NeurIPS 2026 论文列表

Federated Spiking Neural Networks (FedSNNs) provide an energy-efficient and privacy-preserving learning paradigm for edge intelligence. However, their robustness under Non-IID data is limited by a coupled failure mode: label shift distorts membrane potential distributions, drives neurons away from gradient-sensitive regions, and further amplifies surrogate gradient errors, which destabilizes federated optimization. To address this problem, this paper proposes Spatio-Temporal Spiking Adaptive Thresholds (ST-SAT), a framework that mitigates the coupling between label shift and surrogate gradient errors through coordinated neuron-level adaptation and client--server knowledge alignment. At the neuron level, ST-SAT introduces the Spatio-Temporal Parametric Leaky Integrate-and-Fire (ST-PLIF) neuron, which adaptively calibrates firing thresholds, membrane potentials, and decay dynamics to keep local neuronal responses within effective gradient regions. At the collaborative interaction level, ST-SAT adopts a Dual-Scale Knowledge Distillation strategy that combines adaptive logit-level distillation with feature-level alignment of neuronal statistics, thereby reducing class distribution bias and improving knowledge consistency across heterogeneous clients. Extensive experiments on three benchmark datasets show that ST-SAT consistently improves both global model performance and client-local performance, with particularly strong gains under severe label shift. These results validate the effectiveness of the proposed framework for robust federated SNN learning. The anonymous code link is https://anonymous.4open.science/r/ST-SAT-4771.

The process of discovery requires active exploration---the act of collecting new and informative data. However, efficient autonomous exploration remains a major unsolved problem. The dominant paradigm addresses this challenge by using Reinforcement Learning (RL) to train agents with intrinsic motivation, maximizing a composite objective of extrinsic and intrinsic rewards. We suggest that this approach incurs unnecessary overhead: while policy optimization is necessary for precise task execution, employing such machinery solely to expand state coverage may be inefficient. In this paper, we propose a new approach that explicitly decouples exploration from policy optimization and bypasses RL entirely during the exploration phase. Our method uses a tree-search strategy inspired by the Go-With-The-Winner algorithm, paired with a measure of uncertainty to systematically drive exploration. By removing the overhead of policy optimization, our approach explores an order of magnitude more efficiently than standard intrinsic motivation baselines on hard exploration benchmarks. Further, we demonstrate that the trajectories discovered during exploration can be distilled into deployable policies using existing supervised backward learning algorithms, achieving state-of-the-art performance by a wide margin on Montezuma's Revenge, Pitfall!, and Venture without relying on domain-specific knowledge. Finally, we demonstrate the generality of our framework in high-dimensional continuous action spaces by solving the MuJoCo Adroit dexterous manipulation and AntMaze tasks in a \emph{sparse-reward} setting, directly from image observations and without expert demonstrations or offline datasets. To the best of our knowledge, this has not been achieved before.

Recent full-duplex spoken dialogue models have demonstrated compelling progress toward human-like interaction, enabling agents to respond with low latency, produce backchannels, and handle user barge-ins. Yet these improvements in conversational dynamics often come with weaker reasoning and instruction-following abilities, revealing a potential tension between interactive dynamics and intelligence capability. In this paper, we argue that such an **intelligence--dynamics** trade-off is not fundamental: conversational dynamics can instead be learned as a separate real-time decision policy from human dialogue data. To this end, we propose DuplexPO, a reinforcement learning (RL) framework that decouples when to speak from what to say. It preserves the semantic response capability of an instruction-tuned assistant, while optimizing its temporal interaction behavior over selected high-impact windows from long human conversations. To quantitatively optimize these dynamics, we formulate the Factorized Conversational Dynamics Reward (FCDR) to enable fine-grained temporal credit assignment for turn initiation, backchanneling, yielding, and regularized participation. The policy is then optimized with a GRPO-style objective. Experiments show that DuplexPO substantially improves full-duplex behaviors, including timely backchannels, smooth turn-taking, and barge-in handling, while maintaining strong reasoning and instruction-following performance. Moreover, improvements in dynamics-oriented metrics are reflected in better user experience, suggesting that optimizing conversational timing as a standalone objective can promote more natural full-duplex interaction.

Egocentric action transfer aims to decouple the semantic action from egocentric observations and reproduce them in new visual contexts, enabling scalable world simulation, embodied policy learning, and robotic data generation. Existing approaches to transferring actions rely on condition-guided video generation, which converts the observation video into explicit geometric controls (_e.g._, hand poses or meshes) to drive synthesis. However, these methods introduce geometric estimation errors, making it difficult to preserve physically plausible interactions when transferred to new scenes. Alternatively, motion transfer methods directly extract motion patterns from the observation video, yet motion-level features alone cannot encode rich contact dynamics, frequently leading to severe hand structural collapse and implausible contact layout. To address both limitations, we present EgoACT, a test-time framework for egocentric action transfer that operates directly on the denoising process of video diffusion models without requiring additional geometric estimators. EgoACT uses Velocity-guided Structure Anchoring to stabilize reference-consistent hand-object structure in the early denoising stage, and Sparse Correspondence Calibration to refine reliable local correspondences in the mid-to-late denoising stages. Together, these two components preserve transferable action semantics while improving temporal coherence and interaction realism. We further establish EgoActionBench, a benchmark for evaluating action preservation, visual quality, and hand-object plausibility across diverse egocentric manipulation scenarios. Experiments show that EgoACT generates more coherent and physically plausible interaction videos than strong baselines.

Diffusion models achieve strong performance in text-to-image generation, but they can still produce unsafe content. Training-free safety guidance offers a practical inference-time alternative that intervenes during sampling without modifying model weights. However, many existing safeguards still rely on a coupled safety-control chain. In this chain, the same unsafe condition simultaneously specifies what should be avoided, indicates the current unsafe signal, constructs the intervention, and determines when that intervention remains active. This coupling can work when unsafe factors are explicit, but becomes less reliable once harmful and benign semantics are entangled in the current generation state, often leaving residual unsafe content or unnecessarily degrading benign-generation quality or prompt fidelity. We address this problem with Decoupled Safety Control (DSC), a plug-in safety-control algorithm for training-free safety guidance. DSC reformulates training-free safety guidance as an explicit state-dependent control process, so the executed intervention is determined from the current generation state rather than inherited directly from a single unsafe condition. Experiments on standard safety benchmarks show that DSC improves representative latent- and text-space safeguards, achieving stronger unsafe-content suppression while preserving benign-generation quality and maintaining efficient inference.

Cross-modal hashing is essential for efffcient large-scale re-trieval in multimedia analysis. However, existing unsupervised methods often struggle to fully capture interactions between modalities, enforce multi-level alignment, and maintain stable optimization. To overcome these challenges, we propose Decoupled Prototype Contrastive Align-ment Hashing (DPCAH), which employs a cross-modal semantic con-trastive learning module that concatenates image and text features and encodes their interactions via a transformer to produce uniffed represen-tations. feature-level and hash-level contrastive objectives jointly align semantic information and guide discriminative hash code learning across modalities. A decoupled prototype consistency module further models cross-modal correlations independently, enhancing semantic alignment while ensuring stable and robust optimization. Experiments on three benchmark datasets demonstrate that DPCAH outperforms state-of-the-art methods in cross-modal retrieval.

Semi-supervised learning (SSL) relies on two core mechanisms: self-training under the Teacher-Student (T-S) framework and joint optimization of labeled and unlabeled losses. Despite their effectiveness, we find both mechanisms introduce distinct optimization pathologies. First, parameter coupling enforces strict synchronization between teacher and student, where strong regularization on the student degrades the teacher's fitting ability, thereby limiting the permissible generalization intensity. Second, the imbalance in gradient update consistency between labeled and unlabeled losses drives the shared parameters to prematurely converge to labeled-dominated local minima, creating a bottleneck for global optimization. To address both issues, we propose the Pioneer Student (PiS), an auxiliary branch that operates in an independent parameter space and periodically transfers accumulated knowledge back to the T-S model. Extensive experiments show that PiS is a universal plug-and-play module that consistently improves mainstream SSL methods.

Prompt learning adapts vision-language models such as CLIP by optimizing a small set of continuous context vectors. The cross-entropy objective drives the learned prompt toward base-class specialization, while generalization to unseen classes benefits from staying close to the zero-shot feature space. Because both objectives act on the same parameters, their gradients oppose each other at convergence, and single-prompt losses are confined to a fixed empirical base-novel trade-off curve across a wide range of loss designs. We propose Decoupled Mode Connectivity (DMC), which resolves this conflict by assigning each objective to a dedicated prompt. A linear mode connectivity (LMC) corridor in text-feature space enforces low classification loss across interpolated classifiers between the two endpoints. The Visual Anchor regularizer preserves CLIP's pretrained class-similarity structure during specialization; equivalently, it minimizes the KL divergence between the learned and pretrained class-similarity distributions. We introduce class-permutation invariance (CPI) as a necessary condition for regularizer transfer across the base-novel boundary, and prove via Fano's inequality that any CPI violation lower-bounds the drop in novel accuracy by a term proportional to the mutual information between the prompt and base-class labels. DMC improves base and novel accuracy on the majority of dataset-baseline combinations across 11 datasets and three prompt-learning baselines~(CoOp, KgCoOp, MMA), shifting the Pareto frontier rather than trading along it.

In modern parametric model training, full-batch gradient descent (and its variants) suffers due to progressively stronger biasing towards the exact realization of training data; this drives the systematic ``generalization gap'', where the train error becomes an unreliable proxy for test error. Existing approaches either argue this gap is benign through complex analysis or sacrifice data to a validation set. In contrast, we introduce decoupled descent (DD), a novel theory-based training algorithm that satisfies a train-test identity---enforcing the train error to asymptotically track the test error for stylized Gaussian mixture models. Within this specific regime, leveraging approximate message passing theory, DD iteratively cancels the biases due to data reuse, rigorously demonstrating the feasibility of zero-cost validation and $100\%$ data utilization. Moreover, DD is governed by a low-dimensional state evolution recursion, rendering the dynamics of the algorithm transparent and tractable. We validate DD on XOR classification, yielding superior performance compared to GD; additionally, we implement noisy MNIST and non-linear probing of CIFAR-10, demonstrating that even when our stylized assumptions are relaxed, DD narrows the generalization gap compared to GD.

Existing multi-task active learning (MTL-AL) methods predominantly rely on the linear scalarization of heterogeneous objectives, such as uncertainty, diversity, and gradient conflict.We argue that this entangled formulation is fundamentally limited, as linear combination fails to capture Pareto-optimal trade-offs under conflicting objectives. To address this limitation, we propose a paradigm shift from combination to deconstruction and introduce a principled framework that rethinks MTL-AL along three orthogonal dimensions. First, we identify the Conflict Paradox: contrary to conventional wisdom, samples with high gradient conflict are not noise to be avoided but carry maximal information about unresolved inter-task trade-offs. We therefore decouple sample selection, which embraces conflict, from optimization, which resolves it via gradient surgery. Second, we introduce Orthogonal Decomposition, separating the acquisition objective into two distinct axes: Informativeness and Exploration. This two-stage process, consisting of subspace projection followed by manifold coverage, prevents ``diversity dilution'' by excluding uninformative outliers before enforcing diversity. Third, we propose Cross-Task Mutual Information (CTMI), a distribution-free proxy for inter-task dependency derived from a variational lower bound under the Maximum Entropy principle. Extensive experiments on COCO, Cityscapes, and NYUv2 — including comparisons against recent multi-task active learning baselines — demonstrate consistent improvements over state-of-the-art MTL-AL methods, highlighting that structural rethinking of acquisition yields larger gains than incremental heuristic design. Our code is publicly available at: \href{https://anonymous.4open.science/r/multitask_active_learning-1545}{\texttt{https://anonymous/CTMI}}

Identifying subpopulations of neurons that interact with each other from simultaneous recordings of populations of many neurons is key for understanding across-brain communication with cellular resolution. Recent work identified communication subspaces, which capture additive interactions between pairs of high-dimensional neural populations through a small number of source and target activity patterns. However, no current method captures how a third, potentially multivariate variable - such as behavioral state or the activity of a third population - modulates these interactions. Here we extend the communication subspace framework by parameterizing modulation as a low-rank tensor. This identifies multiplicative interaction channels (MICs), defined as triplets of source, target, and modulator activity patterns, in which the modulator pattern gates the source-target interaction. We derive MICs as a bilinear perturbation of reduced-rank regression. We develop a hierarchical fitting pipeline and provide a closed-form decomposition that quantifies whether modulation reshapes the modulator-averaged baseline interaction, recruits private dimensions of one population, or opens new interactions. In simulations, MICs reliably recover the presence and geometry of ground-truth modulation even in the high-dimensional, low-sample regime. Applying MICs to simultaneous calcium imaging of prefrontal axons and interneurons in the visual cortex revealed that behavioral state asymmetrically modulates top-down interactions, reconfiguring the patterns of prefrontal projections that interact with a stable set of visual interneuron activity patterns. By providing an efficient and compact characterization of modulatory interactions, MICs enable asking new questions about how potentially high-dimensional variables shape interactions between neural populations.

Workload forecasting is a fundamental task to realizing electricity-computing synergy in modern data centers, where operational planning must account for the risk induced by fluctuating computing demand. Yet production workloads exhibit substantial volatility, making deterministic point forecasts insufficient for uncertainty-aware decision making. Existing methods mainly model workload uncertainty from temporal correlations in utilization traces, overlooking the job-induced cross-machine dependencies shaped by the scheduling layer. We empirically find that workload volatility exhibits structured and time-varying cross-machine patterns whose strength aligns with job co-occurrence. This observation motivates GD-Diff, a job-aware graph-conditioned diffusion framework for probabilistic workload forecasting. GD-Diff constructs a job-induced weighted dynamic graph from scheduling logs, transforming job co-occurrence into time-varying inter-machine dependencies. Built on this graph, GD-Diff uses a dual-branch noise prediction network within the diffusion process, where the temporal branch captures regular temporal evolution and the graph branch models job-induced dynamics. Experiments on Alibaba and Google cluster traces show that GD-Diff achieves state-of-the-art performance in both point and probabilistic forecasting, and ablation studies further validate the job-induced dynamic graph and dual-branch architecture. The code is available at \url{https://anonymous.4open.science/r/GD-Diff-F5E8/}.

We study two-layer neural networks trained by stochastic gradient descent (SGD) on a multi-index teacher model, focusing on how first-layer parameters orthogonal to the teacher subspace affect generalization. We decompose the first-layer weights into a signal component aligned with the teacher and an orthogonal complement, and analyze their joint SGD dynamics together with the second layer in the proportional limit. We show that although SGD updates to the complement are initially isotropic, the training dynamics induce teacher-dependent spectral spikes in the complement Gram matrix. This emergent anisotropy provides a concrete mechanism by which representation variance is amplified, and generalization degrades. To isolate this effect, we introduce Decomposed Dynamics, an analytically tractable surrogate whose asymptotic test-error dynamics coincide with those of SGD. Using this surrogate, we characterize regimes in which freezing the orthogonal complement strictly improves generalization relative to training it, despite identical training error. Finally, we show that the signal component rapidly aligns with the second-layer weights, revealing strong cross-layer coupling even in this shallow setting. Together, our results provide a precise dynamical explanation for when and how feature learning outside the teacher subspace harms generalization.

Jailbreak attacks that frame harmful queries as professional requests can bypass LLM safety training, but we do not know which ingredients in a professional-framing pipeline actually matter. We introduce a **pre-specified 2×2×2 factorial-ablation design for jailbreak decomposition**, separating access-driving from depth-driving components, and apply it to one concrete pipeline family, **Structured Three-stage Framing (STF)**: a **Persona Adoption** turn (S1) that establishes professional identity, a **Moral Justification** turn (S2) that appeals to harm prevention, and a **Linguistic Substitution** (S3) of framework terminology for colloquial language. Tested on N = 5,000 trials across three closed frontier models (~31,900 total trials across nine LLMs), the decomposition isolates **S1 (Persona Adoption) as the dominant contributor to acceptance** (OR = 6.5), **S3 (Linguistic Substitution) as the dominant contributor to depth** (β = 0.91), and **S2 (Moral Justification) showing no detectable positive effect at the stated equivalence bounds** (pooled OR = 0.83; TOST at [0.67, 1.50]) — a component ordering we write as **S1 > S3 > S2** (Persona > Linguistic Substitution > Moral Justification). This ordering holds across three full-rank factorial replications (defensive system prompt, cybersecurity, social engineering; N ≈ 5,000 each) and nine additional robustness checks. Two defensive targets follow: detect persona claims to block access; restrict output depth when responses use framework terminology. The method is portable beyond STF; the empirical result is specific to one pipeline tested primarily through provider APIs, with open-weight corroborations on local inference (§3).

Structural causal models (SCMs) provide a principled foundation for causal reasoning, and neural causal models (NCMs) extend this framework by parameterizing causal mechanisms with neural networks. The classical formulation of NCMs assumes a specific architectural choice: a single function approximator per structural equation. In this work, we revisit this assumption and show that it represents only one point in a broader family of valid neural causal model parameterizations. Using interventions as an analytical lens and graph-based neural architectures as a concrete construction, we characterize how different parameter-sharing schemes over sub-mechanisms induce distinct members of the NCM family while preserving the same causal graph. This perspective yields two additional regimes beyond the classical formulation, which differ in how causal mechanisms are decomposed and parameterized within a unified structural framework. Together, these regimes demonstrate that architectural choices shape the causal semantics and expressivity of neural causal models, even when graphical structure is fixed. Relaxing structural assumptions further, we identify a class of partially causal models (PCMs) that support limited interventional or counterfactual queries without satisfying full SCM requirements, offering alternative trade-offs between causal fidelity and tractability. We place NCMs and PCMs within a unified expressivity spectrum between purely associational models and fully causal generative models, and illustrate these distinctions empirically using a graph-based variational autoencoder with interventional structure.

In high-stakes settings, decision-makers using conformal prediction (CP) read the predictive interval's width as their operational uncertainty signal. Local feature attribution methods, applied to that width, silently conflate two semantically distinct contributions: a calibration-driven baseline that depends on the held-out calibration set, and an instance-driven term that depends on the test point. We propose a two-game Shapley construction---one game over feature coalitions at the test point, one over feature coalitions across the calibration set---that decomposes the total attribution into calibration-driven and instance-driven components. The construction is method-agnostic on two axes: any local attribution method that can be applied to a scalar uncertainty target can be used, and any split CP family whose uncertainty size admits a known separation into a calibration-only and an instance-only summary satisfies the decomposition. Empirically, the calibration-driven and instance-driven attributions frequently carry opposite signs on synthetic and real data, with representative cases where a feature's sign reverses between the two views; the total attribution then becomes a misleading sum. On a clinical-decision task with semi-synthetic data, this conflation degrades decision quality, whereas jointly considering the calibration-driven and instance-driven attributions recovers high-uncertainty cases missed by total attribution. We further empirically verify that the decomposition holds across multiple CP families that satisfy the separability conditions.

Single-cell multi-omics technologies jointly measure multiple modalities from the same cell, such as gene expression, chromatin accessibility, and surface protein abundance, providing a richer view of cellular state than any single modality alone. A central challenge is how to learn a multimodal cell representation that preserves the different types of information contained in these co-profiled measurements. Existing integration methods often emphasize either cross-modal correspondence, which benefits retrieval and imputation, or fused discriminative structure, which benefits classification, but these objectives can favor different aspects of the data. As a result, a representation optimized for one setting may fail to preserve information needed for another. We introduce scDecomp, a decomposed representation learning framework for co-profiled single-cell multi-omics data. Motivated by the concepts of redundancy, uniqueness, and synergy, scDecomp separates multimodal information into three role-specific components: a redundancy component ($R$) for information shared across modalities, a uniqueness component ($U$) for modality-specific information, and a synergy component ($S$) for interaction-dependent multi-omic signals. This design allows the learned representation to retain shared cell-state structure while also preserving modality-specific and cross-modal regulatory information. On single-cell multi-omics benchmarks, the $R$ component achieves a 5.0\% reduction in FOSCTTM compared with the strongest baseline, while $U+S$ improves cell type classification accuracy by 1.38\%. Branch-level analyses further show that the decomposed components support complementary aspects of representation quality, suggesting that structured decomposition provides a practical strategy for learning more informative multimodal cell representations.

Generative visual verifiers play a crucial role in advancing multimodal intelligence systems. In this paper, to enhance the robustness of visual verifiers in complex and realistic open-world scenarios, we move beyond conventional binary verifiers that produce only True/False discrete judgments. Instead, we introduce a verifier that automatically decomposes complex prompts or environmental rules into fine-grained components and assigns pointwise scores, thereby providing a continuous and more informative critique signal. Under a general reinforcement learning framework, we first empirically demonstrate that the proposed pointwise decomposed verifier significantly outperforms binary verifiers in traditional text-to-image scenarios. We then extend our investigation to a novel paradigm: generative world modeling, where generative models are leveraged for world simulation and world reasoning. From a theoretical standpoint, we further show that when ground-truth images are easy to obtain, a pairwise verification paradigm yields more accurate critiques than the pointwise formulation. Empirical results on diverse world-modeling tasks, such as Maze and Sudoku, further validate the effectiveness of our approach. Overall, our findings suggest a key insight: the transition from generative models to world-modeling agents critically hinges on the availability of accurate pairwise decomposed visual verifiers.

Large Reasoning Models (LRMs) exhibit backtracking and self-verification mechanisms that enable them to revise intermediate steps and reach correct solutions, yielding strong performance on complex logical benchmarks. We hypothesize that such behaviors are beneficial only when the model has sufficiently strong “critique” ability to detect its own mistakes. This work systematically investigates how current LRMs recover from errors by inserting arithmetic mistakes in their intermediate reasoning steps. Notably, we discover a peculiar yet important phenomenon: despite the error propagating throughout the entire chain-of-thought (CoT) without any verbalized correction, the model still reaches the correct final answer after the thinking process finishes. This recovery implies the existence of an internal mechanism helping the model to detect errors and trigger self-correction, which we refer to as the hidden critique ability. Building on feature space analysis, we identify a highly interpretable critique vector representing this behavior. Extensive experiments across multiple model scales and families demonstrate that steering latent representations with this vector improves the model’s error detection capability and enhances the performance of test-time scaling at no extra training cost. Our findings provide a valuable understanding of LRMs’ critique behavior, suggesting a promising direction to control and improve their self-verification mechanism.