NeurIPS 2026 Papers

Biologically plausible learning requires temporal credit assignment without backpropagation through time (BPTT). Hamiltonian Echo Learning (HEL) achieves this by running a neural system twice --- an \emph{inference} phase then a time-reversed \emph{echo} phase --- recovering exact BPTT gradients. We identify spatio-temporal sum-separability of the Hamiltonian as the structural condition making HEL's learning rule both spatially and temporally local, and exploit it to extend HEL beyond diagonal recurrences: a Hopfield-inspired oscillatory RNN with dense recurrent connectivity yields a contrastive Hebbian rule with constant memory and only two forward passes. This rule matches full BPTT and outperforms e-prop and truncated BPTT on time-series classification and regression. We then relax HEL's Hamiltonian-reversibility constraint --- which forces unbiological connectivity and activation patterns --- and derive \emph{Echo Learning} (EL), exact for arbitrary smooth dynamics whenever the network is reversible and self-adjoint with respect to a readout involution. Both conditions are enforced by a spatio-temporally local homeostatic loss, recovering near-BPTT gradient quality without architectural constraints.

Aging cohorts increasingly combine imaging, plasma, and cognitive measurements, but most subjects provide only a partially observed cross-sectional snapshot, leaving latent stage, progression subtype, and stage-varying multimodal coordination to be inferred jointly. Existing methods estimate the cohort-level aging process, model covariance under observed covariates, or fuse modalities for prediction. However, they rarely target the joint problem of endpoint-anchored staging and progression-resolved residual coordination across modalities, even though subject staging, subtype assignment, and multimodal interplay are mutually dependent. We introduce ELPAC, a Bayesian framework with two interlocking constructions. The first integrates subtype trajectories backward from clinically reliable endpoint groups along biologically structured velocity components encoding modality-specific monotone direction and optional sequential ordering priors. The second factorizes residual multimodal variation around the inferred stage axis into sparse coordination axes whose loadings and activations expose which multimodal patterns are operative in each subtype and when they emerge, with stage-resolved variation supplying the auxiliary structure for conditional identifiability. We use a scalable amortized variational inference scheme that enables joint posterior inference at cohort scale with single-pass deployment on new subjects. On synthetic data and a harmonized ADNI+NACC/SCAN Alzheimer's cohort, ELPAC demonstrates accurate latent-structure recovery, well-calibrated prediction intervals, and biologically interpretable multimodal coordination programs that differ by subtype.

Foundation models for EEG analysis are still in their infancy, limited by two key challenges: (1) variability across datasets caused by differences in recording devices and electrode configurations, and (2) the low signal-to-noise ratio (SNR) of EEG, where neural activity is often buried under artifacts and non-brain sources. To address these challenges, we present \textit{EEG-X}, a device-agnostic and noise-robust foundation model for EEG representation learning. EEG-X introduces a location-based channel embedding that enables robust transfer across heterogeneous EEG devices and electrode layouts. To improve robustness against noise, EEG-X employs a novel noise-aware masking-reconstruction strategy that reconstructs artifact-cleaned signals rather than raw noisy EEG and introduces a novel Dictionary-based Convolutional Transformation (DiCT) that improves reconstruction-based pretraining by comparing signals in a structured feature space instead of directly in the raw signal space. Experiments across datasets collected from diverse EEG devices show that EEG-X consistently outperforms state-of-the-art methods across multiple downstream tasks and demonstrates strong cross-domain generalization when pretraining and downstream datasets differ in electrode layouts and recording configurations paving the way towards foundation models. The models and code are available at :https://anonymous.4open.science/r/EEG-X-CEDE/README.md

Vision-language models (VLMs) have made steady progress as in-vehicle driving assistants, yet existing evaluations largely remain offline, focusing on perception and short-term reasoning over pre-recorded driving scenes. A complementary capability is still underexplored: whether a model can continuously monitor a causal driving video stream and issue a calibrated alert when a user-specified condition is met. Existing driving benchmarks mainly assess reactive understanding, while recent streaming video benchmarks are developed largely outside the driving domain. To this end, we introduce DriveStreamBench, a driving and streaming video benchmark that organizes this capability spectrum into four cognitive levels constructed from a shared pool of driving events, to support level-wise capability profiling and gated evaluation. We evaluate various vision-language models along with DriveStream-SFT, a supervised reference baseline trained on the benchmark's training split, under a unified streaming protocol. DriveStreamBench tests a basic but necessary capability, and all evaluated models fall short of it; current vision-language models are not yet ready for user-conditioned alerting in driving. Resources are available at https://anonymous.4open.science/r/DriveStreamBench/ and https://dataverse.harvard.edu/previewurl.xhtml?token=01f6bc4c-d809-4e40-b025-b6d783c86571.

Recent speech brain-computer interfaces (BCIs) increasingly rely on large models and complex multi-stage pipelines for neural decoding. However, in speech BCI, where training data are scarce, noisy, and expensive to collect, the benefits of model scaling remain unclear. This work revisits a fundamental question: $\textbf{are large models truly necessary for accurate neural decoding?}$ We propose $\textbf{\textit{BEST}}$ ($\textbf{\textit{B}}$rain-$\textbf{\textit{E}}$ncoding-$\textbf{\textit{S}}$peech-$\textbf{\textit{T}}$ext), a two-stage framework for efficient neural-to-text decoding. Instead of relying on large generative models or phoneme-centric pipelines, $\textbf{\textit{BEST}}$ introduces hierarchical intermediate representations and a compact speech-oriented decoder that directly maps neural activity to text. This design preserves essential structure while significantly reducing computational cost, enabling robust generalization in low-resource settings and practical deployment under constrained resources. On the Brain-to-Text ’24 and ’25 benchmarks, $\textbf{\textit{BEST}}$ achieves word error rates (WERs) of 6.19\% and 1.62\%, espectively, using only 3.7\% of the parameters required by comparable methods. Beyond these results, we provide three key insights: (1) decoder scaling yields non-monotonic gains; (2) cross-modal alignment is more critical than model size; and (3) overfitting degards sequence-level performance. Representation analysis further reveals a consistent neural→audio→text alignment pathway.

Automated semantic distance tests—which prompt a model to produce a set of words, and score the average embedding distance between them—are increasingly used to measure the "creativity" of large language models (LLMs). However, the validity of semantic distance tests as predictors of *machine* creativity has not yet been established, and these tests already have limited validity as predictors of human creativity. To address this problem, we conduct the first systematic study evaluating the effectiveness of semantic distance tests in predicting the creative achievement of LLMs across three constructs: creative writing, divergent thinking, and scientific ideation. We score each test on two criteria (*validity* and *specificity*), and derive a theoretical limit for the maximum attainable specificity and validity a test can achieve. We find that: **(1)** Test effectiveness varies significantly by construct, and no single test predicts all constructs well. **(2)** None of the tests is a good predictor of scientific ideation ability. **(3)** Existing tests are far below the theoretical limits, indicating meaningful room for the design of improved tests moving forward. Our findings provide clear practical takeaways and directions for future work and suggest that novel tests are needed to reliably predict scientific ideation ability.

Diversity maximization is a fundamental problem in combinatorial optimization with applications in fairness-aware machine learning, clustering, and data summarization, and has attracted significant attention in theoretical computer science. We study the Distant $k$-Subsets Problem (DKSP): given a metric space $(X,d)$ with $|X|=n$ and an integer $k$, find two disjoint subsets $S,T\subseteq X$, each of size $k$, maximizing $\min_{s\in S,\; t\in T} d(s,t)$. This problem arises as a key subroutine in composable coresets for diversity maximization, and improvements can yield better approximations for related objectives such as remote-matching and pseudoforest. It also appears in practice: a variation was featured in the ICPC World Finals 2024. Mahabadi and Narayanan gave a $(4/\epsilon)$-approximation for this subproblem when $n \ge 2k^{1+\epsilon}+k$. For any $\epsilon>0$, we present a deterministic combinatorial algorithm achieving a $\left(1+\left\lceil 1/\epsilon\right\rceil\right)$-approximation under the same condition. In particular, for $\epsilon=1$, this yields a $2$-approximation when $n \ge 2k^2+k$; the factor $2$ is optimal for general metrics unless $P=NP$. More generally, any polynomial-time $(2-\eta)$-approximation for constant $\eta>0$ would imply an algorithm for the $k$-Biclique problem, even though DKSP is polynomial-time solvable when $n=2k$. For $d$-dimensional Euclidean metrics, we obtain improved guarantees: a $(2+\sqrt{2})$-approximation when $n \ge 3\cdot 2^{d-1}k$, and a $(2+\sqrt{d}/c)$-approximation when $n \ge (2d+c^d)k$. In particular, for $d=2$ and $c=1$, this gives a $(2+\sqrt{2})$-approximation when $n\ge 5k$, yielding near-$2$ guarantees under linear constraints on $n$ for constant $d$.

Distributed online convex optimization (D-OCO) is a powerful paradigm for modeling distributed scenarios with streaming data. However, the communication cost between local learners and the central server is substantial in large-scale applications. To alleviate this bottleneck, we initiate the study of D-OCO with compressed communication. Firstly, to quantify the compression impact, we establish the $\Omega(\delta^{-1/2}\sqrt{T})$ and $\Omega(\delta^{-1}\log{T})$ lower bounds for convex and strongly convex loss functions, respectively, where $\delta \in (0,1]$ is the compression ratio. Secondly, we propose an optimal algorithm, which enjoys regret bounds of $O(\delta^{-1/2}\sqrt{T})$ and $O(\delta^{-1} \log T)$ for convex and strongly convex loss functions, respectively. Our method innovatively incorporates the error feedback mechanism into the Follow-the-Regularized-Leader framework to address the coupling between the compression error and the projection error. Furthermore, we employ the online compression strategy to mitigate the accumulated error arising from the bidirectional compression. Our online method has great generality, and can be extended to the offline stochastic setting via online-to-batch conversion. We establish convergence rates of $O(\delta^{-1/2}T^{-1/2})$ and $O(\delta^{-1} T^{-1})$ for convex and strongly convex loss functions, respectively, providing the first guarantees for distributed non-smooth optimization with compressed communication and domain constraints.

Reconstructing audio from brain activity remains challenging. A key limitation lies in current audio diffusion models, which are primarily designed for text-based conditioning and lack mechanisms to incorporate non-linguistic continuous signals such as neural responses. While recent work in the visual domain has explored direct conditioning on brain activity, this approach has not yet been extended to audio diffusion models. We introduce a framework that directly guides a latent audio diffusion model with fMRI signals, avoiding intermediate feature prediction for the conditioning stage. Central to our approach is a transformer-compatible adapter that enables the integration of non-linguistic representations into the cross-attention layers of diffusion transformers (DiTs). Although motivated by neural decoding, this adapter provides a general mechanism for conditioning audio diffusion models on arbitrary continuous inputs, not inherently limited to neural signals. We demonstrate consistent improvements over baselines, including two-stage fMRI-to-latent conditioning, across both acoustic and semantic evaluation metrics. In addition, we introduce a fidelity metric grounded in a computational model of auditory neural processing, which allows us to quantify the contribution of individual auditory cortical regions to reconstructed audio representations.

Many network design tasks must decide which directed arcs to build so that multiple sources can reach a root efficiently. Building fewer or cheaper arcs reduces construction cost, but may force long routing paths; building more arcs shortens routes, but increases construction cost. The Cost--Distance problem formalizes this trade-off, yet it has remained difficult to optimize with gradient-based methods because the selected network, the induced routes, and the routing cost are tightly coupled. We propose Cost Distance Policy Gradient (CDPG), a differentiable framework that treats local next-hop choices as a routing policy, prevents unstable cyclic routing through Dynamic Acyclic Dropout, evaluates the objective with a truncated value solver, and uses the induced acyclic support for quality-preserving TopoRounding. Under the stated conditions, CDPG has a fixed-accuracy \(\mathcal{O}(m\log n)\) efficiency guarantee. Experiments cover 1,487 instances in UAV logistics networks, transportation networks, and four synthetic graph families; across this suite, CDPG shows a strong time--quality trade-off compared with other differentiable baselines, Cost--Distance-specific algorithms, and the commercial solver. Our code is available at: https://anonymous.4open.science/r/cdpg_nips-28D5/.

Optimizing functionals over the space of probability measures is now ubiquitous in machine learning. A widely used approach is to perform the optimization directly over the Wasserstein space, but many objective functionals of practical interest are non-convex along Wasserstein geodesics, making the analysis of standard first-order methods challenging. In this work, we study a class of objectives over the Wasserstein space that admit a difference-of-convex (DC) decomposition and we lift the classical convex-concave procedure (CCCP) to this setting. Under smoothness and strong convexity assumptions on the convex components of the decomposition, we prove almost stationarity along the iterates of the resulting algorithm. Our main focus is on the Maximum Mean Discrepancy (MMD) and the Energy Distance (ED) functionals, for which we develop explicit Wasserstein DC decompositions, and establish local convergence of the scheme under mild assumptions. Empirically, we show that well-chosen DC decompositions yield faster and more stable convergence than Wasserstein gradient descent on these MMD objectives.

Compact medical multimodal models are the most realistic path to clinical deployment, yet they often produce fluent answers that are not actually grounded in the image. We show that this failure has a clear internal signature. In five compact medical backbones and across six medical VQA benchmarks, visual tokens lose their spatial diversity shortly after entering the language backbone, their residual updates are an order of magnitude smaller than text residuals, and attention on them freezes on content free regions of the image. Zeroing the visual residual stream barely changes the output, while zeroing the text residual stream destroys it. We give a short analytical account that links the collapse of visual similarity to the residual dominance ratio, and we propose VGrip, a set of three lightweight losses that act on the three failure points without modifying the backbone architecture or the inference procedure. We also introduce the Visual Dependency Score, a simple and benchmark agnostic measure of how much a model actually uses the image. On Qwen2.5-VL-7B, VGrip lifts accuracy by 4.7 points on average across the six benchmarks and more than doubles the Visual Dependency Score over a strong LoRA baseline, with gains that are stable from two to eight billion parameters.

Fine-scale-faithful neural simulation under fixed storage budgets remains challenging. Many existing methods reduce high-frequency error by improving architectures, training objectives, or rollout strategies. However, under budgeted coarsen-quantize-decode pipelines, fine detail can already be lost when the carried state is constructed. In the canonical periodic incompressible Navier--Stokes setting, we show that primitive and derived fields undergo systematically different retained-band distortions under the same operator. Motivated by this observation, we formulate **Derived-Field Optimization (DerivOpt)**, a general state-design framework that chooses which physical fields are carried and how storage budget is allocated across them under a calibrated channel model. Across the full time-dependent forward subset of PDEBench, DerivOpt not only improves pooled mean rollout nRMSE, but also delivers a decisive advantage in fine-scale fidelity over a broad set of strong baselines. More importantly, the gains are already visible at input time, before rollout learning begins. This indicates that the carried state is often the dominant bottleneck under tight storage budgets. These results suggest a broader conclusion: in budgeted neural simulation, carried-state design should be treated as a first-class design axis alongside architecture, loss, and rollout strategy.

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.

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/}.

Random sampling is a fundamental tool in modern machine learning and numerical linear algebra for reducing the computational cost of large-scale matrix problems. Existing analyses, however, rely primarily on subspace embedding guarantees, which do *not* precisely characterize the statistical bias of *nonlinear* random oblique projections induced by sampling, which arises ubiquitously in subsampled least squares and fast low-rank approximation methods. Because (pseudo)inversion is *nonlinear*, these random oblique projections can be systematically *biased* even when the underlying sketch is unbiased, thereby introducing hidden bias into downstream least squares and low-rank approximation solutions. In this work, we develop a unified non-asymptotic theory for random oblique projections in high dimensions. We show that standard random sampling schemes generally induce a systematic statistical *bias* overlooked by classical subspace embedding-style analyses, and we propose a principled debiasing framework to correct it. We illustrate the power of the theory through two canonical applications. For subsampled least squares, we obtain sharp bias--variance characterizations, reveal previously unrecognized statistical *suboptimality* in widely used sampling schemes, and identify when debiasing yields provable improvements. For fast CUR decomposition, we develop a debiased approach with improved approximation accuracy. Numerical experiments further validate our theoretical findings.

Large language models (LLMs) are increasingly deployed as autonomous agents in offensive cybersecurity. In this paper, we reveal an interesting phenomenon: different agents exhibit distinct attack patterns. Specifically, each agent exhibits an attack-selection bias, disproportionately concentrating its efforts on a narrow subset of attack families regardless of prompt variations. To systematically quantify this behavior, we introduce CyBiasBench, a comprehensive 630-session benchmark that evaluates five agents on three targets and four prompt conditions with ten attack families. We identify explicit bias across agents, with different dominant attack families and varying entropy levels in their attack-family allocation distributions. Such bias is better characterized as a trait of the agents, rather than a factor associated with the attack success rate. Furthermore, our experiments reveal a bias momentum effect, where agents resist explicit steering toward attack families that conflict with their bias. This forced distribution shift does not yield measurable improvements in attack performance. To ensure reproducibility and facilitate future research, we release an interactive result dashboard at \url{https://cy-bias-bench.vercel.app/} and a reproducibility artifact with aggregated session-level statistics and full evaluation scripts at \url{https://anonymous.4open.science/r/CyBiasBench-2026/}.

Long-context LLM inference is fundamentally a retrieval process, transitioning from diffuse to sharply focused attention at a few architecture-determined (CTLs). Existing acceleration methods leave significant costs unaddressed: sparse attention ignores FFNs, KV cache compression neglects prefill, and hidden-state pruning relies on fixed, heuristic rules. We introduce CutAttn, a training-free framework that exploits CTLs via inter-layer Jensen--Shannon divergence, pruning redundant context at chunk granularity while dynamically gated by a normalized-entropy criterion. Operating at the hidden-state level, CutAttn reduces both attention and FFN costs, composing orthogonally with existing acceleration techniques. Evaluated on RULER, InfiniteBench, and LongBench, CutAttn preserves full-attention accuracy while achieving up to a $2.02\times$ prefill speedup on 128K sequences (and $2.81\times$ when composed with sparse attention). Furthermore, our theoretical analysis provides approximation bounds, an entropy-compression limit justifying the gating, and a closed-form speedup ratio.

Deep learning has accelerated automated atomic model building from cryo-EM maps, yet progress remains limited by the lack of large, high-quality datasets and standardized evaluation benchmarks. We address this gap by introducing CryoAtlas, a rigorously curated dataset and unified benchmark for density-guided atomic model building. By integrating and filtering EMDB and PDB entries (up to March 2026), CryoAtlas provides 17,809 map--model--sequence entries, including a curated 16,657-entry training split. From this resource, we construct a 618-target benchmark with a shared evaluation protocol that jointly assesses structural accuracy, model-to-map fit, and stereochemical validity. Using this framework, we benchmark state-of-the-art learning-based methods against Phenix and AlphaFold~3, comprehensively analyzing the impact of map resolution, sequence length, protein category and sequence identity on modeling accuracy. As a case study, retraining the ModelAngelo CNN component on CryoAtlas improves performance on the benchmark. CryoAtlas provides an open, reproducible foundation for future cryo-EM atomic model building research.