Writing study: Reliable Weak-to-Strong Monitoring of LLM Agents

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← ICLR 2026 oral writing corpus

Neil Kale, Chen Bo Calvin Zhang, Kevin Zhu, Ankit Aich, Paula Rodriguez, Christina Knight, Zifan Wang · Official ICLR 2026 oral record

Observed writing choices, not acceptance rules. This study examines the inspected manuscript version. It does not establish why the paper was accepted or that its presentation choices should be copied unchanged.

Figure, table and section ledger

Editorial contribution type: Empirical analysis, diagnosis & auditing (primary) · Datasets, benchmarks & measurement resources.

Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.

Canonical PDF

56 physical pages

Before references / appendix

≈ 9.71 page-equivalents

Main figures / tables

5 / 0

Attached appendix figures / tables

18 / 3

Attached appendix span

≈ 41.89 page-equivalents

Before references / appendix: ≈ 17.34%References / intervening material: ≈ 7.86%Attached appendix: ≈ 74.8%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Section-by-section lengths and source pages

The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.

Top-level heading	Starts on PDF page	Estimated span	Share of entire PDF
1 INTRODUCTION	1	≈ 1.13 pp.	2.02%
2 BACKGROUND	2	≈ 0.68 pp.	1.21%
3 MONITOR RED TEAMING	3	≈ 2.76 pp.	4.93%
4 EXPERIMENTS	6	≈ 3.76 pp.	6.71%
5 FUTURE WORK AND LIMITATIONS	9	≈ 0.57 pp.	1.02%
6 CONCLUSION	10	≈ 0.28 pp.	0.5%
REFERENCES	10	≈ 4.4 pp.	7.86%
A ADDITIONAL RELATED WORK	15	≈ 0.54 pp.	0.96%
B LLM USAGE DISCLOSURE	15	≈ 0.46 pp.	0.82%
C CASE STUDY: ESCALATION TO HUMANS	16	≈ 1 pp.	1.79%
D MONITORING CHUNK SIZE	17	≈ 1.25 pp.	2.23%
E MONITOR FAILURE MODE ANALYSIS	18	≈ 2.75 pp.	4.91%
F LATENCY AND COMPUTATIONAL COSTS	21	≈ 1 pp.	1.79%
G ADDITIONAL SCALING LAW PLOTS	22	≈ 1 pp.	1.79%
H MRT ON BASHBENCH	23	≈ 2 pp.	3.57%
I ATTACK POLICIES	25	≈ 7 pp.	12.5%
J MONITORING PROMPTS	32	≈ 4 pp.	7.14%
K ADDITIONAL DETAILS ON MRT THREAT MODELS	36	≈ 2 pp.	3.57%
L CUA-SHADE-ARENA DETAILS	38	≈ 3 pp.	5.36%
M EXECUTION DETAILS	41	≈ 0.43 pp.	0.77%
N MODEL VERSIONS	41	≈ 1.57 pp.	2.8%
O ATTACKER TRANSCRIPT EXAMPLES	43	≈ 3.53 pp.	6.3%
P MONITOR TRANSCRIPT EXAMPLES	46	≈ 3.53 pp.	6.3%
Q HUMAN-IN-THE-LOOP MONITORING DETAILS	50	≈ 6.83 pp.	12.2%

All detected figure and table locations

Main text

• Figure 1 · p. 2
• Figure 2 · p. 5
• Figure 3 · p. 7
• Figure 4 · p. 8
• Figure 5 · p. 9

Attached appendix

• Figure 6 · p. 16
• Figure 7 · p. 17
• Figure 8 · p. 17
• Figure 9 · p. 18
• Figure 10 · p. 18
• Figure 11 · p. 19
• Table 1 · p. 21
• Figure 12 · p. 22
• Figure 13 · p. 22
• Figure 14 · p. 23
• Figure 15 · p. 23
• Figure 16 · p. 24
• Figure 17 · p. 25
• Figure 18 · p. 26
• Table 2 · p. 29
• Figure 19 · p. 36
• Table 3 · p. 40
• Figure 20 · p. 50
• Figure 21 · p. 55
• Figure 22 · p. 56
• Figure 23 · p. 56

Argument and organization

1. Monitor Red Teaming definition

   Fix datasets, awareness-based threat models, evasion families, metrics, and scaffolding before comparing results.

2. Numbered questions and findings

   Progress from awareness and evasion to capability gaps; place the human-oversight case study in a linked appendix.

Problem, claim and contribution

The paper explicitly distinguishes offline trajectory evaluation from online intervention before presenting reliability claims, preventing a detection benchmark from silently becoming a deployment-control claim.

Section 2, page 3. · Main paper

Argument and section order

Five research questions organize six named findings, while a two-by-three awareness taxonomy isolates what the agent and monitor know from the monitoring architecture itself.

Sections 3.2 and 4, pages 4 and 6–9; Figure 19, page 36. · Main paper

Figures, captions and visual explanation

A shared miniature trajectory lets four side-by-side diagrams expose the difference between full-context, sequential, hierarchical, and averaged hybrid monitoring with very little text.

Figure 2, page 5. · Main paper

Tables, comparisons and evidence

The adversarial-results figure places AUC and low-false-positive recall alongside compact winner tables. Their disagreement is informative: better AUC does not imply the best recall at a 1% false-positive rate.

Figure 4, page 8; Section 5, pages 9–10. · Main paper

The human-study figure pairs suspicion distributions with recall bars, showing why unrestricted feedback can worsen false positives while selective escalation improves recall from 21.0% to 36.0%, a 15-percentage-point change.

Appendix C and Figure 6, page 16. · Main paper

Appendix and reproducibility

Reproduction support includes full prompts, model endpoint identifiers, retry limits, task-pair construction, annotated failure cases, and human-interface screenshots. The cost appendix openly uses estimated latency, though its caption retains placeholder wording.

Appendices F and I–Q, pages 21 and 25–56. · Main paper

What to reuse—and what not to infer

Transferable lesson: Define the threat model before the architecture, then show paired metrics and counterexamples that reveal where an apparently stronger monitor still fails.

Boundary: This is prompting-based offline oversight, not knowledge distillation or proven online safety. Preserve exact API identifiers because the prose uses nonstandard Claude 3.6 labels; do not treat the rough attention-complexity claim as measured API savings, since Appendix F reports higher hybrid token cost and estimated latency.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked

2026-09-19

Reading status

Full text + selected visuals inspected

Main text

Pages 1–10, Sections 1–6; references on pages 10–14.

Appendix / supplement

Pages 15–56, complete Appendices A–Q: related work, disclosure, human study, chunk/ensemble analyses, failures, cost estimates, capability plots, BashBench, prompt templates, threat models, dataset/execution/model details, transcripts, and interface instructions.

Visually inspected PDF pages

5, 8, 16, 21

• ICLR 2026 official proceedings PDF, retrieved 2026-09-19 · 56 pages