{
  "id": "oai:papers.qnfo.org:llm-model-audit-2026-08-13",
  "slug": "llm-model-audit-2026-08-13",
  "title": "Prioritizing Large Language Models for Scientific Research and Agentic AI: A LiveBench-Grounded Audit (August 2026)",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
  ],
  "abstract": "Scientific research programs that rely on large language models (LLMs) for mathematics-heavy derivation, formal proof verification, literature synthesis, and agentic tool orchestration face a model-selection problem that general-purpose leaderboards do not directly answer. This audit evaluates the frontier LLM landscape as of August 13, 2026 against the concrete requirements of a mathematical-physics research program: ultrametric and p-adic analysis, Laws of Form formal algebra, measurement stratigraphy, and multi-agent subagent orchestration. Primary evidence is the LiveBench 2026-06-25 release, a contamination-free benchmark of 23 objective tasks across seven categories, collected directly from the live leaderboard. Supporting evidence includes the Cloudflare Workers AI model catalog (26 text-generation models, enumerated via the account REST API) and the runtime configuration of the program's inference router (DeepSeek V4 flash/pro, 1M-token context, verified via the DeepChat provider list). Findings: (1) no Meta/Llama model appears among the top 42 LiveBench entries, empirically supporting the exclusion of Llama from scientific routing paths; (2) DeepSeek V4 Pro 0813 achieves mathematics score 95.1 at USD 0.044 per benchmark run, the strongest price-performance among frontier-mathematics models and the default workhorse; (3) three open-weights models (Qwen 3.8 Max, Kimi K3, Smaug-Agentic) outperform it on agentic coding (62.2-64.6 vs 54.9) and are preferred for orchestration roles; (4) a five-tier prioritization is defined and deployed. All numeric claims were verified against the live leaderboard and the production router.",
  "abstract_markdown": "Scientific research programs that rely on large language models (LLMs) for mathematics-heavy derivation, formal proof verification, literature synthesis, and agentic tool orchestration face a model-selection problem that general-purpose leaderboards do not directly answer. This audit evaluates the frontier LLM landscape as of August 13, 2026 against the concrete requirements of a mathematical-physics research program: ultrametric and p-adic analysis, Laws of Form formal algebra, measurement stratigraphy, and multi-agent subagent orchestration. Primary evidence is the LiveBench 2026-06-25 release, a contamination-free benchmark of 23 objective tasks across seven categories, collected directly from the live leaderboard. Supporting evidence includes the Cloudflare Workers AI model catalog (26 text-generation models, enumerated via the account REST API) and the runtime configuration of the program's inference router (DeepSeek V4 flash/pro, 1M-token context, verified via the DeepChat provider list). Findings: (1) no Meta/Llama model appears among the top 42 LiveBench entries, empirically supporting the exclusion of Llama from scientific routing paths; (2) DeepSeek V4 Pro 0813 achieves mathematics score 95.1 at USD 0.044 per benchmark run, the strongest price-performance among frontier-mathematics models and the default workhorse; (3) three open-weights models (Qwen 3.8 Max, Kimi K3, Smaug-Agentic) outperform it on agentic coding (62.2-64.6 vs 54.9) and are preferred for orchestration roles; (4) a five-tier prioritization is defined and deployed. All numeric claims were verified against the live leaderboard and the production router.",
  "date": "2026-08-13",
  "first_published": "2026-08-13",
  "last_revised": "2026-08-13T00:00:00Z",
  "modified": "2026-08-13T00:00:00Z",
  "version": "0.1.0",
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  "language": "en",
  "keywords": [
    "large language models",
    "model selection",
    "agentic AI",
    "scientific computing",
    "benchmark evaluation",
    "cost optimization"
  ],
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  "url": "https://papers.qnfo.org/papers/llm-model-audit-2026-08-13",
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  "sha256": "c12bf39b32cc06114706ea186f9613dbcdcaf20eead9cbf874df6aaf4ee4c859",
  "version_url": "https://papers.qnfo.org/papers/llm-model-audit-2026-08-13/v/0.1.0",
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    "creators": [
      {
        "name": "Rowan Brad Quni-Gudzinas",
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    "titles": [
      {
        "title": "Prioritizing Large Language Models for Scientific Research and Agentic AI: A LiveBench-Grounded Audit (August 2026)"
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    ],
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        "subject": "model selection"
      },
      {
        "subject": "agentic AI"
      },
      {
        "subject": "scientific computing"
      },
      {
        "subject": "benchmark evaluation"
      },
      {
        "subject": "cost optimization"
      },
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        "description": "Scientific research programs that rely on large language models (LLMs) for mathematics-heavy derivation, formal proof verification, literature synthesis, and agentic tool orchestration face a model-selection problem that general-purpose leaderboards do not directly answer. This audit evaluates the frontier LLM landscape as of August 13, 2026 against the concrete requirements of a mathematical-physics research program: ultrametric and p-adic analysis, Laws of Form formal algebra, measurement stratigraphy, and multi-agent subagent orchestration. Primary evidence is the LiveBench 2026-06-25 release, a contamination-free benchmark of 23 objective tasks across seven categories, collected directly from the live leaderboard. Supporting evidence includes the Cloudflare Workers AI model catalog (26 text-generation models, enumerated via the account REST API) and the runtime configuration of the program's inference router (DeepSeek V4 flash/pro, 1M-token context, verified via the DeepChat provider list). Findings: (1) no Meta/Llama model appears among the top 42 LiveBench entries, empirically supporting the exclusion of Llama from scientific routing paths; (2) DeepSeek V4 Pro 0813 achieves mathematics score 95.1 at USD 0.044 per benchmark run, the strongest price-performance among frontier-mathematics models and the default workhorse; (3) three open-weights models (Qwen 3.8 Max, Kimi K3, Smaug-Agentic) outperform it on agentic coding (62.2-64.6 vs 54.9) and are preferred for orchestration roles; (4) a five-tier prioritization is defined and deployed. All numeric claims were verified against the live leaderboard and the production router.",
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