NeurIPS 2026 Papers

In high-stakes settings, decision-makers using conformal prediction (CP) read the predictive interval's width as their operational uncertainty signal. Local feature attribution methods, applied to that width, silently conflate two semantically distinct contributions: a calibration-driven baseline that depends on the held-out calibration set, and an instance-driven term that depends on the test point. We propose a two-game Shapley construction---one game over feature coalitions at the test point, one over feature coalitions across the calibration set---that decomposes the total attribution into calibration-driven and instance-driven components. The construction is method-agnostic on two axes: any local attribution method that can be applied to a scalar uncertainty target can be used, and any split CP family whose uncertainty size admits a known separation into a calibration-only and an instance-only summary satisfies the decomposition. Empirically, the calibration-driven and instance-driven attributions frequently carry opposite signs on synthetic and real data, with representative cases where a feature's sign reverses between the two views; the total attribution then becomes a misleading sum. On a clinical-decision task with semi-synthetic data, this conflation degrades decision quality, whereas jointly considering the calibration-driven and instance-driven attributions recovers high-uncertainty cases missed by total attribution. We further empirically verify that the decomposition holds across multiple CP families that satisfy the separability conditions.

Single-cell multi-omics technologies jointly measure multiple modalities from the same cell, such as gene expression, chromatin accessibility, and surface protein abundance, providing a richer view of cellular state than any single modality alone. A central challenge is how to learn a multimodal cell representation that preserves the different types of information contained in these co-profiled measurements. Existing integration methods often emphasize either cross-modal correspondence, which benefits retrieval and imputation, or fused discriminative structure, which benefits classification, but these objectives can favor different aspects of the data. As a result, a representation optimized for one setting may fail to preserve information needed for another. We introduce scDecomp, a decomposed representation learning framework for co-profiled single-cell multi-omics data. Motivated by the concepts of redundancy, uniqueness, and synergy, scDecomp separates multimodal information into three role-specific components: a redundancy component ($R$) for information shared across modalities, a uniqueness component ($U$) for modality-specific information, and a synergy component ($S$) for interaction-dependent multi-omic signals. This design allows the learned representation to retain shared cell-state structure while also preserving modality-specific and cross-modal regulatory information. On single-cell multi-omics benchmarks, the $R$ component achieves a 5.0\% reduction in FOSCTTM compared with the strongest baseline, while $U+S$ improves cell type classification accuracy by 1.38\%. Branch-level analyses further show that the decomposed components support complementary aspects of representation quality, suggesting that structured decomposition provides a practical strategy for learning more informative multimodal cell representations.

Generative visual verifiers play a crucial role in advancing multimodal intelligence systems. In this paper, to enhance the robustness of visual verifiers in complex and realistic open-world scenarios, we move beyond conventional binary verifiers that produce only True/False discrete judgments. Instead, we introduce a verifier that automatically decomposes complex prompts or environmental rules into fine-grained components and assigns pointwise scores, thereby providing a continuous and more informative critique signal. Under a general reinforcement learning framework, we first empirically demonstrate that the proposed pointwise decomposed verifier significantly outperforms binary verifiers in traditional text-to-image scenarios. We then extend our investigation to a novel paradigm: generative world modeling, where generative models are leveraged for world simulation and world reasoning. From a theoretical standpoint, we further show that when ground-truth images are easy to obtain, a pairwise verification paradigm yields more accurate critiques than the pointwise formulation. Empirical results on diverse world-modeling tasks, such as Maze and Sudoku, further validate the effectiveness of our approach. Overall, our findings suggest a key insight: the transition from generative models to world-modeling agents critically hinges on the availability of accurate pairwise decomposed visual verifiers.

Large Reasoning Models (LRMs) exhibit backtracking and self-verification mechanisms that enable them to revise intermediate steps and reach correct solutions, yielding strong performance on complex logical benchmarks. We hypothesize that such behaviors are beneficial only when the model has sufficiently strong “critique” ability to detect its own mistakes. This work systematically investigates how current LRMs recover from errors by inserting arithmetic mistakes in their intermediate reasoning steps. Notably, we discover a peculiar yet important phenomenon: despite the error propagating throughout the entire chain-of-thought (CoT) without any verbalized correction, the model still reaches the correct final answer after the thinking process finishes. This recovery implies the existence of an internal mechanism helping the model to detect errors and trigger self-correction, which we refer to as the hidden critique ability. Building on feature space analysis, we identify a highly interpretable critique vector representing this behavior. Extensive experiments across multiple model scales and families demonstrate that steering latent representations with this vector improves the model’s error detection capability and enhances the performance of test-time scaling at no extra training cost. Our findings provide a valuable understanding of LRMs’ critique behavior, suggesting a promising direction to control and improve their self-verification mechanism.

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/).

Individualized treatment selection with continuous actions requires accurate causal response estimation in decision-relevant regions, rather than uniformly over the entire action space. Estimating a global causal response surface and then choosing the treatment that maximizes it can therefore be suboptimal, since standard estimation objectives allocate modeling effort according to the observed treatment distribution rather than the regions that determine the optimal decision. While decision-aware approaches have been studied in unconfounded settings, this problem remains underexplored in proximal causal inference, where proxy variables and bridge functions enable identification under suitable assumptions even in the presence of hidden confounding. Despite recent progress, proximal methods have primarily focused on treatment-effect and potential-outcome estimation rather than treatment selection and optimal decision-making. To bridge this gap, we introduce a policy-targeted weighted bridge loss that emphasizes decision-relevant treatment regions while retaining global stabilization. We prove a regret bound showing that the proposed weighted bridge loss controls treatment-selection regret through a weighted ill-posedness constant. We instantiate the framework in decision-aware variants of several proximal bridge solvers, yielding practical algorithms that alternate between weighted bridge estimation, response-surface projection, policy update, and weight refinement. Empirically, we find that decision-aware weighting reduces regret across several bridge solvers, suggesting improved treatment selection in proximal settings.

Investigating the causal structures of transformers often entails a trade-off between the granularity of the explanation and computational overhead. Existing approaches either bypass the full internal chain, potentially missing critical decision-relevant links, or struggle with the combinatorial explosion of path enumeration. To address these challenges, we propose an efficient automated path-tracing method that ensures both causal reliability and algorithmic feasibility. Through extensive verification, we find that (i) the identified paths are the primary sources for decision signals, as evidenced by a marked decrease in self-repair compared to non-path components; and (ii) transformers utilize modular, class-shared path mechanisms to process categorical information. Beyond these findings, we show that the potential implications of our method can stem from sparse manipulation, highlighting efficient applicability to downstream tasks such as model debugging and pruning.

Decision-focused scenario learning for contextual two-stage stochastic programming has emerged as a promising research direction, but open questions remain. A general theory for when scenario generators can yield optimal decision policies is currently lacking. Many existing methods require strict assumptions on which second-stage data are allowed to be random. Generally, the non-differentiability of the decision-focused objective is a source of difficulty. This paper makes three theoretical contributions, each aiming to remedy one of these challenges. First, we present a comprehensive theory for when scenario generators can induce optimal policies, showing that learning a single out-of-support scenario component is often sufficient. Second, we demonstrate a theoretical and methodological equivalence result between learning different second-stage data components, relaxing assumptions required for applying existing methods. Third, we study log-barrier smoothing enabled training, presenting representability and optimization results. Finally, leveraging the theory, we propose a novel method inspired by interior-point methods from classical optimization, and validate it on standard benchmarks.

Retrieval-augmented generation evaluation checks whether model claims are factually grounded in retrieved documents. It does not check whether retrieved evidence is attributed to the correct entity. A clinical RAG response can pass every automated check (zero hallucinations, near-perfect faithfulness, real citations) while presenting drug $Y$'s clinical evidence as evidence about queried drug $X$. We term this **deceptive grounding** (DG): a failure invisible to faithfulness, hallucination, and citation checks because every claim is sourced from a real document, about the wrong entity. Using a controlled factorial benchmark across 13 models, we find DG rates spanning 8--87\% at peak adversarial conditions. Medical and biomedical fine-tuned models reach up to 86.7\%; domain specialization amplifies the failure rather than mitigating it. A controlled ablation identifies the mechanism: removing entity-specific clinical evidence from retrieved documents eliminates entity-attribution failure entirely, shifting all failures to confabulation. The two failure modes respond to the same trigger, taking different paths. Production measurement across 740 drug--disease pairs finds 7.8\% overall DG in a deployed RAG system, rising to 13.6\% for recently approved drugs. Entity-attribution verification (checking that cited evidence applies to the queried entity) detects DG at 92.3\% precision and 95.8\% DG recall; no existing framework implements it.

We propose a fully decentralized $Q$-learning dynamic for infinite-horizon discounted Markov potential games in which agents observe the global state and their own realized payoffs, but do not know the reward functions, the transition probabilities, the potential function, or the policies and the actions of the other players. In the proposed dynamic each player maintains a local estimate of its continuation payoff for each state and action and updates its policy through a smoothed best response. Importantly, the smoothing parameter is adjusted by using an online estimate of the discounted state-visitation distribution, so that players explore more in rarely visited states while exploitation is encouraged in states that are frequently visited. We prove that the induced policy sequence converges almost surely to a set of approximate Nash equilibria of the Markov potential game, with an approximation guarantee that depends only on easily computable parameters. We illustrate the performance of the proposed learning dynamic via numerical experiments on a routing game and compare it with existing independent learning schemes based on persistent $\theta$-greedy exploration.

As a variant of the Multi-Agent Pathfinding (MAPF) problem, Multi-Goal Multi-Agent Pathfinding (MG-MAPF) requires planning conflict-free paths for a team of agents to visit sequences of preassigned goal vertices. Existing search-based methods suffer from poor scalability due to their reliance on centralized control, while decentralized learning-based approaches struggle with long-horizon planning under partial observability. Furthermore, existing paradigms rarely address joint goal ordering across agents, where uncoordinated goal sequences cause multiple agents to simultaneously converge on the same region, increasing congestion and conflicts. To address these challenges, we propose the Intent-aware Multi-Goal Multi-Agent Pathfinding (IMMP), a hierarchical framework for decentralized MG-MAPF that explicitly models spatial congestion at the high level and neighbor intent at the execution level. The high-level planner encodes a spatial prior via spatially-balanced goal ordering and path planning, reducing conflict pressure passed to the low-level policy. The low-level module resolves real-time conflicts via a reinforcement learning policy augmented with a neighbor intent prediction module, trained end-to-end through a prediction-guided collaborative proximal policy optimization. Extensive experiments demonstrate that IMMP achieves strong scalability and generalization, Extensive experiments demonstrate that IMMP achieves strong scalability and generalization, reducing SoC by an average of 19.77\% compared to the best-performing decentralized baseline, while maintaining robust performance. Our code is available at https://anonymous.4open.science/r/IMMP-CB2E/

The learning dynamics of decentralized coupled representation learning can be formulated as a non-autonomous stochastic dynamical system, yet the macroscopic behavior of the resulting system on continuous manifolds remains poorly understood. We establish three theoretical results for such coupled slow-fast dynamics. First, under a standard random geometric graph scaling regime, we prove that the infinitesimal generator of the discrete Markov process converges to the generator of an overdamped Langevin diffusion for every $f \in C^3(\mathcal{M})$. Second, under timescale separation, the stochastic parameter dynamics converge to a deterministic averaged flow that admits a row-orthogonality Lyapunov function, controlling row-space degeneracy and ruling out parameter divergence. Third, under a spectral gap condition, the row space of the averaged parameter trajectory converges to the principal eigenspace of the induced covariance matrix. Together, these results give a stochastic-approximation limit theory for the coupled regime in which Markovian sampling and local subspace learning co-evolve, showing that memoryless local interactions can induce a stable and structured macroscopic limit.

We propose WALLA, a decentralized mechanism for aggregating probabilistic predictions from multiple LLMs using wagering mechanisms. Each model reports a prediction and a learned wager reflecting its expected score advantage; predictions are then aggregated using wagers as weights. Our mechanism introduces a leave-one-out baseline that yields three key properties: dominant-strategy incentive compatibility under general belief structures, a best-response wager proportional to expected score advantage, and decoupling of prediction and wager optimization. We instantiate two mechanism variants trading off normality and no-arbitrage, both with bounded worst-case deficit independent of the number of participants. Experiments on QA benchmarks and a forecasting benchmark show that WALLA matches centralized baselines while simultaneously achieving advantage-weighted aggregation, uncertainty-awareness, fully decentralized learning, and incentive-compatibility guarantees.

In this position paper, we argue that successful decentralized AI needs trustworthy, adaptive, and portable governance at scale. The ML community must adopt an architecture that cleanly separates \emph{policy processing} (the evaluation of evidence against governance requirements) from \emph{capability enforcement} (the gating of access to protected digital assets). We argue that the prevailing approach, in which each organization manages bespoke policy logic, custom-built for its infrastructure, produces fragmentation that undermines interoperability, transparency, and auditability of federated systems. Drawing on a survey of governance mechanisms in federated learning and data collaboration frameworks and on a concrete reference architecture built around community-driven policy objects, we argue that this decoupling is technically feasible and architecturally necessary and supports a broad class of decentralized AI governance settings. We further suggest that the AI community should invest in open, standardized policy abstractions rather than proliferate siloed governance solutions. We address counterarguments concerning the costs of standardization and the feasibility of universal policy languages.

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

Direct Preference Optimization (DPO) enables offline alignment of diffusion models from preference pairs without explicit reward modeling, but scaling DPO requires large amounts of expensive human preference data. In this paper, we consider a semi-supervised approach where only a small subset of preference pairs is annotated by humans, while the remaining (much larger) pool is unlabeled and annotated by inexpensive synthetic feedback e.g., vision-language models scoring image pairs or self-training from the current model. However, such synthetic supervision is not a replacement for human judgments: it can be systematically misaligned, so naively mixing human and synthetic preferences will yield misaligned inference. We introduce DeDPO, a debiased objective that integrates a doubly robust estimator from causal inference into DPO to correct synthetic-label misalignment while preserving the simplicity of DPO. Experiments show improved label efficiency and robustness to synthetic labeler quality, allowing DeDPO to outperform standard DPO under the same human-label budget and to approach fully human-supervised performance, despite using four times fewer human-labeled data.

Estimating counterfactual distributions under interventions is central to treatment risk assessment and counterfactual generation tasks. Existing approaches model the counterfactual distribution as a standalone generative target, without exploiting its relationship to the observational data. In this work, we show that under standard assumptions, observational and counterfactual outcome distributions are tightly linked: they have identical support and tail behavior, remain statistically close under weak confounding, and share any features of high-dimensional outcomes which are invariant to confounders. These properties motivate learning counterfactual distributions not from scratch, but via a *deconfounding flow* from the observational distribution. We formulate this problem via flow-matching and derive a semiparametrically efficient estimator based on a novel efficient influence function correction. We subsequently extend our estimator to target minimal-energy flows in high-dimensions, which we show can be especially simple targets between observational and counterfactual distributions. In experiments, deconfounding flows outperform existing debiased counterfactual distribution estimators, while also mitigating known failure modes of flow-based methods.

Deadline-Constrained Cost-Aware Dynamic Workflow Scheduling (D-CADWS) aims to minimize virtual machine (VM) rental cost while maintaining high workflow deadline satisfaction in dynamic cloud environments. The problem is challenging because the deadline impact of assigning a workflow task to a VM is not directly observable at decision time. It depends on long-horizon effects, including downstream task dependencies, VM queueing, and interactions among dynamically arriving workflows. Existing methods usually fold deadline violations into a single reward or penalty term, which obscures the distinction between action safety and action cost. In this paper, we cast D-CADWS as a representation learning problem. The core idea is to learn a deadline-aware representation that can predict, before execution, whether a candidate action is risky with respect to the current task deadline, and then use it to guide cost-efficient scheduling. Based on this idea, we propose a representation-centered deep reinforcement learning (RCDRL) method. RCDRL constructs task-level deadline supervision, trains a predictive deadline model to learn deadline-aware representations, and learns a VM-cost critic on top of this representation. The final policy follows a safe-first rule that rejects risky actions before minimizing VM cost. We further propose a two-phase training strategy to keep the learned representation aligned with the evolving policy-induced data distribution. Experiments on dynamic workflow scheduling benchmarks show that RCDRL achieves substantially lower VM cost than other state-of-the-art heuristic and DRL baselines while maintaining strong workflow deadline success.

In industrial machine vision, removing specular highlights is crucial for accurate surface analysis; however, existing open research remains largely confined to natural images, limiting its efficacy on highly reflective metallic surfaces. When used for zero-shot inference, these methods generate artifacts, making them unreliable for specialized industrial scenes. Since current state-of-the-art methods rely on pixel-level supervision, adapting them to novel industrial subjects is often impractical. Such adaptation requires highlight-free ground truths across hundreds of multi-view scenes, necessitating hardware-intensive cross-polarization, which is difficult to implement at scale in uncontrolled real-world settings. To address these challenges, we propose DeGlare, a flexible training framework for highlight removal in specialized industrial settings. Our approach leverages only a single-view scene by exploiting its multi-illumination observations together with a latent permutation strategy, enabling an encoder-decoder–style architecture to implicitly learn diffuse–specular decomposition. This eliminates the dependency on the burdensome manual annotation required by state-of-the-art supervised methods. We demonstrate the practical effectiveness of our approach on a real-world industrial robotic rig data set, where DeGlare achieves promising performance under limited data and annotation constraints.