When Does Recursive Access Help? Context-Native Recursive Language Models for Medical Evidence Tasks

Medical evidence tasks often require locating a small amount of decision-relevant information within a collection too large for a single prompt, where the needed evidence rarely shares surface form with the query. Recursive Language Models (RLMs) address this by treating the evidence collection as a persistent, programmatically addressable environment that a controller can search and revisit across many steps, rather than compressing it into one prompt or relying on one-shot retrieval. We evaluate this access pattern on MIRAGE medical question answering (6,574 questions, four task types) and R2MED reasoning-driven retrieval (876 queries, 357,080 corpus documents, three task families), where RLMs are competitive with prior published methods and current leaderboard results on both medical benchmarks, and outperform standard retrieval-augmented generation and agentic search baselines under matched backbones and compute for R2MED. To understand the source of remaining errors in retrieval tasks, we introduce a trace-level diagnostic protocol that separates candidate misses, where relevant evidence is never encountered during search, from ordering misses, where evidence is found but subsequently lost or ranked too low to count, finding that ordering misses are the more common of the two. Motivated by this diagnosis, we show that a lightweight, weight-frozen self-improvement mechanism, which distills recurring trajectory failures into reusable retrieval policies, improves retrieval on the subsets it targets without any change to model parameters. Together, these results characterize not only how well recursive access performs on medical evidence tasks, but specifically where it succeeds and fails, and why, thus identifying evidence retention, rather than discovery, as the central bottleneck for future systems.