NeurIPS 2026 Papers

Bayesian persuasion, a central model in information design, studies how a sender, who privately observes a state drawn from a prior distribution, strategically sends a signal to influence a receiver's action. A key assumption is that both sender and receiver share the precise knowledge of the prior. Although this prior can be estimated from past data, such assumptions break down in high-dimensional or infinite state spaces, where learning an accurate prior may require a prohibitive amount of data. In this paper, we study a learning-based variant of persuasion, which we term persuasive prediction. This setting mirrors Bayesian persuasion with large state spaces, but crucially does not assume a common prior: the sender observes covariates $X$, learns to predict a payoff-relevant outcome $Y$ from past data, and releases a prediction to influence a population of receivers. To model rational receiver behavior without a common prior, we adopt a learnable proxy: *decision calibration*, which requires the prediction to be unbiased conditioned on the receiver's best response to the prediction. This condition guarantees that myopically responding to the prediction yields no swap regret. Assuming the receivers best respond to decision-calibrated predictors, we design a provably efficient algorithm that learns a decision-calibrated predictor within a randomized predictor class that optimizes the sender's utility. In the commonly studied single-receiver case, our method matches the utility of a Bayesian sender who has full knowledge of the underlying prior distribution. Finally, we extend our algorithmic result to a setting where receivers respond stochastically to predictions and the sender may randomize over an infinite predictor class.

Tandem mass spectrometry provides a high-throughput framework for identifying and quantifying proteins in complex biological samples. In computational proteomics, predicting peptide MS/MS spectra is a critical task, enabling downstream applications such as large-scale peptide identification and quantification. While deep learning architectures have substantially improved prediction accuracy, three evaluation challenges obscure the true progress of the field. First, inconsistent data preprocessing and incompatible model output spaces hinder fair model comparison. Second, flawed data splitting strategies can permit hidden sequence leakage and inflate reported performance. Third, existing evaluations typically lack comprehensive cross-species benchmarking and systematic assessment of model robustness to influential experimental conditions. To address these challenges, we propose PepSpecBench, a unified benchmark for peptide MS/MS spectrum prediction. PepSpecBench standardizes data preprocessing across complementary public datasets, enforces a strict backbone-disjoint splitting strategy to eliminate sequence leakage, and evaluates diverse architectures within a shared fragment-ion representation space. It further introduces a comprehensive multi-species evaluation suite and physically grounded metadata perturbation probes to assess model robustness and instrument awareness. We uncover previously unrecognized performance discrepancies and robustness limitations across six representative models, providing actionable insights for future model design, evaluation and practical deployment. Code and data are available at https://anonymous.4open.science/r/PepSpecBench-NeurIPS2026/.

Predicting mutation-induced changes in peptide--protein binding affinity ($\Delta\Delta G$) is central to therapeutic peptide optimization, but peptide-specific predictors remain limited by scarce labels, target overlap in supervised benchmarks, and costly molecular simulations. We introduce PepDDG, a zero-shot, training-free predictor that ranks peptide mutations by decomposing binding perturbations into three complementary information channels: energetic perturbation, geometric environment, and evolutionary compatibility. Each channel is computed from a wild-type complex structure, transformed into rank space, and combined by non-parametric Borda aggregation, without fitting fusion weights or fine-tuning neural predictors. PepDDG is motivated by rank-covariance analysis showing cross-channel fusion can improve rank correlation when channels retain complementary signal, whereas increasingly expensive refinement of a single energetic channel has diminishing returns. Empirically, the channels are individually moderate but capture distinct mutation regimes, and rank fusion consistently outperforms single-channel variants and training-free baselines. On our curated benchmark of 332 peptide-chain mutations across 33 targets, PepDDG reaches Spearman $\rho = 0.619$; the calibrated PepDDG-Cal variant reaches $\rho = 0.691$. Performance remains strong on short and cyclic peptide subsets, with $\rho = 0.813$ and $0.872$, respectively. These results support complementary evidence fusion on PDB-derived complex structures as a practical alternative to costly same-channel physical refinement for peptide $\Delta\Delta G$ ranking; a separate diagnostic shows that predicted structures can be used when no crystal structure is provided at inference time.

Predicting spatial gene expression from histopathology images enables transcriptomic analysis on archival H\&E samples where matched spatial transcriptomics data is unavailable. Gene expression is organized through cyclic regulatory circuits: transcription factor complexes are themselves products of regulated genes, aggregating upstream gene activity and modulating co-expressed downstream programs (e.g., the ISGF3 complex coordinating hundreds of interferon-stimulated genes through shared cis-regulatory elements). Existing approaches to histology-to-expression prediction, both deterministic and generative, employ generic architectures that ignore this structure. We introduce regulator attention, a mechanism that encodes this cycle by pooling gene representations into a small set of content-dependent tokens via a learned selector and routing them back through cross-attention to modulate gene-level features, in contrast to learnable token arrays whose identity is independent of the input. Within a flow matching framework conditioned on pathology foundation model embeddings, regulator attention consistently outperforms deterministic and generative baselines on correlation-based metrics across four tissue types spanning human cancer, normal tissue, and cross-species samples, with substantial gains on highly variable genes. Beyond predictive accuracy, 87.5\% of regulator tokens align with known biological pathways without supervision. Replacing them with content-independent learned queries eliminates this specialization and degrades predictive performance, isolating content-dependent token generation as the causal mechanism.

Performative prediction studies feedback loops that arise when predictive models are deployed in consequential domains. In these settings, model updates can shape the population whose patterns they aim to predict, inducing a distribution shift that is *endogenous* to the learning system. This perspective departs from classical treatments of distribution shift, where shifts are typically modeled as *exogenous* changes in the data-generating process. Yet, in practice, distribution shift is rarely one or the other. Predictive models may influence future data through the decisions they support, while the world itself continues to drift for reasons beyond the learner’s control. We study *partially performative prediction*, a framework that captures both endogenous and exogenous sources of distribution shift. The framework generalizes performative prediction by allowing the data distribution to evolve both in response to the deployed model and according to an external, time-varying process. We extend the central notions of performative stability and performative optimality to this setting by defining online analogues that measure performance relative to the evolving partially performative environment. We analyze practical learning heuristics, including repeated retraining, and characterize when they successfully adapt to partially performative environments.

Many applications require joint prediction of interdependent behavioral choices, yet existing models often treat each choice independently (e.g., through parallel prediction heads), overlooking the influence of one on the other. In this work, we propose Progressive Dual-Head Transformer (PDHFormer), a novel framework that performs two-step prediction: the model first estimates one choice and then conditions the second on this upstream estimate through an explicit head-to-head pathway. A shared encoder captures the common structure of two prediction tasks, while the dual-head module explicitly reflects cross-choice dependence. A gated residual mechanism integrated into the embedding layer and the dual-head module further improves the training stability and the prediction performance. Extensive experiments on real-world urban mobility, manufacturing, and food delivery application domains demonstrate that PDHFormer consistently outperforms state-of-the-art machine learning models, deep tabular models, as well as parallel-head Transformer variants across multiple metrics. Moreover, our ablation study confirms that both the proposed progressive dual-head and gated residual mechanism are key contributors to the observed gains in different prediction tasks.

Conformal prediction is a framework that provides valid uncertainty quantification for general models with exchangeable data. However, in the online learning and time-series settings, exchangeability is not satisfied. Existing online conformal methods, such as adaptive conformal inference (ACI), can achieve long-run validity, yet they remain inefficient under covariate heterogeneity because they rely on global calibration. We propose *Online Localized Conformal Prediction (OLCP)*, which combines online adaptation with covariate-dependent localization to better reflect heterogeneity. To reduce sensitivity to the localization bandwidth, we further develop *OLCP-Hedge*, which performs bandwidth selection as an online expert aggregation problem using a constrained online convex optimization framework. Importantly, we provide coverage guarantees for both algorithms and demonstrate through simulations and real-data experiments that the proposed methods attain valid long-run coverage with narrower prediction intervals than existing baselines.

Chain-of-Thought (CoT) enables transformers to solve complex reasoning tasks by generating intermediate reasoning steps. Despite strong empirical success, the mechanisms by which such reasoning abilities emerge during training remain poorly understood, particularly from a theoretical perspective. In this work, we study this question in a stylized yet fully analyzable setting: in-context $\mathrm{K}$-nearest-neighbor ($\mathrm{K}$-NN) prediction with a one-layer transformer. Earlier work has demonstrated that one-layer transformers can be trained to perform $1$-NN without CoT \citep{Li2024One} by having the softmax attention attend to the nearest neighbor in context. However, this result on $1$-NN cannot be extended to $\mathrm{K}$-NN for $K>1$, as softmax attention is much less naturally suited to attending to the 2nd through $K$-th nearest neighbors. We first give a rigorous negative result showing that this limitation already appears on a well-separated class of binary classification tasks for odd $K>1$. We then give an explicit construction of a one-layer transformer, and show that it can solve $\mathrm{K}$-NN via CoT reasoning. We further prove that, under a stylized training setup, gradient descent can recover this construction, thereby showing that transformers can acquire the capability to solve $\mathrm{K}$-NN through training. In addition, we show that the resulting trained model applies to a broader test class than that assumed in the training analysis. Our results shed light on how CoT expands the ability of transformers to learn and solve tasks that are otherwise hard to solve.

Multi-token prediction (MTP) accelerates autoregressive decoding through blockwise prediction, but remains vulnerable to error accumulation across sequentially generated blocks. Existing work primarily focuses on improving MTP’s acceleration ratio while largely overlooking the compounding errors that accumulate during block-wise generation. In this work, we study MTP from an information-theoretic perspective and identify the strength of inter-block dependencies as a key factor governing its performance. Building on the cumulative information loss, we theoretically quantify the gap of block-sequence representation. Motivated by the analysis, we propose Dual-MTP, a novel MTP training framework that explicitly models inter-block dependencies from two complementary perspectives, thus reducing the block-sequence representation gap across decoding steps. Specifically, Dual-MTP introduces a dual-perspective training objective with random masking, where each block is jointly trained for forward prediction and masked context reconstruction, encouraging robust block-level representations. Experiments across five model backbones and six tasks spanning math reasoning and GLUE understanding show that Dual-MTP consistently outperforms standard MTP on both quality and efficiency, attaining the highest average accuracy on every backbone with gains of +0.3 to +3.0 points and a 1.4× to 1.9× wall-clock speedup over standard autoregressive decoding.

AI tools are increasingly integrated into real-world workflows. However, existing measures of reliance on these tools focus on AI output adoption or on self-reported indicators, rather than how task effort is distributed between users and tools. Here, we introduce offloading score, a measure of reliance that quantifies the fraction of cognitive effort offloaded to an AI tool. Offloading score is simulation-based---we construct a counterfactual workflow by estimating how the user would have completed the task without the tool, and then computing the fraction of steps saved by using the tool. We validate offloading score through intrinsic evaluations of metric validity, and a controlled user study ($n=40$) with developers performing programming tasks using AI tools. We vary time pressure to test whether reliance measures capture the known increase in reliance under time pressure. We show that offloading score detects significantly higher reliance in time-constrained settings ($+43\%$, $p=0.018$), while usage-based and self-reported baseline measures do not distinguish the conditions. We complement this with descriptive insights showing that higher reliance manifests as greater delegation and more direct reuse of AI outputs. Finally, we demonstrate an approach of using offloading score in combination with target outcomes of reliance (e.g., task understanding) to identify when reliance may be (in)appropriate. Our framework offers two contributions: an instrument users can apply to measure and reflect on their own reliance, and a quantitative signal that agent designers can utilize to mitigate overreliance.

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.

Protein structure prediction has progressed rapidly, with a flourishing ecosystem of open-source AlphaFold (AF)-style systems delivering real progress. But isolating which architectural and training choices actually drive that progress is difficult. Production-scale training is both computationally prohibitive and not accessibly reproducible; moreover, the public corpora are complex, with structural biases that compound in parameter- and compute-constrained regimes. To address these gaps, we introduce NanoFold, a compact, fixed-data benchmark for AF-style training studies. Its sealed-hidden tracks address three fundamental questions, with a $\textit{limited}$ track for sample efficiency, a $\textit{research large}$ track for whether early gains persist under more optimization, and an $\textit{unlimited}$ track for best fixed-data final performance. NanoFold's split is designed rather than naively sampled, with chains grouped into biological units under MMseqs2 cluster and PDB-entry disjointness, stratified across structural metadata, and allocated to $10{,}000$ training, $1{,}000$ public-validation, and $1{,}000$ sealed hidden chains. We verify the construction through statistically principled diagnostics, including a randomization study over $1{,}000$ alternative valid splits, confirming the dataset is diverse, well-distributed, and statistically typical of its constraint class. We use NanoFold to run comprehensive experiments on model behavior across scales and training regimes, demonstrating that the benchmark is learnable but unsaturated, scales predictably with budget, cleanly separates training primitives, and that the underlying codebase flexibly supports head-to-head comparison. NanoFold makes architectural and training progress in protein structure prediction more transparent, comparable, and reproducible at a compute-accessible scale.

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.

Trade flows are a central variable for studying international trade networks. Traditional economic models, grounded in economic theory and mathematical derivation, provide structured representations of bilateral trade flows. However, these representations often rely on fixed functional forms, have limited ability to incorporate high-dimensional features, do not directly capture trade inertia, and scale poorly to large and dynamic trade networks. In recent years, graph neural networks have been increasingly used to address some of these limitations because they are naturally compatible with networked trade data and offer strong scalability. However, the feature aggregation process in existing graph neural models remains weakly interpretable from an economic perspective. To address this limitation, we propose MRTGNN, a graph neural network model built on a theory-guided MRTGNN layer derived from the fixed-point equations of multilateral resistance in structural gravity. Theoretically, we prove that an $L$-layer MRTGNN can approximate $L$ iterations of a multilateral-resistance fixed-point operator on a compact domain, with an explicit bound on the iteration error. Empirically, we extensively evaluate MRTGNN on panel data covering 176 countries from 2002 to 2019 against various representative baselines. The full model achieves the best RMSE, $R^2$, and Spearman rank correlation across the reported baselines. Under counterfactual tariff shocks involving China, it produces a Network Spillover Ratio one order of magnitude higher than a generic graph-attention baseline. These results show that theory-guided message passing improves trade flow prediction while enabling more interpretable counterfactual reasoning in global trade networks.

Conformal prediction constructs prediction sets with finite-sample coverage guarantees, but its calibration stage is structurally constrained to a scalar score function and a single threshold variable — forcing shapes of prediction sets to be fixed before calibration, typically through data splitting. We introduce multi-variable conformal prediction (MCP), a framework that extends conformal prediction to vector-valued score functions with multiple simultaneous calibration variables. Building on scenario theory as a principled framework for certifying data-driven decisions, MCP unifies prediction set design and calibration into a single optimization problem, eliminating data splitting without sacrificing coverage guarantees. We propose two computationally efficient variants: RemMCP, grounded in constrained optimization with constraint removal, which admits a clean generalization of split conformal prediction; and RelMCP, based on iterative optimization with constraint relaxation, which supports non-convex score functions at the cost of possibly greater conservatism. Through numerical experiments on ellipsoidal and multi-modal prediction sets, we demonstrate that RemMCP and RelMCP consistently meet the target coverage with prediction set sizes smaller than or comparable to those of baselines with data split, while considerably reducing variance across calibration runs — a direct consequence of using all available data for shape optimization and calibration simultaneously.

Self-improving LLM agents typically improve answers, not the $\textit{process}$ by which answers are improved: their search loops and mutation rules are held fixed by design. We introduce $meta^n$, a framework for $\textit{recursive}$ self-improvement in which a single universal meta-operation $\Omega$ is applied repeatedly to build a hierarchy of meta-layers whose depth is sized by convergence rather than prescribed. Each layer observes execution traces from below, diagnoses failure patterns, and emits executable code (strategic guidance via pre-processing and reusable functions via a code library) whose effects cascade downward. Inter-layer conditioning permits up to $\prod_{d=2}^{n} k_d$ effective strategy-tactic combinations across $n$ layers rather than $\sum_d k_d$. An evolutionary orchestration mode maintains a growing archive of candidate chains with fitness-weighted parent selection and cross-candidate inspiration. Across seven benchmarks (combinatorial optimization, two text classification tasks, terminal agent tasks, mathematical discovery, symbolic regression, algorithm speedup), $meta^n$ beats prior self-improving agents on all five (of seven) benchmarks where they apply, with the largest gap on CO-Bench (0.849 test vs. Gödel Agent's 0.000 and OpenEvolve's 0.816). Layers specialize without prescription and their roles emerge at depth: generic primitives at depth 2 propagate as cross-task callables, task-aware routing emerges at depth 3, and deeper layers refine via specialization. Code and data will be released publicly.

This position paper argues that AI for mental health must move beyond two dominant paradigms. The first is diagnostic prediction, which frames mental healthcare as symptom detection, diagnosis, and decision-making. The second is chat-based support, which reduces therapeutic interaction to language-mediated information delivery. Both approaches neglect what therapeutic research shows to be central to healing: experiential, affective, and embodied processes. In many therapeutic traditions, insight emerges not through information-delivery but through emotionally resonant, multisensory experiences that allow individuals to feel, reflect, and reframe their inner world. We propose an alternative framework: co-experiential AI systems that identify emotionally salient moments across modalities, modulate sensory experience to facilitate reflection, and adapt over time to a user's evolving responses and interpretations. Using dreamwork therapy as a stress test for mental health AI, we define three key challenges for AI systems: identifying affective anchors in indeterminate multimodal interactions; modeling user perspective as a situated context; and enabling continual adaptation to individuals without repeated retraining. We posit that addressing these challenges requires rethinking AI's role not as an expert, but as a perspective-aware participant in human meaning-making.

Out-of-distribution (OOD) detection is essential for deploying machine learning models in high-stakes scientific applications, yet methods that flag OOD inputs without retraining the model have been validated almost exclusively on image classification. We introduce MOOD (Materials Out-of-Distribution Detection), a benchmark for evaluating whether these post-hoc OOD detectors transfer to graph neural networks (GNNs) on materials property prediction. MOOD evaluates 15 post-hoc detectors and 2 supervised diagnostic probes across 4 GNN architectures, 5 MatBench tasks, and 15 physically motivated compositional and structural shifts, yielding over 200 evaluation settings and 4,000 AUROC measurements. Our evaluation reveals three key findings. First, current post-hoc detectors perform near chance; all 15 detectors achieve an AUROC around 0.5. Second, detector failures decompose into two regimes: method bottlenecks and encoder bottlenecks. Method bottlenecks arise when supervised probes can separate in-distribution (ID) from OOD samples but unsupervised detectors fail, meaning that the embeddings contain OOD signal that current detectors do not extract. Encoder bottlenecks arise when even supervised probes fail to separate ID from OOD samples, indicating that the relevant shift is not accessible in the frozen GNN representation. Third, when OOD prediction error exceeds ID error, the AUROC of distance-based and density-based detectors correlates with the size of that gap. Collectively, these findings indicate that reliable OOD detection in scientific domains requires representation learning strategies that explicitly preserve OOD relevant signal, particularly for tasks where current GNN architectures encode no extractable signal at all.

Graph Neural Networks (GNNs) are powerful tools for learning from spatio-temporal data, as interactions in space can be naturally described by a graph structure. However, capturing long-range dependencies becomes substantially harder when information must flow through space and time simultaneously. Most existing approaches extend GNNs from static graphs to temporal settings by alternating propagation between time and space. In this work, we introduce STORM, a differential-equation-inspired GNN that exploits oscillatory dynamics to propagate information effectively in the joint spatio-temporal domain. By combining a wave-equation update with dissipative and external forcing terms, STORM balances conservative and non-conservative dynamics. We provide a bottom-up analysis of the model, highlighting its propagation behavior and stability, and show that STORM is universal. We empirically validate our method on diverse benchmarks, including tasks designed for analyzing long-range spatio-temporal dependencies, real-world forecasting, and a new long-range task inspired by physical simulations. Across these settings, STORM consistently matches or outperforms strong baselines, establishing a new state of the art for long-range spatio-temporal graph learning.