NeurIPS 2026 论文列表

Parameter initialization shapes training dynamics, convergence, and performance in neural networks. In spiking neural networks (SNNs), analytical initialization is complicated by three additional mechanisms—binary spike outputs, temporal recurrence (leak), and reset-induced drops in membrane potential—which together suppress activity propagation: (1) over time, where recurrence and reset reduce individual neurons' firing rate, and (2) across depth, where deficits in upstream activity compromise downstream layers, known as spike-rate collapse. Prior initialization methods address only subsets of these challenges. We derive closed-form weight and bias distributions for initialization that jointly account for temporal recurrence, refractory reset, and signal propagation across depth. The method is data-agnostic: it requires only the architecture and neuron hyperparameters. Our derivation centers around a bias correction that yields stationary firing rates and membrane variance, with a principled default spike probability of $p = 0.5$ that maximizes per-neuron Bernoulli entropy—counteracting dead neurons ($p \approx 0$) and saturated neurons ($p \approx 1$) at initialization. In controlled simulations across 30 layers, it maintains target firing rates and membrane variance at the individual-neuron level, unlike alternatives tested. Across UEA multivariate time-series classification, neuromorphic classification, and continuous-control reinforcement learning, our SNNs rank first in every paradigm without per-paradigm tuning.

Most time-series anomaly detectors ask whether an observation can be predicted or reconstructed. We ask a different question: can a detector’s own spiking dynamics become anomaly evidence? We study this question for periodic and quasi-periodic time series and propose SPIRE, a phase-conditioned spiking anomaly detector. SPIRE uses period-aware current formation to expose phase-aligned cross-period residuals to leaky integrate-and-fire neurons. We refer to this conversion as eventification: after residual injection, recurrence violations induce abnormal bursts, silences, or phase-shifted spike trains. SPIRE then constructs phase-conditioned normal templates of spike states and scores deviations in residual energy, firing-rate patterns, and burst/silence statistics, instead of relying solely on output-space prediction or reconstruction errors. This separates the operational protocol, including causal current-step detection, one-step prediction, and non-causal reconstruction, from the source of anomaly evidence, namely output errors, spike dynamics, or both. On TSB-AD-U and TSB-AD-M, SPIRE establishes new best VUS-PR among neural time-series anomaly detectors. Spike-native scores remain competitive without reconstruction error, hybrid scoring consistently improves error-only scoring, and replacing LIF neurons with continuous activations in matched ANN counterparts reduces detection performance under the same period-aware architecture. Periodicity-stratified analyses and spike-state visualizations further show that the gains are strongest when phase recurrence is reliable, supporting the proposed spike-as-detector mechanism. These results suggest that phase-conditioned spiking dynamics can serve as a first-class anomaly detection signal for periodic time series, rather than merely an efficient computational substrate.

Spiking Neural Networks (SNNs) offer a distinctive paradigm for temporal modeling through their intrinsic membrane potential dynamics. However, existing SNN-based forecasting methods focus exclusively on temporal processing, lacking mechanisms to capture spatial dependencies among variables. To bridge this gap, we propose SpikeSTAG, a neuromorphic spatiotemporal architecture that integrates graph-based spatial reasoning into spike-driven temporal computation. Central to our approach is the Dendritic Graph Module, which reinterprets spectral graph convolutions as biological dendritic integration -- coupling multi-scale spatial aggregation with the temporal dynamics of spiking neurons within a single computational unit. To adaptively fuse the resulting spatial and temporal representations, we further introduce a Dual-Stream Integration Module that employs competitive gating and coincidence-based amplification, selectively enhancing consistent spatiotemporal patterns while suppressing conflicting noise. Extensive experiments demonstrate that SpikeSTAG establishes a new state-of-the-art among SNN-based models and surpasses representative ANN baselines, while reducing theoretical energy consumption by approximately 33.3\% compared to Transformer architectures.

Two-photon calcium imaging is a standard tool for recording large neural populations in vivo, yet inferring spikes accurately across the growing diversity of calcium indicators remains an open problem. Existing supervised methods achieve reasonable in-domain accuracy but generalize poorly to unseen indicators, because different indicators induce distinct fluorescence kinetics and signal statistics while existing architectures remain relatively simple generic temporal regressors without dynamics-matched inductive bias. We propose SpikeSSL, a universal spike inference framework whose temporal backbone is a bank of bidirectional IIR state-space layers broadly motivated by calcium dynamics. A multi-modal conditioning encoder maps indicator identity, sampling rate, and trace-level signal statistics into a global conditioning vector that modulates the backbone via Adaptive Layer Normalization, while a heteroscedastic variance head provides calibrated per-frame uncertainty. On a benchmark with five fixed evaluation splits built from 33 public ground-truth datasets, SpikeSSL achieves state-of-the-art performance in both in-domain and zero-shot leave-one-indicator-out settings. We also develop a biophysical simulation pipeline capable of generating paired fluorescence-spike traces with systematically varied kinetic parameters, spike statistics, response nonlinearities, baseline drift, and noise. Using this pipeline, we synthesize approximately 11{,}000 simulated traces. Augmenting training with these data effectively closes the cross-indicator domain gap and improves zero-shot generalization.

Neural avalanches and turbulence-like brain dynamics are usually measured after neural activity has been coarse-grained into continuous fields, leaving open which spike-level mechanisms can generate these macroscopic observables. We build a spatial leaky integrate-and-fire spiking neural network and explicitly transform spikes into activity-density and Hilbert phase fields. A delayed, distance-dependent, passive-dendrite regime reproducibly generates turbulence-like field structure, including high-amplitude phase-defect tracks, structure-function scaling, and power-law-favored continuous-field avalanche tails. Controls show that firing rate, vortex count, and avalanche tail fits are individually insufficient: destroying delayed propagation, dendritic filtering, or spike timing suppresses key field signatures, while random-space nulls can retain many mathematical phase defects but lose spatial scaling and synaptic-delay transfer. Biologically motivated gates further shape the regime: PV-like perisomatic inhibition clamps phase dynamics, SST-like dendritic inhibition and projection-specific inhibitory facilitation preserve long-lived tracks, and a rate-constrained projection-STP regime maintains similar dynamics at 12.25 Hz across 12 seeds. These results use SNNs as a mechanistic testbed for spike-derived brain turbulence observables and show why phase defects and avalanche-like statistics must be interpreted jointly with spatial scaling and spike-level propagation.

Spiking Neural Networks (SNNs) promise energy-efficient inference, but adapting large pre-trained SNNs to new tasks is expensive because BPTT memory and runtime scale with the number of simulation steps $T$. We observe that weights in pre-trained SNNs are strongly concentrated near zero, and under thresholded dynamics many such synapses are functionally silent. Based on this, we propose \textbf{Spike-SFT}, a two-stage adaptation framework. Stage 1 (\textbf{Selective Parameter Enhancement}, SPE) fine-tunes only a small-magnitude subset via masked updates with low-rank regularization, progressive re-selection, and sparse-gradient storage, reducing memory while maintaining competitive accuracy. Stage 2 (\textbf{PickIt}) fuses multiple SPE-adapted models by alignment and interference-aware delta merging with spike-statistics calibration, yielding a single merged model with zero inference-time overhead. Across several SNN backbones and benchmarks, Spike-SFT offers a favorable accuracy--cost trade-off, improving fine-tuning efficiency (time/memory) while retaining strong accuracy, and enabling zero-overhead weight-space fusion via PickIt.

Spiking Neural Networks (SNNs) offer a promising energy-efficient alternative to Artificial Neural Networks (ANNs) through event-driven, multiplication-free computation. However, when applied to downstream tasks such as Neural Radiance Fields (NeRF), SNNs suffer from significant information loss, resulting in a noticeable performance gap. In this paper, we present \textit{Spik-NeRF v2} to advance SNN-based neural rendering. We propose the $\pm$I-LIF spiking neuron, which extends I-LIF to support signed integer values during training, thereby mitigating information loss. During inference, it is converted to ternary spikes $\{-1, 0, 1\}$, preserving event-driven properties with addition-only operations. Furthermore, we introduce a re-parameterization technique that transforms a trained I-LIF-based Spik-NeRF with $t$ timesteps into an equivalent $\pm$I-LIF-based model with $t/2$ timesteps. This enables faster inference while preserving rendering quality, overcoming the suboptimal performance of directly trained $\pm$I-LIF models with reduced timesteps. Extensive experiments on synthetic and realistic datasets demonstrate that Spik-NeRF v2 surpasses existing SNN-based NeRF methods and achieves rendering quality comparable to ANN-based approaches.

We study the problem of learning generative models for discrete sequences in a continuous embedding space. Whereas prior approaches typically operate in Euclidean space or on the probability simplex, we instead work on the sphere $\mathbb S^{d-1}$. There the von Mises-Fisher (vMF) distribution induces a natural noise process and admits a closed-form conditional score. The conditional velocity is in general intractable. Exploiting the radial symmetry of the vMF density we reduce the continuity equation on $\mathbb S^{d-1}$ to a scalar ODE in the cosine similarity, whose unique bounded solution determines the velocity. The marginal velocity and marginal score on $(\mathbb S^{d-1})^L$ both decompose into posterior-weighted tangent sums that differ only by per-token scalar weights. This gives access to both ODE and predictor-corrector (PC) sampling. The posterior is the only learned object, trained by a cross-entropy loss. Experiments compare the vMF path against geodesic and Euclidean alternatives. The combination of vMF and PC sampling significantly improves results on Sudoku and language modeling.

Energy-based models (EBMs) are flexible generative architectures inspired by statistical physics, but their learning and generative properties remain poorly understood. Here, we analyze a solvable EBM in the high-dimensional limit: the spherical Boltzmann machine (SBM). Combining tools from random matrix theory and dynamical mean-field theory, we: solve exact equations describing the training dynamics of the SBM; compute the Bayesian evidence, which acts as a partition function in parameter space and encodes global properties of the trained model; and uncover cascades of phase transitions that occur both during training and as a function of hyperparameters, related to successive alignment and condensation of the top modes of the coupling matrix to the data. We connect these transitions to sampling-time generative phenomena in a teacher-student scenario, including: sampling temperature tuning, double descent as a function of regularization strength, tempered posterior effects, and out-of-equilibrium effects during training that induce biases in the trained model. We provide numerical evidence demonstrating that all these phenomena appear in standard generative architectures, beyond the SBM.

Active view selection for 3D Gaussian Splatting (3DGS) requires information-theoretic grounding, computational efficiency, and strong reconstruction quality, yet existing methods satisfy these only partially. Information-theoretic approaches quantify gain over the full parameter space via costly Fisher computation under a Laplace approximation; faster alternatives sacrifice the grounding they started from. We propose SpheriBED, a Bayesian experimental design framework that defines the information objective over per-Gaussian spherical appearance—the quantity each observation fundamentally measures—rather than the full parameter vector, yielding exact closed-form posteriors without Laplace approximation. Scoring a candidate view requires only a single forward rasterization pass with no backward pass or per-candidate optimization. Extensive experiments across benchmarks demonstrate that SpheriBED consistently improves active reconstruction performance and provides more reliable uncertainty quantification compared to state-of-the-art methods.

Video anomaly detection (VAD) aims to automatically identify events that deviate from normal patterns in untrimmed surveillance videos. Existing methods universally depend on large-scale annotations or task-specific training procedures, severely limiting their rapid deployment to novel scenes. We observe that intermediate-layer features of pre-trained multimodal large language models (MLLMs) already encode rich anomaly semantics, yet existing approaches rely on the language output pathway and fail to exploit the geometric discriminability latent in these representations. Based on this finding, we propose SphereVAD, a fully training-free, zero-shot VAD framework that recasts anomaly discrimination as von Mises–Fisher (vMF) likelihood-ratio geodesic inference on the unit hypersphere, unleashing latent discriminability through principled geometric reasoning rather than learning new representations. Specifically, SphereVAD first applies Fréchet mean centering to unfold feature distributions and eliminate domain biases, then employs Holistic Scene Attention (HSA) to reinforce feature consistency using cross-video priors, and finally performs vMF-guided Spherical Geodesic Pulling (SGP) to align ambiguous segments with directional prototypes on the spherical manifold. This training-free pipeline requires only minimal synthetic images for calibration. SphereVAD establishes new state-of-the-art results among training-free approaches on three major benchmarks and remains competitive with fully supervised baselines. Code will be available upon acceptance.

Omnidirectional or 360° cameras provide embodied AI agents with a holistic, wide field-of-view (FoV) perception of the 3D structure of their surroundings. This capability has generated significant interest in applying Multi-modal Large Language Models (MLLMs) to omnidirectional spatial reasoning. However, most MLLMs are primarily trained on conventional 2D perspective images and therefore struggle with the severe distortions and wrap-around discontinuities introduced by spherical geometry. As a result, enabling MLLMs to generalize effectively to non-Euclidean 3D spaces without retraining remains an open challenge. In this paper, we propose SphMind, a novel, training-free, and plug-and-play framework designed to bridge this gap. The central idea of SphMind is to decouple semantic perception from geometric reasoning. Instead of requiring MLLMs to learn complex spherical geometric principles internally, the framework preserves their strong semantic understanding while handling geometric reasoning externally. To achieve this, we introduce a Spherical Harmonics-based Spatial Graph (SHSG) that models spatial relationships using equivariant transformations on the sphere. We further integrate this with Inference-Time Geometric Grounding (IGG), a model-agnostic closed-loop optimization process that aligns the internal representations of MLLMs with spherical geometric constraints during inference. Extensive experiments on three benchmark datasets demonstrate the effectiveness of SphMind. Without any additional training, the framework achieves more than 21.4% average improvement in directional reasoning on MP3D and Stanford2D-3D, outperforms all prompt-engineering baselines by 8.7% on the real-world ODI-Bench dataset, and achieves 5.9× higher rotational invariance under panorama rotations compared to existing baselines, all without dataset-specific tuning. We also validate the framework on real-world in-the-wild captures, where SphMind successfully resolves directional reasoning queries that baseline vision-language models fail to answer correctly.

Multi-agent orchestration over large language models is increasingly used to improve reasoning accuracy, especially on problems that benefit from critique, verification, specialization, or consensus, but these gains often come with inference costs that are not explicitly controlled. Many systems spend similar compute on straightforward queries and genuinely ambiguous or expert-level ones. Existing methods typically follow fixed collaboration protocols, compile task-level workflows offline, route queries among models or collaboration modes, or use learned controllers whose state does not explicitly identify which reasoning defects remain unresolved. As a result, they do not directly decide during inference whether the current multi-agent trajectory still justifies another agent call. We introduce Defect-Aware Residual Coverage (DARC), a training-free inference-time controller for adaptive multi-agent reasoning that represents the current trajectory using four residual defect dimensions: answer uncertainty, claim-level contradiction, verification failure, and aspect under-coverage. DARC selects the next agent by maximizing cost-normalized submodular marginal coverage over the remaining residuals. Each candidate agent receives a role-induced capability profile from frozen embeddings of its natural-language role description, enabling DARC to operate without task-specific supervision, learned routing parameters, or fine-tuning. Across five reasoning and multimodal suites, MMLU-Pro, GPQA-Diamond, LiveBench, MMMU-Pro, and HLE, DARC achieves the strongest accuracy under the main shared heterogeneous six-agent pool while using substantially fewer tokens and agent calls than recent workflow, adaptive orchestration, and cost-aware routing baselines, with additional large-pool experiments showing consistent scaling behavior. It also outperforms the single-call frontier reasoning models tested, showing that residual-aware selective invocation can improve both answer quality and inference efficiency.

While the Log-Sum-Exp (LSE) function is a foundational numerical primitive for stable log-domain computation in modern machine learning, its fast and efficient computation on generic platforms (\ie, CPUs and GPUs) has been underexplored, potentially creating execution bottlenecks for large workloads. On GPU, the de facto implementation torch.logsumexp dispatches a chain of nine sub-kernels per call, incurring redundant High Bandwidth Memory (HBM) traffic and launch overhead. On CPU, exp and log operations are hardware-hungry, limiting the computing efficiency. To address these bottlenecks, we propose two drop-in kernels to significantly accelerate LSE computation on both GPUs and CPUs: FuseLSE, a single-pass fused kernel that evaluates LSE's exp and log functions on the GPU's Special Function Unit (SFU); and IntLSE, which further replaces the LSE computation with approximated integer arithmetic for CPU computation. Our experiments, validated by benchmarks across workload sizes and hardware platforms, show that both kernels are ${\sim}10-11\times$ faster than PyTorch at small workloads and ${\sim}6\times$ faster on larger ones on GPUs, where the speed-up is attributed to reduced launch overhead and HBM traffic. On CPU, where no SFU is available, LSE computation dominates the cost, allowing IntLSE to gain ${\sim}3-6\times$ execution speed over FuseLSE.

We consider the scheduling problem of online speed scaling where the goal is to minimize the energy consumption of a machine that controls the speed at which jobs are processed. Recent work has leveraged the learning-augmented framework, where the algorithm is provided with predictions about jobs that will arrive in the future, to manage power usage more efficiently. This paper proposes a novel prediction model for speed scaling where the predictions are about the machine speed (the output), instead of the jobs (the input). Machine speed predictions have multiple advantages: they are succinct, admit strong PAC-learnability guarantees, can be provided dynamically, and lead to a natural definition of smoothness. We give an algorithm for dynamic machine speed predictions that is $(1+\epsilon)$-consistent and $O(1)$-robust. For offline machine speed predictions and job speed predictions, we provide an algorithm that achieves the stronger guarantee of $(1+\epsilon)$-smoothness, while maintaining $O(1)$-robustness. These guarantees are comparable to previous work, but do not require predicting the entire input.

Speech tokenizers serve as the critical interface between continuous speech signals and discrete representations, and are widely used in speech systems, e.g., Automatic Speech Recognition (ASR) and Large Audio-Language Models (LALMs). In this paper, we find that speech tokenizers are fragile at the semantic level: slight perturbations can disrupt their semantic encoding process and consequently cause severe degradation in downstream ASR and LALM performance. To study this vulnerability, we propose T-SemAttack, a transferable semantic attack based on a semantic encoder ensemble. By jointly perturbing the representation spaces of multiple semantic encoders, T-SemAttack effectively disrupts the semantic content of speech while preserving perceptual quality, thereby inducing error tokens. We further analyze token fragility and layer-wise representation drift in relation to cross-model transferability and disruption of LALM attention. Our analysis reveals a progressive amplification chain in which small waveform perturbations are magnified through the tokenizer pipeline and lead to semantic collapse, e.g., the attack causes a 99.7% token change in the S3 tokenizer. Building on these findings, we introduce ROSETok, a robust speech tokenizer that combines noisy training with robust semantic distillation to improve reconstruction fidelity and downstream-task robustness. Extensive experiments on multiple datasets, 9 open-source and black-box ASR systems, and 4 LALMs demonstrate that T-SemAttack achieves strong transferable attack performance, while the proposed Robust Speech Tokenizer exhibits robustness under high-fidelity reconstruction and downstream tasks. Our code and demo are available at https://t-semattack.github.io.

Many practical deployments require language models to internalize knowledge that was absent from pretraining, such as proprietary corpora, continually updated facts, or specialized domain skills. Self-distillation has emerged as a natural approach for this task. A model conditioned on the source material acts as the teacher, and an unconditioned student is trained to reproduce the teacher's behavior closed-book. We propose Speculative Self-Distillation (SSD), an efficient per-token mixed-policy method for self-distillation. The student generates each rollout by default, while the teacher intervenes only at positions where its next-token distribution substantially diverges from the student's. This design keeps training rollouts close to the student's test-time behavior, unlike off-policy distillation with teacher-generated prefixes, but avoids spending teacher compute on the many low-signal tokens encountered by fully on-policy methods. In effect, SSD concentrates supervision precisely where privileged context changes the prediction. On three closed-book QA benchmarks measuring fact recall, knowledge updates, and skill acquisition, SSD matches on-policy distillation with 45\% fewer supervised tokens on average and consistently outperforms off-policy distillation on the held-out test set. SSD therefore establishes a new accuracy-efficiency Pareto frontier for self-distillation. Code is available at https://anonymous.4open.science/r/Speculative-Self-Distillation/.

We prove that the speedup of exact speculative decoding is governed by a per-token rate, not by the path-level prefix-overlap capacity that folklore suggests, and that the gap between the two can be unbounded. The two candidates are the per-token Leviathan rate σ_L(P, Q) and the path-level coupling capacity Ω_L(P, Q) = Σ_{t=1}^L ov(P_{≤t}, Q_{≤t}). We show σ_L ≤ Ω_L always, the inequality is strict already on i.i.d. products, and there exists an explicit fixed-precision binary trajectory-exact rejection-style protocol on L = 2 with E[τ] = 19/16 > 9/8 = σ_L, ruling out σ_L as a universal upper bound across all trajectory-exact rounds. Inside the deployable admissible Leviathan-style clipped-ratio class, σ_L is the tight rate, and the speculation decoding complexity satisfies SDC_{B,L}^Q(M; u, T) = Θ(T / (σ_{L,B}(P_M^{u,T}, Q) + 1)), achieved by iterated Leviathan and matched up to constants by Wald. On the path side, Ω_L exhibits a sharp two-sided dichotomy in the prefix-KL profile η_t: Σ_t √(η_t / 2) = o(L) forces Ω_L = L − o(L), while geometrically bounded overlap o_t ≤ ρ^t forces Ω_L ≤ 1/(1−ρ) uniformly in L, with no unconditional KL converse. Two explicit fixed-precision Transformer families of size O(log L) realize the two extremes under the same three-element proposal family Q = {Q_flat, Q_const_0, Q_const_1}: a constant-readout family F⁺ has SDC ≤ T / ((1−ε)L + 1), while an alternating-parity family F⁻ has SDC ≥ T/5, an unconditional Θ(L) architecture separation. Speculative decoding is not path coupling; it is per-token clipped-ratio acceptance, and the architecture of the target controls the rate.

In neural inverse reconstruction, the forward model is typically treated as a fixed simulator that maps a neural scene representation to measurements. This work studies how the parameterization of the forward operator itself shapes the optimization landscape of coherent inverse problems. We introduce Moray, a reconstruction framework built on spectrally parameterized forward operators that bypass conventional time-domain synthesis and instead evaluate measurements directly through closed-form spectral kernels. This parameterization induces a substantially shorter and better-conditioned differentiable graph compared to the time-domain approaches. We further introduce Phase-Coherent Manifold Parameterization, a scene representation that jointly learns scene reflectivity and a deformable surface manifold, allowing the reconstruction to adapt to geometric deviations from the assumed imaging plane and thereby reducing common artifacts in coherent reconstruction. We instantiate these ideas for 77 GHz radar imaging using real measurements from a synthetic-aperture mmWave system. Across multiple challenging scenes, Moray consistently improves reconstruction fidelity, suppresses background artifacts, and degrades more gracefully under data limitations.More broadly, the results suggest that, in neural inverse problems, exposing appropriate analytical structure of the sensing physics to the optimizer can be as important as the choice of neural representation itself.