All papers

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 proposes a system for physical AI that continuously monitors environments to identify intervention needs and execute timely actions.

The paper introduces a multi-agent framework comprising an orchestrator and four specialized agents to automate iterative drug discovery workflows.

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 introduces an open-source framework and recipe for training and evaluating deep-search language agents on complex web exploration tasks.

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

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.

Develops a multimodal search framework that learns to turn intermediate visual discoveries into guidance for subsequent search actions.

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

Proposes a clinical world-model architecture using unified patient states and ordered action histories to anticipate treatment outcomes.

Introduces a benchmark and agent framework for embodied navigation tasks requiring physical environment interactions like opening doors.

Extracts and categorizes mathematical proof mechanisms and lemmas from an open release of synthetic mathematical manuscripts.

Measures post-training activation geometry shifts against run-to-run noise, identifying distinct structural signatures left by supervised fine-tuning.

Presents an open-source research workbench and portable packaging format to preserve relationships between computations, artifacts, and assessments.

Presents a unified encoder that jointly models type definitions and text for cross-domain entity, relation, and event extraction.

Designs an explicit digital-twin world model where robotic agents inspect, simulate, and calibrate physical parameters before acting.

Converts half-duplex speech models into streaming full-duplex conversational models capable of simultaneous listening and speaking.

Combines genetic programming and diagnostic evaluation loops to discover quantitative financial trading factors without backtest overfitting.

Introduces a dynamic spatial audio reasoning benchmark and an architecture tracking acoustic sources across moving indoor scenes.

Demonstrates that the scoring format chosen for an evaluation model strongly restricts its expressive capacity and ranking reliability.

Presents a compliant teleoperation control framework that reduces excessive contact forces during dexterous manipulation data collection.

Uses language-model perceptual descriptions of complex chemical mixtures to predict perceived odor similarity from molecular composition.

Discovers robot capabilities using a generative flow network that creates symbolic task skeletons grounded in physical robot affordances.

Presents a multimodal benchmark testing whether models can explain observed chemical effects by linking structural features to reaction mechanisms.

Introduces a native sparse attention mechanism that combines sliding windows and keypoint selection for linear-time context extrapolation.

Uses reinforcement learning on large routing instances to help diffusion-based solvers generalize beyond their training instance sizes.

Applies regenerative software primitives so programs can be deleted and rebuilt by agents without losing architectural intent and knowledge.

The paper demonstrates recipes to extract single-pass probability decisions directly from open instruction-tuned language models.

The paper proposes a multi-agent framework to coordinate analytical tools and evaluate multi-factor supply chain risks across diverse locations.

The paper demonstrates multi-fidelity training to optimize data generation compute and error scaling for neural PDE surrogates.

The paper presents a benchmark of synthetic paired comparisons to evaluate video reward models across specific visual and motion dimensions.

The paper describes a modular software backend architecture for extracting energy domain information and answering grounded questions from web sources.

The paper presents a reinforcement learning post-training method that scores intermediate generation trajectories in diffusion language models.

The paper proposes information-theoretic tests to measure learned equivariance in intermediate neural network representations without knowing explicit group transformations.

The paper presents a reasoning-conditioned framework that refines chain-of-thought explanations to improve propaganda detection without explanation annotations.

The paper introduces phase-conditioned affordance fields to improve equivariant visuomotor policy learning for robotic manipulation.

The paper introduces a benchmark to evaluate coding agents building executable physical simulations from video observations using Blender.

The paper introduces an adaptive network-slicing orchestration framework for medical IoT applications operating across edge-computing infrastructure.

The paper evaluates small language models for text-to-SQL tasks and tests lightweight iterative retry workflows to improve query accuracy.

The paper introduces decision models that balance fast single-pass predictions with adaptive reasoning for uncertain cases using reinforcement learning.

The paper introduces a diagnostic framework to test whether language model agents genuinely infer opponent beliefs rather than following superficial behavioral patterns.

The paper proposes clustering observations by feature importance profiles to incorporate latent factor representations into deep learning models.

The paper develops an approach to map AI semantic blind spots and evaluates the quality of distinct human ideas.

The paper presents a minimal coding agent baseline for Lean theorem proving using standard bash tooling instead of specialized harnesses.

The paper proposes a budgeted KV-cache repair technique to restore model performance efficiently after deleting obsolete context tokens.

The paper presents a statistical mechanics framework using modern Hopfield models to analyze inference reliability and phase transitions in Transformer attention.

The paper introduces a distribution-level evaluation metric for medical image segmentation to assess prediction sets without requiring paired ground-truth masks.

The paper proposes a hierarchical and attribute-aware supervision method to improve training efficiency and long-tail performance in generative recommendations.

The authors analyze compatibility across multi-schema latent spaces in motion models, showing shared representations require explicit paired data to align.

The paper introduces a benchmark assessing whether merchant websites provide adequate access, terms, and evidence for autonomous shopping agents.

The authors propose an evolutionary framework that optimizes instance coverage and updates language model weights to discover complementary optimization heuristics.

The authors introduce a topology-aware transformer meta-trained across multiple electricity markets to forecast day-ahead electricity prices for unseen grids.