NeurIPS 2026 论文列表

While novel view synthesis (NVS) for dynamic scenes has seen significant progress, reconstructing temporally consistent geometric surfaces remains a challenge. Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) offer powerful dynamic scene rendering capabilities; however, relying solely on photometric optimization often leads to geometric ambiguities. This results in discontinuous surfaces, severe artifacts, and broken surfaces over time. To address these limitations, we present DySurface, a novel framework that bridges the effectiveness of explicit Gaussians with the geometric fidelity of implicit Signed Distance Functions (SDFs) in dynamic scenes. Our approach tackles the structural discrepancy between the forward deformation of 3DGS ($canonical \rightarrow dynamic$) and the backward deformation required for volumetric SDF rendering ($dynamic \rightarrow canonical$). Specifically, we propose the VoxGS-DSDF branch that leverages deformed Gaussians to construct a dynamic sparse voxel grid, providing explicit geometric guidance to the implicit SDF field. This explicit anchoring effectively regularizes the volumetric rendering process, significantly improving surface reconstruction quality, containing watertight boundaries and detailed representations. Quantitative and qualitative experiments demonstrate that DySurface significantly outperforms state-of-the-art baselines in geometric accuracy metrics while maintaining competitive rendering performance. Codes will be publicly available.

Large-scale foundation models achieve strong performance across diverse tasks, but their size makes inference costly, largely due to dense matrix multiplications. Prior work reduces this cost by replacing dense weight matrices with efficient structured forms such as low-rank factorizations. However, these methods approximate weights rather than the output activations that determine inference accuracy. Consequently, small weight-space errors can be amplified by input activations, producing large output errors. In this work, we propose DyRA, an input-adaptive method that improves structured matrix multiplication approximation by correcting residual output errors during inference. We show that matrix multiplication can be approximated more effectively by directly optimizing low-rank factors of the output. DyRA builds on this insight by dynamically approximating and correcting the output error introduced by structured weight approximations. This combines efficient structured computation with input-dependent correction, yielding a more faithful approximation of full matrix multiplication under the same computational budget. Across vision, speech, and language models, DyRA consistently improves the accuracy–efficiency trade-off over structured weight approximations alone. Notably, DyRA achieves a $1.5\times$ end-to-end GPU speedup for DINOv3 while reducing accuracy degradation by more than $3\times$ relative to weight-only baselines.

Consistency-based generative models like Shortcut and MeanFlow achieve impressive results via a target-aware design for solving the Probability Flow ODE (PF-ODE). Typically, such methods introduce a target time $r$ alongside the current time $t$ to modulate outputs between a local multi-step derivative ($r = t$) and a global few-step integral ($r = 0$). However, the conventional "one input, one output" paradigm enforces a partition of the training budget, often allocating a significant portion (e.g., 75% in MeanFlow) solely to the multi-step objective for stability. This separation forces a trade-off: allocating sufficient samples to the multi-step objective leaves the few-step generation undertrained, which harms convergence and limits scalability.To this end, we propose Duality Models (DuMo) via a "one input, dual output" paradigm. Using a shared backbone with dual heads, DuMo simultaneously predicts velocity $\mathbf{v}_t$ and flow-map $\mathbf{u}_t$ from a single input $\mathbf{x}_t$. This applies geometric constraints from the multi-step objective to every sample, bounding the few-step estimation without separating training objectives, thereby significantly improving stability and efficiency. On ImageNet $256 \times 256$, a 679M Diffusion Transformer with SD-VAE achieves a state-of-the-art (SOTA) FID of 1.79 in just 2 steps.Code will be publicly available.

Recent multimodal agents have shown promising progress in embodied planning and web interaction. However, existing evaluations often rely on simplified tasks with limited combinatorial structure, leaving it unclear whether current agents can robustly integrate perception and reasoning for multi-goal planning under temporal, physical, and multimodal constraints. We introduce DualWorldBench, a unified benchmark for evaluating agent planning under controlled symbolic--temporal and grounded--topological complexity. Built around a shared pickup--delivery interface, DualWorldBench consists of three complementary components: TextWorld, which isolates symbolic planning under varying task compositions and release dynamics; SimWorld, which evaluates grounded path planning under controlled topological variation with text, image, or multimodal inputs; and DualWorld-VQA, a diagnostic suite spanning perception, scene understanding, and planning. Experiments show that current state-of-the-art agents remain brittle under increasing combinatorial complexity. Multimodal input does not consistently improve planning and can even degrade performance, revealing difficulties in aligning symbolic task specifications with grounded topology. Diagnostics further show that models handle perception or understanding in isolation, but fail when decision-making requires integrating both. These findings establish DualWorldBench as a challenging testbed for agents with tighter perception--reasoning--planning integration. All datasets and code are publicly available at our project website https://dualworld.netlify.app/.

Existing graph neural network-based methods for Boolean satisfiability (SAT) solving are generally constrained by limited local receptive fields, making it difficult to adequately capture the long-range dependencies between variables and clauses in SAT instances. Moreover, these methods overlook the multi-solution nature of SAT instances. Consequently, their expressive power and generalization performance remain limited. To address these issues, we propose DualSAT, a novel neural framework for the SAT problem, designed to enhance the phase selection heuristic of SAT solvers. Specifically, DualSAT adopts a novel dual-branch graph neural network that separately encodes local structural information and global structural information in the graph, and then fuses them through an attention-based cross-branch feature fusion module. An assignment decision head is further introduced to generate the initial assignment prediction of variables. This prediction process requires only a single forward pass, achieving a favorable balance between expressive power and inference efficiency. Furthermore, to account for the multiple solutions nature of SAT instances, we design a two-stage training strategy that combines supervised and unsupervised learning to improve the generalization performance of the model. Experimental results on multiple datasets show that, as an end-to-end assignment prediction model, DualSAT significantly outperforms NeuroSAT and NeuroBack. In addition, we integrate DualSAT into the classical SAT solver CaDiCaL, where it increases the number of solved instances by up to 11.6% and reduces the number of conflicts by up to 9.21% on SAT competition problem sets.

Modern RL post-training methods such as GRPO and DAPO train on $N$ response sequences of $R$ tokens sampled from a shared prompt of $P$ tokens, but standard FlashAttention replicates all $P$ prompt tokens $N$ times across both forward and backward passes --- duplicating compute and memory on identical hidden states. In large-rollout, long-context RL training ($N{\geq}16$, $P{\geq}8\text{K}$), this redundancy dominates the policy update cost. We observe that in decoder-only models, causal masking makes prompt representations invariant across sequences at every layer, so all per-token operations (norms, projections, MLP) and attention can process the prompt once --- a property not yet exploited at the kernel level for training. We propose \textbf{DualKV}, the first FlashAttention kernel variant that eliminates shared-prompt replication during RL training, via (1) fused CUDA forward and backward kernels that iterate over two disjoint KV regions --- shared context and per-sequence response --- in a single kernel launch, and (2)~a data-pipeline redesign in veRL that repacks $N(P{+}R)$ tokens into $P{+}NR$ tokens per micro-batch, extending the token reduction from attention to the entire model by a factor $\rho = N(P{+}R)/(P{+}NR)$. DualKV is mathematically equivalent to standard attention and introduces no approximation. On Qwen3-8B GRPO training with 8$\times$H100 GPUs ($N{=}32$, 8K-context), DualKV achieves $1.63$--$2.10\times$ policy-update speedup, enables $2\times$ larger micro-batches, and raises MFU from $36\%$ to $76\%$. Similar gains hold for DAPO ($2.47\times$ speedup, $77\%$ MFU). At 30B MoE scale on 16$\times$H100, DualKV achieves $3.82\times$ policy-update and $3.38\times$ end-to-end step speedup over FlashAttention (which requires 4-way Ulysses sequence parallelism to avoid OOM).

Drifting Models have recently achieved state-of-the-art performance in one-step image generation by training generators to follow corrective vector fields. However, their standard reverse-style drift improves samples from the model side, which can severely underutilize training samples and bottleneck early convergence. To address this, we introduce DualDrift, a unified framework that augments the original reverse drift with a novel forward drift. The forward drift lets each data sample assign correction to generated rollouts, providing denser data-side signals and accelerating early training. At the same time, this dense data-side signal can overemphasize dominant regions when used alone. DualDrift therefore uses a parameter-free scheduler driven by empirical batch statistics to balance the fast early correction of forward drift with the stable refinement of reverse drift. Evaluated on ImageNet $256\times256$, DualDrift achieves faster convergence and significantly improves over the baseline Drifting Models in both FID and Inception Score.

This paper develops a dual-space preconditioning framework for variational inequalities and root-finding problems beyond classical cocoercivity and Lipschitz continuity assumptions. To this end, we introduce both a novel cocoercivity-type condition and a relaxed Lipschitz continuity condition, and derive convergence of deterministic and stochastic methods under these generalized conditions. For isotropic preconditioners, the resulting algorithms can be interpreted as variable-stepsize variants of classical methods, recovering and extending recent analyses based on $(L_0, L_1)$-type growth conditions while allowing simpler proofs and larger stepsize ranges. We also show that the framework naturally handles constrained variational inequalities through weak Minty-type arguments. Finally, we study the associated continuous-time dynamics and derive a variational and optimal-control interpretation of the preconditioned flow. Overall, the results show that dual-space preconditioning provides a flexible mechanism for designing and analyzing operator methods beyond the standard Euclidean Lipschitz setting.

Diffusion models achieve state-of-the-art generative performance but suffer from high computational costs during inference due to the repeated evaluation of a heavy neural network. In this work, we propose Dual-Rate Diffusion, a method to accelerate sampling by interleaving the execution of a heavy high-capacity context encoder and a light efficient denoising model. The context encoder is evaluated sparsely to extract high-dimensional features, which are effectively reused by the light denoising model at every step to refine the sample efficiently. This approach significantly accelerates inference without compromising sample quality. On ImageNet benchmarks, Dual-Rate Diffusion matches the performance of standard baselines while reducing computational cost by a factor of $2$--$4$. Furthermore, we demonstrate that our method is compatible with distillation techniques, such as Moment Matching Distillation, enabling further efficiency gains in few-step generation.

Adapting Multimodal Large Language Models (MLLMs) to specialized and diverse downstream applications necessitates fine-tuning, typically through parameter-efficient methods such as *Low-Rank Adaptation* (LoRA). However, fine-tuning MLLMs severely degrades pre-trained upstream capabilities, a phenomenon known as *catastrophic forgetting*. Existing Methods primarily rely on parameter pruning to strike a balance between the downstream adaptation and upstream knowledge retention. Empirical analysis indicates that these methods are subject to *parameter space entanglement*, wherein the imposition of downstream adaptation and upstream retention within a unified parameter space results in suboptimal outcomes for both objectives. To address this, we introduce Dual-Pronged LoRA, a novel fine-tuning framework for MLLMs featuring two components. (1) *Task-Decoupled Contextual Routing* (TDCR) makes the first attempt to explicitly decouple tasks by modeling a downstream contextual support region. It adaptively activates the LoRA branch exclusively for downstream-relevant inputs and bypasses it otherwise, thereby preventing downstream adaptation from interfering with pre-trained knowledge. (2) *Downstream-Performance-Oriented Rank Sculpting* (DPO-RS) maximizes downstream adaptation performance by dynamically sculpting ranks via gradient energy. It effectively strengthens critical layers while compressing redundant ones and is theoretically grounded in the minimization of information loss. Extensive experimental results indicate that Dual-Pronged LoRA achieves near-zero forgetting while attaining new state-of-the-art downstream performance.

For decades, philosophers and musicologists have debated which features of a performance are constitutive of the work and which are expressions of how it is being played. Computational evidence has been hard to assemble: audio embedding models conflate work identity with performance style, and existing interpretability tools for music generators treat learned representations as flat dictionaries that mix the two. We turn the question into an empirical one by introducing the dual-contrastive sparse autoencoder (DC-SAE), a two-branch sparse autoencoder that uses cheap work-level metadata to factor a generative music transformer's residual stream into work-identity and performance-content subspaces. Across jazz-standard, classical-work, and pop-cover corpora, the resulting decomposition is supported by probes and feature galleries that surface musically interpretable concepts on each side. Without performer supervision, the variation branch acquires structure related to performer identity, and steering along these directions can shift the perceived performer of generated audio while preserving the underlying work. Together, these results show that a generative music model has internalized a representation of interpretation that can be recovered, named, and steered.

Decoder-only Transformers compute attention over the KV cache of preceding tokens. Keys (and Values) are typically represented with the same dimensionality, regardless of its distance from the prediction target. In natural language, however, the next word is most strongly influenced by the immediately preceding tokens. We hypothesize that local and distant tokens impose asymmetric demands on representational capacity: local tokens are more critical for predicting immediate outputs and thus require richer representations, whereas distant tokens primarily serve as long-range memory, for which lower-dimensional representations may suffice. We formalize this idea as Distance-Adaptive Representation (DAR), implemented in a controlled setting that preserves full-dimensional representations within a local context window while assigning reduced-dimensional representations (e.g. 1/4 of the original dimensionality) to tokens beyond that window. Across multiple pretraining scales (70M to 410M parameters), as well as continued supervised fine-tuning on a 1B-scale model, this approach closely matches the performance of full-dimensional baselines. In contrast, uniformly reducing dimensionality across all token positions leads to worse performance. These results challenge the common assumption that key and value dimensionality should be uniform across token positions. Our findings suggest a new direction for designing attention architectures that adaptively allocate representational capacity across the sequence, enabling further reductions in KV cache during inference.

Building state-of-the-art (SOTA) predictive models for drug discovery requires expensive search over tools, architectures, and training strategies. Current LLM-based agents can find SOTA solutions through extensive trial and error, but they do not retain the experience accumulated along the way and therefore pay the full search cost on every new task. We propose DrugSAGE (Self-evolving Agent Experience), a framework that accumulates and reuses experience across tasks to build SOTA drug discovery models efficiently. DrugSAGE maintains a cross-task memory of verified skills, statistical evidence about effective strategies, and a record of recurring errors and their fixes. In some cases, DrugSAGE transfers a working solution directly without test-time search. In 33 molecular property prediction tasks, DrugSAGE ranks first among nine SOTA agents in a single-task setting. With memory accumulated from 16 smaller tasks, DrugSAGE achieves a averaged normalized score of 0.935 on 17 held-out tasks in a cross-task evaluation setting and outperforms all baseline agents by 10-30\% in a zero-test-time search regime. In summary, our work shows the advantage of cross-task memory for efficient SOTA model development in drug discovery.

Key-value (KV) cache management has become a critical bottleneck for scaling Large Language Models (LLMs) to long-context scenarios due to linear memory growth. While recent eviction methods have moved beyond simple attention-score heuristics to incorporate value representations, they typically face a fundamental trade-off: the underlying optimization for minimizing output perturbation is an NP-hard combinatorial problem, leading to mathematically suboptimal heuristics or implementations that are difficult to kernelize. In this work, we propose DropKV, a simple principled eviction framework based on Decoupled Residual-Output Perturbation. By decoupling the joint eviction decision into independent per-token scoring, DropKV circumvents combinatorial intractability and admits a provable constant-factor approximation guarantee under a cone condition that we empirically verify on three long-context LLMs. To ensure practical viability, we provide a high-performance implementation using fused Triton kernels that avoid materializing the full attention matrix, delivering a 19.8$\times$ scoring-kernel speedup that translates to up to a 9.9\% matched-batch end-to-end prefill speedup. Extensive evaluations on the RULER and LongBench benchmarks demonstrate that DropKV consistently outperforms state-of-the-art baselines at equivalent cache budgets, achieving the lowest inference latency among eviction methods while also remaining faster than the dynamic cache baseline.

End-to-end autonomous driving models learn to plan the vehicle's future trajectory from raw sensor observations. While earlier driving benchmarks often measured deviation from the human trajectory, current benchmarks such as NAVSIM and Bench2Drive evaluate models with richer simulation-based metrics intended to capture safe and compliant driving. A high benchmark score should reflect that a model can understand the scene in front of it and act accordingly. But how much of that score specifically comes from reacting to the dynamic part of that scene? To probe this, we remove a model's camera input and replace it with memories from prior drives of the same location. The retrieved memories can provide persistent scene information, including road layout and location-conditioned regularities, but because they are historical, they do not reveal the current actors, signal states, or transient conditions. Surprisingly, memory is nearly sufficient on NAVSIM, reaching or even exceeding the performance of leading end-to-end methods without actually observing the evaluated scene. Our results suggest that a high NAVSIM score does not require a planner to react to the current traffic scene and should therefore be treated with caution. However, we do not observe the same behavior on Bench2Drive: driving from memory remains far from camera-based performance, suggesting that Bench2Drive better preserves interaction demands through longer closed-loop routes. To ensure reproducibility, we provide our code at anonymous.4open.science/r/MemoryDrivoR-E64C.

Video retrieval at scale is central to data curation and safety validation in autonomous driving, where users want to find not only scenes but also dynamic events such as cut-ins and hard braking. Existing vision-language and keyword-based retrieval methods often miss these events because the relevant motion may not be explicitly described in text or captured by lexical overlap. Rule-based retrieval can encode such events more directly, but it is brittle: generated or hand-written rules often fail when their assumptions do not match real driving data. We propose STRIVE-D, a data-calibrated retrieval framework for driving videos. STRIVE-D uses weakly labeled in-domain videos to estimate when a query rule is reliable, adapt rules that mismatch observed data, and fuse calibrated rule scores with vision-language and keyword-based retrieval signals. Across three driving benchmarks, including newly released human annotations for event retrieval on DrivingDojo, STRIVE-D achieves up to a 30.4% relative improvement over state-of-the-art retrieval models.

Driver attention prediction is crucial for interpretable driver monitoring and human-like autonomous driving. Existing methods typically formulate this task as a scene-to-heatmap regression problem, failing to reveal the underlying dynamic attention redistribution mechanism. To address this issue, we propose a scene-task-aware competitive allocation framework, which decomposes driver attention as a bottom-up visual saliency prior and a top-down scene-task utility representation. An adaptive product-of-experts allocation rule is designed to fuse these two heterogeneous branches in a competitive manner, where scene-task demand dynamically modulates the dependence on top-down task utility, and driving control urgency adjusts spatial attention concentration via a learnable attention budget temperature. To explicitly differentiate task-critical regions from visually prominent yet task-irrelevant distractors, we incorporate competition-aware contrastive learning to enhance feature discrimination in the latent space. Extensive experiments demonstrate that our method achieves strong and balanced performance, with a Kullback–Leibler divergence of 0.98 and a normalized scanpath saliency of 4.15 on the DR(eye)VE dataset, as well as a Pearson’s correlation coefficient of 0.67 and a histogram similarity of 0.54 on the BDD-A dataset. Ablation studies and diagnostic analyses further validate that our framework yields interpretable attention allocation behaviors, including urgency-induced spatial entropy contraction and scene-task-demand-guided dynamic attention reallocation.

Pretrained foundation models have become an important basis for end-to-end autonomous driving. In contrast to vision-language models pretrained primarily on static image-text pairs, video generative models capture temporal dynamics and motion priors that are naturally suited for driving. We present DriveWAM, a driving world-action model that adapts a pretrained video diffusion transformer into an autoregressive video-action policy. DriveWAM organizes video and action streams into a unified temporal token sequence and trains them under a joint flow-matching objective, preserving the pretrained video-generation architecture while adapting its large-scale video priors to action generation. To incorporate high-level scene understanding, we introduce scene-evolving driving guidance, where a frozen VLM produces chunk-specific semantic intent to guide video-action generation. To keep long-horizon rollout bounded, we further introduce selective KV memory, which maintains bounded modality-aware video and action memory pools through relevance-redundancy cache selection at inference time. Experiments on NAVSIM and the PhysicalAI-Autonomous-Vehicles benchmark show that DriveWAM achieves strong planning performance, and a data-scaling study from 4k to 100k driving clips further confirms the scaling potential of world-action modeling for end-to-end autonomous driving.

Vision-language models (VLMs) have made steady progress as in-vehicle driving assistants, yet existing evaluations largely remain offline, focusing on perception and short-term reasoning over pre-recorded driving scenes. A complementary capability is still underexplored: whether a model can continuously monitor a causal driving video stream and issue a calibrated alert when a user-specified condition is met. Existing driving benchmarks mainly assess reactive understanding, while recent streaming video benchmarks are developed largely outside the driving domain. To this end, we introduce DriveStreamBench, a driving and streaming video benchmark that organizes this capability spectrum into four cognitive levels constructed from a shared pool of driving events, to support level-wise capability profiling and gated evaluation. We evaluate various vision-language models along with DriveStream-SFT, a supervised reference baseline trained on the benchmark's training split, under a unified streaming protocol. DriveStreamBench tests a basic but necessary capability, and all evaluated models fall short of it; current vision-language models are not yet ready for user-conditioned alerting in driving. Resources are available at https://anonymous.4open.science/r/DriveStreamBench/ and https://dataverse.harvard.edu/previewurl.xhtml?token=01f6bc4c-d809-4e40-b025-b6d783c86571.