Vision Large Language Models (VLLMs) show promise for multi-modal understanding; yet, their application to emotion recognition remains limited by insufficient spatial and contextual awareness and by supervised fine-tuning that uniformly penalizes all reasoning tokens—suppressing valid alternatives. We present Set-of-Vision-Text Rewards (SoVTR), a unified, reward-based framework that guides VLLMs with interpretable and verifiable visual attributes, focusing optimization on the emotion-discriminative reasoning steps that truly determine correctness. SoVTR decomposes supervision into three rewards: (i) Face Reward capturing action units, gaze, and micro-expressions; (ii) Body Reward encoding pose and gesture cues; and (iii) Context Reward modeling person–object relations and scene activities. By combining BERTScore attribute matching with GRPO policy optimization, SoVTR produces attribute-grounded explanation traces while preserving non-critical reasoning diversity. Across DFEW, FERV39k, and ExpW, SoVTR consistently improves both recognition accuracy and grounding quality, delivering substantial gains over strong VLLM baselines.

Medical coding converts free-text clinical notes into standardized diagnostic and procedural codes, which are essential for billing, hospital operations, and medical research. Unlike ordinary text classification, it requires multi-step reasoning: extracting diagnostic concepts, applying guideline constraints, mapping to hierarchical codebooks, and ensuring cross-document consistency. Recent advances leverage agentic LLMs, but most rely on rigid, manually crafted workflows that fail to capture the nuance and variability of real-world documentation, leaving open the question of how to systematically learn effective workflows. We present MedDCR, a closed-loop framework that treats workflow design as a learning problem. A Designer proposes workflows, a Coder executes them, and a Reflector evaluates predictions and provides constructive feedback, while a memory archive preserves prior designs for reuse and iterative refinement. On benchmark datasets, MedDCR outperforms state-of-the-art baselines and produces interpretable, adaptable workflows that better reflect real coding practice, improving both the reliability and trustworthiness of automated systems.