NeurIPS 2026 Papers

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.

We present FM-ChangeNet, a pathwise-supervised framework for change detection that reformulates bi-temporal reasoning as continuous transport in feature space rather than static endpoint comparison. Given encoded pre and post-temporal representations, we construct intermediate latent states and learn a time-conditioned velocity field $\hat{v}_\theta(z_t,t)$ along the transformation trajectory. This pathwise formulation constrains the predictor over a continuum of intermediate states, providing a denser and less ambiguous supervision signal than conventional endpoint-only segmentation and enabling the model to capture temporal evolution explicitly. The learned velocity field is not only a transport mechanism but also an interpretable representation of change: its magnitude serves as a spatially localized change cue that helps distinguish true structural variation from nuisance effects such as illumination shifts and spatial misalignment. We develop a hierarchical multi-scale architecture with cross-temporal alignment, time-conditioned coarse-to-fine flow decoding, and a unified objective that couples flow supervision, trajectory consistency, spatial regularization, and segmentation loss. Experiments on remote sensing benchmarks show that the proposed framework produces more structured and robust change representations while achieving state-of-the-art performance.

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.

The expressive limitations of message-passing Graph Neural Networks (GNNs) have motivated a wide range of more powerful graph learning architectures. We advocate Deep Homomorphism Networks (DHNs) as a model particularly well-suited for learning over relational databases, due to their close connection to important fragments of SQL such as conjunctive queries. We study the precise expressive power of DHNs by relating them to various natural fragments and extensions of first-order logic (FO). For DHNs with max, sum, and mean aggregations, we establish connections to the unary negation fragment (UNFO) and to the extensions of UNFO with counting quantifiers and with ratio quantifiers. We further relate sum-aggregation DHNs to the unary quantifier alternation fragment of FO and to an extension of FO with expressive counting. Through the classical correspondence between FO and SQL, these results also illuminate the relation between DHNs and SQL. They also enable us to study the decidability of two fundamental static analysis problems for DHNs, the emptiness problem and the subsumption problem. Finally, we confirm through experiments that the established differences in expressive power are reflected in the performance on suitable prediction tasks.

Entropy minimization (EM) is the dominant objective for test-time adaptation, yet its failure mode, model collapse, remains poorly understood. In this work, we show that distribution shifts can cause feature clusters corresponding to distinct classes in the model’s representation space to merge, while the decision boundary remains fixed. This induces a systematic skew in the predicted class distribution, referred to as prediction bias. Prediction bias refers to a shift in the predicted class distribution, with some classes overrepresented and others suppressed. We show that entropy minimization amplifies this prediction bias by tightening the existing clusters, reinforcing the incorrect groupings until all predictions collapse to a trivial solution. Next, to demonstrate the significance of prediction bias and mitigate it, we further propose Distribution Shift Bias Reduction (DSBR), a bias-correcting objective that specifically targets this failure mode by equalizing the contribution of each predicted class to the unsupervised entropy minimization loss. To study this failure mode, we design suitable adaptation settings using four medical‑imaging datasets and additionally evaluate on ImageNet‑C. We find that DSBR consistently stabilizes test‑time adaptation, prevents model collapse, and matches or outperforms the state‑of‑the‑art methods. Moreover, DSBR operates solely at test-time.

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.

Modern automatic speech recognition (ASR) systems have achieved remarkable accuracy by scaling model depth and capacity, but at the cost of substantial memory and computation. On edge devices where ASR is often most needed, such as watches and glasses, deploying such large models is infeasible due to extremely constrained resources. This raises a fundamental question: can we achieve high representational power in deep ASR models without scaling up parameterization? In this work, we revisit the role of depth and identify layer-wise representational dynamics, in which most layers learn functionally similar transformations. Motivated by this insight, we propose a block-recurrent ASR architecture that replaces parameterized depth with a small set of recurrent blocks, each consisting of weight-shared layers, thereby preserving effective depth while drastically reducing model size. Through representation-guided grouping and knowledge distillation, block-recurrent models retain the accuracy of large reference models while using an order of magnitude fewer parameters. Across Open ASR Leaderboard benchmarks, a model with only two shared blocks recovers $97.4$% of the accuracy of large models on average. The compact backbone further enables efficient on-device personalization through lightweight MoE-LoRA adaptation, allowing user-specific models to match or surpass the accuracy of general-purpose ASR systems with significantly lower inference cost. Together, these results show that high-quality ASR can be achieved without large parameterization, and that structured recurrence provides a principled path towards ultra-compressed, high-efficiency, and personalized speech recognition on the edge.

Proteolysis-targeting chimeras (PROTACs) induce protein degradation through coordinated interactions among a degrader molecule, a target protein, and an E3 ubiquitin ligase. Yet computational prediction is often framed as if degradation were an intrinsic property of the degrader alone, overlooking the tuple-level context that determines activity. This mismatch is especially consequential in public PROTAC datasets, where degradation labels are sparse but molecule–target–E3 records are abundant. We introduce DegradeQuery, a tuple-conditioned framework that uses unlabeled records as structured relational evidence rather than incomplete labeled examples. DegradeQuery first performs counterfactual tuple pretraining, learning to distinguish observed molecule–target–E3 tuples from alternatives generated by replacing the target, the E3 ligase, or both. This objective induces a conditional compatibility representation before degradation labels are used, enabling the model to capture how degrader activity depends on biological context. The pretrained representation is then fine-tuned for high/low degradation prediction. On the official PROTAC-8K benchmark, DegradeQuery achieves 0.9065 AUROC and 0.8500 accuracy, outperforming reported semi-supervised results without pseudo-labeling, teacher models, distillation, or ensembles. Ablations, scaffold holdout, target–E3 holdout, and target-wise few-shot adaptation further support PROTAC degradation prediction as a tuple-conditioned rather than molecule-only problem.

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.

Cross-distribution generalization in longitudinal behavioral data remains a persistent obstacle. Models trained on one cohort degrade on new populations, and prior approaches operating through feature-distribution alignment or model-capacity scaling have not substantially improved cross-distribution performance. We diagnose the limitation as insufficient representational constraints during training. We propose holding the language model frozen, so that the coherence criterion is defined by its pre-trained language prior and cannot shift during training. We introduce PRISM, a frozen-backbone framework that decomposes each behavioral trajectory into temporal, spectral, and semantic streams and integrates them through directed cross-attention with instance-specific gating. Training uses a dual-path objective combining discriminative and coherence constraints on a shared representation. On GLOBEM, a widely used cross-cohort benchmark for behavioral health prediction, PRISM achieves 79.93\% out-of-distribution accuracy, 27.13 points above the previous best, with an in-distribution to out-of-distribution accuracy gap of 1.24 points. PRISM also achieves the highest OOD accuracy on three additional datasets (LifeSnaps anxiety, MFAFY engagement, CrossCheck schizophrenia symptoms), with consistently smaller ID-to-OOD gaps than fine-tuned VLM baselines. Ablations identify the frozen coherence constraint as the component responsible for the distribution-invariance pattern.

Predicting genetic perturbation responses across cell lines is challenging not only for models, but also for the metrics used to evaluate them. We introduce a strict cross-cell-line CRISPRi stress test on Replogle Perturb-seq, training on K562 and RPE1 (or additionally HepG2) and evaluating on held-out Jurkat. We pair this setting with two necessary controls: target-control-copy, which predicts no perturbation effect, and shifted-control baselines, which translate target-line controls by a source-derived mean shift. Across classical baselines, recent deep models, CellFlow, and context-conditioned flow-matching variants with five frozen single-cell foundation-model encoders plus a PCA/null context, we find that aggregate distributional metrics can be strongly driven by target-line cell-state variability rather than perturbation-specific response. Target-control-copy is competitive on aggregate distributional metrics, while shifted-control approaches CellFlow on Energy and substantially outperforms it on perturbation-specific Pearson. Used as fixed per-cell context for a shared downstream flow head, none of five frozen scFM encoders matches a PCA/null encoder in this strict cross-line setting. Finally, anchored mean-preserving residual-flow probes reveal a metric-specific trade-off: full-space residual flows improve $W_2$ but degrade per-gene moment matching, whereas low-rank constrained flows preserve moments but lose most $W_2$ gain. No tested method simultaneously dominates perturbation-specific response and biologically relevant distributional fidelity. These results argue for perturbation-aware cell-level evaluation in cross-cell-line virtual-cell benchmarks.

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.

We introduce Phase-Interference Decision Trees (PIDT), a differentiable routing model for tabular prediction in which each node propagates a bounded complex state rather than only a scalar routing probability. The main model uses two-state norm-preserving branch maps: a gate controls how much amplitude is sent to the left and right children, while learned unitary maps rotate the latent state before subsequent splits. This formulation contains ordinary soft decision trees as the phase-free scalar case, and two-state routing creates explicit score-level cross terms among latent-state trajectories. We give a self-contained PIDT definition with exact mass conservation, prove scalar phase cancellation, establish a scoped containment relation with soft trees, derive an algebraic interference decomposition, give a companion construction for shared two-state parity routing, and derive a fixed-architecture empirical Rademacher upper bound for bounded two-state PIDT scores. We evaluate PIDT on a ten-dataset OpenML suite of low-class-count tabular tasks under matched differentiable-tree controls. Across ten seeds per dataset, the main summaries track accuracy as context and report small descriptive NLL and Brier deltas relative to the scalar PIDT-1S, phase-frozen PFB, and SDT controls. The empirical study is framed as a mechanism-level study for compact differentiable trees, not as a broad tabular-performance claim.