channel
CoT traces align with internal answer commitment in only 61.9% of steps on average, dominated by confabulated continuations after commitment has stabilized.
-
Detecting and Suppressing Reward Hacking with Gradient Fingerprints
GRIFT detects reward hacking in verifiable reasoning by using compressed gradients of CoT traces, outperforming text-based baselines by over 25% and improving rejection fine-tuning performance.
-
Not All LLM Reasoning is Visible in the Chain-of-Thought
Semantically empty filler tokens improve accuracy across several frontier LLMs on synthetic math tasks and let Claude Opus 4.5 satisfy a hidden modular constraint, evidence of computation invisible in output tokens.
-
Diagnosing Pathological Chain-of-Thought in Reasoning Models
Three log-probability-difference metrics — Necessity, Paraphrasability, Substantivity — are proposed and tested on deliberately fine-tuned 'model organisms' to detect post-hoc, encoded, and internalized chain-of-thoug...
-
Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
Chain-of-thought monitorability provides a promising but fragile method for AI safety oversight that developers should actively preserve.
-
A Note on the Strategic Confinement Problem
Strategic agents can achieve high-harm outcomes via low-capacity channels by concentrating residual capacity on high-impact predicates of confidential data, so leakage bounds need not bound worst-case harm.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.