All papers

Presents a cycle-consistent adversarial model enabling continuous sound pressure level adjustments for speech vocal intensity conversion.

Analyzes theoretical connections between generative diffusion models, flow matching, and entropic optimal transport formulations.

Develops a kernel-based maximum mean discrepancy framework to formalize and estimate the external validity and replicability of experimental studies.

Introduces a replay benchmark to evaluate robot motion smoothness and controller latency in vision-language-action manipulation pipelines.

Develops a differentially private and distributionally robust ADMM framework for multiclass classification across distributed client data.

Presents a dual-path architecture combining normalized attention and incidence priors for hypergraph message-passing neural networks.

Applies a surrogate functional framework incorporating ground-state electron density to improve molecular property prediction models.

Examines sampling methods for test-time sharpening in language models and shows simple best-of-N approaches yield comparable gains.

Introduces a benchmark evaluating tabular foundation models across point prediction, distributional calibration, and prediction interval efficiency.

Proposes a scalable variational inference method using sparse autoregressive structures and normalizing flows for non-Gaussian spatial field models.

Compares seven calendar feature encoding techniques across four time series foundation models to evaluate impact on forecast accuracy.

Introduces a parameterized quantum-walk model optimized via backpropagation to rank network node responses to seed perturbations.

Proposes an in-context score operator framework allowing diffusion models to adapt to unseen target distributions without retraining.

Develops a theoretical framework linking training objective losses to sampling accuracy and finite-time convergence in Generative Flow Networks.

Introduces a multi-mode urban mobility benchmark across Seoul to evaluate origin-destination travel demand forecasting models.

Presents a compressed latent world-action model enabling faster robotic manipulation inference on resource-constrained edge hardware.

Introduces two synthetic mathematical task datasets to study how task diversity affects model learning during pretraining.

Empirically evaluates the Bayesian posterior approximation quality and amortization errors of prior-data fitted networks across synthetic prior tasks.

Analyzes convergence rates of a multi-objective optimization method pairing norm-constrained linear minimization oracles with entropic task-weight updates.

Establishes stochastic first-order and zeroth-order algorithms achieving optimal rates for constrained nonsmooth nonconvex optimization.

Introduces randomized momentum transport to track stochastic gradients using probabilistically sampled transport corrections alongside cheap predictors.

Proposes a two-phase variance reduction method that adapts to average, standard, and noiseless smoothness settings without predefined hyperparameter constants.

Develops a sequential conditioning framework enabling Gaussian process linear partial differential equation solvers to enforce linear inequality constraints.

Evaluates combinatorial fusion analysis to build diverse ensembles combining classical models and variational quantum circuits for classification tasks.

Repurposes pretrained tabular foundation models into supervised dataset encoders using dataset-level readouts and contrastive post-training.

Presents Shared Canonical Component Removal to quantify predictive information retained by raw machine learning representations beyond predefined expert features.

Investigates training mechanisms in continuous diffusion language models, proposing design modifications for self-conditioning and embedding geometry.

Examines how chronological versus shuffled pre-training knowledge sequencing influences the sample efficiency of subsequent model knowledge updates.

Evaluates full sandbox snapshot exploration for coding agents, finding benefits primarily when recovery requires restoring runtime dependencies outside Git repositories.

Applies dynamic budget allocation algorithms at inference time to select scaffolds and epitopes during de novo nanobody generative design.

Derives finite-sample convergence guarantees for fully discrete conditional diffusion models with finitely many denoising steps under smooth density assumptions.

Introduces PISO, an optimization framework that combines partially available gradients with zeroth-order residual tracking to reduce variance from random probing.

Analyzes the dimensional requirements of exact recurrent memory states for cumulative learning under convex residual loss functions.

Establishes finite-time error bounds for single-trajectory asynchronous Q-learning that incorporate the hitting time of the underlying Markov decision process.

Introduces Prompteina, an all-atom generative model and dataset for generating de novo protein structures conditioned on natural language prompts.

Develops a multi-scale biophysical spiking neural network model to simulate sleep-wake regulation and discrepancies between objective sleep and subjective restedness.

Applies reinforcement learning to manage bundle scheduling and flow control at relay nodes in deep-space communication networks.

Analyzes round complexity in parallel coordinate samplers, proving a separation between schedule-averaged and output-law Kullback-Leibler divergences.

Proves upper bounds on the topological expressivity of neural networks measured by the Betti numbers of their output spaces.

The paper proposes a complex-valued neural network to fuse audio and visual similarity streams for open-vocabulary audio-visual event localization.

The paper proposes MIDAPN, a unified spatiotemporal network using identity-aware graphs and temporal learning across numerical and narrative-flow time series.

The paper introduces benchmarks and summarizes winning solutions for city-scale audio-visual reasoning and spatial intelligence at the Perception Test 2026.

The paper presents DVLA-RL++, combining complementary semantic purification and reinforcement learning gating to reduce context contamination in few-shot learning.

The paper proposes data-space iteration, a few-step generative method removing flow discretization by directly refining noise predictions in data space.

Introduces an end-to-end diffusion model operating directly in radar space for few-step probabilistic precipitation nowcasting.

Presents a dense correspondence framework combining generative and semantic representations to handle non-rigid image editing and generation.

Designs a vision transformer using a single recurrent block with depth-programmed experts to reduce stored parameters without accuracy loss.

Presents an embodied coding agent framework using active physical and visual probing to refine aerial manipulation policies.

Proposes a hybrid linear and sparse attention framework to accelerate feed-forward 3D foundation models across many views.

Synthesizes stereo manipulation data from monocular robot demonstrations using estimated depth and video diffusion models.

Introduces an asynchronous prefix denoising framework for extreme latent compression in diffusion models.

Introduces a federated vision-language model tuning framework combining global shared adapters with client-specific personalized projection adapters.

Studies how text-to-image anchor properties correlate with downstream video generation quality in image-to-video diffusion pipelines.

Audits joint-embedding predictive learning in medical CT scans, showing a bias that degrades performance on unpredictable localized pathology.

Investigates decoding register tokens alongside patch tokens in vision transformers to preserve global scene appearance details.

Introduces a streaming causal transformer framework for generating human motion continuously from spatial audio inputs.

Aligns robot policy representations with retrieved pretraining examples to maintain generalization during low-data fine-tuning.

Proposes a memory-efficient cross-attention method for distilling large paired audio-visual datasets into compact synthetic sets.

Presents a unified plug-and-play posterior sampling framework combining diffusion and flow models via exact Gaussian conditioning.

Uses a frozen world model to continually steer generalist robot policies onto new tasks without catastrophic forgetting.