Machine-readable watermarks and model-side provenance metadata answer one question: whether a generative system participated in a text, proof, or code artifact. They do not answer the questions that determine inventorship, academic credit, and institutional ownership: what a human conceived, contributed, decided, rejected, or directed, and when those acts occurred relative to later model use or later laboratory work. This note treats the publicly reported September 2026 dispute surrounding AI-assisted finite-time blowup results for forced incompressible fluid equations (Euler, Boussinesq, incompressible porous media; claimed related work on forced Navier–Stokes) as an exemplary use case for the Human Conception Ledger (HCL). The mapping is evidentiary architecture, not adjudication. Public statements are treated as claimed events that an HCL deployment would have timestamped, typed, hashed, and cross-corroborated. The case is useful precisely because the underlying mathematics, the models, the employers, and the announcement sequence are all in collision at once.
Melinda B. Chu· Zenodo (CERN European Organi...· 0 citations
Background: Competitive intelligence work is routinely scattered across company websites, press releases, industry publications, patent and funding databases, and news feeds. Most organizations still assemble this picture by hand, in spreadsheets and static slide decks that are out of date the moment they are finished. Objective: This paper documents Frontier, an AI-enabled platform for continuous competitor and partner intelligence, and its evolution from a single-company research tool into a reusable, general-purpose SaaS application. Frontier lets a user enter the name of any company and receive a live, scored brief covering collaboration candidates, competitors, and market-expansion opportunities, alongside a rolling feed of relevant news. Overview: We describe the system's architecture, its use of large language model (LLM) inference for entity discovery and scoring, and the transparent, documented rubric that underlies every compatibility and opportunity score the platform produces. Limitations of conventional approaches: Manual research does not scale past a handful of competitors, spreadsheets do not capture the reasoning behind a judgment, and periodic reviews (quarterly or annual) miss developments that happen in between. Origin and generalization: Frontier began as a purpose-built research tool for Ecotera Asia's EcoExposure™ platform, with a fixed, hand-researched list of competitors and partners in the environmental-diagnostics and water-quality-monitoring space. It was subsequently rebuilt so that any company name can be analyzed on demand, generalizing the underlying architecture beyond a single industry. Contributions: This work contributes (i) a working, publicly deployed implementation of an on-demand competitor-intelligence pipeline; (ii) a documented, transparent scoring rubric intended to make AI-generated judgments auditable rather than opaque; and (iii) a case study showing how the same architecture served both a narrow, industry-specific need and a general-purpose product. Live application: frontier2.vercel.app Source code: github.com/sanviagarwal7211-a11y/frontier2
Sanvi Agarwal, Melinda B. Chu· Zenodo (CERN European Organi...· 0 citations
Background: Competitive intelligence work is routinely scattered across company websites, press releases, industry publications, patent and funding databases, and news feeds. Most organizations still assemble this picture by hand, in spreadsheets and static slide decks that are out of date the moment they are finished. Objective: This paper documents Frontier, an AI-enabled platform for continuous competitor and partner intelligence, and its evolution from a single-company research tool into a reusable, general-purpose SaaS application. Frontier lets a user enter the name of any company and receive a live, scored brief covering collaboration candidates, competitors, and market-expansion opportunities, alongside a rolling feed of relevant news. Overview: We describe the system's architecture, its use of large language model (LLM) inference for entity discovery and scoring, and the transparent, documented rubric that underlies every compatibility and opportunity score the platform produces. Limitations of conventional approaches: Manual research does not scale past a handful of competitors, spreadsheets do not capture the reasoning behind a judgment, and periodic reviews (quarterly or annual) miss developments that happen in between. Origin and generalization: Frontier began as a purpose-built research tool for Ecotera Asia's EcoExposure™ platform, with a fixed, hand-researched list of competitors and partners in the environmental-diagnostics and water-quality-monitoring space. It was subsequently rebuilt so that any company name can be analyzed on demand, generalizing the underlying architecture beyond a single industry. Contributions: This work contributes (i) a working, publicly deployed implementation of an on-demand competitor-intelligence pipeline; (ii) a documented, transparent scoring rubric intended to make AI-generated judgments auditable rather than opaque; and (iii) a case study showing how the same architecture served both a narrow, industry-specific need and a general-purpose product. Live application: frontier2.vercel.app Source code: github.com/sanviagarwal7211-a11y/frontier2
Sanvi Agarwal, Melinda B. Chu· Zenodo (CERN European Organi...· 0 citations
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