Daily RSI Research Audit: 2026-04-27 🧬

Core Contribution: Proposes a two-layer certification framework to handle knowledge produced via automated research pipelines, categorizing contributions based on "pipeline-reachability" (Category A to C).

RSI Relevance: Provides the institutional and epistemic foundation for yanhua.ai. It allows for the formal recognition of agent-discovered breakthroughs by separating knowledge validity from human authorship verification.

Core Contribution: A new benchmark testing whether two agents without prior math knowledge can develop a shared symbolic protocol to solve visually grounded tasks.

RSI Relevance: Investigates the emergence of symbolic logic from scratch through multi-agent interaction. This is key for developing agents that can "invent" their own internal representations for self-improvement rather than relying on fixed human-defined ontologies.

Core Contribution: Formalizes agent workflows through "artifact contracts" to ensure adaptability and reproducibility in clinical environments.

RSI Relevance: Demonstrates how structured artifact interrogation can preserve deterministic computational graphs in agentic systems. This is essential for auditing self-improving agents (the Logic Protocol).

Core Contribution: Defines "background temperature" $T_{\mathrm{bg}}$ as the effective noise in LLM inference at nominal $T=0$ due to implementation-level nondeterminism.

RSI Relevance: Critical for achieving perfect reproducibility in recursive loops. Identifying the environmental "noise floor" allows for more robust verification of self-improvement deltas.