NeurIPS 2026 论文列表

We study the problem of learning in multi-player general-sum Markov games. In the simulator setting, where the learner can sample the next state conditioned on an arbitrary state action pair, the previous best-known upper bound of the sample complexity for learning an $\varepsilon$-approximate coarse correlated equilibrium (CCE) is $\widetilde{O}(H^4S\sum_{i=1}^m A_i/\varepsilon^2)$ (Li et al., 2022), where $H$ is the horizon, $S$ is the number of states, and $A_i$ denotes the number of actions for the $i$-th player. This work improves the upper bound to $\widetilde{O}(H^4S\max_{i\in[m]} A_i/\varepsilon^2)$, matching the lower bound of $\Omega(H^4S\max_{i\in[m]} A_i/\varepsilon^2)$ (Li et al., 2022). In the online setting, in which the learner can only sample a trajectory, the previous best-known sample complexity upper bound for learning CCE in multi-player general-sum Markov games is $\widetilde{O}(H^6S\max_{i\in[m]} A_i/\varepsilon^2)$ (Song et al., 2021; Jin et al., 2024; Mao et al., 2022). In this work, we improve this to $\widetilde{O}(H^5S\max_{i\in[m]} A_i/\varepsilon^2)$. To our knowledge, this is the tightest upper bound that breaks the curse of multi-agency. The core of our algorithmic design and analysis is the new gap decomposition and, in particular, a bandit algorithm with a high-probability empirical variance regret bound, which might be of independent interest.

Uncertainty quantification for individual treatment effects (ITEs) is a daunting challenge in causal inference. Motivated by recent advances in conformal prediction, several works aim to construct distribution-free prediction sets for ITEs with desired coverage under standard assumptions such as strong ignorability and overlap. In this paper, we show that such goals are fundamentally unattainable in the presence of continuous covariates. Specifically, we establish finite-sample and asymptotic impossibility results demonstrating that any distribution-free prediction set achieving desired coverage for ITEs must be trivial, in the sense that it has infinite expected length. Our analysis relies on a connection between ITE inference and the hardness of conditional independence testing, and highlights the intrinsic limitations imposed by the missing data nature of causal inference. These results provide a new perspective on existing methods, clarifying that their apparent success necessarily relies on additional structural assumptions beyond standard causal assumptions.

We propose a neural exploratory framework for *variational problems over graphons*, i.e., symmetric measurable functions $[0,1]^2 \to [0,1]$, that arise as limits of dense graph sequences. Such problems are central to two parts of the mathematical literature: extremal graph theory, which studies graph parameter optimisation under given restrictions, often for those graphs with a large number of vertices, and the large deviation theory of dense random graphs, which characterises the structure of rare events through constrained graphon optimisation. We represent graphons by implicit neural networks and optimise graphon objectives by gradient descent. Our design combines three ingredients: a multi-scale sinusoidal residual architecture biased toward sharp, step-like graphons; an embedded solver that enforces a single empirical density constraint and is differentiated by implicit differentiation; and symmetry-aware Monte Carlo estimators. On generalised Turán problems that previously required substantial human effort, our framework rediscovers known optimal graphons without human intervention. Applied to open instances, it produces candidate optima, including a previously unreported family of extremal structures. Applied to the variational problems from the large deviation theory of Erdős–Rényi random graphs, the framework produces new candidate optimal graphons across both upper- and lower-tail regimes for multiple pattern graphs; for the case of the triangle graph and the upper-tail regime, these candidates improve upon the best-known reference construction of Lubetzky and Zhao.

Reconstructing the gene regulatory dynamics underlying tissue development from unpaired, cross-sectional spatial transcriptomic snapshots poses a significant challenge in single-cell biology. While recent optimal transport and flow-based methods have advanced trajectory interpolation, their unconstrained latent dynamics lack identifiability, hindering reliable discovery of the underlying regulatory mechanisms. To address this, we propose single-cell Identifiable Feedback-controlled Latent Flow (scIFLF), a generative framework that introduces a physics-inspired structural prior that decomposes the latent flow into a macroscopic developmental drift and a restorative feedback toward functional attractors. Crucially, the gene-regulatory Jacobian is shown to be uniquely identifiable, invariant to the latent ambiguity, enabling principled discovery of gene regulations directly from the learned flow. Implemented with a multimodal Neural ODE, scIFLF aligns latent trajectories using entropy-regularized optimal transport. Benchmark experiments demonstrate that scIFLF outperforms state-of-the-art methods in trajectory interpolation and spatial coherence, while successfully recovering key driver genes of tissue development and organogenesis, bridging the gap between deep generative flexibility and biological interpretability.

Field trials use plot-level crop-state measurements to interpret how crops grow under real field conditions, but repeated measurements of canopy greenness, canopy amount, canopy closure, and growth stage require field visits, sensing campaigns, and post-processing. Modern wheat trials also collect weather records, trial metadata, field-camera images, UAV and satellite observations, proximal sensors, canopy products, and phenological surveys. Turning these records into a machine-learning benchmark is nontrivial because observations are sparse, asynchronous, unevenly collected, missing for structured reasons, and tied to measurement pipelines. We introduce INVITA-WheatFieldState, a benchmark derived from the INVITA wheat field-trial archive for estimating the crop state of a plot on a target date from observations available for that plot and date. Each plot-date example specifies a plot, date, and crop-state target, and is paired with available observations after temporal and source-provenance filtering. The derived dataset contains over 220k examples across NDVI, LAI, FCover, and Zadoks growth stage, covering canopy greenness, canopy amount, canopy closure, and phenological timing. LAI and FCover are released with provenance labels that distinguish product and proxy targets rather than pooled manual ground truth. The benchmark suite provides a plot-disjoint split matched to the plot-level experimental unit, validators, prediction schemas, regression representations, and prediction-level diagnostics. Results show that source, timing, metadata, and observation-availability structure are strong signals. Observation-set representations improve some all-example targets, while sensor-sequence, field-camera, and fusion representations remain target-dependent and coverage-bound. INVITA-WheatFieldState offers a concrete test case for ML methods that must learn from incomplete, asynchronous, and provenance-sensitive measurements used to study crop development in real fields.

In this paper, we introduce INEUS, a meshfree iterative neural solver for partial integro-differential equations (PIDEs). The method replaces the explicit evaluation of nonlocal jump integrals with single-jump sampling and reformulates PIDE solving as a sequence of recursive regression problems. Like Physics-Informed Neural Networks (PINNs), INEUS learns global solutions over the entire space-time domain, yet it offers a more efficient treatment of nonlocal terms and avoids the computationally expensive differentiation of full PIDE residuals. These features make INEUS particularly well suited for high-dimensional PDEs and PIDEs. Supported by a contraction-based convergence proof for linear PIDEs, our numerical experiments show that INEUS delivers accurate and scalable solutions for various high-dimensional linear and nonlinear examples.

Large language models (LLMs) are increasingly used for tasks involving creative problem solving and idea generation. However, However, there is a lack of consensus concerning their creative capabilities:: some studies report superior performances compared to humans, while others highlight structural limitations such as fixation and the homogenization of outputs. Existing evaluation approaches either rely on narrow, decontextualized tasks that do not capture goal-oriented generation or on broader settings that confound multiple aspects of the creative process, making it difficult to isolate the effects of task formulation, prompting, and evaluation design. Significantly, the role of structured prompting strategies in shaping idea generation remains underexplored. Therefore, we introduce IDEAFix, an evaluation framework for analyzing divergent thinking in open-ended idea generation tasks. We prompt models to generate multiple original solutions to controlled variations of short design scenarios, task attributes, and defixation prompting strategies. This design enables systematic analysis of how structured guidance influences LLMs' idea generation. Our results show that both task formulation and attribute selection significantly affect models' performance, and that simple prompting strategies can boost the originality of solutions. However, we also observe persistent output homogenization across models, confirming inherent limits in their ability to generate diverse solutions. Overall, IDEAFix provides a controlled, extensible framework for studying the mechanisms underlying LLMs' creativity.

The Allen--Cahn equation is one of the standard phase-field models for mean-curvature flow (MCF), parameterised by the diffuse-interface width $\varepsilon$. Practical simulations span a working range of $\varepsilon$, so a single model covering this range is needed. PDE-specific neural operators are accurate but must be retrained at every $\varepsilon$; general neural operators cover $\varepsilon$ in one model but lose the phase-field structure of the target. We introduce Interface-Augmented Diffusion-Reaction (IADR), a neural operator that addresses both shortcomings: a target network with a diffusion-and-reaction structure preserves the phase-field structure of the Allen--Cahn equation, while a hypernetwork that maps $\varepsilon$ to the weights of this target network amortises the dependence on $\varepsilon$ across the working range. A single IADR matches the per-$\varepsilon$ specialists on in-distribution data, retains their cross-shape generalisation under both $\varepsilon$- and geometry-out-of-distribution shifts, and runs at the per-$\varepsilon$ specialist's per-step inference cost. We further establish a $K$-step rollout error bound that aligns with the long-horizon behaviour observed in experiments.

Active perception—the ability of a robot to proactively select viewpoints to acquire task-relevant information—is essential for robust operation in real-world environments. However, existing approaches are typically limited to fixed objectives or constrained settings, and struggle to generalize to open-ended perception intents specified in natural language. We propose I-Perceive, a foundation model for language-conditioned active perception in large-scale indoor environments. Given a query image, a set of context images, and a natural language instruction, I-Perceive predicts a 6D camera pose that fulfills the specified perception intent. The model integrates a vision-language pathway for semantic grounding with a geometric reasoning pathway for multi-view 3D understanding, connected via multi-layer semantic fusion to enable language-conditioned geometric reasoning. To support scalable training, we construct a large-scale dataset of language-viewpoint pairs from both real-world scene-scanning data and simulated environments using an automated pipeline. Extensive experiments demonstrate that I-Perceive significantly outperforms strong baselines on prediction accuracy, viewpoint feasibility, and instructions alignment. The model exhibits strong zero-shot generalization to unseen scenes and instructions, and enables closed-loop active perception, progressively refining viewpoints over sequential interactions.

Zero-shot machine unlearning aims to remove the influence of specified training instances from a pretrained model when the retain set is unavailable. Instance-wise unlearning further operates at the level of individual instances, where a forget instance may induce instance-specific representation and margin deviations that are entangled with class-relevant local structure. However, without retain instances, separating these deviations from the structure that retraining would preserve is difficult, exposing retain-forget entanglement in representation space. We propose HLMR, a novel zero-shot instance-wise unlearning approach that formulates unlearning as local margin retraction in hyperspherical representation space. HLMR uses hyperspherical geodesic probes to construct a local reference in representation space, preserving the structure supported by this reference while unlearning instance-specific margin excess. Empirical evaluation across multiple dataset--architecture settings shows that HLMR aligns more closely with the retraining oracle in utility and membership-inference risk than state-of-the-art zero-shot baselines.

Dense Associative Memory (DenseAM) is a promising family of AI architectures that is represented by a neural network performing temporal dynamics on an energy landscape. While hyperparameter transfer methods are well-studied for feed-forward networks, these methods have not been developed for settings in which weights are shared across layers and within the layer, which is common in DenseAMs. Additionally, DenseAMs utilize rapidly peaking activation functions that are rarely used in feed-forward architectures. The confluence of these aspects makes DenseAM a challenging framework for using existing methods for hyperparameter transfer. Our work initiates the development of hyperparameter transfer methods for this class of models. We derive explicit prescriptions for how the hyperparameters tuned on small models can be transferred to models trained at scale. We demonstrate excellent agreement between these theoretical findings and empirical results.

Dynamic feature selection (DFS) is a machine learning framework in which features are acquired sequentially for individual samples under budget constraints. The exponential growth in the number of possible feature acquisition paths forces a DFS model to balance fitting specific scenarios against maintaining general performance, even when the feature space is moderate in size. In this paper, we study the structural limitations of existing DFS approaches to achieve an optimal solution. Then, we propose \textsc{Hyper-DFS}, a hypernetwork-based DFS approach that generates feature subset-specific classifier parameters on demand. We show that the use of hypernetworks compared to mask-embedding methods results in a smaller structural complexity bound. We also use a Set Transformer encoding to create a smooth conditioning space for the hypernetwork, so that functionally similar tasks are also geometrically close. In our benchmarks, \textsc{Hyper-DFS} outperforms all state-of-the-art approaches on synthetic and real-life tabular data. It is also competitive or superior across all image datasets tested, and shows substantially stronger zero-shot generalisation to feature subsets never seen during training than existing DFS approaches.

With the rapid growth of surveillance data, Person Re-Identification (ReID) is evolving toward large scale scenarios, where datasets exhibit substantial increases in both size and diversity. Under this trend, the widely adopted dense backbone architectures, such as Transformers, suffer from an inherent limitation: Shared parameters couple all input tokens, forcing the model to learn generic representations that are suboptimal for heterogeneous data sources. Although Mixture-of-Experts (MoE) architectures offer a promising solution for handling diverse data, they encounter a fundamental challenge in ReID. Specifically, person images exhibit high inter-instance similarity at the coarse level, while discriminative cues lie in subtle fine-grained details that are easily overlooked. As a result, existing MoE methods often suffer from severe routing collapse, leading to insufficient expert specialization and limited diverse knowledge acquisition capacity. To address these problems, we propose a novel framework, termed Hyperspherical Mixture-of-Experts for Large Scale Person Re-Identification (HypMoE-ReID). Specifically, motivated by theoretical analysis in hyperspherical space, HypMoE-ReID incorporates two key components: (1) a Token Purification mechanism that suppresses noisy tokens detrimental to expert specialization, and (2) a Routing Diversity Learning strategy that explicitly separates experts on a unit hypersphere to enforce specialization and prevent collapse. Furthermore, to facilitate the investigation in large scale ReID, we construct a large-scale ReID benchmark comprising 809,701 images by unifying 13 public datasets with significant distribution gaps. Furthermore, extensive experiments demonstrate that HypMoE-ReID outperforms state-of-the-art MoE-based methods by at least 11.3\%/10.3\% in Average mAP/R@1, and surpasses strong dense backbone models by 2.2\%/2.0\% in Average mAP/R@1. Our code will be released.

The efficiency of Large Language Model (LLM) serving has been significantly improved by prefix caching, which enables reuse of previously computed activations. However, extending prefix caching to hybrid linear–softmax language models remains challenging due to the presence of large recurrent states in linear attention modules. In practice, these states cannot be stored at every token position due to their substantial memory footprint, and are instead saved periodically (e.g., at chunk boundaries). This leads to a fundamental mismatch between token-level KV caching and coarse-grained state caching, resulting in reduced effective cache hit rates and additional recomputation overhead. In this work, we propose \sysname, a system designed to address the limitations of prefix caching in hybrid linear–softmax LLMs. \sysname is built on two key observations: (1) recurrent state naturally exhibit different effective memory ranges across heads, and (2) the distribution of state values presents structured outliers that hinder effective compression. Based on these insights, \sysname introduces two novel designs. First, we develop a recurrent-aware state reuse mechanism that selectively stores short-range head states, while reuse previously cached long-range head states, enabling fine-grained alignment with KV caching without incurring prohibitive memory cost. Second, we propose a dual-scale state quantization method that applies independent scaling along row and column dimensions to effectively compress states with structured outliers. We evaluate \sysname on hybrid linear–softmax LLMs under realistic serving workloads. Experimental results show that \sysname significantly improves effective prefix cache utilization, reduces recomputation overhead, and achieves substantial throughput gains without sacrificing model accuracy. These results highlight \sysname as an effective system-level solution for efficient LLM serving in hybrid architectures.

Probabilistic prediction heads in neural networks typically output either a Gaussian mixture or a single conformal region. Neither separates the distinct sources of uncertainty often present in real prediction tasks: a discrete choice among modes, bounded systematic drift within the chosen mode, and irreducible stochastic noise. We introduce the Hybrid Probabilistic Zonotope (HProbZ), an output head that represents these three sources as binary, bounded, and stochastic generators of a zonotope, and admits a closed-form likelihood by convolution. Sharing the bounded generator across prediction steps couples future predictions algebraically, so observing one step refines the predictive distribution at every remaining step in a single forward pass. We establish that the three generators are identifiable from the likelihood up to permutation, and that an HProbZ density is representationally distinct from any finite Gaussian mixture. The same shared structure provides analytic per-mode risk and distribution-free multi-modal conformal sets at inference time. Empirical analysis on representative prediction benchmarks supports the effectiveness of the design relative to same-encoder mixture baselines, while offering structural properties that mixture or convex-conformal predictors do not jointly provide.

Neural surrogates promise large speedups over classical solvers for physical dynamics but fail silently at sharp dynamical events such as shocks, fronts, and contact. We present hybrid neural world models for physical dynamics: a recipe for training and deploying multi-horizon surrogates in physical state space, where a single network with continuous horizon conditioning is trained with direct supervision against textbook reference solvers to predict any future state at horizon $T$ in one forward pass. Although no part of the training data, loss function, or architecture supervises discontinuity location, the trained surrogate encodes it implicitly, recoverable from its forward passes alone as a per-trajectory error map that concentrates on shocks, fronts, and contacts, and stays small elsewhere. The map is competitive with or better than standard label-free baselines including deep ensembles, learned error heads, gradient-magnitude indicators, and locally-adaptive conformal prediction, while using only a single trained network and requiring no calibration set or governing-equation knowledge. The recipe supports two operating points. Mode 1 runs the surrogate alone for maximum throughput, with same-hardware CPU speedups of $26\times$ to $72\times$ against textbook solvers on the PDE environments. Mode 2 uses the error map to gate a reference-solver fallback, deferring uncertain trajectories and roughly halving the surrogate's residual error at the default operating point. The recipe applies without modification across reaction-diffusion, compressible Euler, and rigid-body collision dynamics.

Face de-identification (FDeID) has emerged as a critical privacy-preserving technology, yet its evaluation remains fundamentally fragmented. Existing protocols rely on inconsistent metrics, heterogeneous datasets, and partial annotation coverage, so methods targeting different utility dimensions, such as landmark versus expression preservation, are reported on different benchmarks under different metrics, rendering cross-method comparison infeasible. We revisit FDeID evaluation from both the data and metric perspectives. On the data side, we introduce UTILFACE, a curated, demographically balanced benchmark with high identity diversity, assembled from four large-scale face datasets through identity-aware cleaning, resolution enhancement, and stratified filtering. On the metric side, we propose HiFD, a Hierarchical Face De-identification metric that unifies identity suppression, multi-level utility preservation, and image quality under a single consistency-based paradigm: every component is computed from pretrained estimators’ outputs on the original face and its de-identified counterpart, directly quantifying how much identity is suppressed and how much downstream-perceivable utility survives. HiFD organizes facial signals into a three-level utility hierarchy spanning macro cues (L1), micro cues (L2), and imperceptible cues (L3), and aggregates the five resulting components into a single interpretable score via weighted harmonic mean, with configurable application-specific profiles. Using this unified protocol, we conduct a comprehensive comparative study spanning adversarial, GAN-based, and diffusion-based methods, surfacing trade-offs and failure modes that remain invisible under existing protocols. We will release the benchmark and evaluation toolkit to foster reproducible research in privacy-preserving face analysis (anonymous codebase: https://anonymous.4open.science/r/HiFD).

We measure how much one recurrence is worth to a looped (depth-recurrent) transformer, in equivalent unique parameters. From an iso-depth pretraining sweep across recurrence counts $r \in \{1, 2, 4, 8\}$ spanning ${\sim}50\times$ in training compute, we fit a joint scaling law $L = E + A\,(N_\text{once} + r^{\varphi} N_\text{rec})^{-\alpha} + B\,D^{-\beta}$ and measure a recurrence-equivalence exponent $\varphi = 0.46$. Intuitively, $\varphi$ tells us whether looping a block $r$ times is equivalent in validation loss to $r$ unique blocks of a non-looped model (full equivalence, $\varphi{=}1$) or to a single block run repeatedly with no capacity gain ($\varphi{=}0$). Our $\varphi = 0.46$ sits in between, so replacing unique blocks with shared recurrences increases validation loss at matched training compute. For example, at $r{=}4$ a 410M looped model performs on par with a 580M non-looped model, but incurs the training cost of a 1B non-looped one. We demonstrate the utility of $\varphi$ as a diagnostic tool on two case studies: commonly used truncated backpropagation lowers $\varphi$ to $0.38$, indicating that the loop mechanism is poorly trained under truncation, even though validation loss decreases. Conversely, hyperconnections raise $\varphi$ to $0.65$, a genuine capacity gain. Our method separates true loop improvements from training-side gains, a distinction raw validation loss cannot make.

Kalman filtering provides a principled inference framework for linear Gaussian hidden Markov models, but it leaves open a complementary question of representation: what is the minimum amount of information about the observation history that must be retained to support accurate filtering? We show that this question leads naturally to an indirect rate-distortion problem, in which an encoder observes the history of noisy measurements while distortion is evaluated with respect to the latent state. Directly solving this problem is challenging, since the optimization ranges over arbitrary stochastic kernels from a time-varying observation history to an output representation, resulting in an infinite-dimensional formulation. We overcome this difficulty by proving that any feasible encoder can be replaced by a stochastic encoder acting only on the Kalman posterior mean, achieving the same mean-square error with no larger mutual information. The reduced problem admits a closed-form solution in the form of a linear Gaussian blurring mechanism determined by the spectral decomposition of the Riccati solution. This yields an explicit expression for the rate distortion function $R_t(\epsilon)$, showing that any representation with distortion at most $\epsilon$ must retain at least $R_t(\epsilon)$ nats of information. We further use this characterization to derive information-theoretic lower bounds on the population risk of sequential learning and on the prediction error of temporal GP. Numerical experiments corroborate the theoretical predictions.