NeurIPS 2026 Papers

Robust deployment of clinical electroencephalography (EEG) models requires source-trained models to generalize to unseen hospitals without target-domain calibration, hospital identifiers, or clinical reports at inference. This is challenging because acquisition protocols, hardware, montages, and patient populations perturb low-level EEG statistics, while clinical interpretation is organized around higher-level semantic concepts. We argue that this abstraction mismatch limits conventional signal-level invariant learning: aligning feature distributions alone cannot distinguish clinically irrelevant variation from diagnostic content. We propose Text-Guided Invariant Learning (TG-IL), a deployment-oriented framework that uses clinician-authored reports only during training as semantic anchors for EEG representation learning. TG-IL combines stochastic recording-level aggregation, a variational semantic information bottleneck, and prototype-guided EEG--report alignment to preserve report-derived clinical semantics while mitigating site-specific nuisance variation. At inference, all text-side modules are discarded and the model operates solely on EEG. On a three-hospital clinical EEG benchmark, TG-IL improves zero-shot cross-hospital generalization and worst-case robustness while reducing hospital-specific information in learned representations.

Spatiotemporal point processes (STPPs) model event data in continuous time and space, with applications in mobility, epidemiology, and public safety. Recent neural STPPs span expressive intensities, density models, continuous-time latent dynamics, normalizing-flow spatial decoders, and score-based generative mechanisms. Yet comparison remains fragile because implementations differ in preprocessing, coordinate normalization, splits, likelihood conventions, and evaluation protocols. We present \textsc{Seahorse}, a unified framework for reproducible STPP experimentation. \textsc{Seahorse} provides an encode--evolve--decode interface and an executable benchmarking contract that standardizes dataset handling, configuration resolution, training, tuning, checkpointing, raw-coordinate likelihood reporting, and run artifacts across heterogeneous STPP families. It supports classical baselines, neural likelihood models, continuous-time Neural STPP variants, and sample-based generative models under one external protocol. Using HawkesNest as a controlled synthetic stress-test suite, we show how diagnostic benchmarking can probe neural STPP behavior under increasing spatiotemporal entanglement and other structural changes. Together, \textsc{Seahorse} and these controlled evaluations provide reusable infrastructure for comparing, diagnosing, and extending STPP models beyond implementation-specific leaderboards.

Progress in genomic language modeling depends on pretraining resources that are both diverse and comprehensive, as well as benchmarks that evaluate models across biological scales from genes to whole genomes. This need is especially acute in bacteria, which are extremely abundant, diverse, and central to human health, ecology, and biotechnology. Yet resources for large-scale pretraining and evaluation in bacterial genomics remain limited, with existing frameworks focusing largely on eukaryotic settings. Here, we introduce BacCorpus, a genome-level resource for training genomic LMs comprising 20B proteins, including 4.6B unique sequences, from 7M genomes spanning more than 150,000 species, and BacBench, a comprehensive benchmark spanning 10 datasets and 5 tasks across gene-, system-, and genome-level tasks. We evaluate a diverse suite of genomic LMs on BacBench and additionally develop BacLM, a 350M-parameter model trained jointly on protein sequences and intergenic DNA to further investigate how bacterial genomes are best represented. Across tasks, we find that performance depends strongly on biological scale: methods leveraging protein sequence are competitive on gene-level prediction, whereas models that incorporate wider genomic context excel at problems which involve complex multi-gene interactions. Together, BacCorpus and BacBench provide a practical foundation for training and evaluating robust, genome-aware genomic language models in bacteria at an unprecedented scale.

Generative molecular optimization aims to design molecules with properties surpassing those of existing compounds. However, evaluating candidate molecules is expensive, so sample efficiency, i.e., generating optimized molecules within a fixed evaluation budget, is essential. Moreover, in the offline setting, where a surrogate model approximates candidate evaluation, sample inefficiency compounds with surrogate error, further limiting performance. To address these challenges, we introduce Joint Self-Improvement (JSI), a framework for sample-efficient online and offline molecular optimization. JSI combines **(i) a joint generative-predictive model** trained via a joint likelihood-based loss, which aligns molecule generation and surrogate modeling, and **(ii) inference-time self-improvement**, which iteratively biases the generative component of the joint model toward higher-scoring molecules using direct objective evaluations in the online setting and the predictive component in the offline setting. Experiments across docking score optimization, in both online and offline settings, as well as antimicrobial peptide optimization, demonstrate that JSI outperforms state-of-the-art methods under limited evaluation budgets.

We study the problem of human gaze modeling, which aims to generate the gaze patterns a viewer produces while observing a visual stimulus. Gaze is primarily captured through two modalities: continuous eye-tracking trajectories, which describe fine-grained motion dynamics, and discrete scanpaths, which describe high-level fixation structure. Because gaze varies substantially across viewers and trials, we treat this variability as a defining property rather than noise and model gaze as a stochastic generative process. Existing generative gaze models supervise on only one of these two representations in isolation. We hypothesize that trajectories and scanpaths describe gaze at complementary scales and are jointly informative during training, and test this hypothesis through ST-DiffEye, a joint trajectory-scanpath diffusion framework that couples both modalities by concatenating them as an additional raw input channel, requiring no architectural overhead beyond an input and output channel expansion. We further introduce a principled evaluation framework based on the Continuous Ranked Probability Score (CRPS), which generalizes any existing sequence similarity metric into a proper scoring rule that jointly assesses the accuracy and diversity of generated gaze. Experiments on task-driven visual search, covering both target-present and target-absent scenarios, and on free-viewing benchmarks demonstrate state-of-the-art performance. These results, along with detailed ablations, confirm the benefit of joint modeling and the value of distribution-aware evaluation in capturing the intrinsic variability of human gaze.

Multi-Agent Systems (MAS) built on Large Language Models (LLMs) require effective orchestration to coordinate specialized agents, yet training such orchestrators is hindered by limited supervision and high computational cost. We propose **Orch**estration **R**eward **M**odeling (**Orch-RM**), a self-supervised framework for evaluating orchestration quality without human annotations. Orch-RM leverages intermediate artifacts from multi-agent executions to construct win-lose pairs for Bradley-Terry reward model training. Unlike existing MAS test-time scaling and orchestrator training frameworks that rely on costly sub-agent rollouts, Orch-RM operates directly at the orchestration level, enabling efficient and high-performing reward-guided orchestrator training and MAS test-time scaling. As shown in Figure 1, Orch-RM improves training efficiency by up to 10$\times$ in token usage while improving MAS test-time scaling performance by up to 8\% in accuracy. These gains consistently transfer across multiple domains, including mathematical reasoning, web-based question answering, and multi-hop reasoning, demonstrating orchestration-level reward modeling as a scalable direction for robust multi-agent orchestration. Data, code, and trained models will be released upon publication.

Many scientific instruments produce structured noise that contaminates the signal of interest and cannot be observed in isolation, ruling out standard supervised denoising. We propose BEAM (Background Estimation via Additive Modeling), a generative framework for modeling complex noise in these settings. As ground truth noise observations are unavailable, BEAM learns a generative model of the clean signal and uses it to score candidate noise estimates by how plausible the underlying signal looks. Score matching on signal data turns this into a tractable likelihood that we sample from with Langevin dynamics, which refines a coarse classical estimate of the noise into a more accurate one. We further show that when metadata governing the noise is known in advance, a separate conditional flow model predicts the noise from that metadata alone, enabling pre-observation forecasting of the noise. We instantiate BEAM on stray light in NASA's Transiting Exoplanet Survey Satellite (TESS), where contamination from the Earth and Moon obscures stellar signals. BEAM produces cleaner stray light estimates than the filtering tool used in current TESS pipelines and forecasts stray-light contamination across the observing window for the interstellar comet 3I/ATLAS using only spacecraft--Earth--Moon geometry, supporting prospective scheduling of time-critical observations and a path to recovering faint signals that current pipelines discard.

Tokenization that partitions time series into subsequences has become a foundational paradigm in modern time series modeling. However, we find that current tokenization strategies inherently introduce spectral distortions, forcing the learned representations to diverge from the true underlying patterns such as trend, periodicity, and seasonality, thereby impairing model performance. Through theoretical analysis from a frequency perspective, we derive the boundary conditions under which such distortions occur. To overcome this distortion, we propose **TimeFT**, a nearly distortion-free frequency-based tokenizer for time series data. TimeFT obtains tokens through frequency-domain partitioning, frequency shifting, and Nyquist sampling. The method is parameter-free, incurs negligible computational complexity, and can serve as a drop-in replacement for existing tokenizers. Our extensive experiments on diverse tasks such as forecasting, classification, and anomaly detection demonstrate that TimeFT consistently and significantly improves performance.

Recently, rubrics have been used to guide LLM judges in capturing nuanced, multi-dimensional human preferences, and have further been extended as reward signals for reinforcement fine-tuning (RFT). However, open-ended generation typically lacks a unique canonical target, and its evaluation rubrics are latent rather than directly observed. This makes rubric generation under-determined and difficult to control: rubrics often lack coverage, conflate dimensions, misalign preference direction, and contain highly correlated criteria, degrading judge accuracy and producing suboptimal rewards during RFT. We propose RRD, a practical framework for rubric refinement built on a recursive decompose–filter cycle. RRD decomposes coarse rubrics into fine-grained, discriminative criteria, expanding coverage while sharpening separation between responses. A complementary filtering mechanism removes misaligned and redundant rubrics, and a correlation-aware weighting scheme to prevent over-representing highly correlated criteria, yielding rubric sets that are informative, comprehensive, and non-redundant. Empirically, RRD delivers large, consistent gains across all evaluation and training: it improves preference-judgment accuracy on JudgeBench and PPE for both GPT-4o and Llama3.1-405B judges, achieving top performance in all settings with up to +17.7 points on JudgeBench. When used as the reward source for RFT on WildChat, it yields substantially stronger and more stable learning signals, boosting reward by up to 160% (Qwen3-4B) and 60% (Llama3.1-8B) versus ∼10--20% for prior rubric baselines, with gains that transfer to HealthBench-Hard and BiGGen Bench. Overall, RRD establish recursive rubric refinement as a scalable and interpretable foundation for LLM judging and reward modeling in open-ended domains.

Large language models (LLMs) can translate natural language into optimization code, but *silent failures* pose a critical risk: code that executes and returns solver-feasible solutions may encode semantically incorrect formulations—a feasibility–correctness gap reaching 90 percentage points on compositional problems. We introduce **ReLoop**, which addresses this gap through two complementary mechanisms. *Structured generation* decomposes code production into a four-stage reasoning chain (understand, formalize, synthesize, verify), preventing formulation errors at their source. *Behavioral verification* detects errors that survive generation by testing whether the formulation responds correctly to solver-based parameter perturbation—an external semantic signal that bypasses LLM self-review and requires no ground truth. The two mechanisms are complementary by error structure: structured generation drives the largest gains on compositional problems (**+8.5pp** accuracy on RetailOpt-190 with Claude Opus 4.6), while behavioral verification dominates on localized defects (**+4.4pp** on MAMO-ComplexLP, its largest contribution across benchmarks). Combined with diagnostic execution recovery, ReLoop reaches **100% executable code** on Claude Opus 4.6 and consistently improves accuracy on chat-tuned foundation models across three benchmarks; we further identify a known limitation of narrowly-tuned SFT models, whose learned output formats are brittle to chain-of-thought prompts—an interaction we document and analyze. We release **RetailOpt-190**, 190 compositional retail optimization scenarios targeting the multi-constraint interactions where LLMs most frequently fail. Code and benchmark will be released upon acceptance.

Long-context recall in linear-time sequence models highlights a tradeoff in how they write to memory. State-based linear models, such as state-space models (SSMs) and linear Transformers, write densely, updating the entire state for each newly arrived token, which leads to interference and makes specific past tokens hard to recover. Sliding-window attention (SWA) exhibits the opposite behavior: it writes sparsely by storing explicit token representations, but only within a fixed window, so recall drops once the relevant token is evicted. Interpolating between these models, we introduce Raven, a linear-time sequence model that maintains a fixed set of memory slots and, at each step, decays and updates only a selected subset via learned, input-dependent routing. This lets Raven mitigate SWA's position-based overwriting and hard eviction while reducing interference from dense state updates in SSMs, thereby preserving long-range content much more effectively. Across recall-intensive benchmarks, Raven is competitive with or outperforms prior linear-time baselines, achieving strong long-context recall where both SWA and SSMs sharply degrade. It remains effective when extrapolating to context lengths as large as 16x its training length, with similar gains in hybrid architectures.

Recent advances in sparse voxel-based VAEs have demonstrated remarkable capabilities in high-fidelity 3D autoencoding, yet their generative counterparts consistently lag behind their reconstruction performance. A fundamental cause of this gap lies in the \emph{brittle output representation} in sparse voxel decoders, which parameterize geometry through discrete topological decisions, such as intersection flags or occupancy signs. Small perturbations in these parameters, which are unavoidable under diffusion sampling, can be amplified into abrupt topological ruptures and grid-like artifacts. We argue that a generation-friendly representation should ensure that such perturbations induce only smooth geometric transitions. To this end, we propose Pygmalion, a generation-friendly autoencoding framework that reformulates sparse voxel decoding as a coupled SDF parameterization. To retain explicit mesh-level supervision within this parameterization, which is non-trivial as the SDF jointly governs both topology and geometry, we introduce hinge-based sign correction, case-aware geometry supervision, and rendering-based refinement, achieving high-fidelity reconstruction while preserving generative robustness. Built upon this generation-friendly representation, Pygmalion scales consistently across DiT model sizes and voxel resolutions. Extensive experiments demonstrate state-of-the-art reconstruction fidelity and high-quality 3D generation with complete surfaces, sharp features, and fine geometric details.

Estimating a secondary signal (e.g., behavior) from neural activity over time is central to both causal online decoding and non-causal offline inference in neuroscience, yet existing two-signal latent state-space models rarely support both. In this work, we provide an analytical extension of a linear method (PSID) beyond causal prediction to also support non-causal inference. We provide theoretical derivations extending PSID to enable optimal filtering and optimal smoothing of the secondary signal. We show that, in the PSID setting, the presence of a secondary signal increases identifiability. This allows us to uniquely learn the quantities needed for the optimal Kalman update via a reduced-rank regression step, yielding our first contribution, *PSID with filtering*. We next design a forward-backward construction for smoothing, yielding our second contribution, *PSID with smoothing*. In simulations, we validate that both PSID with filtering and smoothing reach ideal performance. In non-human primate motor cortex data, PSID with smoothing consistently improves over PSID with filtering, which improves over one-step-ahead prediction with standard PSID. Finally, we show that the connection between PSID and an existing nonlinear two-signal model, DPAD, extends naturally to the smoothing setting: applying our forward-backward formulation to DPAD yields *DPAD with smoothing*, which achieves competitive performance with leading methods on the Neural Latents Benchmark (NLB) in behavior decoding and held-out neural prediction across three datasets. Together, this work provides a theoretical foundation for prediction, filtering, and smoothing in the two-signal setting, spanning causal online decoding to offline inference, in both linear and nonlinear settings.

Predicting protein dynamics is a long-standing problem in computational structural biology. Often, protein function critically depends on local directed motions, such as hinge movements, catalytic loop rearrangements and domain reorientations, which can be characterized by directional flexibility and correlated structural motions of the protein backbone. While Molecular Dynamics (MD) simulations provide an established but often prohibitively expensive approach, recent deep generative models aim to reduce this cost by directly predicting conformational ensembles, emulating MD. However, due to their large size and the need to generate several states until the derived dynamical properties converge, these models remain expensive. In this work, we propose a fast SE(3)-equivariant graph neural network to directly predict dynamical descriptors, such as directional backbone flexibility and pairwise dynamic correlations, from an equilibrium structure. In a series of experiments, we show that our model matches the accuracy of substantially larger ensemble generation models while being orders of magnitude faster, and demonstrate that the proposed equivariant architecture is especially well-suited for capturing anisotropic motions in proteins.

Radio maps are essential for wireless decision-making tasks such as access-point placement, coverage planning, and localization, but their fine spatial details are governed by complex propagation effects and are costly to simulate accurately. Machine learning offers a path to high-fidelity radio-map prediction without running expensive high-fidelity simulations for every scene. However, generating high-quality training labels at scale is also difficult: the affordable labels come from finite-ray simulations, which are richer than low-fidelity inputs but carry residual Monte Carlo noise. We address this challenge with Physics-Unrolled Hybrid Neural Operator (PU-HNO), a three-stage cascade that predicts high-fidelity indoor radio maps from low-fidelity ray-tracing outputs and scene priors by progressively capturing reflection, diffraction, and scattering effects, rather than treating radio maps as generic images. We prove that, under conditionally unbiased label noise, the model can learn stable propagation structure and outperform its own training labels. Experiments across diverse floorplans show that PU-HNO outperforms image-to-image baselines, wireless learning models, and monolithic neural operators across both image-quality and wireless deployment metrics.

Generating dance that reflects many aspects of music remains a fundamental challenge in dance motion synthesis. Existing music-conditioned approaches formulate the task as beat-level matching, which captures local synchrony but misses the compositional structure of rhythm, where movement is organized into global beat units. We argue that musical dance arises from the alignment of \emph{periodicity} between motion and music, not from instantaneous beat coincidence. To address this, we propose \textbf{PhaseDance}, a phase-conditioned framework that represents dance as a quasi-musical signal on periodic phase manifolds, enabling unified dance generation across multiple tasks. Alongside this, we ground text descriptions in fifteen music-correlated metrics derived from Laban Movement Analysis to capture the qualitative richness of dance, articulating structured dimensions of motion — effort, shape, and dynamics — that coarse genre labels cannot convey. Experiments show that PhaseDance produces choreography with stronger rhythmic coherence and richer expressive understanding than existing models.

Full-space electron density generation is fundamental to modeling molecular quantum states, ground-state properties, and chemical interactions. DFT provides a principled route to electron density, but high-fidelity calculations require costly self-consistent iterations, limiting large-scale quantum-chemical modeling. Deep learning offers a promising surrogate, yet existing methods often rely on indirect density-related prior, including coordinate queries, predefined basis functions, orbital coefficients, or spherical atomic density grids. These prior introduce reconstruction overhead and can hinder continuous full-space modeling across sharp nuclear cores and sparse vacuum regions. Here, we introduce PI-EDG, a physics-informed voxel-space framework that maps molecular geometries directly to full-space electron-density fields by combining quantum-mechanical prior injection, hybrid FNO-CNN modeling of local and non-local interactions and magnitude-aware DS-LMoE routing for extreme density ranges. On EDBench, PI-EDG achieves an MAE of 3.405$\times$10$^{-3}$, a total electron-number MAE of 0.472, and an average inference time of 133.4 ms for one molecule. Compared with graph-based, basis-based, and voxel-based baselines, PI-EDG demonstrates better predictive accuracy, improved robustness across extreme density regimes, and better physical consistency while maintaining efficient full-space generation. These results suggest that PI-EDG offers a new, direct, and efficient route for accelerating DFT-based electron density calculations.

Existing code benchmarks measure whether an agent can produce any test that reproduces a known bug, or whether it can produce a patch that fixes a described issue. Neither isolates the distinct skill of property-based testing: deriving a semantic invariant from documentation, and then constructing an input-generation strategy precise enough to make a random search reveal the violation. We introduce PBT-Bench, a benchmark of 100 curated property-based testing problems across 40 real Python libraries. Each problem injects one or more semantic bugs (365 in total, mean 3.65 per problem) designed so that default-strategy random inputs almost never trigger them; the agent must read the library's documentation, identify the relevant invariant, and specify a Hypothesis @given strategy that concentrates mass in the trigger region. Bugs are stratified across three difficulty levels (L1–L3) spanning single-constraint boundary bugs to stateful, cross-function protocol violations. We evaluate eight contemporary LLMs under two prompting regimes (open-ended baseline vs. explicit Hypothesis scaffolding) for three independent runs per configuration. Bug recall under the PBT-guided prompt ranges from 42.1% to 83.4% across models; under the open-ended baseline, from 31.4% to 76.7%. Hypothesis scaffolding lifts mid-capability models by over 20 percentage points, but yields smaller gains for the strongest models, with two exceptions showing degradation, suggesting the structured prompt can interfere with certain model behaviours rather than complementing them. The hardest bugs prove model-specific: different architectures fail on different problems, leaving persistent gaps that no single model closes. We release the benchmark, harness, and full evaluation corpus to support downstream work on documentation-grounded semantic reasoning.

Real-world time series forecasting faces the fundamental challenge of non-stationary statistical properties, including shifts in mean and variance over time. While reversible instance normalization (RevIN) has shown promise by stationarizing inputs and denormalizing outputs, it relies on the strong assumption that historical and future distributions remain identical. We observe that in many practical applications, distribution shifts follow cyclical patterns that correlate with periodic positions (e.g., seasonal and holiday volatility). To this end, we propose $\textbf{PAMod}$, a lightweight yet powerful framework that models cyclical distribution shifts via $\textbf{P}$hase-$\textbf{A}$mplitude $\textbf{Mod}$ulation in the normalized feature space. PAMod learns periodic embeddings to modulate representations: phase modulation captures mean shifts, while amplitude modulation adapts to variance changes. Crucially, we prove mathematically that modulating in normalized space is equivalent to applying dynamic denormalization, offering an elegant unification of distribution adaptation and representation learning. Extensive experiments on twelve real-world benchmarks demonstrate that PAMod achieves state-of-the-art performance with fewer computational resources. Furthermore, our modulation mechanism, as a novel plug-and-play technique, can improve existing time-series forecasting methods with simple integration.