Scalable Oversight

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Scalable oversight asks how humans or weaker models can reliably evaluate stronger AI systems, especially when complex outputs are hard to check directly. Approaches include debate and collaborative critique, splitting judgments across multiple calls, training monitors and lie detectors, and constraining agents with tools and access controls. These methods are tested on code, reasoning, preference learning, and ethical decision tasks, while formal analyses examine when oversight can provide guarantees. A central challenge is detecting subtle or adversarial failures without excessive cost, miscalibration, shared blind spots, or vulnerabilities to distribution shift.

Latest papers

15 papers4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

PyINE: A Framework for Scalable Elicitation and Oversight via Code Execution
Oct 3, 2026Pierre-Luc St-Charles, Alessandro Palmas, Damiano Fornasiere +5Scalable Oversight LLM Auditing
LawZero
Can AI Oversight Be Zero Knowledge?
Oct 1, 2026Alessandro Chiesa, Ziyi Guan, Burcu YildizScalable Oversight Zero-Knowledge Proofs
EPFL · MIT
Improving scalable oversight with co-trained monitors
Sep 28, 2026Joseph H. Rudoler, Kevin Tan, Benedict Tessler +2Scalable Oversight Adversarial Robustness
University of Pennsylvania
When Honesty is Not Enough in AI Debate
Sep 24, 2026Rayne Holland, Liming Zhu, Jason XueScalable Oversight Multi-Agent Debate
CSIRO
Sharding Prevents LLM Oversight Failures and Adversarial Exploitation
Aug 5, 2026Victor Akinwande, J. Zico Kolter, Aran NayebiScalable Oversight LLM Evaluation
School of Computer Science 2Neuroscience Institute Carnegie Mellon University
One Human, $N$ Agents: Audit-Budget Allocation for LLM Agent Fleets under Miscalibrated, Correlated Confidence
Jul 30, 2026Cesare Zavattari, Alessandro Tommasi, Giuseppe PrencipeScalable Oversight AI Agent Reliability
Dipartimento di Informatica, Università di Pisa, Pisa, Italy
Steerability via constraints: a substrate for scalable oversight of coding agents
Jul 2, 2026Thomas WinningerScalable Oversight Coding Agents
Télécom SudParis, Évry-Courcouronnes, France · ENS Paris-Saclay, Gif-sur-Yvette, France
Scaling Trends for Lie Detector Oversight in Preference Learning
Jul 2, 2026Oskar J. Hollinsworth, Ann-Kathrin Dombrowski, Sam Adam-Day +2Scalable Oversight Deception in Language Models
Collaborative Disagreement Resolution for Scalable Oversight
Jun 2, 2026Yuyang Jiang, Chacha Chen, Teng Wu +4Scalable Oversight Multi-Agent Debate
Department of Computer Science, University of Chicago, Chicago, IL, USA · Microsoft, Redmond, WA, USA · Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA +1
Weak Critics Make Strong Learners: On-Policy Critique Distillation for Scalable Oversight
May 29, 2026Can Jin, Jiakang Li, Rui Wu +2Scalable Oversight Weak-to-Strong Generalization
Department of Computer Science, Rutgers University, Piscataway, United States.
Calibrating Conservatism for Scalable Oversight
May 27, 2026William Overman, Mohsen BayatiScalable Oversight AI Agent Safety
Stanford Graduate School of Business, Stanford University, Stanford, CA, USA.
Debate Helps Weak Judges Reward Stronger Models
May 26, 2026Ethan Elasky, Frank Nakasako, Naman GoyalScalable Oversight LLM-as-a-Judge
Palaestra Research · Independent
Behavior Cue Reasoning: Monitorable Reasoning Improves Efficiency and Safety through Oversight
May 7, 2026Christopher Z. Cui, Taylor W. Killian, Prithviraj AmmanabroluScalable Oversight LLM Safety
University of California, San Diego · 2Brigham Young University
Automated alignment is harder than you think
May 7, 2026Aleksandr Bowkis, Marie Davidsen Buhl, Jacob Pfau +1Scalable Oversight AI Alignment
AI Security Institute
Knowledge Divergence and the Value of Debate for Scalable Oversight
Mar 5, 2026Robin YoungMulti-Agent Debate Scalable Oversight
Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

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PyINE: A Framework for Scalable Elicitation and Oversight via Code Execution

Pierre-Luc St-Charles, Alessandro Palmas, Damiano Fornasiere, Storm Lei+4

LawZero

Scalable Oversight LLM Auditing Shortcut Learning

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Reasoning models can remain capable of solving a task while still defaulting to cheaper but misleading shortcuts. This creates a central oversight problem: when a model gives an answer with plausible but incomplete reasoning, can an overseer determine whether that output should be trusted? To study this problem, we introduce PyINE, a framework for scalable elicitation and oversight using instrumented Python programs as a verifiable execution substrate. In PyINE, programs define task environments, execution traces provide authoritative labels for outcomes and intermediate facts, and task variants can be generated mechanically rather than through static human annotation. We instantiate the framework in PyINE-v1, a first release built from nearly one million deterministic execution traces and over 500,000 matched LLM-generated code variants used for counterfactual evaluation. Using standard RL with verifiable rewards on cue-varied tasks, we train a shortcut-following model that improves substantially at predicting execution outcomes while still making systematic errors when misleading human-facing cues conflict with the program's realized behavior. We then evaluate activation probes, trained text classifiers, prompted judges, and a lightweight debate protocol as overseers of this model. We find that performance pooled at the dataset level can hide weak coverage of the failures that matter most: cheap learned overseers often miss rare shortcut-driven errors, while stronger model-based checks are more balanced but substantially costlier and harder to turn into reliable thresholded decisions. PyINE-v1 turns this failure-mode coverage problem into a reusable experimental setting for developing oversight methods that are verifiable, failure-mode-aware, and cost-sensitive.