Reasoning-conditioned embeddings can improve retrieval by progressively refining semantic constraints. However, additional reasoning may introduce over-generalized or semantically noisy abstractions that weaken retrieval specificity. Existing methods largely supervise reasoning through final retrieval outcomes, providing limited guidance over the refinement process itself. We propose AURA, a framework that directly supervises intermediate reasoning states according to how well they separate relevant documents from hard distractors. AURA further trains a lightweight utility estimator over prefix-conditioned embeddings to adapt reasoning depth at inference time. On the reasoning-intensive BRIGHT benchmark, AURA-4B achieves 34.7 average nDCG@10 while using 2.8 reasoning steps and 96 generated tokens per query. Under matched deployment conditions, adaptive stopping reduces latency from 4.6 to 0.76 seconds relative to full-trajectory reasoning. Results on MS MARCO further suggest that this specialization need not substantially degrade conventional in-domain retrieval.

Automated LLM workflow design often relies on expensive per-task search, which is computationally expensive and does not explicitly reuse structural knowledge across tasks. We propose SWIFT (Synthesizing Workflows via Few-shot Transfer), which reuses prior search trajectories and demonstrations to synthesize target-task workflows without iterative target-task optimization. SWIFT distills compositional heuristics and output-interface contracts from source-task trajectories, then conditions a single LLM generation pass on these priors and cross-task workflow demonstrations to produce an executable workflow. Across 5 benchmarks, SWIFT outperforms the SOTA search-based baseline while reducing marginal per-task optimization cost by three orders of magnitude given an existing source bank. It also improves performance on 4 additional held-out benchmarks and transfers from GPT-4o-mini to Grok, Qwen, and Gemma execution backbones. These results suggest that prior workflow search can expose reusable structural priors that reduce optimization cost while preserving or improving accuracy on related tasks.

Large language models (LLMs) hold promise for advancing patient–provider communication, yet a persistent gap remains between benchmark-driven model development and the realities of clinical practice. This work presents a systematic, clinically grounded review of text-based medical datasets for LLM training and evaluation. We propose a scenario-based taxonomy derived from established clinical frameworks to map major knowledge-based and conversation-based corpora against core communication scenarios. We further synthesize core communication skills from gold-standard clinical assessment instruments and meta-analyze state-of-the-art medical LLM performance, highlighting how dataset properties, fine-tuning strategies, and evaluation metrics shape both knowledge acquisition and communicative competence. To empirically validate these findings, we conducted controlled fine-tuning experiments across representative LLMs, demonstrating that data composition and scenario alignment critically affect model performance. Our findings highlight the urgent need for scenario-rich datasets and standardized, human-centered evaluation protocol to advance clinically relevant medical LLMs.