Publications

Why Inoculation Prompting Fails on Sycophancy

ICML 2026 Hypothesis Testing Workshop ยท Poster ยท

Nok Man Chan

Tests whether inoculation prompting, a training-time intervention that pairs an unwanted behaviour with an explicit instruction, reduces sycophancy on a reconstructed greatest-common-divisor benchmark. Instead of genuine correction, it mostly produced behavioural substitution, such as refusing the task or appearing to disagree while keeping the user's false answer.

Articles & reports

Not peer reviewed.

How does Fable 5.1 hide messages in images?

LessWrong ยท Singapore AI Safety Hub (SASH) Fellowship ยท

Nikita Kezins, Bryan Chan, Kyle Reynoso, James Chua

One Fable 5.1 session drew an ordinary-looking SVG that hid a secret message, without being told how to encode it. A separate, fresh session received only the rendered PNG and was asked whether it contained a hidden message. In the evaluation, the decoder recovered 39 of 50 messages (78%) with no agreed encoding scheme.

The Sentinel Engine: Solving the Observability Trilemma via Differential Precision Probing

Apart Research ยท Technical AI Governance Challenge ยท

Ramakrishna Doradla Venkatesh, Bryan Chan

Frames the trade-off between inference efficiency, cognitive observability and adversarial assurance in AI monitoring, and proposes treating quantisation noise as a probe for deceptive behaviour. Early-stage sprint work: the proposed detection method was not completed during the event.

Ongoing