NeurIPS 2026 Papers

Conformal risk control is an emerging framework for the safe deployment of machine learning models with finite-sample guarantees. To accommodate a broader class of risk notions, quantile risk control extends this framework to quantile-based risk measures. However, existing methods either suffer from excessive conservatism or lack rigorous finite-sample guarantees. To address these limitations, we introduce Occupancy-based Quantile Risk Control (OQRC), a novel method that provides tight risk control bounds with finite-sample validity. Our key idea is to formulate risk control as a finite-occupancy problem by partitioning the loss space with the ordered calibration losses. Specifically, we estimate the distribution of test losses across the resulting bins and upper-bound the risk by the maximum loss attained within each bin. We then select the parameter $\lambda$ such that this upper bound does not exceed a predefined threshold $\alpha$ with high probability $1-\delta$. Theoretically, we establish a finite-sample guarantee showing that OQRC yields tight risk control bounds that converge to the optimal bounds at a provable rate of $\mathcal{O}_p(n^{-1/2})$. Extensive experiments demonstrate the effectiveness of our method, reducing the risk gap by up to 78.64\% on common benchmarks.

Occupancy world models use historical occupancy states to forecast future 3D scenes, but their robustness under corrupted temporal inputs remains poorly understood. Existing evaluations primarily emphasize clean forecasting accuracy and provide limited evidence about how errors enter, persist, and propagate through the occupancy perception-forecasting chain. This paper introduces OccStress, a robustness stress-testing benchmark for the occupancy forecasting chain. OccStress contains 61 corruption categories and 80k+ anchors. OccStress covers both 3D occupancy perception and 4D occupancy forecasting through two complementary tracks. This design separates realistic pipeline errors from the intrinsic sensitivity of 4D forecasting models to corrupted occupancy states. OccStress further defines temporal injection protocols to test whether errors in the current state, recent history, or earlier history affect future forecasts differently. OccStress provides aggregate metrics to evaluating robustness along occupancy forecasting chain. Experiments on OccStress reveal that current occupancy models are substantially affected by both upstream prediction errors and direct state corruptions, and that clean performance alone is an insufficient indicator of temporal robustness. Dataset: https://hf.co/datasets/OccStress/OccStress. Code: https://anonymous.4open.science/r/OccStress.

Transformers replace recurrence with a memory that grows with sequence length and self-attention that enables ad-hoc look ups over past tokens. Consequently, they lack an incentive to compress history into compact latent states with consistent transition rules. This often leads to learning solutions that generalize poorly. We introduce **Next-Latent Prediction** (NextLat), which extends standard next-token training with *self-supervised* predictions in the latent space. Specifically, NextLat trains a transformer to learn latent representations that are predictive of its next latent state given the next output token. Theoretically, we show that these latents provably converge to *belief states*, compressed information of the history necessary to predict the future. This simple auxiliary objective injects a recurrent inductive bias into transformers, while leaving their architecture, parallel training, and inference unchanged. NextLat effectively encourages the transformer to form compact internal world models with its own belief states and transition dynamics—a crucial property absent in standard next-token prediction transformers. Empirically, across benchmarks in world modeling, reasoning, planning, and language modeling, NextLat demonstrates significant gains over standard next-token training in downstream accuracy, representation compression, and lookahead planning. Furthermore, NextLat enables *variable-length self-speculative decoding*, accelerating inference by up to $3.3\times$ in the language domain. NextLat stands as a simple and efficient paradigm for shaping transformer representations toward stronger generalization.

Understanding relationships between variable sets is fundamental in machine learning, particularly in medical data analysis. Existing methods typically provide only coarse shared/private partitioning of variables, limiting their ability to reveal finer-grained predictive structures across views. We propose Mutual Predictability Decomposition (MPD), a framework that decomposes the variables in each view into three subsets: mutually predictable variables that can be inferred across views, auxiliary variables that are themselves not cross-view predictable but are necessary for predicting the mutually predictable subset, and unpredictable variables that do not contribute to cross-view predictability. MPD is formulated as a coupled bi-directional prediction problem with sparse variable assignment, and optimized through a differentiable relaxation implemented using gated neural networks. Experiments on synthetic and real-world medical datasets demonstrate that MPD recovers interpretable cross-view structures, improves cross-view prediction, and provides more informative decompositions than existing relationship analysis methods. Code will be available on GitHub upon acceptance.

Predicting how a person's first-person view will evolve (what action will follow, what plan completes a task, whether an in-progress shot will score) is fundamentally under-specified: the same context admits many plausible futures, and a model trained to minimize prediction error is forced to hedge or average across them, getting it wrong either way. Two findings shape our approach. First, the future camera trajectory, the path the head carves through space, lets the model commit to one of those futures: it carries the operator's intent in a form fine enough to determine how an action will unfold, substantially outperforming language as a conditioning signal. Second, this same intent makes the trajectory itself partially predictable from the context at hand, enough that trajectory need not be observed at test time to recover most of the gain. We instantiate these findings as TrajPilot, a model that predicts candidate future trajectories from egocentric context and uses them to pilot action prediction in an action-aligned embedding space where language shapes the structure but is never used as a conditioning input. TrajPilot beats VLM and structured-planner baselines on procedural planning across Ego-Exo4D atomic, Ego-Exo4D Keystep, Ego4D GoalStep, and EgoPER, with the trajectory advantage widening with horizon (exactly where prior planners collapse) and holding under RGB-only camera-pose estimation. With the goal masked at inference, the same model performs goal-free anticipation, beating VLM baselines on Ego-Exo4D atomic and extending to EPIC-Kitchens-100 and basketball shot-outcome prediction.

Predicting tandem mass spectra (MS/MS) from molecular structures represents a central task in analytical chemistry with direct relevance to clinical metabolomics, systems biology, and adjacent disciplines. In this work, we revisit the problem through the lens of object detection on molecular graphs. Molecular fragmentation, a central step in MS/MS prediction, can be approximated as detecting a set of subgraphs (i.e., fragments) and their associated spectral contributions. Existing fragment-based models follow a two-stage paradigm—first generating candidate fragments and then scoring them—analogous to two-stage R-CNNs in computer vision. Towards higher accuracy and faster inference, we introduce GLACIER, a single-stage transformer-based fragment detection neural network for molecular graphs. This unified formulation eliminates the need for candidate enumeration, enabling scalable and globally consistent modeling of molecular fragmentation. GLACIER is faster and more accurate than existing state-of-the-art methods by a significant margin, achieving 62.4% and 62.1% Top-1 retrieval accuracy with and without contrastive finetuning on the MassSpecGym dataset (from 55.3%), and 55.2% and 38.2%, respectively, on the NIST'20 dataset (from 33.5%). Furthermore, GLACIER provides nearly 3-fold inference speedup over existing two-stage models.

Neuroscientists have recently turned to intracranial brain recording methods, like electrocorticography (ECoG), for human experiments because of the fine spatial and temporal resolution that they afford. Models trained on this data, however, are fundamentally restricted by the patient populations that can receive the implants necessary for recording. We propose using non-invasive fMRI to bridge the gap in training data. Using spoken language representations fine-tuned on fMRI, we build encoding models of ECoG. These representations showed improved prediction performance in ECoG, even though the temporal resolution of fMRI is two orders of magnitude worse. Prediction improved in frequency bands well beyond what is directly measured in fMRI. Next, to test the procedure's generalization ability, we fine-tuned models on fMRI responses that were temporally downsampled by a factor of 2. Despite the loss in resolution, these models were able to predict fMRI and ECoG responses at levels comparable to the original fMRI-tuned models. Finally, we showed that ECoG performance steadily scales with the amount of fMRI-tuning data. Our results show that "slow" data like fMRI can be a valuable resource for building better models of "fast" brain data like ECoG. In the future, integrating across multiple recording methods may further improve performance in other applications, like decoding.

Conformal Prediction (CP) is a widely used technique for quantifying uncertainty in machine learning models. In its standard form, CP offers probabilistic guarantees on the coverage of the true label, but it is agnostic to sensitive attributes in the dataset. Several recent works have sought to incorporate fairness into CP by ensuring conditional coverage guarantees across different subgroups. One such method is Conformal Fairness (CF). In this work, we extend the CF framework to the Federated Learning setting and discuss how we can audit a federated model for fairness by analyzing the fairness-related gaps for different demographic groups. We empirically validate our framework by conducting experiments on several datasets spanning multiple domains, fully leveraging the exchangeability assumption.

3D semantic occupancy prediction provides dense geometric and semantic scene representations for autonomous driving, where both high accuracy and low response latency are crucial for safe downstream forecasting and planning. Existing methods usually exhibit different accuracy-latency characteristics. Performance-oriented models provide stronger geometric and semantic predictions, while deployment-friendly models provide faster responses. This motivates EvoOcc, a fast-slow evolutionary occupancy prediction framework that jointly exploits their complementary strengths to improve the accuracy-latency balance without changing the upstream architectures. EvoOcc models evolutionary occupancy prediction as a continuous time controlled dynamical system, enabling flexible adaptation to irregular prediction arrival of both fast and slow system. The framework further decomposes the evolution process into dual state evolution where ego state evolution for the deterministic coordinate shifts and scene state evolution for the genuine geometric and semantic scene changes. Extensive experiments on three different fast-slow system pairs and irregular prediction arrivals show that EvoOcc consistently improves over fast system baselines while maintaining much lower latency than the slow system, indicating a favorable accuracy-latency balance and stable behavior under practical inference situations.

Organic crystal structure prediction (CSP) is a requirement for computational modelling of organic solids. Traditional CSP is accurate but relies on exhaustive search, costing several CPU-years per molecule. Generative models such as OXtal dramatically reduce this cost by sampling stable organic crystal structures directly. However, OXtal forgoes explicit lattice parametrization in favour of modelling large crops of the bulk material with expensive triangle layers, which can incur a computational cost of minutes per molecule. In this paper, we reduce this to seconds with Clari, a large-scale flow matching model that generates redundancy-free unit cells and replaces triangle layers with pure pair-bias attention. Clari requires only atom types and bonds as input and does not need an RDKit-sanitizable input molecule, which expands its applicability to challenging chemistries such as fullerenes, metal complexes, and atom clusters. We further ablate key design choices such as auxiliary losses, timestep distributions, noise priors, and self-conditioning. Because Clari also models explicit hydrogens, it supports inference-time scaling via direct energy ranking, without any decoration or relaxation step. On OXtal's aggregated test set, we generate 1000 crystals and select the best 30 ranked by energy, surpassing OXtal's solve rate while obtaining a speedup of $5$-$8\times$. We also introduce a new test split of diverse and complex molecules for future benchmarking. Our contributions enable CSP within seconds, making large-scale virtual screening of organic solids practical.

In high-stakes risk prediction, interval-valued predictions provide a natural way to represent predictive uncertainty. However, standard evaluation tools such as the receiver operating characteristic (ROC) curve and the area under the curve (AUC) are designed for point-valued predictions and do not capture how uncertainty affects discrimination performance. To address this gap, we develop an uncertainty-aware ROC framework and corresponding interval-based AUC (iAUC) metrics for evaluating the performance of interval-valued risk predictions. The framework constructs two ROC-style curves with associated areas $\mathrm{AUC}_L$ and $\mathrm{AUC}_U$, and shows that these quantities induce a three-region decomposition of positive-negative rankings into confidently correct, ambiguous, and confidently incorrect orderings. Under valid class-conditional coverage, $\mathrm{AUC}_L$ and $\mathrm{AUC}_U$, together with the pairwise miscoverage rate, provide theoretical bounds on the optimal AUC, linking interval coverage to achievable discrimination. The framework is compatible with a range of methods for quantifying epistemic uncertainty in risk prediction, including Bayesian, bootstrap, and ensemble-based procedures, and provides a practical way to compare predictive models and interval-generation methods. Experiments on clinical benchmark data and MIMIC-IV mortality prediction show that models with similar point AUC can have different uncertainty-aware ranking profiles, and that the proposed decomposition can support risk-aware model selection and threshold-based abstention.

Adaptive systems often need to make task-specific decisions about a person from limited prior evidence: a tutor may need to anticipate how a learner will approach a new problem, a game may need to adapt when a player enters a new level, and a human-AI system may need to infer whether a partner will persist with a plan or switch goals. These decisions depend on person-level behavioral tendencies that shape how people solve related tasks, but such tendencies are difficult to infer from standard behavioral evidence. One approach is to use aggregate outcome summaries, such as scores, completion rates, or productivity measures; these summaries are compact and available across tasks, but can collapse distinct behavioral processes into similar outcomes. Another approach is to use process-level traces, which record how behavior unfolds over time; however, process modeling within a single task can entangle stable person-level tendencies with task-specific layout, timing, and affordances. In this work, we study early cross-task behavioral inference: whether partial process traces from multiple source tasks can reveal transferable person-level behavioral structure that predicts strategy in a held-out target task. We introduce Process-Level Latent Variable Models (PLVM), which encode task-specific traces and fuse them into a shared person-level latent representation for cross-task prediction. In PowerWash Simulator, a naturalistic telemetry dataset of real human gameplay, PLVM uses partial traces from two earlier cleaning tasks to predict whether a player will exhibit locally persistent Zone Planner behavior or frequent Zone Hopper behavior in the held-out Fire Station level. Controlled simulations with known latent behavioral types show that cross-task fusion helps when different source tasks reveal complementary dimensions of a shared latent behavioral process. These results suggest that process-level cross-task modeling can support early prediction of target-task behavioral strategy when waiting to observe sufficient target-task behavior is impractical.

Generative models have made rapid progress in ordered crystal structure prediction, yet many functional materials are intrinsically disordered, with substitutional mixing, vacancies, or interstitial species controlling their properties. Existing crystal generators either assume deterministic site occupations or require site-level disorder annotations, which are often unavailable when the chemical formula is the primary input. We formulate disordered crystal structure prediction through an Occupancy Distribution Matrix (ODM), a continuous site-by-species representation that unifies ordered crystals, solid solutions, vacancy disorder, and interstitial occupancy. A valid ODM must satisfy coupled site-wise occupancy, mass-conservation, and non-negativity constraints, placing each sample on a formula-dependent transportation polytope. We propose Entropic Polytope Flow (EP-Flow), a marginal-constrained flow matching framework that canonicalizes heterogeneous polytopes into a shared double-centered space, learns a marginal-preserving flow, and recovers feasible occupancies through a Sinkhorn inverse map. By jointly generating occupancies, fractional coordinates, and lattice parameters, EP-Flow achieves state-of-the-art performance on formula-conditioned disordered CSP benchmarks derived from COD and MPDS, substantially outperforming adapted ordered-crystal generators. Analyses further show that EP-Flow recovers sparse and chemically meaningful local disorder patterns rather than merely matching global composition statistics.

In this work, we consider decision-focused learning (DFL) for a Markov decision process (MDP), where existing methods differentiate through the KKT conditions of the Bellman equation and require solving a linear system over all state-action pairs, limiting its scalability. We address this by reformulating the MDP as an occupancy measure-based linear program (LP), whose feasible region is induced by predicted dynamics, and we derive a closed-form gradient by identifying the active constraints in the feasible polyhedron via the pivoting algorithm. This occupancy measure-based LP layer raises two challenges: (1) LP's solution gradient is discontinuous when active constraints change, and (2) the LP backward cost still scales with the state size, which is costly for large or continuous state spaces. We address the challenges with an augmented Lagrangian surrogate and smooth the boundary jumps by random row sketching of the constraints, and a learnable soft state-aggregation layer and its function-approximation generalization that scales the LP to large finite and continuous-state MDPs. Across multiple tasks, our methods reach lower regret than KKT-based DFL and two-stage baselines with significantly lower computation cost. The source code for all experiments is available [here](https://anonymous.4open.science/r/State_Aggregation_Project-3E64/).

Traffic flow prediction is a fundamental task in intelligent transportation systems. Existing methods primarily rely on temporal dependency modeling and thus face two critical limitations: struggling to effectively capture diverse periodicity; overlooking the relationship between spectral structures in the traffic flow and spatiotemporal traffic behaviors. To address these limitations, we propose DSSNet (Deep Spectral Structure Profiling Network), an adaptive spectral profiling framework. DSSNet follows the paradigm of "spectral structure profiling" and then "dual-domain modeling". Initially, a Spectral Structure Learning module captures the dominant frequency bands of the spectrum to enhance regional representations while simultaneously serving as discriminative spatiotemporal identity between regions. Based on these identities, we construct a global Resonance Graph to uncover cross-region dependencies and introduce a Dual-Domain Knowledge Integration module to aggregate structural knowledge across both time and frequency domains. Extensive experiments on multiple benchmark datasets demonstrate that DSSNet achieves state-of-the-art performance, while highlighting the potential of frequency domain modeling for understanding spatiotemporal traffic behaviors.

Predicting gene expression under perturbations from single-cell data is a fundamental task for modeling cellular responses, with applications in discovering gene function and designing therapeutics. However, existing methods often struggle to generalize across datasets due to differences in experimental protocols, biological contexts, and species. We present \textbf{CytoWave}, a perturbation-centric pretraining framework for single-cell genetic perturbation response prediction. We leverage large-scale cross-species perturbation datasets unified via orthology and train CytoWave in a two-stage manner. In the first stage, we learn reference control cell states via masked gene expression recovery on unperturbed cells. In the second stage, we pretrain the model to predict perturbed gene expression given control states and perturbation signals, together with a contrastive objective that aligns latent representations of perturbation responses with perturbation signals. We perform strict dataset-held-out evaluation across multiple condition combinations and case studies of pathway activity changes, demonstrating consistent and effective performance of CytoWave in predicting single-cell genetic perturbation responses.

Conformal Prediction (CP) provides a model-agnostic framework for constructing finite-sample prediction sets with coverage guarantees and has become a tool for reliable decision-making in high-risk settings. Recent advances have extended Split Conformal Prediction to increasingly complex learning problems, including multilabel, high-dimensional, and functional outputs. In this work, we study CP for Distribution-to-Distribution Regression, a setting of growing interest across many application domains. We show that a naive application of Split CP yields prediction sets that, while marginally valid, are non-interpretable, e.g., lacking a well-defined volume or a tractable sampling procedure, and of limited practical value. To overcome this, we propose a new conformal framework that operates in an orthogonal basis space, producing interpretable prediction sets while preserving coverage guarantees. We further develop an adaptive variant with asymptotic conditional coverage under mild assumptions. Empirical results on synthetic and real-world datasets validate the effectiveness of the proposed approach.

Link prediction remains difficult in sparse graphs, where diffusion heuristics lack connectivity and geometric methods suffer spectral instability when coupling learned representations with diffusion operators. We introduce **Concorde**, an energy-based framework that resolves this by decomposing link likelihood into three structurally decoupled branches: **(i)** a parameter-free spectral anchor that establishes permutation-invariant topological stability; **(ii)** a mixed-curvature residual on a learnable $\mathbb{H}\_{c\_h}^{d\_h} \times \mathbb{S}\_{c\_s}^{d\_s}$ product manifold, regularized by a feature-aware Ollivier--Ricci curvature proxy; and **(iii)** a continuous vector field on $\mathcal{T}\mathbb{H}\_c^d$ for non-local structural alignment across topological gaps. The branches are fused via learned gating and trained with an $N$-pair ranking loss aligned with retrieval-style evaluation. Across five benchmarks spanning sparse, dense, featureless, and attributed regimes, **Concorde** establishes state-of-the-art early retrieval precision, nearly doubling $P@10\\%$ over leading baselines in sparse settings.

Modeling interventions that include timing is an important task in many domains, such as treatments in healthcare, transactions in finance, and others. However, estimating these interventional distributions is challenging because standard next-token predictors, typically implemented using transformers, do not naturally support altering the timing of specific future events. We develop \emph{Competing Event Models} (CEMs), an autoregressive generative approach that simplifies the estimation of interventions on both what interventions are applied and \emph{when}. Drawing from the competing risks literature, CEMs model independent probability distributions for latent times of each event type, where the earliest realized time determines the next event. We show that this framework enables straightforward interventions on event timing and efficient sampling, and, through careful design choices, can also model concurrent events. We evaluate CEMs on a cancer tumor volume simulator and on prediction tasks using the MIMIC-IV dataset. Our results show that CEMs accurately recover causal effects of sequential treatments in simulations while surpassing standard transformer baselines in predictive performance on 3 out of 4 real-world medical data tasks.