Significance
As Artificial Intelligence begins to co-author scientific discovery, the lack of a standardized audit trail creates a potential crisis of trust. This Editorial launches Gauge Freedom Journal, the first physics venue to treat “cognitive provenance” as a fundamental scientific variable. We introduce a cryptographically verifiable protocol—pairing human-readable Integrity Reports with machine-verifiable Content-Addressable Receipts (CARs)—to ensure that the coming era of Human-AI science remains open, reproducible, and trustworthy.
Abstract
Science is entering an era in which Human–AI collaboration is not an exception but a default mode of work. This change is already producing legitimate novelty, including peer-reviewed work where AI-assisted exploration contributed to key steps later subjected to independent verification. However, it also brings a new class of failure modes: opaque AI influence, fragile reasoning masked by fluent prose, and a growing difficulty in distinguishing rigorous advances from plausible artifacts.
Gauge Freedom Journal (GFJ) launches to help establish a new norm: provenance-first, open, energy-aware physics publishing. We encourage a dual-layer disclosure standard for AI-assisted research: AI Integrity Reports as concise human-readable summaries and Content-Addressable Receipts (CARs) as machine-verifiable audit trails. We invite the builders of this new era—independent researchers, interdisciplinary teams, and early adopters of agentic science—to join us. Our aim is simple: as AI accelerates discovery, the scientific method must become more visible, not less.
Key Findings
Ensemble Research Standard: Establishes the “Generator-Verifier” workflow (using independent AI models to cross-check results) as a recommended standard for reducing hallucination risk in theoretical physics.
Dual-Layer Provenance: Introduces a disclosure requirement that separates narrative explanation (AI Integrity Report) from cryptographic proof (Content-Addressable Receipt/CAR).
Energy as a Metric: Formally treats computational energy cost as a variable of scientific quality, requiring stewardship reporting for high-compute AI workflows.
Separation of Concerns: Demonstrates a governance model where editorial decisions are strictly merit-based and independent of the corporate entity providing the verification infrastructure.
Transparency Statement
AI Contribution: This editorial is a product of Human–AI collaboration. The authors are responsible for all ideas, framing, and scientific claims. Generative AI systems were used to test alternative structures, synthesize literature, and polish the final prose. To exemplify GFJ’s provenance-first standard, we provide an accompanying AI Integrity Report and Content-Addressable Receipt (CAR). These artifacts were generated using the journal's pilot verification infrastructure (Intelexta), though the standard allows for equivalent third-party provenance tools.
