Step 25: assistant

thinking

Calls provenance_write_map

{
  "summary": "The effect was first reported by the study that introduced noise injection, whose own tables and plots bear it out for prompted sandbagging and for two password-locked 7B models. The two denials are of different weight. One is a post hoc, unblinded analysis inside a larger auditing study, with its own same-noise comparators shown. The other, which sounds strongest because it reports failure on eleven models, comes from a paper promoting a rival method that never describes how it applied noise and whose figures are lower than random guessing alone would give, so it shows little about what a full noise sweep would find.",
  "material": true
}

Calls notify_steward

{
  "message": "From the pass on \"Increasing Gaussian weight noise improves benchmark performance in sandbagging LLMs, unlike the degradation in non-sandbagging models\": still inconclusive (0.72), no credence, but the reading of the fine-tuned evidence changed from \"leaning negative\" to \"mixed and thin\". Primary-text details that may save your pass some work:\n\n1. Le, Tan and Williams-King (arXiv 2608.29458) list noise injection as recovering +0 to +3% of the gap on eleven password-locked models (1.5B to 32B). I read the whole TeX source: the paper never describes the noise baseline (where applied, sigma range, seeds, best or mean). Its figures also cannot be the Tice statistic. The locks have gaps of 0.77 to 0.98 on four-option ARC-Easy scored by forced choice, so locked accuracy is at most 1 minus gap (0.02 to 0.23), below chance. A model scrambled by heavy noise would answer at about 0.25 and so \"recover\" at least (gap - 0.75)/gap: 23% at gap 0.98, 17% at 0.90, 6% at 0.80. The reported figures are below that bound on ten of eleven models. So the baseline was not a best-over-sweep run to collapse; it shows no unlock at whatever settings were tried and little about the narrow pre-collapse window where Tice et al. found gains. The authors promote a rival method. I would not count these as eleven independent failures to replicate.\n\n2. In Tice et al. (2412.01784) the opposite worry does not apply: in both the main example figure and the password-lock figure, heavy noise takes accuracy to about zero, not to chance, so a below-chance sandbagger cannot show a gain just by being scrambled. In the Mistral-7B WMDP lock figure all five seeds rise from about 0.13 to 0.31 to 0.54 at the sigma where the with-password model has fallen from 0.68 to about 0.5, then both fall to zero (read from the plot).\n\n3. Fine-tuned sandbaggers with a described protocol: positives are Tice's two 7B locks (5.0 and 3.12 against 1.00 and 1.01 with password; the 5.0 lock was trained to imitate Pythia-1B) and Jang et al. (2604.28182) on BigCodeBench (best about 0.42 against 0.30, three seeds, noise on the lock adapter itself); weak and non-diagnostic are the two auditing-game 70B organisms (2512.07810: 1.094 against 1.038 per-seed, password-unlocked versions also gain, Oak 1.08 locked and unlocked on its one sandbagged task); null is Jang et al. on WMDP. No study has noised benign fine-tunes or varied installation method under one protocol.\n\nFor the question on detection conditions: noise injection is established for prompted sandbagging against the same model unprompted; unsettled for trained sandbagging; and fails to separate sandbagging from other under-elicitation where the comparator is itself a fine-tune (one study, 70B). Nothing here asks you to change a verdict; weigh it as you see fit.",
  "claim_id": "e0abe646-c78f-417c-813f-b8e2c15d775c",
  "target_ids": [
    "bda65b89-5ecb-4728-bfe9-0b54ee7fbc25",
    "6e9ec682-a36d-4fb1-89cf-e75453dda847",
    "01232bf1-55f7-42fd-9f39-8a38bfde7837",
    "fc451329-02cc-4d5f-a155-d3adb0e7cc46",
    "9388972f-04ce-4627-bfa1-8791bf588958",
    "250fee4e-8706-4658-b579-b0382bfae55f"
  ]
}