All papers

Applies multi-directional spatial context modeling and high-frequency guidance for cross-view geographic localization.

Uses fMRI encoding representations to guide targeted emotional edits on images while preserving visual content.

Combines deep segmentation networks with conformal prediction to estimate pixel-level uncertainty on thyroid ultrasound images.

Develops a class-conditioned boundary evidence method for open-set recognition of visually similar insect species.

Reconstructs physical 3D objects with controllable material properties and interaction dynamics via video diffusion models.

Designs a global-local memory interface with multi-precision spectral encoding for consistent video world models.

Introduces a state-isolation routing mechanism for Vision Mamba models to enhance spatially heterogeneous underwater images.

Presents perturbation techniques to protect personal video data from unauthorized identity replication by generative models.

Improves 2D Gaussian Splatting rendering of near-field reflections using observed image environment data.

Reconstructs images and videos from fMRI signals using cortex-inspired serial decoding and multimodal pre-training.

Evaluates shortcut learning and out-of-distribution failure in geospatial foundation models on cross-disaster damage assessment tasks.

Provides an attribution method for selective state-space video models by tracing accumulated recurrence in a single forward pass.

Benchmarks heuristic, machine learning, and deep learning methods for identifying rare dead cells in fluorescence microscopy.

Proposes a simulation-grounded framework rendering real-world 3DGS scenes to supervise autonomous driving world action models.

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.

Pre-trains vision transformers on multi-frame event camera data using masked auto-encoding for action recognition.

Reviews satellite-based building damage assessment across data, evaluation protocols, and method formulations.

Animates flowing water in static 3D Gaussian Splatting scenes using 2D optical flow lifted into a 3D Eulerian motion field.

Uses self-supervised thermal-RGB fusion to detect drivable wheel tracks on snow-covered roads for autonomous vehicles.

Analyzes LiDAR object detection in snow to separate atmospheric noise from performance gains caused by increased point reflections.

Replaces random crop sampling in 3D medical self-supervised learning with a guided probe bank based on anatomical priors.

Formulates robot action diffusion policies that condition directly on physical timestamps using time-aware rotary positional embeddings.

Improves multimodal diffusion transformer generation by suppressing image-to-text feedback loops during iterative denoising.

Introduces a shared latent interface enabling handoff and translation of denoising trajectories across heterogeneous image diffusion models.

Presents a multi-agent generation pipeline and benchmark for creating physically grounded, articulated 3D digital assets.

The paper proposes embedding multi-scale wavelet-domain watermarks into initial diffusion noise to enable robust provenance tracking under image removal attacks.

The paper evaluates label-only backdoor defenses and demonstrates that inspecting representations in unpoisoned feature spaces improves poison detection accuracy.

The paper presents an adversarial perturbation defense that disrupts the value vector diversity in image-to-video diffusion models to prevent unauthorized generation.

The paper proposes a deep convolutional, quantization-free watermarking framework to embed metadata into electrocardiogram signals without hurting diagnostic details.

The paper models structured sentiment analysis as dependency graph parsing implemented through sequence labeling across five languages.

The paper proposes a four-tensor attention architecture that jointly normalizes attention over semantic and temporal fibers for scene prediction.

The paper defines evaluation metrics to show that language models continue generating reasoning traces even when instructed not to think.

The paper introduces a dual-path parametric memory method that combines pooled history representations with sequential adapter updates.

The paper demonstrates that entropy-based byte patching misses predictable calculation points and proposes a boundary-dependence signal to fix it.

The paper develops an LLM-based pipeline to automatically detect outcome switching and presentation spin in clinical trial reports.

The paper benchmarks a specialized decision model against general-purpose LLMs on English and Chinese stance detection tasks.

The paper introduces a method to unlearn target knowledge while recovering collateral capability loss in non-target tasks.

The paper proposes an event-centric memory framework that converts dialogue history into query-relevant graph working memory for conversational agents.

The paper describes training a competitive decoder-only search reranker from scratch using modest computational resources and common datasets.

The paper benchmarks a specialized multiple-choice decision model against 19 general-purpose LLMs across knowledge, reasoning, and multilingual tasks.

The paper reviews LLM-based first-order logic autoformalization, distinguishing ontology extraction from logical translation and outlining key benchmarking challenges.

The paper introduces an educational tool combining Arabic knowledge graphs and passage retrieval to generate narrative-based questions.

The paper meta-analyzes 25 years of automated verbal deception detection models and identifies a persistent accuracy ceiling around 70 to 75 percent.

The paper shows that whole-context KV cache rehearsal harms compression under tight budgets, proposing targeted rehearsal to preserve answer-relevant entries.

The paper trains 123 models across 54 tokenizers to show tokenizer choice matters more for languages with less training data.

Analyzes interaction failures between KL regularization and rewards in group policy optimization and proposes a calibrated surrogate objective.

Releases BERT models pre-trained with yearly chronological cutoffs to reduce lookahead bias in scientific and patent text mining.

Evaluates whether language models can act as world models to predict empirical experimental outcomes across AI research workflows.

Designs an inference serving system optimized for dynamic, token-level routing across multiple language models.

Proposes a scope-then-rank retrieval method using document topology and relational structure rather than pure semantic similarity.

Introduces a post-training framework that trains text-to-SQL agents directly inside interactive database execution environments.

Identifies distribution shift during autoregressive decoding and proposes a decoding-aware pruning method to accelerate model inference.

Presents a symmetry-aware value cache merging method to compress key-value memory during long-context decoding without architectural changes.

Proposes a method to select a compact subset of procedural skills that provide effective training signals during model distillation.

Introduces a multilingual tokenizer that uses structural discovery and linguistic constraints to improve downstream model representations.

Presents a framework that uses language models offline to generate executable feature-engineering code for tabular risk models.

Presents an arena benchmark where pairs of coding agents dynamically generate, test, and debug code against each other.

Introduces a lightweight correction mechanism to restore token dependencies during parallel draft generation in speculative decoding.

Proposes an unsupervised framework using contrastive learning and clustering to induce, name, and annotate event types from text.