Data-aware post-training quantization (PTQ) minimizes a per-token reconstruction loss on a small calibration corpus—an objective natural for average-token preservation, but one that implicitly weights positions by their empirical frequency. For **Automatic Speech Recognition (ASR)**, this weighting is misaligned with tail-sensitive recognition risk: names, numerals, technical terms, and domain-specific words are practically important but receive proportionally little calibration mass.

We propose **Tail-Aware Reconstruction Quantization (TARQ)**, a label-free PTQ framework that shifts calibration toward the lexical tail. **TARQ** introduces **rareBAL**, a closed-form per-Linear-layer rule that equalizes the calibration mass contributed by common and tail positions, plus a metric-consistent residual correction that aligns the sequential PTQ sweep with the reweighted objective. **TARQ** requires no entity labels, no curated calibration set, no validation decoding, and no additional training.

Across eight ASR backbones and eight datasets under 4-bit weight-only quantization, **TARQ** improves mean rare-WER, the **Word Error Rate (WER)** restricted to rare-word reference positions, while maintaining competitive aggregate WER. It achieves the lowest cross-corpus rare-WER swing, **0.63 percentage points (pp)**, among compared methods, and transfers without modification to entity-rich evaluation on ProfASR and ContextASR-Speech-En with no entity supervision.

Medication recommendation predicts medications for patient visits, but existing methods still face two key challenges. At the model level, traditional drug recommendation methods only predict structured drug codes with limited evidence grounding, while LLM agents can use richer clinical context but may lack safety verification and traceability. At the task level, existing benchmarks often use broad medication categories, which ignore subgroup-level safety differences and can lead to risk overestimation. We introduce the first fine-grained medication recommendation setting based on fourth-level ATC code generation. We propose Safe Prescription Agent (SafeRx-Agent), a knowledge-grounded multi-agent framework that uses patient context, external clinical knowledge, and safety verification to recommend traceable medication sets. Experimental results on MIMIC-III and MIMIC-IV datasets show that SafeRx-Agent improves fine-grained medication prediction accuracy while controlling drug interactions, contraindications, and medication set size. Code accompanying this work is available at https://github.com/HWu3150/SafeRx-Agent/tree/main.