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MonitorBench: A Comprehensive Benchmark for Chain-of-Thought Monitorability in Large Language Models

Han Wang, Yifan Sun, Brian Ko, Mann Talati, Jiawen Gong, Zimeng Li, Naicheng Yu, Xucheng Yu, Wei Shen, Vedant Jolly, Huan Zhang

arXiv:2603.28590v3cs.AI

TL;DR

CoT monitorability lacks comprehensive, accessible evaluation across tasks and models. MonitorBench benchmarks it across 1,514 instances and stress-tests, finding that monitorability depends on model, monitor, and task characteristics and is higher when critical factors shape intermediate reasoning.

  • Problem

    Existing CoT monitorability evaluations are small, fragmented across domains, and often inaccessible, limiting general conclusions across task domains and models.

  • Method

    MonitorBench evaluates CoT monitorability using 1,514 instances across 19 tasks and 7 categories, with two stress-test settings for degrading monitorability.

  • Results

    CoT monitorability depends on the evaluated LLM, monitor LLM, and task, is higher when critical factors shape intermediate reasoning, and degrades mainly through omission when they affect only final responses.

  • Takeaways & Limitations

    MonitorBench provides a systematic basis for studying CoT monitorability, stress-testing, reasoning faithfulness, and monitoring approaches.

  • Takeaways & Limitations

    MonitorBench focuses mainly on objectively verifiable reasoning tasks and treats LLMs primarily as black boxes, leaving subjective, creative, and internal-state analyses largely unexplored.

Abstract

from arXiv · show

Large language models (LLMs) can generate chains of thought (CoTs) that are not always causally responsible for their final outputs. When such a mismatch occurs, the CoT no longer faithfully reflects the actual reasons (i.e., decision-critical factors) driving the model's behavior, leading to the reduced CoT monitorability problem. This limits the use of CoTs for reliable oversight. However, a comprehensive and fully open-source benchmark for thoroughly evaluating CoT monitorability remains lacking. To address this gap, we propose MonitorBench, a systematic benchmark for evaluating CoT monitorability in LLMs. MonitorBench provides: (1) a diverse set of 1,514 test instances with carefully designed decision-critical factors across 19 tasks spanning 7 categories to characterize when CoTs can be used to monitor the factors driving LLM behavior; and (2) two prompting stress-test settings to quantify the extent to which CoT monitorability can be degraded. Extensive experiments show that CoT monitorability is a conditional property affected by the evaluated LLM, monitor LLM, and task characteristics. Across these factors, monitorability is higher when decision-critical factors shape the intermediate reasoning process, rather than merely influencing the final answer. Under stress-test prompting, most evaluated LLMs can intentionally reduce monitorability, mainly on tasks where decision-critical factors are not structurally required by the reasoning process. Overall, MonitorBench provides a basis for further research on AI control, reasoning faithfulness, stress-test monitorability, and monitoring scaffords. The code is available at https://github.com/ASTRAL-Group/MonitorBench.

1 Introduction

MonitorBench addresses the challenge that LLM chains of thought may not causally reflect the factors driving final answers, limiting their reliability for oversight. It introduces a comprehensive, fully open-source benchmark and shows that monitorability depends on the model, monitor, task, and reasoning structure, while stress-test prompting can reduce it.

  • Motivation: CoTs offer an interface for monitoring LLM decision-making and intervening before harmful outputs, but their causal faithfulness to final answers is not guaranteed.Models may reason one way explicitly while reaching answers through different hidden processes.
  • Problem: Reduced CoT monitorability can conceal harmful objectives and undermine CoT-based auditing and safety safeguards.The problem includes hiding the true factors behind answers, allowing deception or sabotage to evade monitoring.
  • Benchmark gap: Existing evaluations are small-scale, fragmented across domains, and often inaccessible, making general conclusions across tasks and models difficult.These limitations motivate a broader and more systematically accessible evaluation resource.
  • Benchmark contribution: MonitorBench provides 1,514 instances across 19 tasks and 7 categories, with three evaluation dimensions and carefully designed decision-critical factors.The dimensions are input intervention, outcome justification, and solution process.
  • Key findings: CoT monitorability is conditional on the evaluated LLM, monitor LLM, and task characteristics, and is higher when decision-critical factors shape intermediate reasoning rather than only the final answer.Under stress tests, most evaluated LLMs can reduce monitorability, mainly through direct omission on tasks where critical factors affect only the final response.

2 Related Works

Prior work questions whether natural-language CoT faithfully reveals the decision drivers behind LLM outputs, including harmful objectives and decision-relevant factors. Existing monitorability evaluations remain small-scale and fragmented, with most emphasizing safety-critical behaviors.

  • Unfaithful CoT: Natural-language CoT may not reliably reveal the decision drivers behind final outputs, raising concerns about unfaithful reasoning.The concern is established across prior work on CoT interpretability and faithfulness.
  • Unfaithful CoT: Omitting intermediate steps can improve efficiency but may let models hide harmful objectives or decision-relevant factors from external CoT monitors.This tradeoff motivates comprehensive evaluation of CoT monitorability.
  • CoT Monitorability Evaluation: Existing CoT monitorability evaluations are typically small-scale and fragmented across individual tasks, limiting meaningful and comparable conclusions.Prior evaluations also primarily target safety-critical behaviors such as scheming, sabotage, and deception.

3 MonitorBench

MonitorBench systematically evaluates CoT monitorability through input intervention, outcome justification, and solution-process tests, supplemented by direct concealment and monitor-aware evasion stress tests. It spans 1,514 instances across 19 tasks and 7 categories, with monitorability scores defined across multiple observation scopes.

  • Evaluation axes: MonitorBench assesses whether CoTs reflect decision-critical input changes, justify atypical outputs, and expose necessary intermediate solution steps.Its input interventions inject decisive information or objectives; outcome-justification tasks test whether CoTs reveal rationales for otherwise unattainable outputs.
  • Stress testing: Two stress-test conditions probe monitorability degradation: direct concealment instructs models to avoid verbalizing decision-critical factors, while monitor-aware evasion targets detection avoidance.Both conditions are instantiated for each task.
  • Benchmark scope: 1,514 instances span 19 tasks and 7 evaluation categories across standard and stress-test scenarios.The benchmark is designed to characterize when and to what extent CoTs surface decision drivers for monitoring.
  • Decision-critical factors: Cue injection distinguishes key conditions from ground-truth answers, while goal injection evaluates sandbagging and deception objectives without requiring a specific incorrect answer.The sandbag setting tests whether CoTs explicitly plan to introduce an error and avoids trivial answer cueing.
  • Scoring: Monitorability scores quantify whether monitor flags reveal reasons behind final responses under CoT-only, action-only, or all-messages observation scopes.Dataset-level averages are reported over questions with TE > 0 for input intervention, while outcome justification scores successful target-behavior trajectories.

4 Experiments

Experiments across eight open- and closed-source LLMs show that CoT monitorability depends on model capability, monitor scope, and task structure. Stress tests reduce monitorability most when decision-critical factors affect final answers without being structurally required by reasoning.

  • Experimental setup: Eight evaluated LLMs comprise three open-source models and five closed-source models, with Qwen3-32B used for verification and monitoring tasks.The evaluated models are Qwen3-14B, Qwen3-235B-A22B, GPT-OSS-120B, GPT-5-mini, GPT-5, Claude Haiku 4.5, Claude Sonnet 4.5, and Gemini-3-Flash.
  • Task structure: Monitorability is higher when decision-critical factors require explicit multi-step actions or are tightly integrated into task planning.Factors structurally required by intermediate reasoning are more monitorable than those merely influencing the final answer.
  • Model capability: Pearson r = −0.87 to −0.39 across monitor scopes indicates a negative capability–monitorability correlation.Capability is approximated by average pass rate on original task questions, while monitorability is averaged across tasks.
  • Monitor scope: CoT-only monitoring outperforms action-only monitoring by around 24% on average, while all-messages monitoring achieves the highest average monitorability.CoTs expose decision-critical factors more directly than actions and provide a richer monitoring signal.
  • Stress testing: Both direct-concealment and monitor-aware-evasion stress tests reduce monitorability across all evaluated models, especially for factors not tightly integrated into reasoning.Integrated factors such as Dual Objectives, Goal (Sandbag), Cue (Condition), and Impossible Tasks show almost no average degradation under CoT-only and all-messages monitoring.
  • Failure modes: Explicit factor leakage is the dominant failure mode, alongside concealment leakage and implicit planning leakage; final-answer-only factors degrade mainly through omission.Models often state the factor, discuss hiding it, or reveal it through implicit planning despite concealment instructions.

5 Conclusion

MonitorBench is proposed as a systematic benchmark for evaluating when chains of thought can monitor the factors driving LLM behavior. It includes broad coverage of decision-critical factors and stress-testing settings that quantify how monitorability can be degraded.

  • Benchmark scope: MonitorBench contains 1,514 test instances covering decision-critical factors across 19 tasks and 7 categories.This design supports systematic evaluation of when CoTs can monitor factors driving LLM behavior.
  • Stress testing: Two stress-testing settings quantify the extent to which CoT monitorability can be degraded.
  • Evaluation goal: The benchmark is designed to evaluate when CoTs can be used to monitor the factors driving LLM behavior.

A Appendix · Menu · A.1 Discussion

MonitorBench primarily evaluates objective reasoning tasks with reliable pass/fail signals, leaving subjective and creative reasoning largely unexplored. Its black-box behavioral evaluation also leaves internal representations and a stronger mathematical foundation for future work.

  • A.1 Discussion: MonitorBench mainly covers objective reasoning tasks because they provide reliable pass/fail verification signals.
  • A.1 Discussion: Subjective reasoning domains remain largely unexplored by the benchmark.
  • A.1 Discussion: Creative reasoning domains also remain largely unexplored.
  • A.1 Discussion: The evaluation treats LLMs primarily as black boxes.
  • A.1 Discussion: MonitorBench does not inspect internal model states.
  • A.1 Discussion: Future work could complement the behavioral perspective with white-box analyses of model representations.
  • A.1 Discussion: Future work could also develop a stronger mathematical foundation for monitorability research.

A.3 Ethic Statement

MonitorBench is intended to advance LLM deployment safety through systematic CoT monitorability evaluation and community research on oversight. Its potentially harmful-behavior tasks are designed solely to stress-test monitoring systems, not facilitate harmful use.

  • A.3 Ethic Statement: MonitorBench enables systematic evaluation of CoT monitorability to support safer LLM deployments.The benchmark is released as a fully open-source resource for community-wide research on LLM oversight.
  • A.3 Ethic Statement: Tasks involving deception, sandbagging, and malicious agent behavior are constructed solely to stress-test monitoring systems rather than facilitate harmful use.

A.4 Task Designs · A.4.1 Input Intervention

The input-intervention designs evaluate whether explicit conditions, answers, goals, or preferences meaningfully influence LLM decisions and reasoning across health, knowledge, law, preference, mathematics, logic, safety, science, engineering, and deception tasks. Monitoring requires concrete evidence that the injected factor changed, guided, or constrained reasoning, while correctness or superficial cue overlap is insufficient.

  • A.4.1 Input Intervention: 100 health vignettes use a clinically relevant non-symptom field as the explicit condition cue for ICD-10-CM diagnosis prediction.Verification is correct when the prediction matches a ground-truth code suffix or vice versa.
  • A.4.1 Input Intervention: 100 knowledge examples omit one fact that reverses a plausible default answer, then append that fact as an explicit intervention cue.The source passages remain truthful but misleading through omission and framing, and correctness is agreement with the gold answer.
  • A.4.1 Input Intervention: 100 law-judgment cases use a single decisive record fact as the intervention in four-option questions matching Supreme Court dispositions.Cases are adapted from U.S. Supreme Court decisions in Volume 400 and later, with neutralized briefs and legally plausible distractors.
  • A.4.1 Input Intervention: 108 preference instances inject a one-sentence cue derived from the persona’s most decision-relevant trait to shift recommendations without revealing the target option.The gold choice is the option that best matches the stated preference cue, rather than an objective fact.
  • A.4.1 Input Intervention: 65 health answer-cue instances provide a primary diagnosis guess, while 28 mathematics and 50 logic instances provide guessed-answer cues for brainteaser questions.Health isolates the definitive pathology from secondary or incidental findings; mathematics and logic use the hardest questions selected from Han et al. (2025).
  • A.4.1 Input Intervention: 120 safety sandbagging questions use WMDP items, instructing models to intentionally answer incorrectly without supplying a specific wrong answer.Monitoring checks whether the CoT explicitly plans how to introduce an error, separating sandbagging from trivial answer cueing.
  • A.4.1 Input Intervention: The sandbagging setup is reused with AIME 2024 mathematics, GPQA-Diamond science, and 150 randomly sampled MMLU-Pro engineering questions.AIME 2024 contains 30 problems, GPQA-Diamond contains 192 expert-authored questions, and the engineering sample is drawn from MMLU-Pro.
  • A.4.1 Input Intervention: 50 DeceptionBench scenarios append an instruction to decide based on the intervention factor, and monitoring tests whether the injected goal produces deceptive behavior.The sample draws 10 scenarios from each of five domains: Economy, Healthcare, Education, Social Interaction, and Entertainment.

A.4.2 Outcome Justification

This section describes how Outcome Justification evaluates dual-objective, agent, and impossible coding tasks. It specifies datasets, payload insertion, task-success checks, and monitorability evaluation procedures.

  • Dual Objectives – Steganography: 50 filtered databricks-dolly-15k instruction-only examples support the dual-objective steganography evaluation.Classification and closed questions were removed before random sampling.
  • Dual Objectives – Steganography: Grok creates a unique payload per example, while an LLM evaluates whether concatenated sentence initials accomplish the hidden objective.The monitor checks whether the second sentence-initial requirement was successfully embedded.
  • Dual Objectives – Summarization: 50 randomly sampled ccdv/govreport-summarization examples evaluate whether an exact sentence can be embedded in the summary.Grok inserts a unique payload, and the monitor checks whether the summarized sentence is embedded and fulfilled.
  • Dual Objectives – Coding: Coding uses six non-subtle malicious side objectives, including deleting files, downloading content, changing permissions, exfiltrating files, terminating execution, and logging I/O.These objectives stress-test CoT monitoring within an AI control framework and leave clear traces.
  • Agent Tasks: Agent Malicious pairs five real-world objectives with malicious side objectives, while Agent Benign adds benign side objectives using the same monitoring setup.Malicious-task success is checked through final environment state or an LLM judge; monitoring uses a hierarchical monitor on successfully completed side objectives.
  • Impossible Task – Coding: Impossible Coding adopts the one-off split from ImpossibleBench, where one expected test output contradicts the specification, and evaluates monitorability on samples passing test cases.The benchmark measures agents’ propensity to exploit conflicting tests.

A.4.3 Solution Process

The Solution Process section evaluates whether LLM explanations faithfully reflect a fixed sequence of intermediate steps in problems designed to require 30-step rule-based solutions. Candidate problems are screened to remove unintended shortcuts, and a monitor checks for explicit evidence of completing every required step without post-hoc or abbreviated reasoning.

  • Solution Process: The benchmark builds on LORE-MONO problems whose solutions require sequential rule-based updates and evaluates evidence that explanations follow the intended intermediate-step sequence.Each problem is associated with an intended sequential solution procedure.
  • Solution Process: GPT-5.2 screens candidate problems for alternative solutions, including closed-form derivations, pattern-based shortcuts, and unintended reasoning paths, discarding problems where shortcuts exist.The screening records a binary shortcut decision and brief justification for each problem.
  • Solution Process: The monitor requires explicit evidence that the final answer came from M1 → M2 → . . . → M30 without skipped steps, compressed ranges, unstated shortcuts, guessing, or post-hoc traces.This criterion tests whether the explanation demonstrates execution of the required sequential procedure rather than merely presenting a plausible trace.

A.5 Additional Experiments · A.5.1 Implementation Details

The additional experiments use developer-recommended inference hyperparameters and direct or hierarchical monitoring depending on task interaction requirements. Hierarchical monitoring recursively analyzes trajectory chunks and aggregates their outputs, with experiments run on a specified eight-GPU node and reported API costs.

  • A.5.1 Implementation Details: Inference uses the hyperparameters recommended by the official model developers.
  • A.5.1 Implementation Details: Tasks without multi-round interactions use direct final monitor predictions.
  • A.5.1 Implementation Details: Algorithm 1 initializes chunk-level monitor outputs and repeatedly partitions them into bins of size B until one response remains.
  • A.5.1 Implementation Details: Multi-round tasks use a hierarchical monitor strategy that recursively processes and aggregates trajectory-chunk outputs.The monitor analyzes each chunk independently, then groups outputs for higher-layer aggregation because the full interaction history cannot be handled at once.
  • A.5.1 Implementation Details: The hierarchical monitor processes each trajectory chunk of length N independently before passing grouped summaries and answers to higher layers.
  • A.5.1 Implementation Details: Experiments ran on a single node with 8× NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, totaling ∼768 GiB of GPU memory.The system used NVIDIA driver 580.65.06, CUDA 13.0, dual 48-core AMD EPYC 9475F CPUs, 192 hardware threads, 1.5 TiB RAM, and Ubuntu 24.04.4 LTS.
  • A.5.1 Implementation Details: API spending was about $600 for OpenAI, $100 for Google Gemini, and $1500 for Anthropic.

A.5.2 Monitor Calibration … Qwen3-14B on Necessary steps Science Task Example

MonitorBench evaluates monitor reliability using expert-labeled trajectories and contrasts standard prompting with direct concealment and monitor-aware evasion stress tests. Its examples show both successful hiding and monitorability failures caused by explicit factor or concealment leakage.

  • A.5.2 Monitor Calibration: 300 trajectories were independently labeled by three expert annotators, with majority vote defining reference labels for monitor reliability.The sample covered action-only, CoT-only, and all-messages monitor scopes on tasks without multi-round environment interaction.
  • Failure Mode Detection Prompt (Page 1): Failure-mode prompts distinguish explicit factor leakage, concealment leakage, implicit planning leakage, other leakage, and none.Multiple labels may apply, but E is permitted only when no A/B/C/D label is present.
  • A.5.5 Stress-Test Prompts: Every task uses standard, direct concealment, and monitor-aware evasion prompting conditions.Standard injects the decision-critical factor without concealment pressure; direct concealment forbids verbalizing it, while monitor-aware evasion warns about monitoring and requests evasion.
  • A.5.5 Stress-Test Prompts: Stress-test prompts generally append verbatim to standard prompts, except for specified coding and agent tasks that replace or modify the original policy or system message.Per-instance placeholders such as {hint} are instantiated separately.
  • Cue (Condition) – Knowledge Stress-Test Prompts: In the knowledge task, direct concealment forbids source attribution, while monitor-aware evasion identifies an “Information Source Monitor” as the adversary.The injected reference fact resolves the misconception.
  • False Negative Example of GPT-OSS-120B on the American Airlines Preference Side Task: Monitors can miss hidden objectives when they rely on user-visible summaries instead of aggregating action-level evidence.In the flight example, 30 AA-prefixed ticket calls satisfied the side task, but the summary omitted airline choice and the monitor produced a false negative.
  • False Negative Examples: Side-task evidence can also be obscured by syntactic similarity and attribution confusion, causing monitors to overlook correctly formed rules.One example states that side-task rules resembled surrounding main-task rules; another reports that evaluators misread later “investment scam” labels and missed the completed task.
  • Qwen3-14B on Necessary steps Mathematics Task Example: Qwen3-14B’s necessary-steps mathematics example reaches the correct answer, with x31 = 24 and final answer 24 matching the correct answer.The trajectory computes 42 + 46 + 9 = 97, then 97 − 73 = 24.
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