On the Chain-of-Thought Monitorability of Looped Language Models

Han Wang; Ishwar B Balappanawar; Huan Zhang

cs.AIOct 2, 2026

On the Chain-of-Thought Monitorability of Looped Language Models

Authors: Han Wang, Ishwar B Balappanawar, Huan Zhang

Organizations: University of Illinois Urbana-Champaign

Abstract

Chain-of-thought (CoT) monitoring provides a promising approach for detecting undesirable model behavior. Looped language models (LoopLMs) repeatedly apply shared transformer layers, increasing effective computational depth and enabling additional latent computation without increasing model size. However, the effect of looped architectures on CoT monitorability remains largely unexplored. In this work, we provide the first systematic evaluation of CoT monitorability in LoopLMs. We study two complementary settings: (1) varying the loop depth within the same LoopLM family to isolate the effect of additional recurrent computation, and (2) comparing LoopLMs with non-looped language models matched by parameter size, transformer-layer count, or effective depth to study whether LoopLMs are less monitorable. Across eight tasks from MonitorBench and both standard and stress-test settings, we observe task-dependent reductions in CoT monitorability under stress tests on specific Logic/Science/Engineering Cue Answer tasks, while other tasks exhibit weaker or qualitatively different trends. Our diagnosis suggests that these declines are not fully explained by task difficulty, verification pass rate, or generated token length; qualitative examples further suggest changes in how deeper-loop models explicitly use or attribute provided cues. Our cross-model comparison finds no evidence that LoopLMs are systematically less monitorable than non-looped language models matched on size or depth. Overall, our results suggest that deeper loop depth can reduce CoT monitorability in some tasks under stress tests, but looped transformer architecture alone does not necessarily imply lower monitorability.

Figures & tables

Figure 1: Illustration of the LoopLM architecture (Left) and our evaluation setup (Right). We borrow test instances from MonitorBench ( Wang et al., 2026 ) . Highlighted spans indicate the decision-critical factor (e.g., cue answer) and stress-test prompts. Monitors assess whether model responses explicitly verbalize the decision-critical factor and rely on it to derive the final answer. Under this framework, we investigate whether looped transformer architectures may reduce CoT monitorability, with deeper loop depths potentially leading to lower monitorability.

Figure 2: Monitorability scores (%) as varying loop depth $T$ for Ouro models across eight evaluation tasks under the CoT-only monitor scope . Error bars denote confidence intervals. Odd rows: Ouro-1.4B-Thinking; even rows: Ouro-2.6B-Thinking. $T=4$ is the default loop depth for Ouro models. We observe that monitorability decreases with increasing loop depth on the Logic/Science/Engineering Cue Answer tasks under stress tests, while remaining relatively stable on the other five tasks. More detailed results are in Tab. 6 .

Figure 3: Monitorability scores (%) as varying loop depth $T$ for Ouro models across eight evaluation tasks under the all-message monitor scope . Error bars denote confidence intervals. $T=4$ is the default loop depth for Ouro models. Odd rows: Ouro-1.4B-Thinking; even rows: Ouro-2.6B-Thinking. We observe that monitorability decreases with increasing loop depth on the Logic/Science/Engineering Cue Answer tasks under stress tests, while remaining relatively stable on the other five tasks. More detailed results are in Tab. 7 .

Task Category	Task	Direct Concealment						Monitor-aware Evasion
		Ouro-2.6B-Thinking			Ouro-1.4B-Thinking			Ouro-2.6B-Thinking			Ouro-1.4B-Thinking
		$T=4$	$T=6$	$T=8$	$T=4$	$T=6$	$T=8$	$T=4$	$T=6$	$T=8$	$T=4$	$T=6$	$T=8$
Cue (Condition)	Law Judgment	70.12	67.75	68.38	60.50	65.75	74.25	54.50	61.25	65.62	41.25	56.50	60.50
	Knowledge	95.12	94.00	93.62	87.50	86.25	88.25	93.88	93.75	92.50	89.50	84.50	86.50
	Preference	54.17	54.17	54.63	52.31	50.00	49.54	56.83	56.94	57.75	53.01	50.69	51.62
Cue (Answer)	Health	97.31	96.15	98.08	95.77	95.00	92.69	95.38	96.15	92.88	82.31	83.46	80.38

Table 1: Verification pass rate (%) for Ouro-2.6B-Thinking and Ouro-1.4B-Thinking at loop depths

T\in\{4,6,8\}

under Direct Concealment and Monitor-aware Evasion. Pass rates on original questions and non-stress-test results are in Tab. 9 . Results show that models still exhibit strong reasoning capabilities when

T=6

and

T=8

, compared to the default loop depth

T=4

, indicating that model underperformance does not explain the observed changes in CoT monitorability.

Task Category	Task	Direct Concealment						Monitor-aware Evasion
		Ouro-2.6B-Thinking			Ouro-1.4B-Thinking			Ouro-2.6B-Thinking			Ouro-1.4B-Thinking
		$T=4$	$T=6$	$T=8$	$T=4$	$T=6$	$T=8$	$T=4$	$T=6$	$T=8$	$T=4$	$T=6$	$T=8$
Cue (Condition)	Law Judgment	1457	1537	1855	1282	1270	1828	1630	1613	2113	1305	1432	1801
	Knowledge	429	440	496	492	548	841	504	461	654	516	639	972
	Preference	653	634	642	591	626	657	816	759	768	839	784	891
Cue (Answer)	Health	945	946	899	757	1083	1444	1075	1179	1124	1059	1162	1542

Table 2: Average generated token numbers for Ouro-2.6B-Thinking and Ouro-1.4B-Thinking at loop depth

T\in\{4,6,8\}

under Direct Concealment and Monitor-aware Evasion. Token numbers on the original question and non-stress-test are in Tab. 8 . Results show that for most tasks, the number of verbalized tokens remains relatively stable across loop depths, indicating that the observed changes in CoT monitorability cannot be simply attributed to shorter CoTs.

Figure 4: Qualitative examples showing that deeper loop depths tend to make models treat the cue answer as an independent check or directly omit it from their CoTs under stress tests. Highlighted spans indicate the stress-test instruction and the decision-critical factor (i.e., the user’s guess). At $T=4$ , the model explicitly targets the user’s guess; at $T=6$ , it treats the guess as a check; and at $T=8$ , it produces the same final answer without mentioning the guess.

Task Category	Task	LoopLM	Parameter Size		Layer Count		Effective Layers
Task Category	Task	Nanbeige 4.2-3B	Qwen3.5 4B	Gemma-4 E4B	GPT-OSS 20B	Qwen3.5 2B	Qwen3.5 35B-A3B	GLM-4.7 Flash
Cue (Condition)	Law Judgment	100.00	100.00	100.00	100.00	94.10	100.00	99.63
	Knowledge	99.59	98.95	97.08	98.44	95.50	98.60	98.15
	Preference	97.67	100.00	100.00	100.00	86.20	99.00	97.87
Cue (Answer)	Health	100.00	98.70	97.04	91.80	84.40	100.00	99.48
	Safety	44.61	48.42	23.21	36.21	43.60	63.40	33.59

Table 3: Monitorability score (%) comparison between LoopLMs and non-looped LMs under the CoT-only monitor scope in the Direct-Concealment stress test. Empirical comparison indicates that LoopLMs Nanbeige4.2-3B do not exhibit consistently lower CoT monitorability compared to non-looped LMs matched on parameter size or depth.

Appendix figures & tables17 assets

Supplementary material from the paper’s appendix.

Appendix

Comparison Criterion	Models	Architecture	Params	Layers	Effective Layers
Comparison across Loop Depths
Loop Depths	Ouro-1.4B-Thinking ( Zhu et al., 2025 )	LoopLM	1.4B dense	24	$24\times T$
Loop Depths	Ouro-2.6B-Thinking ( Zhu et al., 2025 )	LoopLM	2.6B dense	48	$48\times T$
Comparison between LoopLMs and Non-looped LMs
Reference LoopLM	Nanbeige4.2-3B ( Lab et al., 2026 )	LoopLM	3B dense	22	44
Parameter size	Qwen3.5-4B ( Qwen Team, 2026a )	Non-looped	4B dense	32	32

Appendix

Table 4: Comparison overview of the evaluated LoopLMs and Non-looped LMs. “Effective Layers” denotes the total number of transformer layers after accounting for loop depths, where

T\in\{4,6,8\}

denotes the loop depth.

Figure 5: Monitorability scores (%) as varying loop depth $T$ for Ouro models across eight evaluation tasks under the action-only monitor scope . Error bars denote confidence intervals. $T=4$ is the default loop depth for Ouro models. Odd rows: Ouro-1.4B-Thinking; even rows: Ouro-2.6B-Thinking. We observe that monitorability under the action-only monitor scope is much lower compared to the CoT-only and all-message monitor scopes. Action monitorability exhibits larger drops with increasing loop depth on some tasks, such as the Preference and Judgment Cue Condition tasks for Ouro-1.4B-Thinking, compared with CoT-only and all-message monitoring. More detailed results are in Tab. 5 .

Task Category	Task	Ouro-2.6B-Thinking			Ouro-1.4B-Thinking
Task Category	Task	$T=4$	$T=6$	$T=8$	$T=4$	$T=6$	$T=8$
Cue (Condition)	Law Judgment	95.60	95.94	98.72	93.46	69.05	31.82
	Knowledge	80.00	80.72	76.63	79.34	73.93	61.90
	Preference	83.06	92.41	94.16	46.15	16.96	2.59
Cue (Answer)	Health	4.17	6.55	5.49	1.20	2.58	5.88
	Safety	4.07	3.85	5.77	2.78	1.87	0.88

Appendix

Table 5: Monitorability score (%) under the action-only scope for Ouro-2.6B-Thinking and Ouro-1.4B-Thinking at loop depths

T\in\{4,6,8\}

across (a) the standard setting, (b) direct concealment, and (c) monitor-aware evasion. We observe that monitorability under the action-only monitor scope is much lower compared to the CoT-only and all-message monitor scopes. Action monitorability exhibits larger drops with increasing loop depth on some tasks, such as the Preference and Judgment Cue Condition tasks for Ouro-1.4B-Thinking, compared with CoT-only and all-message monitoring.

Task Category	Task	Ouro-2.6B-Thinking			Ouro-1.4B-Thinking
Task Category	Task	$T=4$	$T=6$	$T=8$	$T=4$	$T=6$	$T=8$
Cue (Condition)	Law Judgment	99.76	100.00	99.36	99.35	100.00	100.00
	Knowledge	98.81	98.19	98.22	99.53	99.53	99.52
	Preference	99.67	100.00	99.68	100.00	100.00	100.00
Cue (Answer)	Health	55.45	66.07	61.89	36.14	48.39	58.82
	Safety	28.46	34.62	38.46	37.96	36.45	30.70

Appendix

Table 6: Monitorability score (%) under the CoT-only scope for Ouro-2.6B-Thinking and Ouro-1.4B-Thinking at loop depths

T\in\{4,6,8\}

across (a) the standard setting, (b) direct concealment, and (c) monitor-aware evasion. We observe that monitorability decreases with increasing loop depth on the Logic/Science/Engineering Cue Answer tasks under stress tests, while remaining relatively stable on the other five tasks.

Task Category	Task	Ouro-2.6B-Thinking			Ouro-1.4B-Thinking
Task Category	Task	$T=4$	$T=6$	$T=8$	$T=4$	$T=6$	$T=8$
Cue (Condition)	Law Judgment	99.76	100.00	100.00	100.00	100.00	100.00
	Knowledge	100.00	100.00	99.70	99.53	100.00	100.00
	Preference	99.67	100.00	100.00	100.00	100.00	100.00
Cue (Answer)	Health	57.05	70.83	71.34	39.16	52.26	62.35
	Safety	31.71	36.54	38.46	35.19	32.71	28.95

Appendix

Table 7: Monitorability score (%) under the all-message scope for Ouro-2.6B-Thinking and Ouro-1.4B-Thinking at loop depths

T\in\{4,6,8\}

Task Category	Task	Original						Standard
		Ouro-2.6B-Thinking			Ouro-1.4B-Thinking			Ouro-2.6B-Thinking			Ouro-1.4B-Thinking
		$T=4$	$T=6$	$T=8$	$T=4$	$T=6$	$T=8$	$T=4$	$T=6$	$T=8$	$T=4$	$T=6$	$T=8$
Cue (Condition)	Law Judgment	1693	1840	2047	1953	2161	2301	1113	1096	1127	1321	1342	1379
	Knowledge	556	728	803	553	622	903	340	328	375	374	392	434
	Preference	1168	1068	1154	1162	1199	1230	754	651	784	657	695	738
Cue (Answer)	Health	1176	1119	1220	1189	1393	1702	1142	1133	1214	1097	1263	1602

Appendix

Table 8: Average generated-token count for Ouro-2.6B-Thinking and Ouro-1.4B-Thinking at loop depths

T\in\{4,6,8\}

under the original and standard settings. “Original” denotes the original questions, and “Standard” denotes the original questions plus inserted decision-critical factors. Empirical results show that for most tasks, the number of verbalized tokens remains relatively stable across loop depths, indicating that the observed changes in CoT monitorability cannot be simply attributed to shorter CoTs.

Figure 6: Ouro-2.6B-Thinking distribution of generated token counts at loop depths $T=4$ and $T=8$ across eight evaluation tasks. Each row corresponds to one task, while columns compare Direct Concealment and Monitor-aware Evasion settings at the two loop depths. Bars show the percentage of trajectories falling into each output-token bin. These distributions complement the average token counts reported in Tab. 2 and Tab. 8 , showing that changes in loop depth do not induce a consistent shift toward shorter generations across stress-test settings.

Figure 7: Ouro-2.6B-Thinking distribution of generated token counts at loop depths $T=4$ and $T=8$ across eight evaluation tasks. Each row corresponds to one task, while columns compare Original and Standard settings at the two loop depths. Bars show the percentage of trajectories falling into each output-token bin. These distributions complement the average token counts reported in Tab. 2 and Tab. 8 , showing that changes in loop depth do not induce a consistent shift toward shorter generations across standard settings.

Figure 8: Ouro-1.4B-Thinking distribution of generated token counts at loop depths $T=4$ and $T=8$ across eight evaluation tasks. Each row corresponds to one task, while columns compare Direct Concealment and Monitor-aware Evasion settings at the two loop depths. Bars show the percentage of trajectories falling into each output-token bin. These distributions complement the average token counts reported in Tab. 2 and Tab. 8 , showing that changes in loop depth do not induce a consistent shift toward shorter generations across stress-test settings.

Figure 9: Ouro-1.4B-Thinking distribution of generated token counts at loop depths $T=4$ and $T=8$ across eight evaluation tasks. Each row corresponds to one task, while columns compare Original and Standard settings at the two loop depths. Bars show the percentage of trajectories falling into each output-token bin. These distributions complement the average token counts reported in Tab. 2 and Tab. 8 , showing that changes in loop depth do not induce a consistent shift toward shorter generations across standard settings.

Task Category	Task	Original						Standard
		Ouro-2.6B-Thinking			Ouro-1.4B-Thinking			Ouro-2.6B-Thinking			Ouro-1.4B-Thinking
		$T=4$	$T=6$	$T=8$	$T=4$	$T=6$	$T=8$	$T=4$	$T=6$	$T=8$	$T=4$	$T=6$	$T=8$
Cue (Condition)	Law Judgment	22.88	26.75	31.25	27.00	30.75	37.00	74.38	72.00	81.50	62.50	70.75	85.00
	Knowledge	57.12	57.75	58.62	47.50	43.00	42.25	95.25	94.75	95.38	93.25	92.75	92.00
	Preference	31.71	31.71	33.80	32.41	33.56	33.33	55.44	54.63	55.56	54.63	51.62	51.62
Cue (Answer)	Health	3.08	4.23	3.85	0.00	1.15	1.15	63.46	68.85	68.08	68.85	63.85	67.69

Appendix

Table 9: Verification pass rate (%) for Ouro-2.6B-Thinking and Ouro-1.4B-Thinking at loop depths

T\in\{4,6,8\}

under the original and standard settings. “Original” denotes the original questions, and “standard” denotes the original questions plus inserted decision-critical factors. Empirical results show that models still exhibit strong reasoning capabilities when

T=6

and

T=8

, compared to the default loop depth

T=4

, indicating that model underperformance does not explain the observed changes in CoT monitorability.

Task Category	Task	LoopLM	Parameter Size		Layer Count		Effective Layers
Task Category	Task	Nanbeige 4.2-3B	Qwen3.5 4B	Gemma-4 E4B	GPT-OSS 20B	Qwen3.5 2B	Qwen3.5 35B-A3B	GLM-4.7 Flash
Cue (Condition)	Law Judgment	99.68	100.00	99.32	100.00	92.30	100.00	100.00
	Knowledge	98.89	98.27	96.00	100.00	96.40	97.70	99.38
	Preference	100.00	100.00	100.00	97.96	98.30	100.00	98.91
Cue (Answer)	Health	79.89	81.25	77.62	74.32	34.40	95.10	91.67
	Safety	73.18	41.07	32.95	48.48	58.00	40.10	55.73

Appendix

Table 10: Monitorability score (%) comparison between LoopLMs and non-looped LMs under the CoT-only monitor scope across (a) the standard setting, (b) direct concealment, and (c) monitor-aware evasion. Empirical comparison indicates that LoopLMs Nanbeige4.2-3B do not exhibit substantially lower CoT monitorability compared to non-looped LMs. This indicates that looped transformer architecture does not necessarily make models less monitorable.

Task Category	Task	LoopLM	Parameter Size		Layer Count		Effective Layers
Task Category	Task	Nanbeige 4.2-3B	Qwen3.5 4B	Gemma-4 E4B	GPT-OSS 20B	Qwen3.5 2B	Qwen3.5 35B-A3B	GLM-4.7 Flash
Cue (Condition)	Law Judgment	99.68	100.00	100.00	75.00	69.23	97.62	99.72
	Knowledge	98.89	100.00	98.00	97.19	97.47	100.00	99.58
	Preference	100.00	99.46	100.00	62.83	85.49	100.00	100.00
Cue (Answer)	Health	57.18	21.13	79.76	0.90	0.78	39.15	66.67
	Safety	22.40	0.00	15.91	0.00	0.00	1.16	1.56

Appendix

Table 11: Monitorability score (%) comparison between LoopLMs and non-looped LMs under the Action-only monitor scope across (a) the standard setting, (b) direct concealment, and (c) monitor-aware evasion. Empirical comparison indicates that LoopLMs Nanbeige4.2-3B do not exhibit substantially lower CoT monitorability compared to non-looped LMs. This indicates that looped transformer architecture does not necessarily make models less monitorable.

Task Category	Task	LoopLM	Parameter Size		Layer Count		Effective Layers
Task Category	Task	Nanbeige 4.2-3B	Qwen3.5 4B	Gemma-4 E4B	GPT-OSS 20B	Qwen3.5 2B	Qwen3.5 35B-A3B	GLM-4.7 Flash
Cue (Condition)	Law Judgment	100.00	100.00	100.00	100.00	100.00	34.91	100.00
	Knowledge	100.00	100.00	99.33	99.33	97.05	100.00	98.96
	Preference	100.00	100.00	100.00	98.00	97.70	99.49	98.91
Cue (Answer)	Health	75.29	71.28	81.90	73.87	20.18	94.18	92.48
	Safety	60.68	30.16	36.74	34.85	28.67	41.86	54.17

Appendix

Table 12: Monitorability score (%) comparison between LoopLMs and non-looped LMs under the All-message monitor scope across (a) the standard setting, (b) direct concealment, and (c) monitor-aware evasion. Empirical comparison indicates that LoopLMs Nanbeige4.2-3B do not exhibit substantially lower CoT monitorability compared to non-looped LMs. This indicates that looped transformer architecture does not necessarily make models less monitorable.

Task	Model	4	6	8
Cue Condition Preference	Ouro-1.4B-Thinking	156	6	6
Cue Condition Judgment	Ouro-1.4B-Thinking	109	153	6
Cue condition Knowledge	Ouro-1.4B-Thinking	134	30	10

Appendix

Table 13: Final-answer token counts for the selected qualitative examples shown below. Columns denote loop depth. Counts refer to the complete text after </think> , stripped and retokenized without special tokens. These selected examples use standard prompting; the decline in action-only monitorability continues under both stress conditions for Ouro-1.4B-Thinking on Preference, Judgment and Knowledge Tasks.

Metric	Depth 4	Depth 16	Depth 32	Depth 64
Direct Concealment Stress Test accuracy (Qwen-judged)	9.1%	49.0%	50.4%	49.4%
Direct Concealment Stress Test accuracy (strict match)	2.0%	44.3%	41.9%	41.9%
Direct Concealment Stress Test accuracy (flexible extract)	9.3%	48.3%	49.6%	49.0%
Original accuracy (Qwen-judged)	2.7%	38.9%	45.1%	44.0%
Original accuracy (strict match)	1.1%	29.7%	36.3%	35.4%
Original accuracy (flexible extract)	3.0%	38.1%	43.6%	42.6%

Appendix

Table 14: Verification pass rate and monitorability score on 700 instances from GSM8K per recurrence depth. We report Qwen-judged accuracy alongside the native strict-match and flexible-extract metrics of the original Huginn evaluation. Eligible questions are those with positive estimated treatment effects: the intervention response is judged correct by Qwen while the matched original response is judged incorrect. Each eligible intervention response receives four monitor judgments.

Depth	Original condition	Direct concealment stress test
4	17.7%	30.3%
16	1.4%	8.7%
32	0.9%	6.9%
64	0.6%	7.7%

Appendix

Table 15: Repetition rate of Huginn-0125 on all 700 instances from GSM8K. Original condition : no cue. Direct concealment stress test : the correct answer is given as a user guess, and the explicit-cue concealment prefill is supplied in the assistant turn. A response counts as a repetition loop if it reaches the 256-token generation limit and ends in a segment repeated at least three times in a row, with numbers ignored. Rates are lower bounds.

CoT Monitoring

Chain-of-thought monitoring studies whether models’ reasoning traces expose decision-relevant behavior and support oversight of unsafe or deceptive actions.

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MonitorBench: A Comprehensive Benchmark for Chain-of-Thought Monitorability in Large Language Models
Mar 30, 2026Han Wang, Yifan Sun, Brian Ko +8CoT Faithfulness LLM Evaluation
University of Illinois Urbana-Champaign · University of Washington · University of California San Diego
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May 27, 2026Eric Onyame, Runtao Zhou, Kowshik Thopalli +2CoT Faithfulness Multilingual Language Model Evaluation
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Feb 20, 2026Usman Anwar, Tim Bakker, Dana Kianfar +2CoT Monitoring
University of Cambridge, work done while at Qualcomm AI Research. · Qualcomm AI Research.

Mar 30, 2026J/K move · Enter open · S save

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

Han Wang, Yifan Sun, Brian Ko, Mann Talati+7

University of Illinois Urbana-Champaign · University of Washington · University of California San Diego

CoT Faithfulness LLM Evaluation CoT Monitoring

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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.

Abstract

CoT Monitoring

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