NeurIPS 2026 Papers

Medication recommendation is a multi-label prediction task, yet existing prediction-set evaluation and calibration methods rely mainly on exact-overlap metrics and therefore treat all missed medications as equally severe. However, missing a medication but predicting a therapeutically close drug is less severe than matching it only with distant predictions. To address this, we introduce **Conformal Severity Control (CSC)**, a post-hoc framework for calibrating medication prediction sets under hierarchy-aware mismatch severity. CSC uses the Anatomical Therapeutic Chemical (ATC) hierarchy to quantify mismatch severity and controls patient-level severity without retraining the predictor, supporting both average severity and upper-quantile severity for the highest-severity mismatches. Experiments on MIMIC-IV show that CSC tracks user-specified severity tolerances and shifts prediction sets away from distant mismatches toward exact or close matches.

Mask-guided CT synthesis bridges structural annotations with imaging appearance, facilitating data augmentation and clinical tasks such as radiotherapy planning. Despite plausible visual realism, current methods seek optimal solutions by treating all tissues uniformly, failing to capture intrinsic CT properties such as tissue-specific distributions and strict anatomy, leading to semantic misalignment and structural hallucinations. To address this, we decompose CT synthesis into semantics-structure alignment through the proposed Semantics and Structure Loss. Specifically, the Semantic Intensity Distribution (SID) Loss partitions the broad Hounsfield Unit (HU) range into semantic subspaces to achieve tissue-specific intensity alignment. Building on SID-based semantic alignment, the Structural Anatomy (SA) Loss further improves structural fidelity by regularizing gradient manifolds within clinically-informed spatial domains. Extensive experiments across GAN, Diffusion, Flow, and foundation models demonstrate the broad compatibility and consistent gains of our approach. A user study further confirms the clinical realism and mask fidelity, while downstream segmentation demonstrates the practical utility. Code will be released after acceptance.

Facial video–based remote photoplethysmography (rPPG) estimates physiological signals from subtle spatiotemporal variations in facial videos, but remains vulnerable to head motion, illumination changes, and environmental perturbations. Existing methods mainly distinguish rPPG-related variations from motion and lighting artifacts at the signal or feature level, while largely neglecting the optical reflection properties of human skin. In this work, we formulate a skin reflection exponential model to describe the nonlinear relationship among skin reflectance, illumination, motion-induced variations, and blood-volume changes. Guided by this model, we propose a physically inspired rPPG estimation framework that explicitly incorporates skin reflectance priors into signal extraction. Given a facial video, facial keypoints are first detected and encoded to represent motion dynamics, while illumination-related features and candidate rPPG representations are jointly modeled under the proposed exponential formulation. By constraining rPPG estimation with skin-reflection physics, the framework improves separation of physiological components from motion and illumination disturbances. Experiments demonstrate improved robustness under challenging conditions.

Wearables are widely used for mobile health monitoring, and photoplethysmography (PPG) is a key sensing modality for heart rate and related physiological measurements. However, public in-the-wild PPG datasets remain largely wrist-centric or limited to short, controlled studies, constraining research on emerging wearable form factors. We present Multi-site PPG, an in-the-wild physiological dataset collected from four custom-developed unobtrusive wearables: a smart earring, ring, watch, and necklace. Each device records green and infrared reflective PPG, 3-axis acceleration, and temperature with timestamps for cross-device alignment, while a Polar H10 chest strap provides reference electrocardiogram (ECG). Participants wore the devices for one to multiple days during daytime activities while continuing their normal routines. The dataset contains over 350 hours of raw data and 230–290 hours of preprocessed, modeling-ready 8-second windows data per wearable. We benchmark heuristic, supervised, and self-supervised heart-rate estimation methods, showing substantial body-site differences: the best methods achieve mean absolute errors (MAEs) of 2.30 bpm on the earring, 5.13 bpm on the ring, 8.37 bpm on the watch, and 8.68 bpm on the necklace. We further analyze motion effects and evaluate multi-site and PPG–accelerometer fusion, demonstrating the dataset’s value for robust physiological sensing across emerging wearable form factors.

Large language models are increasingly used for mental health support, yet their conversational coherence alone does not ensure clinical appropriateness. Existing general-purpose safeguards often fail to distinguish therapeutic disclosures from genuine clinical crises, and progress on developing better ones is bottlenecked by a lack of clinically grounded evaluation resources. To address this gap, we introduce a risk taxonomy, developed in collaboration with PhD-level clinical psychologists, that identifies actionable harm (self-harm and harm to others) while preserving space for safe, non-crisis therapeutic content. We release MindGuard-testset, a dataset of multi-turn conversations annotated at the turn level by clinical experts, which is, to our knowledge, the first public benchmark with turn-level clinical risk labels for multi-turn mental health support. We also release an automated red-teaming (ART) framework designed to measure how safety classifiers affect downstream model behavior in adversarial multi-turn interactions. Using synthetic dialogues generated via a controlled two-agent setup, we train MindGuard, a family of lightweight safety classifiers (with 4B and 8B parameters). Our classifiers reduce false positives at high-recall operating points and, when paired with clinician language models, help achieve lower attack success and harmful engagement rates compared to general-purpose safeguards. We release all models, human evaluation data, and the ART framework.

Clinical evidence is inherently relational: medical images are interpreted with prior studies, cross-modal examinations, reports, and localized findings, and retrieving relevant evidence is therefore central to clinical interpretation. Recent foundation models increasingly encode these heterogeneous sources into shared embedding spaces, yet existing embedding evaluation often focuses on isolated matches, leaving unclear whether current models retrieve relevant evidence across changes in modality, appearance, and granularity. We introduce MedHEB, a unified benchmark with $107$ retrieval tasks from $54$ datasets, including both in-distribution and held-out-source evaluation, across three axes: evidence form (2D images, 3D volumes, text), pairing type (image--image, image--text, text--text, and modality-bridging visual matching), and relevance granularity (global, question-conditioned, region-level). Under a single ranking protocol, MedHEB varies clinically relevant evidence definitions to test whether embedding proximity remains meaningful across heterogeneous evidence, including modality-bridging visual retrieval and region-level text-to-region retrieval. Across representative models, performance is uneven across retrieval families: models strong on conventional image--text or text--text retrieval are less reliable on modality-bridging and region-level tasks. Current embedding spaces capture some clinical relationships well but do not yet provide balanced retrieval across heterogeneous evidence. We further provide MedEmb, an MLLM-based reference embedding model trained on the MedHEB ID split, as a reproducible baseline for unified 2D--3D--text medical retrieval. We release MedHEB with preprocessing scripts, evaluation code, access manifests, and MedEmb to support research on clinically meaningful medical embeddings.

Doctor agents are moving beyond single-turn answer generation toward evolving clinical decision systems that acquire evidence within episodes and reuse experience across episodes. Fixed-input medical QA and many interactive benchmarks provide limited support for evaluating how episode-level decisions interact with cross-episode experience. We introduce MedEvoEval, an executable longitudinal evaluation framework based on action-gated simulated outpatient episodes. Each case is converted into role-specific patient, examination, and manager views; evidence is revealed through valid actions, and structured traces link observations, actions, final outputs, scores, and optional experience write-back. Experiments show that these traces expose process costs hidden by final-answer scoring, characterize how MDT-style consultation reallocates resources, and support analyses of memory maturation, held-out transfer, update-stage response, and backward retention. On 200 held-out cases, the mature-memory condition with sequential write-back reduces interaction turns by 0.915 and improves composite utility by 0.0483 after Holm correction. MedEvoEval provides a concrete basis for evaluating experience-driven improvement, transfer, and retention. The accompanying artifact provides 700 processed episode records with provenance, role-specific schemas, and execution, scoring, and analysis code.

Dense stereo disparity estimation provides the geometric basis for 3D perception in computer-assisted endoscopy. Endoscopic stereo matching is difficult because reliable tissue correspondences often appear next to weak-texture, specular, occluded, or deformed regions within the same frame. Existing stereo and stereo-monocular methods improve cost aggregation or introduce monocular priors, but they often treat monocular guidance as a global feature source or an independently predicted candidate. Without evaluating the local support of the current stereo match during refinement, the update can reinforce corrupted correspondences in ambiguous regions or allow monocular priors to override reliable metric matches. To tackle these problems, we introduce Manifold-Stereo, a reliability-gated latent-flow framework for endoscopic stereo disparity estimation. The method refines a monocular disparity candidate through a conditional latent residual flow on an image-aligned correction space induced by stereo cost-volume geometry, then calibrates the corrected candidate to the current stereo disparity scale. A geometric consistency gate computed from left-right feature alignment regulates the candidate contribution during recurrent refinement, preserving stereo estimates in well-matched regions while supplying monocular context where local matching is ambiguous. Experiments on in-domain SCARED and zero-shot SERV-CT and Hamlyn show strong average disparity accuracy and competitive thresholded outlier rates against recent stereo and endoscopic baselines.

Survival analysis provides statistical methods to model the time until an event occurs. Reporting delays arise when event times are not observed at their occurrence but are only revealed upon reporting. This issue is particularly critical for timely risk evaluation when the observation window is short due to administrative censoring. In this study, we incorporate right-censored reporting delays by jointly modeling parametric hazards for the event and reporting processes. We then construct a consistent estimator for the model parameters and develop a Monte Carlo expectation-maximization algorithm to compute it. To address the challenges posed by administrative censoring, we leverage these findings and propose a transfer-learning procedure. Experimental results demonstrate that our method improves the accuracy of timely risk evaluation under administrative censoring.

Modern datasets across many disciplines increasingly consist of time-evolving, potentially infinite-dimensional random objects, such as dynamic functional data, which are naturally modeled in Hilbert spaces. In these settings, characterizing probability measures, for example, through densities, can be ill-defined or technically challenging. Motivated by clustering applications, we propose a Gaussian mixture framework for Hilbert-space-valued data based on kernel mean embeddings and develop efficient optimization algorithms for estimation. We establish theoretical guarantees showing that the proposed algorithm is well defined and that the model yields a dense class of approximations in infinite-dimensional spaces. We evaluate the framework through extensive experiments on diverse structures and data geometries, including $L^2$-functional data and random graphs in Laplacian spaces arising in modern medical applications.

Beat-level Electrocardiography (ECG) arrhythmia detection aims to assign an arrhythmia class to each beat in a recording, yet many existing systems treat beats as isolated local instances. This is limiting because beat labels often depend on multi-beat rhythm context, including timing, compensatory pauses, and beat-to-beat morphological consistency. We present $\textbf{DeepArrhythmia}$, a tool-grounded multimodal framework for segment-contextualized beat-level ECG arrhythmia classification. Given a multi-beat ECG segment, DeepArrhythmia combines the raw ECG signal and a rendered waveform image, localizes R peaks to identify beat instances, and produces structured beat-level predictions. The framework decouples physiological measurement from evidence integration using specialized tools for beat localization, numerical rhythm--morphology extraction, and morphology-focused textual analysis. DeepArrhythmia uses segment-level confidence to route between minimal and rich evidence states, since richer physiological evidence is not uniformly useful. This agentic design integrates rhythm context, explicit physiological grounding, and selective evidence acquisition for decision making.

Cell-type deconvolution, the task of estimating the proportions of constituent cell types in a heterogeneous biological sample, is a core problem in computational biology. Methods that rely on epigenetic marks such as DNA methylation typically operate on aggregated methylation estimates, discarding the pattern-level information carried by individual DNA reads. Existing read-level approaches that exploit this information are scarce, and all remain restricted to few-class settings; scaling them further is an open problem because, at scale, non-discriminative reads dominate and hard labels conflict with the many-to-many mapping between methylation patterns and cell types, preventing classifier convergence. To overcome this, we propose data-driven soft labels that estimate the conditional cell-type distribution for each read, and integrate this scheme into *Syto*, a new modular framework for read-level classification-based deconvolution. On a whole-body atlas of 39 human cell types, *Syto* reduces MSE by $2.56\times$ over SoTA, with gains transferring to an out-of-distribution dataset spanning 16 tissues. *Syto* lays the foundation for modeling increasingly large cell-type panels, with improved applications in biology and healthcare. The proposed soft-labeling scheme is further translatable to any setting with a many-to-many signal-to-label mapping.

Scientific AI agents increasingly retrieve evidence, reason across sources, and synthesize conclusions used in consequential decisions. Yet, their ability to do so in high-stakes domains such as health remains unclear. We introduce SciConBench, a large-scale live benchmark of 9.11K questions and expert-written conclusions from systematic reviews to evaluate open-domain scientific conclusion synthesis. The benchmark draws on an expert-validated automated evaluation pipeline that decomposes conclusions into atomic facts and measures correctness and comprehensiveness via factual precision and recall. To mitigate data leakage, we further introduce SciConHarness, a clean-room evaluation harness that equips agents with controlled web interaction to ensure valid measurement. Evaluating 8 frontier models and deep research agents, we find that factual quality remains low: under clean-room settings, the best agent achieves only a factual F1 of 0.337. Our clean-room setting consistently reduces performance relative to unconstrained evaluation, suggesting that leakage inflates estimates of models' true synthesis capabilities. Finally, we audit consumer-facing agents (e.g., Google AI Overview, OpenEvidence) and find they frequently generate incomplete and sometimes contradictory conclusions, even when the ground-truth answer is available. Overall, our results show that reliable synthesis of scientific conclusions remains an open challenge, and that clean-room evaluation is essential for assessing open-domain AI agents.