NeurIPS 2026 论文列表

Phylogenetic inference plays a central role in understanding evolutionary relationships, with applications ranging from tracking pathogen spread to reconstructing the history of life. Conventionally, practitioners obtain posteriors over the phylogenetic tree of species using MCMC methods. However, such likelihood-based methods are only tractable for simple evolutionary models with restrictive assumptions. For more complex and realistic evolutionary models, conventional methods are prohibitively expensive, inaccurate, or even impossible. We instead advocate for a simulation-based inference approach, by using simulated data from an evolutionary model to train a neural network that predicts tree topologies conditioned on sequences. To accurately represent the complex posterior distributions over tree topologies that can arise, we present flexible models that iteratively generate trees using three natural paradigms: top-down, middle-out, and bottom-up. We use a discrete diffusion framework to train these models efficiently on large-scale simulated datasets of phylogenetic trees. For all three generative paradigms, our models fit the data substantially better than the previous state-of-the-art simulation-based method, Phyloformer 2, and obtain more accurate posteriors on real datasets. Finally, our models outperform misspecified conventional methods on data following complex evolutionary processes.

Many real world processes can be represented as compositions of functions along a Directed Acyclic Graph (DAG). In causal modelling these correspond to the underlying mechanisms, in engineering to the multiple fidelities present, and in gene-regulatory networks to the transcription factors. These functions are partially observed across the DAG, with noisy and heterogeneously sampled measurements, posing significant challenges for reconstruction, uncertainty propagation, and inference. To tackle these we place priors over functions and naturally arrive at Deep Gaussian Processes over DAGs. We theoretically study their prior-collapse behaviour, and the effect of graph topology and intermediate observations on the preservation of information. We obtain almost-sure lower bounds on the asymptotic frequency of depths at which distinction between inputs is preserved, identify broad kernel classes for which these hold, and prove an observation by \citep{dunlop2018} on the role of input connections. We offer a structured variational approximation that retains graph dependencies, satisfies compositional uncertainty, and captures the explaining-away behaviour of colliders. Finally, we empirically validate our theoretical results and our methodology, and model a latent-collider DAG, a protein signalling network, and a multi-fidelity heavy-ion collision task, attaining state-of-the-art performance while recovering the low-fidelity contributions and yielding explainability over the simulator hierarchy.

Double Q-learning is a classical control algorithm that mitigates the maximization bias of Q-learning. To do so, it explicitly trains two independent action-value functions and uses them to decouple action-selection and action-evaluation when computing bootstrap targets. Double DQN adapts target bootstrap decoupling to deep reinforcement learning (RL), but explicitly trains only a single action-value function and does not fully decouple its estimators. Consequently, the two estimators remain correlated, and overestimation persists. In this paper, we introduce Deep Double Q-learning (DDQL), a deep RL algorithm that explicitly trains two Q-functions through Double Q-learning. DDQL stabilizes training through a combination of techniques, including lower replay ratios, longer target network update intervals, and shared layers. Across 57 Atari 2600 games, DDQL improves aggregate performance over Double DQN, outperforming it on 47 games while further reducing overestimation. In addition, we study key design choices when adapting Double Q-learning to deep RL, including the network architecture, replay ratio, and minibatch sampling strategies.

The optimal transport (OT) map provides a geometric transformation for aligning probability distributions and has become a useful tool in machine learning. However, existing estimators of the OT map still exhibit a gap between sharp statistical guarantees and practical parametric estimation based on stable training objectives. Theoretical estimators achieve minimax optimal convergence rates, but they are typically nonparametric and can incur demanding implementation design or inference costs. Practical estimators are parametric and scalable, but their statistical guarantees remain underexplored, and their min-max, adversarial-like training objectives can be sensitive to optimization algorithms. We propose BROT (Barycentric Regression for OT), a simple two-step method that first computes the unregularized OT plan and then fits a deep neural network (DNN) to the induced barycentric targets by least-squares regression. Under standard regularity conditions, we prove that the DNN estimator of BROT attains the minimax convergence rate, when the ground-truth OT map is Lipschitz. Numerical studies on synthetic datasets and an image dataset show that BROT provides accurate map estimates, strong target distribution matching, and competitive transport costs, compared to existing estimation methods. Experiments on two downstream tasks, single-cell perturbation prediction and unsupervised domain adaptation, further suggest that the accurate estimation of BROT can translate into stronger task performance.

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.

Language model loss follows remarkably regular scaling laws over model and data size, yet it remains unclear why the aggregate loss should exhibit a power-law form. Existing explanations often attribute this regularity to a heavy-tailed spectrum of pattern difficulty in natural language, but this view has not been directly validated at token-level granularity in large-scale real-data training. We present a token-level framework that deconstructs scaling laws into localized learning events of individual contextualized tokens. By fitting token loss trajectories with sigmoids, we show that token learning is concentrated in localized transitions, giving rise to a learning-time spectrum that dominates the scaling-law shape. Across more than one hundred pre-training runs on large and diverse real-language corpora with modern LLM architectures, scaling up to 6B parameters and 300B training tokens, the measured learning-time spectrum quantitatively reconstructs the validation loss derivative along the training-step $T$, data-scale $D$, and model-scale $M$ axes. We further show that the same signal is actionable: by reshaping the training distribution according to when tokens become learnable, we alter the optimization trajectory and achieve 11\% faster validation-loss reduction. These results provide direct empirical evidence that scaling laws are governed primarily by the distribution of token-level learning times, and that this distribution can be used not only to explain scaling behavior but also to improve training performance.

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).