NeurIPS 2026 论文列表

While agentic frameworks have advanced repository-level code localization, their reliance on static snapshots often leads to a $\textit{traceability gap}$—the loss of implicit links between issue symptoms and code implementation. To bridge this information deficit, we introduce $\textbf{T}$raceability $\textbf{A}$ugmented $\textbf{Co}$de Localization (TACO), a framework that recovers retrievable and usable traceability from repository evolution to guide localization agents. TACO offline crystallizes historical pull requests into a dual-index knowledge base through a prompt auto-tuning technique, capturing high-value semantic hints and architectural rationales. In the online phase, TACO employs a synergistic dual-track retrieval workflow, cross-validated by an arbiter and aligned temporally to handle version drift. Extensive evaluations on SWE-bench Lite and SWE-bench Verified across five state-of-the-art agentic baselines demonstrate that TACO significantly improves exact-match localization accuracy (Acc@1) by an average of 11.29\%, 13.20\%, and 12.87\% at the file, module, and function levels, respectively. Moreover, TACO significantly streamlines the exploration process, achieving an average reduction of 29.2\% in token usage and 30.4\% in monetary costs, with minimal offline maintenance overhead.

Although score-based generative models (SGMs) have achieved remarkable success in real-world sample generation tasks, it is still far from sufficient to understand them from a mathematical perspective. In this paper, we aim to provide enhanced convergence guarantees for two existing SGMs in $\mathcal{W}_2$-distance beyond log-concavity. More precisely, we develop a novel framework of error analysis for Euler-Maruyama (EM) and Poisson midpoint (PM) time discretization schemes for SGMs. Under a non-log-concavity condition, we show that $\tilde{\mathcal{O}}(\sqrt{d}/\epsilon)$ iterations suffice to approximate the target distributions in $\epsilon$-accuracy for the classical EM-based SGM, considerably improving upon the existing iteration complexity $\tilde{\mathcal{O}}(d/\epsilon^2)$. Notably, the novel framework of error analysis enables us to establish an $\tilde{\mathcal{O}}(\sqrt{d}/\epsilon^{2/3})$ iteration complexity for the PM-based SGM in $\epsilon$-accuracy, significantly outperforming current state-of-the-art Wasserstein convergence guarantees for SDE-based diffusion samplers.

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 and score estimation are classically linked through Tweedie’s formula, which relates the posterior mean under Gaussian corruption to the Stein score of the noisy marginal. In this work, we extend this perspective beyond Gaussian noise to a broad class of elliptical, energy-based noise distributions, with particular emphasis on generalized Gaussian corruptions. We derive the Energy–Tweedie identity: when the denoising posterior is viewed through the lens of proper scoring rules, the path derivative of a matched, possibly non-Euclidean energy score recovers the Stein score of the noisy marginal. Thus, the familiar correspondence between Gaussian noise, posterior means, squared loss, and Tweedie’s formula is lifted to a distributional correspondence between generalized Gaussian noise, full posterior laws, Mahalanobis energy scores, and the Energy–Tweedie identity. Among its consequences, this identity gives a posterior-sample-based route to score estimation, yields a principled criterion for noise-parameter calibration, and supplies a score-based perspective on recent diffusion-style generative methods trained with scoring rules.

Deep generative models, particularly Flow Matching frameworks, have emerged as powerful tools for synthesizing complex physical systems. However, standard architectures remain purely statistically driven and agnostic to the underlying governing equations, often violating fundamental physical constraints during inference. Existing zero-shot approaches designed to enforce these constraints typically rely on rigid algebraic projections. While effective for sparse boundary conditions, applying such discrete geometric corrections to dense, full-domain PDEs disrupts the probability path, pulling samples off the learned manifold and introducing intractable computational overhead. To address these challenges, we introduce Energy-Guided Transport, a novel training-free framework that formulates non-linear physical constraints as a smooth potential energy landscape. Rather than interrupting the ODE integration, our method applies a continuous dissipative force directly to the intermediate generative states, seamlessly steering them toward the physical manifold. By evaluating this force implicitly via reverse-mode automatic differentiation, our framework preserves the flow's natural dynamics while maintaining an efficient $\mathcal{O}(N)$ algorithmic complexity. We extensively evaluate our framework across various 1D and 2D physical systems, including the chaotic Navier-Stokes equations. Our method achieves stable generation without structural divergence, significantly improves the stability-accuracy trade-off compared to state-of-the-art methods, and substantially accelerates inference with respect to exact projection strategies. Code available at: \url{https://anonymous.4open.science/r/Projection-free-energy-guided-flow-matching-A302}.

Structure-informed protein representation learning is essential for effective protein function annotation and $\textit{de novo}$ design. With the rapid progress of protein structure prediction and experimental structure determination, a central challenge in the field is shifting from merely obtaining structural models to reliably leveraging predicted or measured structures for downstream learning. However, both crystal and AlphaFold-like predicted structures can contain structural uncertainty, which can bias local neighborhood construction and make robust protein representation learning challenging. To address these issues, we propose a novel equivariant Transformer-State Space Model (SSM) hybrid framework, termed $E^3$former, designed for efficient and robust protein representation. Our approach uses energy function-based receptive fields to construct proximity graphs that adaptively stabilize neighborhood selection under structural deviations, and incorporates an equivariant high-tensor-elastic selective SSM within the transformer architecture. These components allow the model to adapt to complex geometric interactions and extract structural features with a higher signal-to-noise ratio. Empirical results demonstrate that our model outperforms existing methods in structure-intensive tasks, such as inverse folding and binding site prediction, particularly when using predicted structures, owing to its enhanced tolerance to data deviation and structural uncertainty. Our approach offers a novel perspective for conducting biological function research and drug discovery using imperfect but increasingly available protein structure data. Our code is available on \url{https://anonymous.4open.science/r/E3former-207E}.

Real-world endoscopic diagnosis unfolds across native video, depending on continuous traversal of gastrointestinal organs where a single positive finding triggers a multi-minute reasoning chain over location, morphology, severity, and intervention. Existing endoscopy multimodal benchmarks evaluate only static images or single-disease videos that fail to capture the temporal and clinical reasoning demands of real-world endoscopy reporting. We introduce EndoSCOP-V, a video-grounded multi-turn evaluation framework for long-horizon clinical reasoning in endoscopy. The benchmark contains 1,307 real-world endoscopy and colonoscopy videos (63.2 h; 97% 1080p; mean ~174 s) — the time-scale at which endoscopists actually reason about a finding. The corpus is paired with 20,275 question–answer turns deterministically instantiated from clinician-filled structured endoscopy-reporting EHRs, avoiding synthetic or LLM-rephrased per-case annotations. Questions are derived from 1,033 clinician-reviewed templates spanning a clinically aligned 9-task × 30-sub-task reporting taxonomy across 54 World Endoscopy Organization (WEO) MST 3.0 diseases at ~18 templates per disease, substantially deeper per-disease examination than prior endoscopy benchmarks. To mirror real-world case mix, 22% of procedures carry co-existing pathologies, up to six per case. To model the hierarchical and conditional nature of clinical reporting, EndoSCOP-V introduces a Markovian context-pruned oracle-forcing protocol that evaluates multi-turn dialogues turn-by-turn. By injecting the gold answer before each subsequent turn, the protocol isolates per-turn reasoning from cascading dialogue errors and enables fine-grained analysis of clinical reasoning capabilities. Clinician-marked key-frame anchors further separate temporal-localization from frame-level comprehension failures. A panel of 15 board-certified endoscopists anchors a 78% strict-accuracy baseline on a 1,500-question audit. Across 14 open-weight LMMs, the best 27B-class model reaches 59.8% strict accuracy, falling to 53.4% once format-trivial disease-rejection items are excluded. On a between-subjects spot-check, the best of 3 frontier proprietary models trails the clinician panel by 23 pp on strict accuracy. Sub-task analysis reveals consistent gaps in quantitative measurement, multi-variable grading, and intervention reasoning on long-horizon video, providing a clinically grounded benchmark for evaluating multimodal reasoning in endoscopy.

Neuro-symbolic systems for sequence classification couple a neural perception network with a finite automaton over abstract events. Certifying their trajectory-level decisions under input perturbations is critical for safety-sensitive deployment. We address the open problem of end-to-end formal robustness certification for such Neurosymbolic Automata (NeSyA) under norm-bounded input perturbations. Naive layerwise linear bound propagation fails on this composition: per-step relaxation slack compounds through the temporal recurrence (interval explosion), and the standard log-space transition score subtracts two log-sum-exp terms over overlapping coordinate sets that independent relaxations cannot tighten. To address both, we introduce contrastive logit-gap (CLG), a reparameterization whose two log-sum-exp heads act on disjoint coordinates and admit substantially tighter linear bounds, coupled with a fused temporal operator that yields corner-tight bounds from a single linearization. On NeSyA sequence benchmarks, our formulation scales significantly beyond recursive baselines, maintaining tight safety certificates at long horizons while reducing verification wall time by up to ${\sim}6{\times}$. To our knowledge, this is the first end-to-end formal verification framework for temporal neuro-symbolic systems.

Learning identifiable representations in deep generative models remains a fundamental challenge, particularly for sequential data with regime-switching dynamics. Existing approaches establish identifiability under restrictive assumptions, such as stationarity or limited emission models, and typically rely on variational autoencoder (VAE) estimators, which introduce approximation gaps that limit the recovery of the latent structure. In this work, we address both the theoretical and practical limitations of this setting. First, we establish identifiability of a broad class of recurrent nonlinear switching dynamical systems under flexible assumptions, significantly extending prior results. Second, we introduce $\Omega$SDS, a flow-based estimator that enables exact likelihood optimization using expectation-maximisation. Through empirical validation on both synthetic and real-world data, our results demonstrate that $\Omega$SDS achieves improved disentanglement compared to VAE-based estimators and more accurate forecasting of underlying dynamics.

Optimizing high-dimensional physical systems under expensive evaluations and complex constraints is a central challenge in science and engineering. In offline model-based optimization (MBO), methods must improve designs using only a fixed dataset and no test-time access to the true objective. However, existing methods typically suffer from conservatism, unconstrained surrogate exploitation, or function as static inference-time samplers that cannot refine candidates against precise physical objectives. In this paper, we propose Differentiable Diffusion Conditioning (D2C), and show that deterministic sampling makes the reverse denoising chain differentiable with respect to the conditioning signal. D2C turns a frozen conditional diffusion model into an end-to-end gradient-based optimizer by directly optimizing conditioning variables at test time via gradients from frozen surrogate objectives and physics-informed penalties backpropagated through the reverse chain. We implement this idea in a composed proposer-evaluator framework and evaluate it on laser pulse optimization for inertial confinement fusion, sustainable data-center workload scheduling, and four Design-Bench tasks. Our method achieves the strongest results on the two real-world tasks and the best average rank across four Design-Bench tasks.

Test-time sampling can improve Vision-Language-Action (VLA) policies, but only if the model can efficiently select a good action from multiple candidates. Prior work often relies on separately trained external verifiers, adding extra models and inference overhead. We propose \textbf{Token Probability Bucketing (TokenPB)}, a self-verification approach that discretizes continuous action chunks into bucket tokens and co-trains a single VLA checkpoint with a joint objective: a flow-matching loss for action generation and an auxiliary bucket loss $\mathcal{L}_{\text{bucket}}$ that teaches the VLM backbone to model observation-conditioned distributions over bucket-token sequences. At inference, TokenPB samples $M$ candidates, scores each via teacher-forced evaluation under the learned distribution, and selects the best---enabling efficient batched scoring with observation-prefix KV-cache reuse. We provide theory connecting the training objective to a monotonic ranking property and bounds on expected selection regret that separate candidate-set effects from calibration. Across multiple backbones and benchmarks, TokenPB yields consistent success-rate improvements over single-sample inference from the same checkpoint (+2.7 in simulation, +17.1 in real-world, +16.4 under out-of-distribution shifts), and further improves over external-verifier baselines under the same sampling budget.

Text-to-image (T2I) diffusion models are increasingly distilled into few-step variants and being deployed to enable fast inference. However, their ability to generate harmful or undesired content poses significant safety risks. Data-driven unlearning methods suppress targeted generations by fine-tuning model weights using specialized unlearning objectives. Crucially, these objectives implicitly rely on multi-step denoising dynamics, an assumption that breaks down for few-step distilled (FSD) models, resulting in ineffective forgetting. Furthermore, performing unlearning on the non-distilled base model and subsequently re-distilling it to obtain an unlearned FSD model incurs substantial computational and time overhead, making it impractical in many settings. Hence, we address this limitation with a preference-driven unlearning framework that revisits Direct Preference Optimization (DPO) for diffusion models. We show that standard DPO and its unlearning derivatives, formulated around noise-prediction error, transfer poorly to FSD models due to their altered generation dynamics. To overcome this, we introduce a modified preference optimization formulation explicitly aligned with the few-step generation properties, $\textit{enabling direct concept removal in FSD models}$ while preserving few-step efficiency and maintaining strong retention of desirable (non-targeted) capabilities. We evaluate our framework primarily on identity and NSFW (nudity) removal tasks and also extend our method to object-level unlearning. Extensive experiments demonstrate consistent and effective forgetting, and strong retention performance, establishing our method as a practical and principled solution for unlearning in FSD models.

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.

While existing time series foundation models primarily rely on large-scale unimodal pretraining, they lack complementary modalities to enhance time series understanding. Building multimodal foundation models is a natural next step, but it faces key challenges: 1) lack of a unified multimodal pretraining paradigm and large-scale multimodal corpora for time series analysis; 2) how to effectively integrate heterogeneous modalities and enhance model generalization. To address these challenges, we take an early step toward multimodal foundation models for time series analysis. We first propose a multimodal pretraining paradigm that leverages time series with endogenous modalities (derived images and text) and exogenous knowledge (news), providing a comprehensive multi-view perspective for time series analysis. To support this, we develop an automated data construction pipeline to curate MM-TS, the first large-scale multimodal time series dataset spanning six domains, with up to one billion points. Then we propose HORAI, a frequency-enhanced multimodal foundation model. It integrates two core components: the Frequency-enhanced Cross-Modality Encoder and the Time-Frequency Decoder, designed to effectively fuse multimodal features and enhance model generalization across modalities and domains. After pretraining on MM-TS, HORAI achieves state-of-the-art zero-shot performance on time series forecasting and anomaly detection tasks, demonstrating strong generalization.

Diffusion models promise iterative refinement, yet masked diffusion language models (MDMs) suffer from two shortcomings: (1) inability to revise tokens, and (2) sparse training signal. The root cause is structural: MDMs control information flow only through input masking, so unmasked positions cannot be supervised, and self-correction is never learned. We propose the Leave-One-Out Transformer (LOOT), which addresses both issues. With LOOT, MDMs predict the distribution over potential replacements conditioned on the rest of the sequence at each position, in a single forward pass. Supervision can then be applied at masked and unmasked positions alike, yielding a lower-variance training objective with a dense loss signal at every token. Our method empowers MDMs to learn self-correction during training and supports continuous refinement at inference time via Gibbs sampling. LOOT achieves a new best validation perplexity on LM1B in a parameter-matched setting. Finetuned from a MDM checkpoint on OpenWebText, LOOT establishes a generative quality-vs-diversity frontier that surpasses prior remasking methods across NFE budgets.

When large language models (LLMs) are used for creative tasks where the evaluation is largely subjective, two crucial activities are ideation (in which a list of candidate ideas is produced) and pruning (in which the initial candidates are prioritized through a process of comparison). In this framework, the subjective results are influenced by inherent biases in the LLM's generation and evaluation, and it is therefore important to understand how these subjective biases operate. We define and explore a set of stylized tasks that allow us to highlight the effects of these subjective biases in LLM evaluation. In the process, we find evidence for several key underlying principles. First, there are multiple subjective LLM biases at play, and to understand the overall evaluation we must analyze the relative strengths of these biases and their interactions. Second, different prompts -- even when they seem superficially similar -- can have the effect of making different subjective LLM biases more or less salient. And third, there are complex interactions between the LLM's initial idea-generation phase and the subsequent decisions that go into prioritizing and pruning these ideas.

We study methods for simultaneous analysis of many noisy and biased estimates, each paired with an even noisier estimate of its own bias. The analyst's goal is to construct short calibrated intervals for each parameter. The standard debiasing approach, which subtracts the bias estimate from each biased estimate, inflates variance and yields long intervals. In this paper, we propose an empirical Bayes rebiasing strategy that starts from the fully debiased estimates and learns from data how much bias to reintroduce by estimating the unknown bias distribution. We provide convergence rates for the coverage of our intervals when the bias distribution is estimated using nonparametric maximum likelihood. Furthermore, we demonstrate substantial precision gains in prediction-powered inference, including pairwise LLM win-rate evaluations, as well as for inference of direct genetic effects in family-based GWAS.

Empathetic spoken dialogue is a sophisticated cognitive process that requires not only recognizing emotions but also inferring a user’s latent mental states to provide appropriate support. However, current SpeechLLMs often treat empathy as a direct input-to-response mapping, leading to "superficially warm" but emotionally hollow interactions. In addition, since empathy relies on a multi-stage process with strong inter-step dependency, errors at any intermediate step can cascade through subsequent steps and lead to inappropriate responses, while existing training paradigms lack mechanisms to precisely localize and improve such errors. In this work, we propose EmpathyChat, a unified framework that reformulates empathetic spoken dialogue as a structured cognitive reasoning process integrating perception, mental-state reasoning, and response generation. To support this paradigm, we first construct EmpathySet-400K, an acoustically rich dataset for multi-stage empathetic supervision. During the SFT stage, we strengthen acoustic grounding through proposed Acoustic-Anchored Attention (AAA). During the RL stage, we further introduce a novel stage-aware optimization objective with Step-Decomposed Credit Assignment (SDCA) to localize reasoning errors and mitigate cascaded error propagation. In addition, we introduce EmpathyEval, an expert-annotated benchmark for multi-dimensional empathy evaluation. Extensive experiments demonstrate that EmpathyChat achieves state-of-the-art performance in perception, reasoning, and response alignment. All resources, including model, code, dataset, and benchmark, will be publicly released.

Humans have a remarkable ability to judge causal relationships from a limited number of unreliable observations. Past work on causal cognition has largely focused on normative accounts of human behavior, leaving unknown how biologically plausible neural systems could learn causal relationships from observations and update their representations of causal structure with additional evidence. Here, we leverage task-optimized recurrent neural networks to discover candidate implementation-level neural mechanisms of causal judgment. We propose a novel cognitive task in which a subject observes stochastic samples from an unknown causal structure (e.g. among variables A, B, and C with unknown causal relationships), and must judge whether a specific causal relationship is present given a query (e.g. "does A cause C?"). We found that, after training, recurrent neural networks perform the task with high accuracy, adopt strategies that incorporate the behavior of non-queried variables to form their judgments, and, despite being trained only on pairwise queries ("does A cause B?", "does C cause A?", etc), form implicit beliefs about the complete graphical structure underlying the observations. Lastly, we use dynamical systems analysis to identify a set of low-level neural mechanisms that implement causal judgment and representation of causal graphical structure. Together, these findings lay the groundwork for a "bottom-up" approach to causal cognition, providing a potential basis for subsequent experimental study in the brain.