All papers

Monitors model explanation drift on evolving graphs to provide early warnings before predictive uncertainty guarantees fail.

Presents an evaluation benchmark and multi-dimensional protocol for patent machine translation across Chinese, English, Japanese, and Korean.

Accelerates video diffusion models by using closed-loop calibrated caching instead of fixed heuristic thresholds across denoising steps.

Applies rate-distortion theory to structure ordered visual codes so text captions form the initial low-rate representations for images.

Uses a learned scene-coordinate localization module to maintain consistent 3D spatial context for image-goal robot navigation.

Introduces a diagnostic evaluation framework to systematically test generalization and perturbation robustness in vision-language-action robotic models.

Improves reinforcement learning for reasoning models by sampling continuations from truncated traces to provide more accurate intermediate rewards.

Presents a deep learning framework that integrates heterogeneous surface EEG and intracranial EEG signals for multimodal seizure classification.

Classifies languages at the token level across 111 language-script combinations in mixed-language and code-switched texts.

Dynamically balances relevance and visual diversity per sample to compress visual tokens in vision-language models more effectively.

Proposes a generative adversarial network guided by differentiable segmentation models to edit image colors realistically and with precise control.

Generates prompt-adapted adversarial images that block unauthorized visual questions on vision-language models while still answering legitimate prompts correctly.

Incorporates human understandability scores directly into feature selection to make tabular machine learning explanations easier for users to interpret.

Combines physical models and state space neural networks in a closed loop to forecast long-term chemically reactive air quality.

Introduces a general visual memory planner that retrieves, creates, and updates visual evidence across shots for long-horizon video generation.

Improves long-context handling in Mamba state space models without retraining by using surprisal scores to guide state routing.

Reconstructs divergence-free 3D fluid velocity fields from sparse, irregular observations in a single forward pass using a set-to-field transformer.

Tests a hosted decision model to show that adding irrelevant options systematically shifts output probability distributions between top candidates.

Detects adversarial nodes on evolving graphs without fraud labels by measuring the consistency between node features and aggregated neighborhood contexts.

The paper analyzes the impact of teacher rationales before distilling medical reasoning capabilities into compact language models.

The paper introduces an architecture that incorporates token queue dynamics into wireless communications for foundation model inference services.

The paper proposes VAttnPO, using teacher visual attention to refine token-level policy optimization for multimodal reinforcement learning.

The paper analyzes pre-sink hidden-state geometry to detect and modulate attention sink formation in language models.

The paper proposes evaluating partial generation outputs to stop harmful model interactions early while avoiding unnecessary prompt rejections.

The paper presents TabWM, a tabular world model that infers system dynamics from past executions to predict database edits from actions.

The paper proposes GeoVec-VLM, integrating geospatial vector geometry as a native input modality to improve spatial reasoning in vision-language models.

The paper introduces LIBERO-Industry, a benchmark evaluating robotic manipulation policies under industrial process constraints such as moving material flows and tolerances.

The paper introduces InfoVis, a 9B multimodal model along with a benchmark and training dataset for slide image generation and editing.

The paper reveals a numerical side channel in floating-point bias addition that enables recovering full logit vectors from API queries.

The paper presents StableJudge, an external lightweight sentry model that sanitizes prompt-injection attacks before LLM-as-a-judge scoring.

The paper investigates progressive depth growth during self-supervised vision transformer pretraining to improve downstream accuracy while reducing computation.

The paper introduces a direction- and topology-aware graph transformer to predict neural network architecture attributes like accuracy and latency.

The paper proposes an unsupervised drift monitor using internal embedding distances to flag unfamiliar inputs in deployed deepfake speech detectors.

The paper presents an empirical study evaluating syntactic language models at scales up to 1B parameters across linearizations and masks.

The paper presents multi-objective bandit algorithms to identify a small set of arms that minimizes degradation across multiple objectives.

The paper presents an LLM-based metadata pre-screening method to filter candidate videos before downloading, reducing data curation costs.

The paper proposes CLIVO, a target-aware method combining semantic clustering and learned validation objectives for differentiable multimodal instruction data mixing.

The paper argues that full robotic value alignment is impossible and examines how to manage acceptable misalignment in autonomous systems.

The paper formalizes transparency as role-dependent answerability using information flow networks to evaluate AI-supported decision systems.

The paper proposes Neural Bayesian Networks, replacing classical probability tables with differentiable tensor operations for scalable probabilistic reasoning on GPUs.

The paper applies an information-flow modeling methodology to assess public-sector AI recruitment tools under European Union AI Act requirements.

The paper analyzes legal eDiscovery review data to compare generative AI tools against continuous active machine learning and manual human review.

The paper proposes an alignment framework using human feedback signals to personalize search query rewriting without causing intent drift.

The paper analyzes how Cover Tree nearest-neighbor implementation details and query modes materially change runtime performance across datasets.

The paper introduces a large-scale benchmark of synthetic underwater audio records to evaluate audio-language models on acoustic recognition tasks.

The paper investigates how fine-tuning language models on descriptions of user motivations influences their downstream trust and cooperative behavior.

The paper evaluates the impact of sarcasm on Turkish hate speech detection models using a targeted external test dataset.

The paper introduces an in-context learning method that selects demonstration examples by mining a vision-language model's own past prediction mistakes.

The paper introduces an industrial generative retrieval framework featuring collaborative tokenization and preference optimization for WeChat search systems.

The paper presents a structured step-by-step workflow designed to help non-programmer scientists implement and validate published machine learning models.

The paper proposes an offline reinforcement learning method that optimizes generative decision chunks in latent space using conditional velocity fields.

The paper introduces an autonomous driving model unifying diffusion-based trajectory generation with autoregressive chain-of-thought reasoning to reduce latency.

The paper proposes a context-efficient foundation model for generating realistic tabular data from limited context observations.

The paper analyzes forty legal instruments to examine how copyright governance protects scholarly authors whose works train generative AI models.

The paper proposes a manifold-aware graph diffusion network to generate valid functional brain connectomes while capturing fine-grained geometric structure.

The paper introduces ProbabilityBench to evaluate model uncertainty calibration against analytically computed, ground-truth instance-level probabilities across three text environments.

The paper studies low-rank adaptation in Whisper to evaluate whether transcription gains require group-specific adapters rather than pooled fine-tuning.

The paper benchmarks three quantum kernel SVM encodings against classical models for imbalanced credit card fraud detection under NISQ constraints.

Introduces a framework that discovers and separates physical neural operators from unlabeled trajectory data containing multiple mechanisms.

Introduces a visual document retrieval benchmark focused on multi-page and cross-document evidence configurations in long documents.