What I learned from cheating clankers

Presented by

James Wilson
James Wilson

Technology Editor

In this solo Risky Business Features episode, James Wilson looks at the string of recent AI benchmark cheating escapades and how they’ve reshaped his thinking about LLMs and vulnerability research.

He explores the current benchmarks and exactly how agents go about gaming the testing system to achieve their tasks. And why understanding that has changed the way he interacts with his agents.

What I learned from cheating clankers
0:00 / 57:52

Show notes

OpenAI incident account and grading distinction

METR and Redwood independent assessment

FrontierCode original evaluation methodology

BrowseComp aggregation experiments

Sonnet 4 point 5 coding scaffold and parallel selection

Opus 4 point 6 BrowseComp setup

BrowseComp evaluation awareness and contamination investigation

Sonnet 5 point 5 effort and review subagents

FrontierCode 1 point 1 revision and rubric changes

Astra launch comparison and effort conditions

Original OSWorld task construction and worked examples

Sonnet 4 point 6 system card

Original OSWorld benchmark

OSWorld 2 environment and evaluation design

GPT 5 point 4 comparator table

GPT 5 point 5 comparator table

BrowseComp construction and grading paper

GPT 5 point 6 single model and Ultra configurations

METR investigation and artifact evidence

Released task definition

Released scorer for reference only

OpenAI paper sections 3 point 1 to 3 point 3

OpenAI accompanying explanation

Sycophancy to Subterfuge paper

Training a Misaligned Reward Seeker and evaluation appendix

GPT 5 point 2 AIME result