Zero-shot vision-language models (VLMs) such as CLIP demonstrate strong open-vocabulary recognition capabilities, yet their prediction confidences are often poorly calibrated. Existing calibration methods often estimate prediction stability by applying multiple augmentations in input space, introducing substantial computational overhead and limiting practical deployment. To this end, this paper proposes an embedding-space posterior calibration approach for zero-shot VLMs, which shifts the sampling process from the input space to the embedding space. Our approach defines and computes a confidence stability score that measures how stable the sample remains under posterior embedding perturbations. This score is further converted into a sample-wise temperature, so that unstable samples receive stronger confidence smoothing while stable samples remain close to their original predictions. Compared with the original CLIP model, our method reduces the mean Expected Calibration Error from 5.83 to 2.05, achieving a 64.8% relative reduction. Meanwhile, our method achieves this improvement with only a minor increase in GFLOPs, from 8.83 to 8.84.

Sequential diagnosis requires balancing diagnostic accuracy against resource costs through iterative information gathering. Existing Large Language Model (LLM) approaches exhibit a critical knowledge-reasoning gap: despite encoding extensive medical knowledge, they struggle to reason systematically under cost constraints, often resorting to excessive testing. We propose GraphDx, a knowledge-enhanced framework with two core innovations. First, we design an automated pipeline that leverages LLMs to construct Medical Diagnosis Knowledge Graphs (MDKGs) with quantized typicality, action-centric topology, and dual-objective attributes for both diagnostic relevance and cost-sensitivity. Second, we introduce three collaborative agents (Perception, Reasoning, and Decision) where the Perception and Decision Agents handle language understanding and generation, while the Reasoning Agent performs deterministic evidence scoring and cost-aware planning on the MDKG. Experiments on MedQA and MIMIC-IV across three LLM backbones (DeepSeek-V3, Kimi-k2, Llama-3.3) show that GraphDx improves diagnostic success rates from 50–68% to 79–93% while reducing test costs by 20–54%, providing a robust, economical, and interpretable solution for automated clinical diagnosis.