Run step
Step 16: tool results
write_argument
Input
{
"content": "AuditBench (56 Llama 3.3 70B models with 14 implanted behaviours, a Claude Haiku 4.5 investigator run 16 times per model and tool) found scaffolded text completion, user sampling and prefilling the three best tools (58.0%, 53.2%, 51.6% against a 35.5% chat-only default), while no white-box tool (steering, SAEs, logit lens, activation oracles) matched them on any training configuration. A separate secret-elicitation study with an LLM auditor likewise found prefill attacks best in every setting and white-box methods helpful but weaker. If these testbeds are representative of agent-based auditing, the best black-box tools outperform white-box ones.",
"argument_id": "d8ea5982-ca7f-4102-b2e1-d25c7a440b07"
}{"success":true,"message":"Written form saved for argument \"The argument from AuditBench and secret-elicitation tool comparisons\"."}write_argument
Input
{
"content": "In Anthropic's 2025 investigator-agent study on the Marks et al. reward-model-sycophancy model (built by synthetic-document training then SFT and RL to conceal), a Claude Sonnet 4 agent with chat tools alone essentially never found the root cause, while adding interpretability tools (SAE features, steering) or semantic search over training data brought single-agent success to 10 to 13%. There, white-box access beat the black-box chat affordances the agent had, so black-box superiority is not general across testbeds.",
"argument_id": "851336c7-7768-4848-9448-3179bfa582a6"
}{"success":true,"message":"Written form saved for argument \"The argument from the Marks et al. auditing game, where interpretability tools were key\"."}