Agentic RAG agents must generate effective queries across diverse retriever backends, yet current agents produce retriever-oblivious queries, leading to large performance gaps when the backend changes. We observe that different retrievers, despite dissimilar architectures, exhibit structured behavioral patterns in their document-return overlap, forming a continuous behavioral space that can guide retriever-adaptive query generation. We propose RAMP (Retriever-Adaptive Multi-retriever Policy), which probes a black-box retriever with shared queries, encodes the resulting overlap patterns into a compact behavioral embedding, and injects it as soft tokens to condition query generation. A single RAMP model replaces four retriever-specific specialists without per-retriever retraining and generalizes to unseen retrievers by interpolating in behavioral space. On four QA benchmarks, RAMP matches 96–98% of four retriever-specific specialists (+3.8 EM over unconditioned training) and reaches 91.8% of the retrained upper bound on unseen retrievers, outperforming routing, few-shot, and fusion alternatives.

Although LLMs perform well on static medical QA, adapting them to multi-turn diagnostic dialogue remains challenging: agents forget confirmed symptoms, ask redundant questions, and fail to narrow differentials, largely because they lack an explicit representation of their evolving clinical understanding. Our key insight is that a structured belief state—encoding accumulated evidence, ranked differential diagnoses with confidence, and prioritized next questions—can simultaneously serve as a supervision target, a basis for rule-based reward computation, and a grounding context for deciding what to ask next. We propose BridgeGap, a two-stage framework in which supervised fine-tuning on silver-annotated belief states teaches the agent to maintain structured clinical reasoning, while reinforcement learning with a belief-progress reward refines questioning strategy through dense, environment-verified per-turn feedback without LLM judges or learned reward models. On HealthBench, BridgeGap (8B) surpasses Doctor-R1 (8B) by 2.84 points and the 4× larger Baichuan-M2-32B by 5.85 points; on AgentClinic, it achieves 44.6% diagnostic accuracy in 10.5 average turns, compared to 37.6% in 15.5 turns for Doctor-R1. Across evaluations, BridgeGap advances interactive diagnosis while preserving factual knowledge on static medical benchmarks.