NeurIPS 2026 论文列表

Physical control systems, such as building HVAC systems, thermal processes, and power-grid assets, often involve delayed dynamics, safety constraints, and costly online interaction. Because the effect of a control action may appear after several time steps, effective control often requires reasoning over histories of states, actions, and outcomes. Pretrained sequence controllers, such as Decision Transformers, provide a natural way to model this history-dependent control problem by learning policies from offline trajectories, reducing the need for expensive online retraining. However, their performance can degrade when deployment conditions differ from the training data. In building control, for example, actuator wear, sensor drift, occupancy changes, and weather variation can all affect the observed response of the system to the same control action. Online adaptation is therefore needed, but raw prediction error is not always a reliable signal for updating the controller. The key challenge is to identify which part of the observed mismatch is action-sensitive and should guide policy adaptation. To address this challenge, we propose DARPAN, a Drift-Aware Residual Policy Adapter Network for pretrained physical control. DARPAN keeps the pretrained Decision Transformer frozen and performs online adaptation only through lightweight residual adapters, preserving the offline policy and limiting the number of trainable parameters. Instead of updating from raw prediction error, DARPAN uses a controllability filter trained in simulation to extract the action-sensitive part of the residual and filter out mismatch that is weakly related to the controller's actions. During deployment, a frozen predictor estimates the next state, the controllability filter produces a filtered residual, and a short history of filtered residuals forms a drift signature. This signature is used to select an existing adapter or create a new one when a persistent drift pattern appears. We benchmark DARPAN on SustainGym under actuator faults, sensor drift, delays, and external disturbances. DARPAN reduces the drift-induced performance loss by 31.7% and uses 63% fewer online parameters than Online DT. We release anonymized code and experiment configurations at https://huggingface.co/Darpan2026.

We study model-free reinforcement learning (RL) in non-stationary finite-horizon episodic Markov decision processes (MDPs) without prior knowledge of the non-stationarity. We focus on the piecewise stationary (PS) setting, where both rewards and transition dynamics can change at unknown times. We first revisit existing state-of-the-art approaches and identify theoretical and practical limitations that change the current landscape of performance guarantees. To characterize the difficulty of the problem, we establish the first minimax lower bounds for PS-RL in tabular and linear MDPs. We then introduce *Detection Augmented Reinforcement Learning* (DARLING), a modular wrapper for PS-RL that applies to both tabular and linear MDPs, without knowledge of the changes. In tabular MDPs, under change-point separability and reachability conditions, DARLING improves the best known dynamic regret bounds and matches our minimax lower bound. In linear MDPs, DARLING matches the minimax lower bound when the relevant reachability parameters are known, and our analysis clarifies the structural obstacles that distinguish this setting from the tabular case. Finally, through extensive experimentation across diverse non-stationary benchmarks, we show that DARLING consistently surpasses the state-of-the-art methods.

Pathological whole slide images (WSIs) from different hospitals often exhibit severe domain shifts due to variations in scanning devices, staining protocols, and tissue preparation procedures. This leads to significant performance degradation when a trained model is applied to data from different sources. Although unsupervised domain adaptation (UDA) has achieved strong performance in transferring a model trained on a source domain to an unlabeled target domain in natural images, it is much less studied in the computational pathology field. This is due to the unique properties of WSIs, including ultra-high resolution and strong inter-slide and intra-slide heterogeneity. As a result, existing UDA methods are often unstable and may even cause negative transfer in WSI classification. To address this issue, we propose Dual-level Adversarial learning with domain-aware REgularization (DARE) for WSI Classification. Specifically, we design an adaptive pseudo-labeling framework with a teacher-student architecture to provide stable supervision for the target domain and perform adversarial alignment on both patch-level and slide-level embeddings. This enables joint optimization of patch-level and slide-level semantic embeddings for dual-level alignment across domains. We also introduce domain-aware attention masking to regularize attention and improve cross-domain generalization. We evaluate our method on four public WSI datasets through extensive experiments. The results show that our method consistently outperforms state-of-the-art approaches across various domain shifts.

LLM-based theorem proving in Lean 4 has advanced rapidly, but provers are difficult to leverage newly contributed lemmas, because retrieval skews toward popular foundational lemmas, leaving the right premises buried among hundreds of thousands of candidates. Existing neural selectors rank candidates by semantic similarity, with three intrinsic limitations: frequency-skewed retrieval, isolated pairwise ranking, and degradation on out-of-distribution queries such as competition problems and informal language. Given that mathematics is structured by dependencies rather than similarities, we restore this signal to both the training data and the model. We curate two underused dependency resources. We conduct the first systematic mining of 53 Lean Blueprint projects, a previously untapped corpus of 3,805 manually curated informal+formal nodes that capture rare, research-level premises absent from Mathlib4. We further pair it with the extraction of comprehensive, typed, multi-level Mathlib4 dependencies. We then introduce DAPS, a dependency-aware premise selector with a structural neighborhood encoder, a group-level contrastive objective, and a Mathlib-then-Blueprint adaptation procedure. DAPS reaches Recall@32 of 89.31 on Mathlib4-Heldout (+11.47 over the strong selector LeanHammer). DAPS holds its lead across both out-of-distribution benchmarks, and improves three general-purpose LLMs on the informal IMOProofBench. Dependency graphs, benchmarks, and code are available 1 and will be released under a permissive license upon publication.

Deep research tasks require agents to navigate large knowledge spaces, synthesize evidence across many sources, and adapt their plans as intermediate findings emerge. Directed acyclic graph (DAG)-based multi-agent systems are well suited to this setting because they support parallel execution and isolate each sub-task within a focused dependency context. Yet existing DAG-based agents typically instantiate a task-level plan before execution and then repair the graph only after failures or missing evidence are observed. This Plan-then-Patch strategy is brittle for deep research: the system commits most strongly when its evidence is weakest, and later revisions often waste computation on branches that should not have been planned in the first place. To bridge this gap, we propose DAGent, a DAG-based multi-agent framework that introduces Evaluate-then-Grow incremental planning. Instead of committing to a full DAG upfront, an Orchestrator grows the task graph one batch at a time, conditioning each new expansion on confidence and uncertainty signals from completed nodes. To support long-horizon evidence use without overloading each sub-task, DAGent maintains a hierarchical context layer that propagates compact QueryDocs by default while preserving full execution traces for on-demand recall. The cleanly recorded DAG topology in turn admits structural RL signals that an outcome-only recipe cannot define; we instantiate this with DAGRPO, a GRPO adaptation that injects topology-conditioned credit on Executor rollouts and a structural compliance regularization on Orchestrator plans. Across BrowseComp-Plus, GAIA, and xbench-DeepSearch, DAGent surpasses the strongest open-source baseline by 5.3 / 5.8 / 2.0 points at the Qwen3-235B-A22B scale, with the lead replicating across four open-source backbones from four vendors and extending to GPT-5 at 327K context. At the Qwen3-8B scale, DAGRPO further improves over a same-budget outcome-only GRPO baseline by 3.0 average Pass@1 points. A same-architecture comparison further shows that incremental, evidence-conditioned planning recovers accuracy at lower per-task token, tool-call, and step footprints than its Plan-then-Patch counterpart, supporting the view that DAGent's gains come from more targeted evidence expansion rather than additional computation. Anonymized code is available at \url{https://anonymous.4open.science/r/DAGent-F2D6}.

Self-supervised Vision Transformers in the DINO family encode rich semantic structure within their self-attention maps, yet existing parameter-efficient fine-tuning (PEFT) methods apply static, content-agnostic transformations that ignore this internal signal. We present DAGA (Dynamic Attention-Guided Adaptation), a PEFT module that repurposes a frozen backbone's emergent attention maps as instance-specific guidance: per-image attention is converted into channel and spatial gates that condition a lightweight bottleneck adapter. With only 1.4\% additional parameters, DAGA reaches 85.9\% top-1 on ImageNet-1K with DINOv3-ViT-B and surpasses recent PEFT baselines, with strong gains on dense prediction (+12.1\% mIoU on ADE20K) and fine-grained classification (+1.6 / +3.5 on CUB-200 / FGVC-Aircraft). DAGA's effectiveness is tied to the semantic quality of the backbone's attention: gains are large on DINOv2/v3 and iBOT but small on MAE, CLIP, and DeiT, positioning it as the first PEFT module to operationalize emergent SSL attention as in-loop guidance and complementing self-distillation methods that refine attention itself. Anonymized code: https://anonymous.4open.science/r/ID1680.

Existing deep learning architectures for survival prediction from electronic health records (EHRs) largely assume unimodality, failing to capture the complex relational structure across diverse clinical modalities. Graph learning can allow us to robustly adapt to the underlying clinical structure of the data to have more predictive and interpretable representations for time-to-event analysis. However, unconstrained graph learning produces dense, bidirectional adjacencies that overfit. We introduce M-CADENCE, a multimodal survival model that integrates structured acyclic priors with attention-based propagation. Each modality has its own learnable adjacency, regularised toward acyclicity through a log-determinant characterisation and toward sparsity through soft thresholding. A cross-modal meta-adjacency keeps the cross-modal parameter count quadratic in the number of modalities rather than in their feature dimensions. Propagation is then performed by multi-head attention biased by the learnt structure. The propagation step degrades gracefully under sparse or misspecified structure, addressing a known weakness of hard-routing graph variants. We evaluate M-CADENCE on five EHR benchmarks covering intensive-care mortality, circulatory failure, and emergency-department competing risks, against multiple survival, graph, and structure learning baselines. M-CADENCE achieves the best concordance on all datasets, the best early-warning AUC on four of five, and competitive precision-recall against the strongest deep survival baselines. Removing the DAG bias lowers concordance, as does substitution with a density-matched random adjacency, which confirms that the specific learnt structure is required. Discrimination is stable across four orders of magnitude in regularisation strength, and evaluation by a panel of ten language models confirms M-CADENCE learns clinical edges that are more physiologically plausible than baseline structure-learning methods.

Ensuring robust safety alignment of large language models (LLMs) is increasingly difficult as adversarial attacks evolve and outpace alignment pipelines built on pre-collected data. Recent co-evolutionary frameworks let the defender chase a moving attacker, yet their training dynamics expose two compounding pathologies: \emph{attack strategy collapse}, where the attacker overfits to a narrow set of high-reward rewrites, and \emph{defense adversarial forgetting}, where the defender loses competence against earlier attacks as the attack distribution drifts. Existing remedies are partial: attacker-side diversity rewards score textual novelty rather than strategy novelty, and defender-side replay relies on discrete judge scores that ignore estimation uncertainty, response stochasticity, and defender non-stationarity. We introduce DACE, a diversity-driven adversarial co-evolution framework that targets both pathologies jointly. On the attacker side, DACE couples an explicit $12\times10$ strategy space (risk category $\times$ attack style) with a \emph{normalized marginal coverage gain} reward, providing a bounded, non-vanishing exploration signal at the strategy layer. On the defender side, DACE maintains a unified \emph{Bayesian adversarial replay pool} whose Beta--Bernoulli threat posteriors are refreshed by time decay and sampled via Thompson sampling, anchoring defender training to an evolving threat landscape. Across standard safety, automated-attacker, and general-capability benchmarks, DACE improves robustness to out-of-distribution attacks while preserving general capabilities, and yields broader attacker strategy coverage.

Reinforcement learning with verifiable rewards (RLVR) has become a dominant recipe for improving mathematical and code reasoning in open-weight language models, but existing methods rely on fixed hyperparameters despite highly non-stationary training dynamics. We present DACE-RL (Diagnosis-Aware Compute-Efficient RL), a closed-loop framework that promotes training diagnostics to first-class signals. DACE-RL tracks four lightweight statistics—token entropy, sample diversity, advantage saturation, and answer consistency—and feeds their EMA-smoothed state to a controller that adaptively sets per-prompt rollout budget, sampling temperature, asymmetric clipping bounds, and entropy regularization strength. We formalize adaptive rollout allocation as a difficulty-conditioned multi-armed bandit and provide a regret bound that improves over uniform-budget GRPO under heterogeneous prompt difficulty; we also give a stability result for the dual-loop entropy-diversity controller. Across three small base models (Qwen2.5-Math-1.5B, Qwen2.5-7B, and Llama-3.1-8B-Instruct) and six math reasoning benchmarks, DACE-RL matches or improves Pass@1 while reducing rollout-GPU-hours by 38–52%, and improves Pass@32 by 4.7 absolute points on average. Pilot analysis further shows that diagnostic inflection signals appear 100–300 update steps before reward plateau, indicating that diagnosis-aware control is a key lever for compute-efficient RLVR.

Diffusion large language models are a compelling alternative to autoregressive models, yet existing RL methods for diffusion treat all denoising steps as equally important and rely on biased, high-variance likelihood estimates. We identify two fundamental weaknesses: the absence of temporal credit assignment across the denoising trajectory, and the systematic bias of mean-field likelihood estimates used for policy optimization. To address these, we propose Denoising-Aware Credit Assignment for GRPO (DACA-GRPO), a lightweight, plug-and-play enhancement for any GRPO-style trainer. DACA-GRPO introduces two complementary mechanisms: Denoising Progress Scores, which extract per-token importance weights from intermediate predictions at no additional forward cost, and Stratified Masking Likelihood, which partitions token positions into strata so that each token is predicted with most of the sequence as context, reducing the mean-field bias. Applied on top of three GRPO base methods, DACA-GRPO achieves consistent improvements across seven benchmarks spanning mathematical reasoning, code generation, constraint satisfaction, and constrained generation, with gains of up to 5.6pp on math reasoning, 7.4pp on code generation, 36.3pp on constraint satisfaction, and 5.9pp on JSON schema adherence.

The rapid evolution of Embodied AI has enabled Vision-Language-Action (VLA) models to excel in multimodal perception and task execution. However, applying Reinforcement Learning (RL) to these massive models in large-scale distributed environments faces severe systemic bottlenecks, primarily due to the resource conflict between high-fidelity physical simulation and the intensive VRAM/bandwidth demands of deep learning. This conflict often leaves overall throughput constrained by execution-phase inefficiencies. To address these challenges, we propose D-VLA, a high-concurrency, low-latency distributed RL framework for large-scale embodied foundation models. D-VLA introduces "Plane Decoupling," physically isolating high-frequency training data from low-frequency weight control to eliminate interference between simulation and optimization. We further design a four-thread asynchronous "Swimlane" pipeline, enabling full parallel overlap of sampling, inference, gradient computation, and parameter distribution. Additionally, a dual-pool VRAM management model and topology-aware replication resolve memory fragmentation and optimize communication efficiency. Experiments on benchmarks like LIBERO show that D-VLA significantly outperforms mainstream RL frameworks in throughput and sampling efficiency for billion-parameter VLA models. In trillion-parameter scalability tests, our framework maintains exceptional stability and linear speedup, providing a robust system for high-performance general-purpose VLA Models.

Speculative decoding accelerates LLM inference by having a small drafter propose tokens that a larger target model verifies in parallel. Current SOTA diffusion-based parallel drafters such as dFlash predict the full $B$-token block in one forward pass, allowing deeper drafters and higher accepted length. However, across the field, from autoregressive tree drafters to parallel block drafters, training losses use fixed per-position weights that do not adapt to the drafter's current bottleneck position. We derive per-position training weights directly from the expected accepted block length, so that each position's weight reflects its actual contribution to acceptance. The resulting loss, D-PACE (Dynamic Position-Aware Cross-Entropy), automatically identifies where the block's acceptance bottleneck lies and shifts training signal there as the drafter improves. D-PACE consistently improves both wall-clock speedup and accepted length across diverse benchmarks and positions as a drop-in replacement with negligible training overhead and requires no changes to the drafter architecture or inference pipeline, making it broadly applicable to general model architectures.

The landscape of high-performance image generation models is currently shifting from the inefficient multi-step ones to the efficient few-step counterparts (e.g, Z-Image-Turbo and FLUX.2-klein). However, these models present significant challenges for direct continuous supervised fine-tuning. For example, applying the commonly used fine-tuning technique would compromise their inherent few-step inference capability. To address this, we propose D-OPSD, a novel training paradigm for step-distilled diffusion models that enables on-policy learning during supervised fine-tuning. We first find that the modern diffusion models, where the LLM/VLM serves as the encoder, can inherit its encoder's in-context capabilities. This enables us to formulate the training as an on-policy self-distillation process. Specifically, during training, we make the model act as both the teacher and the student with different contexts, where the student is conditioned only on the text feature, while the teacher is conditioned on the multimodal feature of both the text prompt and the target image. Training minimizes the two predicted distributions over the student's own roll-outs. By optimizing on the model's own trajectory and under its own supervision, D-OPSD enables the model to learn new concepts, styles, etc., without sacrificing the original few-step capacity.

Out-of-domain (OOD) robustness is challenging to achieve in real-world computer vision, especially in unsupervised domain adaptation scenarios, where shifts in image background, style, and acquisition instruments often degrade model performance. Generic augmentations show inconsistent gains under such shifts, whereas dataset-specific augmentations require expert knowledge and prior analysis. Moreover, prior studies show that neural networks adapt poorly to domain shifts because they exhibit a learning bias to domain-specific frequency components. Perturbing frequency values can mitigate such bias but overlooks pixel-level details, leading to suboptimal performance. To address these limitations, we propose D-GAP, a Dataset-agnostic and Gradient-guided augmentation method for the Amplitude spectrum (in frequency space) and the Pixel values. Unlike conventional handcrafted augmentations, D-GAP computes sensitivity maps in the frequency space from task gradients, which reflect how strongly the deep models respond to different frequency components, and uses the maps to adaptively interpolate amplitudes between source and target samples. We further propose a dual-space augmentation that jointly controls spectral bias and spatial fidelity by introducing a complementary pixel-space blending branch. This way, D-GAP turns augmentation from fixed, random, or manually designed perturbation into a model-response-adaptive intervention. Extensive experimental results show that the proposed method consistently outperforms both generic and dataset-specific domain adaptation methods, improving average OOD performance by +5.3% on four real-world datasets and +1.9% on three benchmark datasets.

We propose D-DOIT (Discrete Doob-Oriented Inference-time Transformation), a training-free and efficient adaptation method for discrete diffusion models with generic rewards. D-DOIT formulates adaptation as sampling from a reward-tilted target distribution and realizes this transport through Doob's h-transform of the discrete diffusion reverse kernel, using only reward values rather than reward gradients. Unlike continuous diffusion, masked discrete diffusion samples categorical token-reveal transitions rather than continuous state updates. D-DOIT derives the corresponding discrete Doob's h-transform, which guides sampling by reweighting reverse transition probabilities instead of adding a drift correction. To make this transformation practical, D-DOIT avoids expensive future rollouts. At each guided step, D-DOIT samples candidate next states, uses the model prediction head to complete each candidate into a clean sequence, evaluates each completion with the reward oracle, and resamples the next state with probabilities proportional to the rewards. An optional late-stage best-of-$K$ refinement further improves sample quality by branching trajectories only near the end of denoising, avoiding the $K$-fold cost over the full trajectory. Empirically, on regulatory DNA sequence design benchmarks, D-DOIT consistently outperforms training-free guidance baselines and achieves performance competitive with training-based methods, improving both enhancer activity and cell-type-specificity while preserving sequence naturalness.

Large language models (LLMs) deliver strong performance, but their high compute and memory costs make deployment difficult in resource-constrained scenarios. Weight-only post-training quantization (PTQ) is appealing, as it reduces memory usage and enables practical speedup without low-bit operators or specialized hardware. However, accuracy often degrades significantly in weight-only PTQ at sub-4-bit precision, and our analysis identifies two main causes: (1) down-projection matrices are a well-known quantization bottleneck, but maintaining their fidelity often requires extra bit-width; (2) weight quantization induces activation deviations, but effective correction strategies remain underexplored. To address these issues, we propose D$^2$Quant, a novel weight-only PTQ framework that improves quantization from both the weight and activation perspectives. On the weight side, we design a Dual-Scale Quantizer (DSQ) tailored to down-projection matrices, with an absorbable scaling factor that significantly improves accuracy without increasing the bit budget. On the activation side, we propose Deviation-Aware Correction (DAC), which incorporates a mean-shift correction within LayerNorm to mitigate quantization-induced activation distribution shifts. Extensive experiments show that D$^2$Quant achieves superior weight-only PTQ performance at sub-4-bit precision. We will release the code and quantized models of D$^2$Quant.

Predicting gene expression under perturbations from single-cell data is a fundamental task for modeling cellular responses, with applications in discovering gene function and designing therapeutics. However, existing methods often struggle to generalize across datasets due to differences in experimental protocols, biological contexts, and species. We present \textbf{CytoWave}, a perturbation-centric pretraining framework for single-cell genetic perturbation response prediction. We leverage large-scale cross-species perturbation datasets unified via orthology and train CytoWave in a two-stage manner. In the first stage, we learn reference control cell states via masked gene expression recovery on unperturbed cells. In the second stage, we pretrain the model to predict perturbed gene expression given control states and perturbation signals, together with a contrastive objective that aligns latent representations of perturbation responses with perturbation signals. We perform strict dataset-held-out evaluation across multiple condition combinations and case studies of pathway activity changes, demonstrating consistent and effective performance of CytoWave in predicting single-cell genetic perturbation responses.

Paired translation between quasiperiodic physiological waveforms (i.e., recovering a target oscillatory signal from the source) is central to the interpretation of cardiovascular signals derived from wearables placed at different body locations. This source-to-target mapping in these problems carries inherent geometric structure: the phase wraps around the cycle and must be treated as a circular variable, the amplitude remains strictly positive, and the beat-to-beat alignment can drift unpredictably across cycles and subjects. While deep neural networks have been used for phase estimation and complex-valued signal modeling, prior work does not explicitly learn phase transport between paired signals. Consequently, neither endpoint-supervised regression nor the standard affine path used in flow matching accounts for this phase--amplitude structure. We introduce \emph{cylindrical geodesic flow matching} for paired cardiovascular waveform translation. We show that the standard affine path used in flow matching distorts intermediate amplitude and instantaneous frequency when interpolating between quasiperiodic signals; replacing it with a closed-form geodesic on the phase--amplitude cylinder eliminates these artifacts and converts each training pair into dense, geometry-consistent velocity supervision. On zero-shot photoplethysmography and limited-support seismocardiography adaptation benchmarks, our method consistently outperforms interpolation baselines and matches or exceeds direct supervised prediction, reducing Hilbert Transform, $L_2$, and Dynamic Time Warping distance by up to ${\sim}15\%$ over the strongest competing baseline. These results suggest that bridge geometry is a critical inductive bias for flow matching on oscillatory signal translation.

We cast image segmentation refinement as a \textit{discrete} diffusion process. To solve this task we introduce Cyclic Discrete Diffusion (CDD), a novel forward–reverse formulation with a cyclic label topology that maps a discrete label set to the uniform distribution and back. Instead of injecting Gaussian noise as is done for continuous diffusion, CDD evolves segmentation masks through label jumps: at each step, every pixel either remains in its current class or jumps to the next label with a fixed rate. This jump-or-stay mechanism defines a finite-state Markov process with bounded forward and reverse steps, featuring analytically consistent and tractable reverse dynamics. CDD operates natively in a multi-class label space, enabling joint refinement of all classes in a single pass without requiring class-wise decomposition, as commonly needed in prior binary refinement approaches. The proposed formulation admits a finite and controllable number of refinement steps, for which we provide a theoretical characterization based on spectral gap analysis. We evaluate CDD across diverse segmentation tasks, including multi-class semantic, instance-level, and binary segmentation refinement. Our method consistently improves both region accuracy and boundary quality across multiple backbones and datasets.