NeurIPS 2026 论文列表

Light-field (LF) spatial super-resolution hinges on exploiting cross-view correspondences to recover high-frequency details while preserving geometric structure. However, existing methods often rely on implicit feature interaction or coarse disparity maps, which suffers noticeable performance degradation in regions with large disparities. In this paper, we propose Fine-Grained Epipolar Attention Diffusion (FEAD), which adopts epipolar attention as an explicit mechanism to characterize cross-view geometric correspondence along epipolar lines. To enable more accurate feature alignment and long-range spatial–angular interaction, we introduce a diffusion-based epipolar attention refinement strategy that progressively improves the initial attention. With the refined attention as guidance, cross-view features are explicitly warped and fused to enforce geometric consistency and aggregate complementary details across views for fine-grained reconstruction. Extensive experiments demonstrate that FEAD achieves state-of-the-art performance, with particularly strong gains in large-disparity scenarios.

A foundational challenge in neuroscience and AI is understanding how physical space is mapped into neural representations. While artificial neural networks can generate brain-like spatial representations, such as those observed in place and grid cells, their "black-box" nature makes it difficult to determine if these representations arise as general solutions or as artifacts of a chosen architecture, objective function, or training protocol. Critically, these models offer no guarantee that learned solutions for core navigational tasks, like path integration (updating position from self-motion), will generalize beyond their training data. To address these challenges, we introduce a first-principles framework based on the exponential map. Bypassing gradient-based optimization entirely, we use generator matrices to map physical locations to neural population vectors. Within the proposed formalism, essential navigational capabilities emerge analytically as intrinsic geometric properties. Exact, trajectory-invariant path integration naturally dictates commuting generators. Furthermore, skew-symmetric generators inherently yield equinorm representations with translationally invariant similarities. Translational similarity invariance is critical for stable egocentric navigation in open fields and empirical models typically enforce them via explicit regularization or architectural constraints. Extending our geometric perspective, we demonstrate that preserving the metric of flat space restricts the spatial wavevectors (derived from the generator eigenvalues) to form sets of roots of unity. The proposed framework supports diverse, biologically relevant spatial tuning profiles, including periodic grid fields, localized place fields, and context-dependent remapping. Beyond explaining mechanisms that underpin recent deep learning models of spatially-tuned cells, our work provides a formal connection between continuous attractor dynamics and oscillatory interference models. Finally, we show how the framework natively supports goal-oriented, multi-map navigation by identifying superpositions of representations alongside remapping as hyperdimensional computing operations. By grounding spatial representations in strict algebraic laws, this work offers a transparent alternative to empirical representation learning, revealing the exact conditions required for a coherent neural map of space.

Light Field (LF) deraining relies on leveraging redundant information between views to restore regions occluded by rains. Existing LF deraining methods typically rely on predicted depth maps to guide cross-view information interaction, while learning a brute-force mapping to separate rain from background content. However, the semi-transparent nature of rain streaks often leads to inaccurate depth estimation, which undermines geometric alignment and degrades restoration quality. More critically, the blending of rain and background during restoration makes brute-force mappings ineffective, resulting in visible rain artifacts in the output. In this paper, we first construct a consistency-constrained LF rain model by considering the consistency and the accumulation effects of rain layers. Then, we propose a novel Consistency-Guided Recovery Network (CGRNet) specifically designed for LF deraining. Inspired by the fact that background regions occluded by rain streaks often exhibit inconsistencies across all views, we introduce consistency maps to perceive degradation by aggregating pixel-wise consistency information along epipolar lines. Guided by consistency maps, the proposed model adaptively samples and aggregates consistent and redundant features from clean background regions to restore the details of degraded areas. Experiments validate our method's superiority over state-of-the-art approaches, and show that our dataset effectively enhances practical performance under real-world conditions.

LLM agents excel when environments are mostly static and their required information fits in a model’s context window, but they struggle with IT enterprise diagnostic tasks—such as incident management, IT vulnerability analysis, and FinOps anomaly explanation—where operators iteratively mine massive observability data over a curated resource graph to identify the origins that explain observed symptoms, enabling correct remediation. The data in these domains inherently has hidden dependency structure: entities interact, signals co-vary, and the importance of a fact may only become clear after other evidence is discovered. To cope with bounded context windows, agents must summarize intermediate findings before their significance is known, increasing the risk of discarding key evidence. ReAct-style agents are especially brittle in this regime. Their retrieve-summarize-reason loop makes conclusions sensitive to exploration order and introduces run-to-run non-determinism, producing a reliability gap where Pass-at-k may be high but Majority-at-k remains low. Simply sampling more roll-outs or generating longer reasoning traces does not reliably stabilize results, since verifying a diagnosis requires remediation actions that carry operational risk---demanding consistent correctness, not occasional success---and ReAct provides no mechanism for belief revision as evidence accumulates. In addition, ReAct entangles semantic reasoning with controller duties such as tool orchestration and state tracking; execution errors and plan drift degrade reasoning while consuming scarce context. We address these issues by formulating the investigation as abductive reasoning over a dependency graph and proposing EoG (Explanations over Graphs), a disaggregated framework where an LLM performs bounded local evidence mining and labeling (cause vs symptom) while a deterministic controller manages traversal, state, and belief propagation to compute a minimal explanatory frontier. On representative ITBench diagnostics tasks, EoG improves accuracy and run-to-run consistency over ReAct baselines, including a 7x average gain in Majority-at-k F1 score.

Autoregressive scientific forecasters often enforce physical or structural constraints by repairing each predicted state before feeding it back into the model. However, it remains unclear when stronger physical rule enforcement becomes reliable and when it becomes a source of distribution shift. We study this question through operator exactness, meaning whether the repair map is the identity on the target manifold and is aligned with the target geometry. We compare raw forecasting, post hoc repair, and in-loop repair across periodic incompressible Navier--Stokes, non-periodic CFDBench flows, and a hierarchical-forecasting support task. In the exact periodic regime, Fourier projection substantially improves rollout accuracy. On the NS-128 benchmark, a strong Raw-FNO has a final-step rollout MSE at horizon 100 of $(9.390 \pm 6.290)\times 10^{-5}$, and post hoc and in-loop projection reduce it to $(1.130 \pm 0.165)\times 10^{-6}$ and $(5.370 \pm 0.113)\times 10^{-7}$. However, once an exact projection is unavailable and only approximate boundary-preserving cleanup is available, the ordering changes. Across cavity, tube, dam, and cylinder flow, stronger Poisson-based cleanup can reduce divergence while worsening rollout error; target-distortion MSE predicts this harm far better than a linear-system residual. Controlled mismatch, screened cleanup, adaptive gating, and external-backbone checks show that the best approximate-regime operating point can be raw or near-identity. Hierarchical forecasting gives the same broader pattern. Exact forecast reconciliation is a stable baseline, whereas blended top-down repair, a validation-tuned interpolation toward historical-proportion top-down reconciliation, is dataset-dependent. Thus, constraint enforcement should be benchmarked by operator--data alignment before enforcement strength. Use in-loop projection when the operator is exact, and validate approximate cleanup strength using rollout metrics otherwise.

Diffusion and flow-based models learn powerful data priors by training a denoiser to reverse Gaussian corruption. To use this prior to solve a linear inverse problem, one needs to sample from the posterior, but the score that the prior provides is the unconditional score, not the posterior score. Existing methods either steer a fixed pretrained denoiser with approximate measurement-matching corrections, or train a conditional restoration model that abandons the denoising structure of the prior. We derive the exact posterior score in closed form for linear Gaussian inverse problems under general Gaussian interpolants, and show that posterior sampling reduces to a denoising problem at an operator-dependent shifted pivot under an anisotropic noise covariance. We turn this identity into **Exact Posterior Score (EPS)**, a denoising training objective that preserves the input/output structure of standard pretraining and can therefore be trained from scratch or fine-tuned from a pretrained denoiser. At inference, EPS uses the same sampler as the underlying backbone, with no likelihood gradients or projections. We evaluate EPS on five linear inverse problems across FFHQ and ImageNet, where it outperforms training-free and training-based baselines on fidelity, perceptual, and distributional metrics, while using roughly an order of magnitude fewer denoiser evaluations than gradient-based posterior samplers.

We study the problem of propagating the mean and covariance of a general multivariate Gaussian distribution through a deep (residual) neural network using layer-by-layer moment matching. We close a longstanding gap by deriving exact moment matching for the probit, GeLU, ReLU (as a limit of GeLU), Heaviside (as a limit of probit), and sine activation functions; for both feedforward and generalized residual layers. On random networks, we find orders-of-magnitude improvements in the KL divergence error metric, up to a millionfold, over popular alternatives. On a variational Bayesian neural network, we show that our method attains hundredfold improvements in KL divergence from Monte Carlo ground truth over a state-of-the-art deterministic inference method. We also give an smooth-distance error bound showing that, under regularity assumptions, moment matching removes the leading low-variance errors and propagates higher-order local accuracy through the layers of a network.

Free-form language generation poses a challenge for uncertainty quantification because responses do not belong to a fixed global label space. Semantic-entropy methods address this issue by sampling responses, clustering them by meaning, and estimating uncertainty over prompt-specific semantic classes. However, they mainly measure semantic dispersion and do not provide a principled decomposition into aleatoric and epistemic components, which in our setting correspond to ambiguity among plausible semantic meanings and insufficiency of model support, respectively. Moreover, cluster frequencies depend on the sampling budget and should not be treated as evidential strength. We propose a prompt-level evidential probing framework for semantic uncertainty decomposition in LLM generation. For each prompt, sampled responses define a dynamic semantic frame and provide initial support for the corresponding semantic classes. A lightweight evidence-retention probe learns how much of this support should be retained as reliable semantic evidence. The retained evidence induces a subjective opinion in which dissonance captures conflict among supported semantic alternatives and vacuity captures insufficient retained evidence. To train the probe, we introduce Augmented Semantic Uncertainty Cross-Entropy (AS-UCE), an LLM-specific evidential objective that augments the prompt-specific semantic frame with an unsupported class, allowing unreliable or hallucinated support to be routed away from observed semantic clusters during training. Experiments across four LLM backbones show that our framework consistently improves task-relevant AUROC and AUPR, with average AUROC gains of 7.4\% for OOD detection and 4\% for ambiguity detection over the competitive baselines, while requiring fewer sampled responses than perturbation-based decomposition methods.

When approximating an intractable density via variational inference *VI* the variational family is typically chosen as a simple parametric family that very likely does not contain the target. This raises the question: *Under which conditions can we recover characteristics of the target despite misspecification?* In this work, we extend previous theoretical results on robust VI with location-scale families under target symmetries in two substantial ways: (1) We open them up to a wider range of divergences by providing sufficient conditions for exact recovery of the target mean and correlation matrix when using the forward Kullback-Leibler divergence and $\alpha$-divergences. (2) By doing so, we find that we can drop the restrictive assumption of a log-concave target made in previous work, allowing us to give guarantees for a wider range of targets, including multi-modal ones. In our experiments, we show how our guarantees can serve as guidelines for the choice of the variational family and $\alpha$-value and we illustrate on a diverse set of examples how and why optimization can fail in the absence of our sufficient conditions.

ECG, with mature physiological simulators and public benchmarks, is a useful testbed for evaluating what synthetic pretraining supports. We introduce a reusable framework for ECG representation learning that compares patient-free physiological simulators, patient-derived real ECGs, and learned synthetic ECGs from real-ECG-trained generators. Holding the encoder and MAE objective fixed, we study three matched settings. Transfer-protocol analysis compares abnormal-inclusive real ECGs, normal-only real ECGs, and simulator pretraining under frozen probing and full fine-tuning on 26 abnormal-ECG tasks from PTB-XL, G12EC, and CPSC2018 using a single-lead-II pipeline. Compute-normalized PTB-XL scaling tests whether simulator pretraining matches real-data pretraining under equal processed-sample budgets. A fixed 100k-sample generator-class comparison evaluates VAE, DCGAN, SSSD-ECG diffusion variants with and without labels, and two simulators, SimECG-M and SimECG-N. The transfer analysis shows that protocol determines the supported claim: abnormal-inclusive real ECG pretraining yields more linearly separable pathology features under frozen probing, whereas full fine-tuning narrows the gap and can reverse it when the simulator corpus is sufficiently scaled. Under the fixed-100k protocol, task-wise F1 averaged over five runs ranks SimECG-N first on 22/26 tasks; no evaluated learned generator ranks first under this fixed-100k protocol; and SimECG-N obtains higher mean F1 than both diffusion variants on all 26 tasks. These results delineate where abnormal-inclusive real exposure remains beneficial and where patient-free physiological simulation can serve as an effective, protocol-bound substitute under the evaluated single-lead-II MAE pretraining and full-fine-tuning setting. We release pretraining corpora, splits, and code to support reproducible evaluation of synthetic ECG pretraining sources.

Foundation models of brain activity aim to learn representations from large-scale neural recordings that generalize across sessions, subjects, species, and downstream tasks. Transformer-based models have emerged as a promising architecture for this goal and begin all with the same first step --- tokenization: transforming sparse binary events into vector representations that downstream networks can consume. In other areas of machine learning, tokenization is its own design problem, distinct from the downstream foundation model. Several mostly implicit tokenization approaches dominate the literature, but their impact on downstream performance has not been systematically evaluated. We propose four explicit desiderata for a neural tokenizer---reconstruction fidelity, downstream utility, cross-instance generalization, and compression---and a framework of metrics spanning seven dimensions, including firing statistics, encoding and decoding, and latent structure and robustness. We assemble a benchmark of ten datasets spanning species, brain areas, recording technologies, and behavioral paradigms, and evaluate a representative set of discrete and continuous tokenizers, both single-neuron and population variants. Across the strategies evaluated, we show how tokenizers trained only on reconstruction can support downstream tasks. Yet reconstruction alone is misleading: reconstruction quality and downstream utility can be sharply dissociated. Our multi-axis framework is a response to this failure mode. These findings highlight tokenization as a critical and underexplored design axis in brain foundation models, and suggest that further progress will require continued systematic study of tokenization strategies.

The goal of asymmetric pansharpening is to synthesize a high-resolution multispectral (HR-MS) image by integrating panchromatic (PAN) and low-resolution multispectral (LR-MS) images. While State Space Models (SSMs), particularly Mamba, offer a promising paradigm for global dependency modeling, their standard selective scan mechanisms operate in discrete Cartesian grids, which are ill-suited for distinguishing between structural and spectral features in complex-valued frequency representations. To resolve this, we propose Euler-Mamba, a physics-inspired framework that reformulates the selective scan as a continuous state evolution on polar manifolds. We develop an Eulerian Selective Scan (ESS) mechanism that treats the fusion task as a trajectory of frequency growth. By performing radial scans within the polar domain, ESS governs complex-valued state transitions through the decoupled architecture: the Explicit Phase Rotation (EPR) block facilitates explicit phase rotations for geometric alignment, while the Implicit Magnitude Mapping (IMM) block performs magnitude scaling for spectral calibration. This polar-decoupled evolution allows the model to capture the continuous transition from global spectral backgrounds to fine structural details with linear complexity. Consequently, by modeling the fusion as a continuous functional evolution flow, Euler-Mamba achieves resolution-invariant performance and zero-shot generalization across varying spatial scales, bypassing the computational complexity inherent in traditional discrete-integration frameworks. Extensive experiments demonstrate that Euler-Mamba establishes a new Pareto frontier for asymmetric pansharpening, achieving a superior balance between physical interpretability, computational efficiency, and reconstruction quality.

We study semi-offline value estimation, where a small on-policy sample supplements a large off-policy dataset. This regime is common in recommender systems and pre-deployment A/B testing, and the challenge is to use the limited on-policy data to correct systematic bias in a given value predictor. Recent work on Bellman calibration (BC) gives a post-hoc, model-agnostic correction along the predicted value. We propose Error-Guided Bellman Calibration (GBC), which trains an error model on the on-policy sample and calibrates the predictor along this learned error axis. We prove a completeness-free refinement guarantee, a theorem showing that the correction adapts to the intrinsic dimension of the prediction bias, and a finite-sample bound on the value-estimation error that isolates our advantage in the refinement term. Across synthetic, CRM, and D4RL benchmarks, GBC improves over existing calibration baselines.

Mapping fine-scale 3D chromatin landscapes is critical for understanding gene regulation, yet the resolution of Hi-C data remains fundamentally constrained by both physical bottlenecks and sequencing costs. Although deep learning has shown promise in super-resolving Hi-C data, existing methods suffer from absolute coordinate loss during localized patching and a lack of structural and epigenomic priors in unconditioned generation. We propose a novel epigenomics-guided flow matching framework EpiFlow for 3D genome super-resolution. EpiFlow introduces two key innovations: (1) an EpiCond module that deterministically maps 1D epigenomic features to 2D spatial constraints via orthogonal expansion with built-in positional grounding, avoiding quadratic attention complexity; and (2) a Toeplitz-regularized classifier-free guidance mechanism that enforces distance-decay priors using a learnable symmetric prototype matrix. Extensive evaluation shows that our method achieves state-of-the-art cross-cell-type loop detection, improving recall by $2.8\times$ over raw Hi-C on unseen H1-hESC (Loop $F_1$ of $0.325$ vs. $0.199$) and outperforming all baselines on same-cell HFFc6 ($F_1=0.520$). Aggregate Peak Analysis (APA) confirms biological validity, and strong generalization to a held-out cell line demonstrates that the model learns transferable principles of 3D genome folding rather than cell-specific biases.

Patch-based transformers have emerged as efficient and improved long-horizon modeling architectures for time series modeling. Yet, existing approaches rely on temporally-agnostic patch construction, where arbitrary starting positions and fixed lengths fracture temporal coherence by splitting natural transitions across boundaries. This naive segmentation often disrupts short-term dependencies and weakens representation learning. We propose a novel Entropy-Guided Dynamic Patch Encoder (EntroPE), as a temporally informed framework that dynamically detects transition points via conditional entropy and dynamically places patch boundaries. This preserves temporal structure while retaining the computational benefits of patching. EntroPE consists of two key modules, namely an Entropy-based Dynamic Patcher (EDP) that applies information-theoretic criteria to locate natural temporal shifts and determine patch boundaries, and an Adaptive Patch Encoder (APE) that employs pooling and cross-attention to capture intra-patch dependencies and produce fixed-size latent representations. Extensive experiments on long-term forecasting, classification, and anomaly detection demonstrate that the proposed method improves both accuracy and efficiency, establishing entropy-guided dynamic patching as a promising new paradigm for time series modeling.

Large Language Models (LLMs) often generate factually incorrect outputs, commonly termed hallucinations, that undermine trust and limit deployment in high-stakes settings. Existing hallucination detection methods typically require multiple forward passes, or access to model internals. In this work, we provide theoretical background and empirical evidence that the *distribution* of token-level entropies, beyond the mean captured by perplexity or length-normalised entropy, serves as a fingerprint of hallucination, with distributional shape and tail behaviour carrying substantial independent signal. We formalize hallucination detection as a statistical hypothesis test and propose the **C**alibrated **E**ntropy **S**core (**CES**), a lightweight algorithm requiring only a single forward pass and black-box access to token logits. CES combines the mean signal with the maximum signal of the generated entropy through a calibrated reference CDF, producing scores that are directly comparable across models and tasks. We establish finite-sample calibration guarantees via a novel random-length Dvoretzky--Kiefer--Wolfowitz inequality, and also prove that CES detects hallucinations with probability converging to one exponentially fast in the generation length. Across eight QA benchmarks and ten generator models spanning open-source and API access models, CES achieves the highest detection performance among all single-pass black-box methods while providing formal error guarantees that existing heuristics lack. Remarkably, CES is statistically indistinguishable from multi-sample methods that require far greater computational cost, closing the gap between lightweight and expensive detection and making it suitable for real-time, large-scale deployment.

Large language model (LLM) agents typically perform function calling by retrieving relevant tools and reinserting their specifications as textual prompts at inference time. Although retrieval reduces the number of candidate tools, selected tool specifications must still be repeatedly re-encoded, incurring substantial latency and memory overhead, especially in on-device environments. We argue that this inefficiency stems from treating tool knowledge as transient text rather than reusable execution memory. To address this, we propose EngramState, a state-centric framework that compiles tool specifications offline into reusable recurrent state priors and directly loads them at runtime for query-only inference. EngramState combines retention-aware state construction, channel-wise state compression, a lightweight same-backbone state retriever, and feedback-driven state routing. On the DroidCall benchmark with an RWKV-7 1.5B backbone, EngramState reduces prompt tokens from 985 to 22 (97.8\%) under the Top-4 retrieval setting and substantially lowers time-to-first-token (TTFT) on a Galaxy S25 Ultra while maintaining or improving function-calling accuracy. Furthermore, the proposed state retriever achieves competitive retrieval quality using only 8.24\,MiB of retrieval storage. These results demonstrate that reusable recurrent states can serve as an efficient execution-time memory interface for scalable on-device function calling.

Denoising score matching (DSM) is the cornerstone of scalable diffusion model training. However, DSM becomes inapplicable on curved manifolds: the curved geometry makes the forward diffusion process nonlinear and the transition kernel is no longer tractable. Existing approaches therefore revert to implicit score matching (ISM) with expensive Brownian simulation, approximate the intractable heat kernel, or bypass the transition kernel altogether via flow matching with the logarithm map, each incurring significant computational cost or approximation error on general manifolds. We propose \emph{splitting diffusion}, a new framework that restores DSM-like training on general Riemannian manifolds by replacing the intractable Brownian forward process with a splitting scheme that alternates two steps: (1) an Ornstein-Uhlenbeck velocity update in the tangent space, whose transition kernel has a closed-form solution; (2) a deterministic geodesic transport step. This splitting yields a fully tractable transition kernel locally, enabling DSM-style training while requiring only the exponential map as a geometric oracle. The effectiveness of splitting diffusion is demonstrated on complicated manifolds and high-dimensional data.

Embedded devices from wildlife monitoring stations to clinical wearables require local AI inference due to latency, communication, or privacy constraints. Optimizing models for heterogeneous microcontrollers (MCUs) requires simultaneously satisfying hard physical constraints on memory, power, and temperature while preserving accuracy, a multidimensional optimization that is today performed manually by experts. We ask whether an LLM agent can autonomously navigate this complex, multi-turn pipeline guided by real hardware feedback, and introduce a hardware-in-the-loop agent arena in which the agent iteratively refines both model and firmware---compiling, flashing, and measuring on real hardware---to enable iterative optimization. Frontier models, including Claude Opus 4.7 and Gemini 3.1 Pro, fail entirely without hardware feedback (0\% deployment success), whereas our closed-loop formulation achieves the first successful deployment within three iterations and can surpass human expert results within seven. This agentic co-optimization achieves 250× compression for vision models with <3.3\% accuracy loss and 400× for audio with <6\% Feature Error Rate loss, enabling battery-free operation on a commercial MCU via solar harvesting. We demonstrate practical impact in two real-world systems: an elk-detection camera trap (96.7\% accuracy) and a phonetic-transcription wearable (8.44\% FER) for child development research.