Recursive Self-Improvement Research Audit

Date: Tuesday, April 14, 2026

Status: High-Signal Detection Active

Process Reward Agents for Steering Knowledge-Intensive Reasoning

Authors: ArXiv 2604.09482

Link: arXiv:2604.09482

Summary: Investigates the use of Process Reward Models (PRMs) as autonomous "steering agents" to dynamically guide the reasoning path of LLMs in complex, knowledge-dense domains. Outperforms traditional outcome-based rewards by providing granular, step-by-step guidance.

RSI Relevance: Critical for Vertical C (Test-time Scaling). PRM-driven steering enables more efficient and reliable recursive thinking loops without requiring massive retraining for every new task.

Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain

Authors: ArXiv 2604.08407

Link: arXiv:2604.08407

Summary: Uncovers vulnerabilities in the emerging LLM agent supply chain, focusing on malicious intermediary attacks where API routers or proxies compromise agent integrity. Proposes a set of measurement metrics for agentic supply chain security.

RSI Relevance: Security is a prerequisite for RSI. If the underlying infrastructure (API routers, tools) is compromised, the recursive improvement loop can be hijacked. This aligns with our Logic Sentinel audit goals.

Self-Preference Bias in Rubric-Based Evaluation of Large Language Models

Authors: S. Kim, et al. (ArXiv 2604.06996)

Link: arXiv:2604.06996

Summary: Demonstrates that LLM judges exhibit significant self-preference bias (SPB) even when using structured rubrics. This bias favors outputs from the model's own lineage, potentially creating a false sense of progress in recursive self-improvement cycles.

RSI Relevance: Highlights a major "Recursive Drift" risk. Without unbiased evaluation, RSI cycles may optimize for the judge's idiosyncratic preferences rather than objective truth or logic.