NeurIPS 2026 Papers

Aging cohorts increasingly combine imaging, plasma, and cognitive measurements, but most subjects provide only a partially observed cross-sectional snapshot, leaving latent stage, progression subtype, and stage-varying multimodal coordination to be inferred jointly. Existing methods estimate the cohort-level aging process, model covariance under observed covariates, or fuse modalities for prediction. However, they rarely target the joint problem of endpoint-anchored staging and progression-resolved residual coordination across modalities, even though subject staging, subtype assignment, and multimodal interplay are mutually dependent. We introduce ELPAC, a Bayesian framework with two interlocking constructions. The first integrates subtype trajectories backward from clinically reliable endpoint groups along biologically structured velocity components encoding modality-specific monotone direction and optional sequential ordering priors. The second factorizes residual multimodal variation around the inferred stage axis into sparse coordination axes whose loadings and activations expose which multimodal patterns are operative in each subtype and when they emerge, with stage-resolved variation supplying the auxiliary structure for conditional identifiability. We use a scalable amortized variational inference scheme that enables joint posterior inference at cohort scale with single-pass deployment on new subjects. On synthetic data and a harmonized ADNI+NACC/SCAN Alzheimer's cohort, ELPAC demonstrates accurate latent-structure recovery, well-calibrated prediction intervals, and biologically interpretable multimodal coordination programs that differ by subtype.

Discharge summaries are crucial clinical documents for patient care, containing clinical context of a patient's overall admission, and are routinely reviewed by medical experts during patient readmission, ongoing care, and diagnostic decision-making. In practice, medical experts often must synthesize information across multiple lengthy discharge summaries by iteratively obtaining information from the documents, while also verifying the evidence supporting each answer. Although large language models (LLMs) are increasingly explored for clinical document question answering, existing benchmarks do not sufficiently capture this clinical setting: they either evaluate exam-style medical knowledge or focus on single-turn question answering with limited evidence-grounding evaluation. We introduce **EHRNote-ChatQA**, the first benchmark for evidence-grounded multi-turn clinical question answering over patients' multiple discharge summaries. Built from de-identified MIMIC-IV discharge summaries, EHRNote-ChatQA contains 967 patient-level multi-turn samples spanning one to five notes and 16,072 medical-expert-verified QA pairs (8,036 content questions, each paired with an evidence-grounding question) across eight clinical categories. We construct the benchmark through an expert-informed pipeline that combines a comprehensive discharge-summary structuring schema, expert-curated multi-turn QA templates, and LLM-based generation. Every single QA sample is then reviewed and revised by 11 medical experts over three weeks. Benchmarking 22 LLMs (both open and closed-sourced) reveals several findings; evidence grounding is consistently harder than content answering, multi-turn errors compound across turns, and strong performance on existing single-turn clinical QA benchmarks does not reliably transfer to this setting. These results establish EHRNote-ChatQA as a rigorous and practical benchmark for evaluating clinical QA systems.

The design of the forward diffusion process is a foundational yet under-theorized aspect of diffusion modeling. Although it determines how data are corrupted and how quickly the forward dynamics approach a reference distribution, forward-process design remains largely governed by canonical defaults rather than a unified analytical framework. We propose Eigenvalue-Guided Control and Analysis (EGCA) for diffusion models, a spectral framework that uses the principal eigenvalue of the infinitesimal generator to analyze and guide forward-process design. Under suitable ergodicity conditions, this eigenvalue governs the exponential convergence rate to stationarity, providing a compact and interpretable summary of forward dynamics. We establish sufficient uniqueness and ergodicity conditions, derive eigenvalue-based convergence bounds, and adapt a practical numerical estimator for the principal eigenvalue. Empirically, we show that the eigenvalue strongly tracks forward convergence speed and serves as an organizing coordinate for the efficiency--quality trade-off across tractable forward-generator families. Experiments across multiple image datasets and diverse diffusion settings, including training formulations, architectures, and samplers, demonstrate that eigenvalue-guided design maintains or improves generation quality while reducing training cost. Beyond a single generator choice, our framework offers a spectral perspective for understanding and comparing forward-process choices in diffusion models.

Despite the critical need for sustainable deep learning, model energy efficiency remains largely neglected in Hardware-Aware Neural Architecture Search (HW-NAS). Discovering energy-efficient models is severely bottlenecked by costly on-device measurements and the brittleness of training-free accuracy proxies, which often yield unbalanced "glass cannon" architectures. We propose Energy-Efficient Neural Architecture Search (EENAS), a strictly zero-shot HW-NAS framework. EENAS introduces a PCA-based anomaly router that dispatches tasks to decision trees or tabular transformers, achieving <10\% MAPE zero-shot energy prediction on entirely unseen GPUs. Simultaneously, we robustly estimate accuracy by ensembling training-free proxies via a novel Log Z-score aggregation. Combining these predictors unlocks Absolute Hardware Bounding, discovering Pareto-optimal architectures for strict energy budgets entirely offline in under 30 minutes. Evaluated on ImageNet-1K, our models achieve 40\% less energy consumption at comparable accuracy, or a 2.5\% accuracy improvement under the same energy budget compared to efficient baselines.

Foundation models for EEG analysis are still in their infancy, limited by two key challenges: (1) variability across datasets caused by differences in recording devices and electrode configurations, and (2) the low signal-to-noise ratio (SNR) of EEG, where neural activity is often buried under artifacts and non-brain sources. To address these challenges, we present \textit{EEG-X}, a device-agnostic and noise-robust foundation model for EEG representation learning. EEG-X introduces a location-based channel embedding that enables robust transfer across heterogeneous EEG devices and electrode layouts. To improve robustness against noise, EEG-X employs a novel noise-aware masking-reconstruction strategy that reconstructs artifact-cleaned signals rather than raw noisy EEG and introduces a novel Dictionary-based Convolutional Transformation (DiCT) that improves reconstruction-based pretraining by comparing signals in a structured feature space instead of directly in the raw signal space. Experiments across datasets collected from diverse EEG devices show that EEG-X consistently outperforms state-of-the-art methods across multiple downstream tasks and demonstrates strong cross-domain generalization when pretraining and downstream datasets differ in electrode layouts and recording configurations paving the way towards foundation models. The models and code are available at :https://anonymous.4open.science/r/EEG-X-CEDE/README.md

Electroencephalography (EEG) foundation models increasingly rely on multi-dataset training and evaluation, yet public EEG datasets still lack a shared task specification layer that can turn heterogeneous recordings into reusable benchmark units. Existing standards organize files, metadata, and provenance, but they do not specify EEG tasks under a common language and rulebook, leaving critical task semantics scattered across papers, code, and manual interpretation. We investigate whether heterogeneous public EEG datasets can be standardized through a structured task specification language paired with a shared rulebook. Our methodology represents each benchmark entry as a task document synchronized with an executable task kernel, with the rulebook defining task fields, evidence requirements, document-kernel alignment, review states, and machine-checkable constraints. Using this methodology, we release a community-reviewed EEG benchmark corpus centered on 53 completed and reviewed entries with 245 task definitions spanning diverse paradigms, and we introduce NeuroDoc and NeuroAudit as the operational support layer for rulebook-guided drafting, upgrading, review, amendment, and release management. We further examine whether the resulting benchmark units can be instantiated in a shared downstream setting across four EEG foundation model backbones, providing execution-based evidence for reusable, auditable, and executable EEG benchmarking infrastructure. Project resources are available at https://anonymous.4open.science/r/anonymous_reviewe_neurodoc.

Multimodal question answering (MMQA) is based on integrating heterogeneous data sources, selectively leveraging relevant modalities, and ignoring distractors. Existing approaches based on Multimodal Large Language Models (MLLMs) improve performance by splitting the task into intermediate steps, but do not quantify how each modality contributes to reaching the final answer. We argue that quantifying the effect that each modality has on the answering process improves process traceability and final performance. Thus, we propose Entropy-Driven Multimodal Answering (EDMA), which uses logical entropy to formalise multimodal QA as an iterative process of entropy reduction and to quantify the contribution of each modality in distinguishing correct from incorrect candidate answers. This enables the construction of quantifiable answering trajectories, where each step is associated with a measurable entropy reduction. EDMA introduces (i) modality-conditioned partitioning to estimate unimodal entropy reduction, (ii) entropy-driven multimodal fusion to capture complementary information across modalities, and (iii) entropy-based selection of the answering trajectory that minimises the most logical entropy. Experiments on multimodal QA benchmarks show that EDMA consistently outperforms prompting-based baselines and state-of-the-art methods. Averaged across datasets, EDMA achieves gains of $12.8$ and $19.8$ F1 points over the strongest prompting baseline and state-of-the-art system, respectively. Moreover, we show that higher entropy correlates with more false positives and, through controlled interventions on the choice of the answering trajectory, provide evidence that lower-entropy partitions causally reduce false positives, validating entropy level as a reliable proxy for performance. We share our code at https://anonymous.4open.science/r/EDMA/README.md.

Disentangling writing style from semantic content is a fundamental challenge in literary text modeling. Style-content entanglement causes models to rely on semantic content for authorship attribution (AA) tasks and memorize author-specific content for style imitation (SI) tasks. We propose EDITORS (Editing LoRA Subspaces), a diagnose-then-deflate framework that adapts pretrained large language models and prioritizes stylistic features, making it effective for both AA and SI tasks. EDITORS first trains a diagnostic LoRA adapter using style-neutral statements derived from the training corpus to identify a content-orientedsubspace. It then trains the final style adapter under activation-space deflation that projects input activations away from the identified content-oriented directions. We demonstrate that EDITORS is empirically effective: On three AA benchmarks spanning literature, social media, and news, EDITORS achieves state-of-the-art accuracy, outperforming the strongest existing baseline by up to 29%. On SI, EDITORS generates stylistically faithful and diverse passages, outperforming baselines in achieving style similarity while adhering to content specifications.

Euclidean combinatorial optimization problems (ECOPs), such as the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP), possess inherent symmetries under the two-dimensional Euclidean group E(2), including rotations, reflections, and translations. Existing learning-based methods, including recent diffusion-based methods, rely on data augmentation or regularization to approximate E(2)-equivariance. This paper presents EDISCO, the first discrete diffusion model for ECOPs with exact E(2)-invariant generative distributions over node-index solutions. EDISCO introduces an E(2)-equivariant edge-score network coupled with a categorical continuous-time Markov chain over discrete edge variables, and exact posterior sampling provides efficient multi-step inference. This design gives EDISCO a local geometric inductive bias: edge neighborhoods with the same relative geometry and combinatorial context are represented consistently regardless of absolute position or orientation, making learning more efficient and inference more robust than non-equivariant methods. EDISCO outperforms previous learning-based state-of-the-art solvers on synthetic TSP from 100 to 10000 nodes and CVRP from 50 to 2000 customers, while using only 33--50\% of the training instances. Trained only on uniform synthetic data, EDISCO also outperforms competing learning-based baselines under spatial distribution shift and CVRP constraint-tightness shift. Code is available at https://anonymous.4open.science/r/EDISCO-7F54.

Deep learning has become the dominant paradigm for vision-based spacecraft pose estimation, where models are predominantly trained on synthetic data due to the prohibitive cost of acquiring real in-orbit imagery. When deployed, these models suffer significant performance degradation due to the sim-to-real domain gap, which existing benchmarks address by pairing synthetic training data with Hardware-in-the-Loop (HIL) real imagery. Yet a critical factor remains consistently overlooked: in real orbital scenarios, illumination varies with the spacecraft's position along its orbit, inducing severe appearance shifts that existing benchmarks neither control nor study. We introduce ECLIPSE, the first benchmark designed to isolate illumination as an independent experimental variable. ECLIPSE pairs a large-scale photorealistic synthetic training set with a fully labeled HIL evaluation set of 14 approach trajectories, each replayed identically under 4 controlled lighting conditions, enabling rigorous evaluation of illumination-robust and domain-adaptation methods. Through this controlled design, our experiments confirm that lighting is the dominant driver of the sim-to-real performance gap in spacecraft pose estimation.

Reinforcement learning (RL) is a critical stage in post-training large language models (LLMs), involving repeated interaction between rollout generation, reward evaluation, and centralized learning. Distributing rollout execution offers opportunities to leverage more cost-efficient inference resources, but introduces challenges in wide-area coordination and policy dissemination. We present ECHO-2, a distributed RL framework for post-training with remote inference workers and non-negligible dissemination latency. ECHO-2 combines centralized learning with distributed rollouts and treats bounded policy staleness as a user-controlled parameter, enabling rollout generation, dissemination, and training to overlap. We introduce an overlap-based capacity model that relates training time, dissemination latency, and rollout throughput, yielding a practical provisioning rule for sustaining learner utilization. To mitigate dissemination bottlenecks and lower cost, ECHO-2 employs peer-assisted pipelined broadcast and cost-aware activation of heterogeneous workers. Experiments on GRPO post-training of LLMs ranging from 4B to 32B parameters under real wide-area bandwidth regimes show that ECHO-2 significantly improves cost efficiency while preserving RL reward comparable to strong baselines.

While Multimodal Large Language Models (MLLMs) show promising performance in automated electrocardiogram interpretation, it remains unclear whether they perform actual step-by-step reasoning or just rely on superficial visual cues. To investigate this, we introduce **ECG-Reasoning-Benchmark**, a novel multi-turn evaluation framework comprising over 6,400 samples to systematically assess step-by-step reasoning across 17 core ECG diagnoses. Our comprehensive evaluation of state-of-the-art models reveals a systematic shortfall in executing multi-step logical deduction. Although models possess the medical knowledge to retrieve clinical criteria for a diagnosis, they exhibit consistently low success rates in maintaining a complete reasoning chain, primarily failing to ground the corresponding ECG findings to the actual visual evidence in the ECG signal. These results demonstrate that current MLLMs bypass actual visual interpretation, highlighting a limitation in their visual grounding capabilities and motivating robust, reasoning-centric medical AI. The code and data are available at https://anonymous.4open.science/r/ecg-reasoning-benchmark-anonymized-BB3C.

The availability of open 12-lead electrocardiogram (ECG) datasets has significantly increased the number of studies focusing on automated ECG analysis. However, existing datasets often have limitations due to unverified annotations, overly processed data, or the absence of features such as fiducial points of the ECG. In this paper, we present a comprehensive collection of ECGs, with each record meticulously annotated by a team of seven cardiologists. Over 2,500 ECG records were annotated, with most records reviewed by at least four physicians. 1,527 ECG records of the dataset contain detailed annotations of PQRST segments, verified by two cardiologists. The dataset constitutes the largest collection of manually delineated ECG records to date, offering a valuable resource for researchers and practitioners in cardiology. This dataset, the first of its kind available for open use with multi-expert annotations, is suited for a variety of research tasks. It can be used for ECG abnormality classification, for analyzing patterns and annotation complexities across different heart abnormalities, and for tasks related to ECG delineation.

Deploying large language models on edge devices requires mitigating two major bottlenecks: the computational cost of the model and the memory overhead of the KV cache. While combining model quantization and KV-cache eviction seems like a natural solution, we reveal that this naive pipeline suffers from a critical failure mode. The issue stems from a hidden conflict: modern eviction methods rely on attention scores to identify important tokens, yet quantization inherently adds noise to these exact scores. Consequently, quantization does not merely degrade the numerical precision of the KV cache—it actively alters the eviction decisions, often causing the model to discard crucial context. To overcome this non-orthogonal degradation, we propose EAT, a training framework that integrates discrete eviction behavior into quantization-aware training. EAT computes eviction from quantized attention, applies the resulting mask in the forward pass, and uses a masked straight-through estimator in the backward pass. The method requires no architectural changes and adds no inference-time parameters. Systematic evaluations show that EAT mitigates the failure mode of naive quantization-plus-eviction pipelines. Under W4A16 + KV-INT8 with 50% KV eviction, eviction-unaware composition reduces the five-task average from 38.37 to 29.04, whereas EAT recovers it to 39.44 under the same cache budget. Real-world deployment on the MediaTek Dimensity 9500 demonstrates that, under a 50% KV-eviction budget, EAT improves decoding throughput by 64.1% and reduces total memory traffic by 20.4% compared to quantized full-KV inference. Mechanistic analyses suggest that the improvement is associated with reduced attention sink behavior rather than with sharper attention.

Vision–Language–Action (VLA) models offer a unified framework for robotic manipulation by integrating visual perception, language understanding, and control generation. However, existing VLA systems still struggle to generalize across diverse tasks, scenes, and camera viewpoints, and often produce coarse or unstable actions. We argue that these limitations are closely tied to the structural properties of actions in VLA settings, including the inherent multi-peaked nature of action distributions, the token-based symbolic reasoning of pretrained VLM/VLA backbones, and the effective finite resolution imposed by real-world robotic control. Motivated by these properties, we introduce E0, a tweedie discrete diffusion framework that formulates action generation as iterative denoising over quantized action tokens. By operating in a discrete action space with a principled diffusion process, E0 naturally aligns with token-based reasoning, supports fine-grained yet executable action control, and avoids the distributional mismatch of masking-based discrete diffusion. Experiments on LIBERO, VLABench, ManiSkill, and a real-world Franka arm demonstrate that E0 achieves state-of-the-art performance across diverse environments, outperforming strong baselines by 8.3\% on average.

With the rise of large language models (LLMs), generative engines have become powerful alternatives to traditional search, reshaping retrieval tasks. In e-commerce, for instance, conversational shopping agents now guide consumers to relevant products. This shift has created the need for generative engine optimization (GEO)---improving content visibility and relevance for generative engines. Despite its growing importance, current GEO practices are largely ad hoc, and their impacts remain poorly understood, especially in the e-commerce setting. We address this gap by introducing E-GEO, the first dataset built specifically for e-commerce GEO. E-GEO contains 13,747 realistic, multi-sentence consumer product queries, each paired with 10 relevant Amazon listings, capturing rich intent, constraints, preferences, and shopping contexts that existing datasets miss. Using this dataset, we conduct the first large-scale empirical study of e-commerce GEO across five frontier generative engines, seven popular LLM rewriters with a simple prompt, and fifteen hand-crafted rewriting heuristics. We further formulate GEO as a tractable optimization problem and develop a lightweight prompt meta-optimization algorithm that significantly improves over heuristic baselines. Notably, the optimized prompts reveal a stable, domain-agnostic pattern, suggesting the existence of a "universally effective" GEO strategy. Finally, we red-team the GEO system through both heuristic and optimization-based attacks and show that, under a simple in-prompt defense, gains from GEO reflect genuine content improvement rather than manipulation, anchoring GEO as a substantive, well-defined optimization problem.

Tracking plasma profiles, such as electron temperature and rotation, remains a challenging task in tokamak nuclear fusion. While RL-based approaches offer a promising path for multi-input-multi-output control systems, existing frameworks overlook two key obstacles: partial observability of plasma dynamics and the diagnostic mismatch between offline training and online execution. We propose a novel offline RL framework to address these challenges. Specifically, we train the control policy to condition on the latent context that is extracted from a trained plasma dynamics model. This dynamics-informed latent context encodes useful information about operating regimes, enabling the policy to adapt as the plasma evolves. To mitigate the gap between offline and real-time observation, we learn a converter that maps plasma diagnostics reconstructed offline to their real-time-observable counterparts, thereby exposing policy training to the real-time plasma diagnostics available in live experiments. In offline evaluations, the proposed method substantially improves tracking performance and robustness across a diverse set of discharges. We report real-time deployment results on DIII-D Tokamak Fusion Facility, showing feasibility under challenging actuator conditions.

Autoregressive Diffusion Transformers have emerged as a powerful paradigm for long video generation. However, they are often constrained by prohibitive computational overhead and substantial memory footprints. This inefficiency arises from dense computation mechanisms that allocate uniform processing resources across all spatiotemporal regions, failing to leverage the inherent temporal redundancy of video where motion is typically confined to sparse areas. In this paper, we propose **Dynamics-Aware Sparse Attention (DASA)**, a unified framework that shifts from dense computation to sparse, motion-driven generation to eliminate both storage and computational redundancies. Inspired by video codec principles, our approach explicitly decouples video content into dynamic and static regions. To mitigate memory bottlenecks, we selectively compress the historical KV cache by retaining only dynamic features within the generated chunk. Furthermore, to reduce computational overhead, we employ a region-level computation allocation strategy based on localized dynamics. Additionally, a dynamics-guided distillation method is introduced to enhance performance via a lightweight distillation training process. Experiments on VBench demonstrate that our approach accelerates autoregressive video generation by 2.16$\times$, while preserving high visual fidelity.

Large language models are remarkably capable, yet how computation propagates through their layers remains poorly understood. A growing line of work treats depth as discrete time and the residual stream as a dynamical system, where each layer's nonlinear update has a local linear description. However, previous analyses have relied on scalar summaries or approximate linearizations, leaving the full spectral geometry of trained LLMs unknown. We perform full Jacobian eigendecomposition across three production--scale LLMs and show that training installs a monotonic spectral gradient through depth---from non-normal, rotation-dominated early layers to near--symmetric late layers---together with a cumulative low-rank bottleneck that funnels perturbations into a small fraction of the residual stream's effective dimensions. Our experiments reveal that this gradient and the dimensional collapse are learned rather than architectural, and is largely dissolved when structured non-normality is removed. We further show that the topological positioning of graph communities predicts whether the Jacobian amplifies or suppresses them, with the sign of the coupling determined by the local operator type, a relationship absent at initialization. These results map a learned spectral geometry in LLMs that links perturbation propagation and compression to the network's functional topology.