NeurIPS 2026 论文列表

We introduce DIAL (Directional Intervention, Asymptotically Limited), a parameter-efficient adapter that adds a bounded, monotone, scalar-controlled residual $\delta(s,c) = \sigma(g) \odot \tanh(c/T) \odot \tanh(Dz)$ with $z = D^\top \phi(s)$ to a base language model's hidden state. The architectural form yields a closed-form upper bound $\lVert \delta \rVert_\infty \le \sigma(g_{\max})$ on the residual $L_\infty$ norm, computable from model weights alone. Across 5 base models (3.8B to 70B parameters, 4 architecture families; one checkpoint per family, with 5-seed csweep variance at Llama-3.1-8B), the residual saturates at the predicted bound with mean ratio $1.000 \pm 0.001$ (worst-case family deviation 0.2 percentage points). On Llama-3.1-8B under GPT-4o judge ($n=100$ per bench, zero content-filter blocks), DIAL exceeds a FiLM baseline trained with the same multi-$c$ objective on every benchmark and judge. Semantic vs strict judge: HarmBench DIAL +0.52 / +0.55 vs FiLM +0.44 / +0.39; AdvBench DIAL +0.63 / +0.66 vs FiLM +0.46 / +0.50; XSTest DIAL +0.33 / +0.25 vs FiLM +0.26 / +0.22; both judges agree on the ranking. Outside training range, DIAL's residual saturates at $\sigma(g_{\max})$ while FiLM's grows unboundedly: at $c = 10^4$, FiLM's residual $L_\infty$ norm is 534 times larger than DIAL's. The bound holds across all control values, not only trained ones; concrete deployment scenarios (configuration error, multi-tenant policy ceilings, adversarial composition) are where this distinction matters in practice. The bounded-monotone form produces analogous saturation behavior on two continuous-control RL environments (HC-Vel $n=35$, Ant-Goal $n=30$) under matched oracle-$c$; the prior fails when $c$ must be inferred online (meta-RL). Ablations confirm both the bound and the saturating temperature are necessary; a frozen-gate variant retains the bound at lower controllability magnitude. We release adapter train reports and canonical bench generations, algebraic derivations (with a SymPy reproducibility script), the full GPT-4o judge cache (semantic and strict), and a human-labeled validation set ($n=50$, Cohen's kappa = 0.895). Full adapter checkpoints are deferred to camera-ready release.

Accurate forecasting of Earth's dynamic systems is vital in geophysics, where neural operators offer a powerful data-driven paradigm. Standard operators often treat the Earth as a flat Euclidean plane, introducing geometric artifacts. In contrast, Spherical Neural Operators (SNOs) resolve this mismatch by operating within the spherical spectral space, naturally adapting to spherical geometries. However, for computational efficiency, existing SNOs typically rely on the rotation equivariance assumption. This assumption simplifies the transitions of physical states into isolated spectral modal evolutions, thereby over-idealizing the Earth as a homogeneous system. To solve this problem, we no longer rely on the rotation equivariance assumption that isolates spectral modal evolution, but directly derive an explicit modal interaction kernel and propose Diagonal Spherical Neural Operators named DIAGNO. DIAGNO explicitly parameterizes cross-modal interactions through spectral modal decomposition, thereby preserving the inherent heterogeneity of Earth dynamics. Furthermore, leveraging the unique diagonal structure of the spherical spectrum, we design inter- and intra-diagonal interaction mechanisms to natively capture essential zonal and meridional dynamics, respecting intrinsic geophysical regularities. Extensive experiments across simulated fluid dynamics and real-world Earth system reanalysis (including atmosphere and ocean) datasets demonstrate DIAGNO’s superior capability, consistently achieving state-of-the-art performance in multi-step forecasting. Beyond this, we validate DIAGNO’s practical analytical value in professional geophysical domains by assessing its fidelity in capturing climatological anomalies and maintaining physical consistency.

Dynamic Reconfigurable Battery (DRB) systems enable flexible interconnection among cells through power electronic switches, crucial for electric vehicles and energy storage systems. However, sensor failures in practical deployments lead to partial observation loss, posing severe challenges for safe control. Existing methods assume full state observability and often adopt overly conservative strategies when observations are missing, failing to achieve effective trade-offs between safety and performance under uncertainty. More fundamentally, these methods lack mechanisms to adaptively adjust decision conservatism based on inference reliability, leading to unstable decisions under complex failure scenarios. We propose the $\textbf{D}$iffusion-$\textbf{G}$uided $\textbf{R}$isk-$\textbf{A}$daptive $\textbf{F}$ramework (DGRAF) to address this challenge. The framework handles spatiotemporally coupled failure patterns through joint diffusion inference with quality quantification, and integrates inference quality with distributional decision-making to enable automatic modulation between aggressive optimization and conservative maintenance. Through extensive simulations and hardware experiments, we demonstrate that DGRAF consistently outperforms baseline methods across complex failure patterns, achieving superior energy efficiency, decision stability, and risk resilience.

Scientific measurements inherently contain variance. Standard regression, however, treats noisy data as absolute ground truth, leading models to memorize noise rather than physical laws. This overfitting hampers generalization, particularly when transferring from abundant theoretical proxies to scarce experiments. We introduce DETS, a framework replacing rigid point estimation with Physical Tolerance Modeling. Our Interval-Censored Evidential Engine (ICEE) maximizes probability mass within acceptable error margins, explicitly decoupling aleatoric noise from epistemic uncertainty. Using this filtered uncertainty signal, a Thermodynamic Sampling strategy dynamically selects theoretical data to align source and target domains. Experiments across thermodynamics, drug affinity, and bandgap prediction show DETS outperforms state-of-the-art methods. Crucially, it exhibits superior robustness in high-noise, data-scarce settings.

Video multimodal large language models have made strong progress on open-ended video understanding, but they still lack precise local spatiotemporal perception. When two videos share almost the same global semantics and differ only in a short time span or a small region, current models often fail to find the change and provide reliable evidence. We propose **DeltaVid**, a verifiable proxy-task framework that enhances fine-grained spatiotemporal perception with cross-video differences. The key idea is to turn cross-video spot-the-difference into a trainable perception signal, where a model identifies local changes, judges temporal boundaries, and organizes spatial evidence by comparing similar videos. To make this signal scalable to train and reliable to evaluate, we further introduce DeltaVid-10K and DeltaVid-Bench, which convert controllable local differences in real videos into evidence-labeled training and test samples. Experiments show that DeltaVid substantially improves performance on cross-video difference understanding and transfers the learned local evidence ability to general video understanding benchmarks, including MMVU, MLVU, Video-MME, VideoHolmes, VideoMMMU, LVBench, TempCompass, and LongVideoBench. These results show that cross-video differences are not only an effective way to diagnose fine-grained perception failures, but also a scalable proxy supervision that moves Video MLLMs from coarse semantic understanding toward fine-grained spatiotemporal evidence reasoning.

EEG foundation models (EFMs) have improved EEG representation learning by transferring knowledge from large-scale multi-subject pretraining to downstream tasks. However, their pretrained embeddings often retain substantial subject-related variation, placing an additional burden on the downstream classifier, especially in cross-subject settings. To address this issue, we propose DISENTANGLED EFM FINE-TUNING (DEFT), a plug-and-play adapter for EFM fine-tuning. DEFT encourages branch disentanglement of pretrained tokens into task-oriented content and subject-consistent style, then performs downstream prediction from the content branch. It instantiates this factorize-before-classify design with recursive Gaussian refinement, temporal-variance regularization, subject-aware contrastive learning, and reconstruction. Experiments across multiple EEG datasets and four EFM backbones show that DEFT improves matched fine-tuning baselines. Further ablations and analyses support the intended content/style branch organization.

AI-generated image detectors face significant challenges when deployed in real-world environments, particularly when test samples are produced by unseen generators that deviate from the source training distribution. Existing detectors often rely on a single type of evidence, such as semantic representations or low-level generation traces, making them vulnerable when the corresponding cue becomes unreliable. Moreover, adapting detectors to newly emerging generators usually requires target labels or retraining, which is costly and impractical. To address these challenges, we propose Dual-Evidence Disagreement-Constrained Adaptation (DEDCA), a test-time adaptation framework for generalized AI-generated image detection. DEDCA constructs a dual-evidence detector by combining CLIP-based semantic evidence with trace evidence extracted from shuffled entropy and residual statistics, and introduces a reliability-aware gate to perform sample-specific adaptive fusion. Our key idea is to exploit the disagreement between the two evidence streams as an unlabeled adaptation signal, enabling the detector to adjust to target-domain shifts without relying on target labels or blindly trusting a single prediction stream. Experiments show that DEDCA achieves superior performance on the GenImage benchmark by outperforming state-of-the-art AI-generated image detectors and test-time adaptation methods with 92.0\% average accuracy.

Fact-checking systems with search-enabled large language models (LLMs) have shown strong potential for verifying claims by dynamically retrieving external evidence. However, the robustness of such systems against adversarial attack remains insufficiently understood. In this work, we study adversarial claim attacks against search-enabled LLM-based fact-checking systems under a realistic input-only threat model. We propose DECEIVE-AFC, an agent-based adversarial attack framework that integrates novel claim-level attack strategies and adversarial claim validity evaluation principles. DECEIVE-AFC systematically explores adversarial attack trajectories that disrupt search behavior, evidence retrieval, and LLM-based reasoning without relying on access to evidence sources or model internals. Extensive evaluations on benchmark datasets and real-world systems demonstrate that our attacks substantially degrade verification performance, reducing accuracy from 78.7% to 53.7%, and significantly outperform existing claim-based attack baselines with strong cross-system transferability.

Foundational visual features such as DINO have played a critical role across modern computer vision, and have recently become key components in multi-view feed-forward geometry estimators. In this work, we demonstrate that by re-distilling these multi-view models-their internal knowledge of 3D geometry-into a single-view estimator, we can obtain enhanced 3D consistent foundational features. Our key idea is to construct a multi-view teacher by fusing pretrained 2D foundation features with multi-view geometric features, and refining the fused representation with a discriminative ranking objective. Through our discriminative distillation framework, we enforce the learned features to be both 3D consistent and locally distinctive, while keeping them aligned with the feature space of the original foundation model to preserve the semantic structure of the pretrained representation. Consistency and local discriminability are critical for 3D computer vision problems such as forming semantic and geometric correspondences across images. To demonstrate the effectiveness of our method, we perform comprehensive experiments spanning multiple angles: direct feature analysis, dense prediction transfer, and explicit 3D lifting and rendering. Across these evaluations, our method consistently produces stronger 3D-aware foundation features that improve multi-view consistency and local discriminability while preserving the semantic transferability of the original representation.

LLM-based coding agents have advanced rapidly on single-process SWE tasks, with frontier models now clustering in the high-70s on SWE-bench Verified. Distributed-system debugging, however, remains an under-explored regime: bugs span processes, nodes, and protocol interactions, with root causes rarely recoverable from source alone and brute-force exploration intractable across non-deterministic interleavings. This leaves two gaps in LLM and agent evaluation: no code-repair benchmark targets distributed-system bugs, and no controlled study isolates how much externally provided debugging context changes agent success on them. We introduce DDBench, a code-repair benchmark of 60 historical bugs mined from 13 open-source distributed systems, partitioned into three difficulty tiers. DDbench evaluates every case under two matched conditions: a symptom-only condition where the agent receives only the bug symptom and repository, and a context-augmented condition where it additionally receives a bounded debugging context (logs, traces, runtime state, and targeted code-investigation notes), isolating the effect of debugging context from model capability. The evaluation of ten LLMs on DDBench reveals several findings. First, distributed debugging exercises a reasoning dimension that single-process benchmarks do not surface: models’ pass rates span 61 pp, and pairwise bootstrap separates 9 of 15 top-tier model pairs at p < 0.05 on DDBench's hardest case-set. Second, bounded debugging context lifts aggregate pass rate by +18.1 pp, and the lift is asymmetric: weaker models gain pass rate, while stronger models gain efficiency. Third, debugging context requires careful curation, as even faithful debugging context can sometimes mislead LLMs.

Communication enhances cooperative multi-agent reinforcement learning (MARL) under partial observability. In real networks, however, each agent's available bandwidth varies over time due to changing link quality, interference, and congestion. Most existing MARL communication policies decide whether to transmit based on message importance or novelty, or are trained under fixed bandwidth conditions. As a result, they cannot adapt sending frequency to bandwidth variations, leading to packet loss when bandwidth decreases or missed information when bandwidth recovers. To address this, we propose Dual-Decoupled Adaptive Communication (DDACOM)---a framework that decouples message sending and message generation from the action policy. The message-sending module ranks each message by its deviation from teammates' information and the sender's history, and uses a dynamic token bucket to convert the probe-estimated safe rate into a real-time token budget, enabling the communication frequency to track the safe rate online. The message-generation module is trained independently with receiver-side attention feedback and counterfactual marginal value, ensuring each transmitted message meaningfully contributes to cooperation. On SMAC and MPE, DDACOM achieves leading cooperative performance among baseline methods. Under dynamic network conditions in ns-3 simulation, DDACOM sustains over \(95\%\) safe-rate utilization and packet loss below \(0.3\%\) while reducing communication frequency to less than half of full broadcast, confirming its adaptability to runtime network variation.

Dataset distillation has provided a reliable solution for data compression, where models trained on the resulting smaller synthetic datasets achieve performance comparable to those trained on the original datasets. To further improve the performance of synthetic datasets, various training pipelines and optimization objectives have been proposed, greatly advancing the field of dataset distillation. Recent decoupled dataset distillation methods introduce soft labels and stronger data augmentation during the post-evaluation phase and scale dataset distillation up to larger datasets. However, this raises a question: Is accuracy still a reliable metric to fairly evaluate dataset distillation methods? Our empirical findings suggest that the performance improvements of these methods often stem from additional techniques rather than the inherent quality of the images themselves, with even randomly sampled images achieving superior results. Such misaligned evaluation settings severely hinder the development of DD. Therefore, we propose DD-Ranking, a unified evaluation framework, along with new general evaluation metrics to uncover the true performance improvements achieved by different methods. By refocusing on the actual information enhancement of distilled dataset, DD-Ranking provides a more comprehensive and fair evaluation standard for future research advancements.

Software vulnerability detection plays a critical role in ensuring system security, where real-world auditing requires not only determining whether a function is vulnerable but also pinpointing the specific lines responsible. However, existing approaches either rely on a single information source---sequential, structural, or semantic---failing to jointly exploit the complementary strengths across modalities, or treat statement-level localization merely as a byproduct of function-level detection without explicit line-level supervision. To address these limitations, we propose DCVD (Dual-Channel Cross-Modal Vulnerability Detection), a unified framework that performs joint function-level detection and statement-level localization. DCVD extracts control-dependency and semantic features through two parallel branches and integrates them via contrastive alignment coupled with bidirectional cross-attention, effectively bridging the cross-modal representation gap. It further introduces explicit supervision signals at both the function and statement levels, enabling collaborative optimization across the two granularities. Extensive experiments on a large-scale real-world vulnerability benchmark demonstrate that DCVD consistently outperforms state-of-the-art methods on both function-level detection and statement-level localization.

Training and evaluation in multi-channel imaging (MCI) remains challenging due to heterogeneous channel configurations arising from varying staining protocols, sensor types, and acquisition settings. This heterogeneity limits the applicability of fixed-channel encoders commonly used in general computer vision. Recent Multi-Channel Vision Transformers (MC-ViTs) address this by enabling flexible channel inputs, typically by jointly encoding patch tokens from all channels within a unified attention space. However, unrestricted token interactions across channels can lead to feature dilution, reducing the ability to preserve channel-specific semantics that are critical in MCI data. To address this, we propose Decoupled Vision Transformer (DC-ViT), which explicitly regulates information sharing using Decoupled Self-Attention (DSA), which decomposes token updates into two complementary pathways: spatial updates that model intra-channel structure, and channel-wise updates that adaptively integrate cross-channel information. This decoupling mitigates informational collapse while allowing selective inter-channel interaction. To further exploit these enhanced channel-specific representations, we introduce Decoupled Aggregation (DAG), which allows the model to learn task-specific channel importances. Extensive experiments across three MCI benchmarks demonstrate consistent improvements over existing MC-ViT approaches.

High-compression tokenizers are essential for scaling latent image generative models. However, aggressive compression creates a fundamental tradeoff between reconstruction fidelity and generation efficiency: high compression image encoder always increase the learning diffucity of diffusion training, resulting slow model convergence. Recent representation autoencoders speed-up the diffusion training by improving the latent feature's expressive capability by replacing VAE encoders with pretrained semantic encoders, yet they are typically limited to moderate compression and lose pixel-level details necessary for faithful reconstruction. To achieve both high compression and fast diffusion training, we propose DC-SAE, a Decoupled Compact Semantic Autoencoder designed for high-compression image generation with accelerated diffusion model convergence. DC-SAE consists of two key components: (1) a macro-level architecture design that leverages semantic encoders to enable higher compression ratios, and (2) a pixel-level encoder that preserves low-level details, ensuring high-fidelity image reconstruction. We empirically demonstrate that DC-SAE performs strongly on image generation tasks, achieving both compact latent representations and efficient training dynamics. % Specifically, on ImageNet dataset with $512 \times 512$ resolution, DC-SAE achieves $32\times$ spatial compression, with \textbf{29.79} PSNR and \textbf{3.05} gFID, substantially outperforming the previous state-of-the-art high-compression tokenizer baselines DC-AE by 13.5\% and 59.7\% on PSNR and gFID respectively, maintaining comparable throughput and faster diffusion model training convergence.

Modeling neurodegenerative pathology from observational cohorts is challenging because subjects are typically observed as unpaired endpoint snapshots rather than aligned longitudinal trajectories. Existing approaches to pathology transport are either endpoint-conditioned or population-level, yet neither extreme alone yields the best terminal-distribution fit. We introduce the Doob-Bridge Posterior Sampler (DBPS), a controlled-SDE posterior sampler with balanced endpoint-population control that blends individual endpoint guidance with population-level guidance. This ρ-weighted controller, obtained as the Cole–Hopf log-gradient of a geometric interpolation between endpoint and population desirability functions, is the exact optimal feedback policy of a linearly-solvable stochastic optimal control problem with closed-form terminal and running costs. Endpoint-conditioned bridge sampling and population Doob sampling are the ρ = 0 and ρ = 1 boundary cases; ρ ∈ (0, 1) realizes the genuine balanced regime. We evaluate DBPS on tau PET pathology transport in Alzheimer’s disease across ADNI and NACC cohorts (84 brain regions). Compared to representative baselines from all major unpaired-transport families, DBPS achieves state-of-the-art performance across multi-cohort settings (union training: SWD 0.760 vs. 0.821; held-out ADNI: 0.822 vs. 0.859), and remains competitive in single-cohort training (ADNI: 0.868 vs. 0.876). Beyond distribution matching, the learned controls recover canonical Braak-stage pathology organization without anatomical supervision, suggesting that balanced SOC objectives capture biologically meaningful structure in latent neurodegenerative progression.

Diffusion large language models (dLLMs) have shown advantages in text generation, particularly due to their inherent ability for parallel decoding. However, constrained by the quality--speed trade-off, existing inference solutions adopt conservative parallel strategies, leaving substantial efficiency potential underexplored. A core challenge is that parallel decoding assumes each position can be filled independently, but tokens are often semantically coupled. Thus, the correct choice at one position constrains valid choices at others. Without modeling these inter-token dependencies, parallel strategies produce deteriorated outputs. Motivated by this insight, we propose DAWN, a training-free, dependency-aware decoding method for fast dLLM inference. DAWN extracts token dependencies and leverages two key observations: (1) masked positions dependent on unmasked certain positions become more reliable; (2) coupled masked positions strongly influence each other's predictions. Given those findings, DAWN leverages a dependency graph to select more reliable unmasking positions at each iteration, achieving high parallelism with negligible loss in generation quality. Extensive experiments across multiple models and datasets demonstrate that DAWN speedups the inference by **1.80–8.06**× over baselines while preserving the generation quality.

Symbolic regression (SR) aims to discover interpretable mathematical expressions from data and plays a key role in scientific discovery. However, existing methods face a common bottleneck: the enormous search space and severe combinatorial explosion make it difficult to achieve a favorable trade-off between accuracy and algorithmic efficiency. We argue that, in addition to designing increasingly complex solver-specific SR strategies, an equally promising direction is to equip them with a shareable auxiliary strategy that can provide reliably search guidance. To this end, We propose DASS, a solver-agnostic Dynamic Auxiliary Search Strategy that improves SR by constructing and refining a high-potential auxiliary search subspace. DASS represents the auxiliary search space as a functional subspace spanned by a set of high-potential basis function terms. It estimates term utility through multi-environment evaluation, filters unstable guidance, and updates terms' potential score with a Gibbs posterior, thereby calibrating the subspace to provide more reliable guidance for downstream solvers. The High-quality solutions produced by the solver are used to update the set of basis terms. This forms a virtuous closed-loop optimization process: the subspace guides the downstream solver, and the solver feedback reshapes the subspace for subsequent exploration. DASS can be seamlessly integrated with various types of SR solvers. Extensive experiments on LLM-SRBench show that DASS significantly improves the accuracy of original solvers with a manageable increase in runtime.

Subgraph mining aims to discover meaningful subgraphs from a large graph given a seed node, with applications in community detection, functional module identification, anti-money laundering, and knowledge discovery. Existing methods typically adopt static approaches that cannot dynamically and intelligently adapt to diverse graph structures. Moreover, training models for subgraph mining faces inherent challenges including exposure bias and the difficulty of learning when to stop expansion. To address these limitations, we propose DASM (Dynamic Autoregressive Subgraph Mining), a novel two-stage framework that models subgraph mining as a sequential decision process. In the first stage, we employ step-wise supervised learning with multi-label objectives and asymmetric F1 loss to teach the model how to select correct next nodes. In the second stage, we apply Group Relative Policy Optimization (GRPO) to align the model with end-to-end mining quality, enabling it to learn optimal stopping decisions through trajectory-level feedback. Our dynamic autoregressive approach allows the model to make state-aware decisions at each step, automatically determining subgraph boundaries without predefined size constraints. Experiments on multiple datasets demonstrate that DASM significantly outperforms existing baselines in terms of F1 score and IoU.