NeurIPS 2026 论文列表

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.

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.

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.

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.

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.

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.

Context or prompt-level reweighting has emerged as a central algorithmic lever in Reinforcement Learning with Verified Rewards (RLVR) for improving the reasoning capability of large language models, yet the principle determining what constitutes an optimal weighting remains poorly understood. We address this gap by formulating prompt reweighting as a functional derivative of a utility functional defined in the pass-rate function space, yielding a unified optimality framework that accommodates existing schemes, including REINFORCE and GRPO. Building on this optimality framework, we propose a distribution-aware prompt reweighting approach, called CurveRL, based on a quantile coordinate transform, in which the weight assigned to each prompt depends not on the absolute value of pass rates but on its rank and density to reflect the distributional structure of the pass rates in the learning dynamics. Extensive experiments across multiple benchmarks demonstrate that our proposed CurveRL consistently outperforms GRPO and other RLVR baselines. Our study identifies context-distribution control as a principled axis for analyzing and designing prompt-reweighted RLVR algorithms.

We consider the problem of Cost-Aware Learning, where sampling different components of a finite-sum objective incurs different costs. The objective is to reach a target error while minimizing the total cost. We propose Cost-Aware SGD, which uses a distribution based on gradient norms and costs to sample components. We provide a thorough analysis of this algorithm, including cost-improvement bounds over baselines, a characterization of distribution proxy sub-optimality, and a lower bound. We apply our theoretical insights to reinforcement learning with language models, where the computational cost of sequence-level policy gradients varies with length. We find that the advantage magnitude serves as a high-fidelity proxy for gradient norms, and use this to introduce Cost-Aware GRPO. Empirical results on 1.5B, 4B, and 8B LLMs demonstrate that this algorithm significantly reduces the tokens used in policy optimization while matching or exceeding baseline accuracy.

We study contextual bandit problems with correlated arms and access to surrogate reward signals produced by a machine learning model, motivated by applications such as large language model (LLM) routing. Unlike classical contextual bandits that rely solely on bandit feedback and assume conditional independence across arms, our setting allows context-dependent inter-arm correlations and auxiliary reward information that may be noisy or misspecified. We propose algorithms that leverage such surrogate rewards through two complementary designs. A coupled reward-mixing approach pools true and surrogate rewards to accelerate learning when surrogate signals are reliable, while a decoupled prediction-mixing approach maintains separate estimators for bandit feedback and surrogate rewards and adaptively combines their predictions. This decoupling yields robustness to surrogate misspecification, recovering regret guarantees comparable to reward-only bandit methods in the worst case, while achieving improved regret when surrogate predictions are sufficiently informative. We provide theoretical regret analyses for both approaches and evaluate them on LLM routing benchmarks under varying accuracy versus cost trade-offs. The results demonstrate improved sample efficiency and consistently better accuracy–cost trade-offs compared to standard contextual bandit baselines and strong static routing methods.

Model merging integrates task-specific fine-tuned models into a single multi-task model, but often suffers from parameter interference caused by conflicting task-vector updates. Existing methods typically mitigate conflicts by pruning task vectors based on weight magnitude or random heuristics, treating Transformers as unstructured ``bags of parameters'' and overlooking their inherent modularity. In this paper, we propose \textbf{C}ontribution-\textbf{A}ware \textbf{S}tructured \textbf{S}parsity (CASS), a unified framework that reduces parameter interference by identifying and preserving task-specific components. At the core of CASS is a contribution-aware structured mask that identifies task-relevant attention heads and FFN neurons. We instantiate this mask in two settings: CASS-Merging, the primary post-hoc setting where masks serve as a plug-and-play denoising filter for existing merging operators, and CASS-Tuning, an extension for scenarios with fine-tuning access where masks constrain gradients to reduce structural overlap between task vectors. Our analysis shows that task-relevant components are sparse and partially disjoint, supporting structured component-level filtering as an effective way to reduce merging interference. Extensive experiments across vision (ViT, 20 tasks) and language (RoBERTa, 8 tasks; Qwen2.5, 4 tasks) benchmarks demonstrate that CASS improves a range of representative merging baselines.

Constrained learning has been increasingly applied to various domains to ensure explicit feasibility requirements due to fairness, safety, robustness, regularization, and physics or logic constraints. Understanding how training samples influence the solution (e.g., learned parameters) of constrained learning is crucial for interpretability and robustness. The classical influence function may become unreliable in constrained settings: data perturbations can reshape both the objective and the feasible region, leading to estimates that violate feasibility. In response, we propose the Directional Influence Function (DIF), a new estimator that explicitly incorporates the constraints into influence estimation. DIF formulates the optimality conditions of constrained learning as a variational inequality (VI) and analyzes how perturbing training data affects this VI. We validate DIF in constrained linear regression and demonstrate that it recovers leave-one-out retraining results, whereas IF and penalty-based IF exhibit significant bias. We further apply DIF to fairness-constrained CNNs, where DIF accurately predicts test loss changes under data removal and aligns closely with actual retraining. Our results establish DIF as an efficient and reliable tool for data attribution in constrained learning.

Diffusion and flow models enable powerful generative modeling, but controlled editing remains challenging due to the need to balance source fidelity and edit compliance along the generative trajectory. We propose a principled, inversion-free framework for flow-based image editing from a trajectory centric perspective. Rather than relying on inversion or heuristic guidance, we introduce a one-step look-ahead mechanism that evaluates the effect of candidate updates on both edit and fidelity rewards estimated via denoising, and adjusts the current update based on this evaluation. At each step, our method applies reward-driven corrections guided by the look-ahead evaluation under an explicit fidelity constraint, resulting in stable and coherent trajectory evolution. This look-ahead design mitigates gradient interference between edit and fidelity objectives by anticipating their interaction in future states, leading to improved trade-offs. Experiments demonstrate that the proposed approach achieves stronger semantic edits while better preserving source content compared to existing strong baselines.

Federated Learning (FL) enables collaborative model training across clients while preserving data privacy. However, FL typically relies on voluntary client participation with uniform rewards, which can lead to high-quality clients dropping out due to inadequate incentives and low-quality clients taking a free-ride, both of which degrade overall model performance. However, existing incentive mechanisms fail to capture the true contribution value of a client’s data to the global model, and often skew client selection toward cost compression rather than quality enhancement, thereby exacerbating confounding bias. To address this, we propose CausalAFL, a causal-based framework that reduces information asymmetry and corrects for confounding factors. Specifically, CausalAFL builds a complete causal chain connecting client information, bid, and marginal contribution. By jointly optimizing an inference network and a generative network within a variational inference framework, it precisely adjusts for causal bias. Maximizing the evidence lower bound objective allows CausalAFL to separate true contribution from noise and highlight the value of high-quality clients. A social welfare objective that quantifies both natural direct and indirect effects guides the server to prioritize these clients, resulting in improved global model accuracy and social welfare. Experiments on multiple datasets demonstrate that CausalAFL can guide clients to adjust their bids, leading to winners with higher contributions, confirming its effectiveness.

Uncertainty quantification (UQ) for large language models (LLMs) aims to provide reliable measures of predictive confidence, yet current methods often fail to remain stable under simple, meaning-preserving perturbations. We identify an important source of instability: semantically equivalent paraphrases of the same input can induce substantial variability in predictive confidence, even for methods with formal guarantees such as conformal prediction. To address this issue, we propose a paraphrase-aware UQ framework that explicitly enforces invariance to semantic rewordings. Instead of relying on a single input, our approach constructs uncertainty scores by aggregating predictions across a set of its paraphrases, using a lightweight proxy model to produce calibrated and comparable outputs. This aggregation yields uncertainty estimates that are both more stable and more informative, while remaining compatible with conformal calibration techniques to retain coverage guarantees. Across multiple UQ benchmarks and model families, our method achieves nominal coverage with smaller and more stable prediction sets under paraphrase perturbations. Code is available at https://anonymous.4open.science/r/PA_Score-8C0D.

Link prediction remains difficult in sparse graphs, where diffusion heuristics lack connectivity and geometric methods suffer spectral instability when coupling learned representations with diffusion operators. We introduce **Concorde**, an energy-based framework that resolves this by decomposing link likelihood into three structurally decoupled branches: **(i)** a parameter-free spectral anchor that establishes permutation-invariant topological stability; **(ii)** a mixed-curvature residual on a learnable $\mathbb{H}\_{c\_h}^{d\_h} \times \mathbb{S}\_{c\_s}^{d\_s}$ product manifold, regularized by a feature-aware Ollivier--Ricci curvature proxy; and **(iii)** a continuous vector field on $\mathcal{T}\mathbb{H}\_c^d$ for non-local structural alignment across topological gaps. The branches are fused via learned gating and trained with an $N$-pair ranking loss aligned with retrieval-style evaluation. Across five benchmarks spanning sparse, dense, featureless, and attributed regimes, **Concorde** establishes state-of-the-art early retrieval precision, nearly doubling $P@10\\%$ over leading baselines in sparse settings.

A unified cross-modal person re-identification targets robust cross-modal retrieval with queries from diverse modalities like visible, infrared, and text.Such framework requires multi-modal data and centralized training, raising data costs and privacy risks. Hence, we propose a modality-heterogeneous federated ReID task to learn a unified cross-modal framework from clients with different cross-modal data.However, it faces two challenges.First, varying inter-modality complexity across tasks leads to different retrieval patterns and parameter space. Second, different intra-modality complexity results in imbalanced contributions when merging LoRA modules.Existing federated learning methods use uniform merging and transmit full models, which hinders cross-modal knowledge integration and increases communication overhead. To address these issues, we adopt Low-Rank Adaptation (LoRA), a lightweight module for fine-tuning large models through low-rank updates, enabling clients to send compact parameters.On top of LoRA, we develop MIRAGE (Modality-heterogeneous Intelligent federated ReID Aggregation with Global Efficiency), adapting aggregation to inter- and intra-modality complexity.When merging visible LoRA, MIRAGE selects key ranks to protect retrieval patterns while retaining shared ranks for common ability.As for infrared and text LoRA, it reduces conflicts with an adaptive pruning method. In both strategies, task complexity is incorporated into aggregation to balance contributions. Experiments on cross-modal retrieval tasks demonstrate the superiority of MIRAGE.The code and dataset will be publicly released.

3DGS enables real-time novel view synthesis, but practical deployment under varying compute budgets requires a single Gaussian set that remains effective when truncated to a prefix of primitives. This raises how capacity should be allocated as the budget decreases.We observe two findings. First, image complexity correlates with reconstruction error in both image and 3D Gaussian space. Second, this structure degrades under strong budget reduction, indicating suboptimal allocation.These observations motivate using image complexity as an optimization signal. We propose a continuous level-of-detail method based on 3DGS-MCMC that uses it for (i) relocation toward difficult regions during training and (ii) importance-based retention of simple and detailed regions at inference. A variance constraint on residuals between full- and reduced-budget renderings further enforces uniform error under compression. Our method yields smoother LOD degradation than prior work, with largest gains in low-budget regimes. Code will be released.

INT2 KV-cache quantization is attractive for long-context LLM serving, but it remains difficult to make both accurate and deployable. Simple rotations such as Hadamard transforms reduce outliers, but still degrade at INT2 because they are not aligned with downstream attention. We propose CoQuant, an Ultra-low-bit KV Cache quantization method that estimates *attention-aware covariance* structures offline and uses them to derive fixed rotations and clipping thresholds for quantization. In this way, it aligns KV quantization with the covariance structures that attention actually consumes. More importantly, we not only provide theoretical justification but also develop a fully deployable CoQuant system with a custom INT2 attention kernel that remains compatible with paged KV-cache serving and fused kernel pipelines, enabling seamless integration into modern LLM serving frameworks such as SGLang and vLLM. We evaluate our methods on recent reasoning models with reasoning traces of up to 32k tokens across 5 tasks. On Qwen3-4B-Thinking-2507 and Qwen3-8B, CoQuant reduces the BF16 accuracy gap to 3.78 and 1.42 points, respectively, while naive rotation INT2 collapses to nearly zero. We further scale CoQuant to Qwen3-32B and GLM-4.7 (358B params), where it remains effectively on par with BF16. On long context - RULER-NIAH up to 128K, CoQuant remains robust on both Qwen3 models, while naive rotation INT2 collapses. System-wise, CoQuant reduces KV-cache memory by approximately 8×, improves throughput by up to 7× at large batch sizes under the same memory budget, and accelerates batch-size-1 decoding by up to 3× over BF16 due to reduced memory bandwidth overhead.

Reliable deployment of time series classifiers in high-stakes domains requires explanations that capture the interplay between localized patterns and global temporal context. However, existing post-hoc methods often treat local features in isolation, failing to account for long-range dependencies. We propose CoLaX, a context-aware explanation framework that explicitly embeds local dynamics within a global temporal structure. By employing a principled context-conditioning mechanism, CoLaX models how global context modulates the importance of local patterns, revealing not only what is important but also when and why it matters. Extensive benchmarks demonstrate that CoLaX yields significantly more faithful and stable explanations than state-of-the-art baselines, demonstrating that joint local-global modeling is essential for reliable time-series interpretability.