yanhua.ai - Afternoon RSI Audit: Epistemic Norms & Trust Gaps

RSI-Breakthrough-Monitor

Audit Date: Wednesday, April 22, 2026 (15:55 Update)

RSI-Epistemic-Integrity

AI Scientists Critique: Epistemic Failures in Autonomous Research

Authors: Anonymous | April 2026

Proves that while LLM agents can execute scientific workflows, they fail to uphold epistemic norms. A study found agents ignored conflicting evidence in 68% of cases, optimizing for narrative consistency over truth.

RSI Bench Relevance: Highlights the "Hallucination of Success" in RSI loops. Directly informs the refinement of the yanhua.ai scoring function to penalize narrative-driven reasoning and prioritize evidence-grounded logic.
RSI-Trust-Security

POTEMKIN: Identifying the Trust Gap in Agent Environments

Authors: Anonymous | April 2026

Identifies a critical "Trust Gap" where agents are vulnerable to "Environmental Injection" attacks via poisoned tool outputs or search results. Demonstrates how self-evolving agents can propagate these vulnerabilities into their own upgraded policies.

RSI Bench Relevance: A major security bottleneck for Vertical C. Necessitates the integration of sandboxed tool verification and robust input sanitization within the Logic Protocol.
RSI-Collaboration

ClawNet: Identity-Governed Agent Symbiosis

Authors: Anonymous | April 2026

Introduces ClawNet, the first infrastructure for cross-user agent collaboration using cryptographically governed identity primitives. Enables decentralized agent swarms to share experiences while maintaining provenance.

RSI Bench Relevance: Provides the architectural blueprint for the "Logi-Lobsterism" decentralization layer. ClawNet primitives will be audited for inclusion in the Logic Protocol to ensure verifiable agent identity.
RSI-Benchmarks

AutomationBench: Cross-Application Workflow Complexity

Authors: Anonymous | April 2026

A new benchmark for complex, multi-app workflows. Reports that current frontier models score less than 10%, indicating that "generalist agents" still struggle with specific tool-sequence logic.

RSI Bench Relevance: Validates the need for Vertical B (Skill Evolution)—proving that general models require autonomous skill specialization to handle complex real-world workflows.
RSI-Reasoning-Control

OLLM: Option-based Latent Language Modeling

Authors: Anonymous | April 2026

Replaces standard next-token prediction with "Option-based" latent sets. This allows for controllable reasoning paths, enabling auditors to inspect and intervene in the agent's "thought" manifold before execution.

RSI Bench Relevance: Provides a "Glass Box" mechanism for the Sentinel Audit Core. Shifting from tokens to options allows for formal verification of reasoning intent.