Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching
Flow matching approach for quantifying aleatoric uncertainty in medical image segmentation, modeling expert annotation variability.
Flow matching approach for quantifying aleatoric uncertainty in medical image segmentation, modeling expert annotation variability.
ShadowNPU enables efficient on-device LLM inference by redesigning attention operator for NPU execution, improving privacy and performance.
MedShift addresses domain gap between synthetic and real X-ray images using conditional transport for improved generalization to clinical settings.
LifeAlign framework for lifelong LLM alignment across sequential tasks using memory-augmented preference optimization without catastrophic forgetting.
Training-free prompt engineering strategy using state reconstruction and history reminders for efficient multi-turn LLM dialogue.
Chiplet-based RISC-V SoC architecture with modular AI acceleration for edge AI devices with improved yield and efficiency.
StateX post-training method improves recall ability in RNNs and state-space models for long-context information retrieval.
Component-level energy assessment framework analyzing transformer efficiency to enable green AI development.
Explainable bias-aware generative framework combining multimodal attention, attribution methods, and iterative feedback for fair generation.
Template Infilling conditioning strategy enables diffusion language models to handle flexible structural prompting beyond prefix-based generation.
Knowledge Reasoning Language Model unifies language models with knowledge graphs for inductive reasoning over unknown entities and relations.
RLAIF-SPA uses structured AI feedback to improve emotional expressiveness and semantic-prosodic alignment in text-to-speech synthesis.
Methods for evaluating and mitigating fairness issues in LLMs at inference time to reduce harmful behaviors and drift.
Eigen-Value method for efficient data valuation using eigenvalue-based approach, focusing on out-of-distribution robustness.
Data-efficient approach for adapting humanoid robot whole-body motion control from single motion examples using walking priors.
Routing-based architecture for multimodal LLMs enabling continual learning across sequential tasks while preventing catastrophic forgetting.
Snowflake's Cortex AISQL production engine integrates semantic operations into SQL for querying structured and unstructured data.
LLM-based automated feedback system for physics problem solving using evidence-centered design methodology.
CB-APM applies deep learning with interpretability-by-design to stock market prediction using analyst consensus data.
MedMistake pipeline automatically extracts and replicates LLM errors in medical conversations to create evaluation benchmarks.
PhyAVBench benchmark evaluates physics-plausibility of audio in text-to-audio-video generation models.
Framework using sparse autoencoders to identify and steer high-order semantic features in LLMs for reliable control of language generation behaviors.
IBISAgent improves pixel-level visual reasoning in medical multimodal LLMs for biomedical object segmentation through enhanced training strategies.
Research paper analyzing LLM truthfulness under contextual perturbations, showing self-consistent facts can collapse under mild interference.
Research paper proposing predictive reasoning to replace costly physical execution in ML agent workflows using internalized execution priors.
ReaMIL, a multiple instance learning approach for histopathology with reasoning-aware evidence selection under sparsity constraints.
WISP system for distributed LLM inference at the edge using dynamic drafting and SLO-aware batching to balance workload across networks.
Cross-domain few-shot learning for hyperspectral image classification using mixup foundation models to reduce overfitting.
R3G framework for vision-centric visual question answering using reasoning, retrieval, and reranking to select and integrate relevant images.
QUASAR, a universal autonomous system integrating LLMs for atomistic simulation and materials science discovery with flexible tool-calling for production workflows.
Study on hierarchical gating and calibration for human value detection from sentences using Schwartz higher-order categories.
Deep learning and GNN methods for traffic forecasting that incorporate incident data as external disturbances to improve predictions.
Graph-theoretic analysis of computational complexity in learning ground state phases of Heisenberg antiferromagnets using variational methods.
Derives deterministic operational semantics for Grassroots Logic Programs (GLP), a multiagent concurrent logic programming language for serverless platforms.
MedXIAOHE, a medical multimodal foundation model with entity-aware continual pretraining, achieves state-of-the-art on clinical benchmarks.
Method to detect backdoor attacks in LoRA adapters without test inputs by analyzing weight space, addressing security vulnerabilities in shared model repositories.
Study on human-agent co-creative collaboration patterns in shared workspaces, revealing capability gaps for concurrent interaction vs sequential delegation.
Agora platform uses LLMs with AI personas to teach civic competence and consensus-finding skills through deliberative democratic practice.
MM-tau-p²: Persona-adaptive evaluation framework for multi-modal LLM agents with dual-control settings exposing user personality and behavior adaptation.
HyCon: Hyperbolic control mechanism for steering text-to-image models away from unsafe concepts using parallel transport instead of Euclidean adjustments.
Frequency-based data curation method for selecting calibration data to preserve LLM performance during post-training pruning and quantization.
Examines prompt framing effects on LLM decision-making in threshold voting tasks across model families under isolated, non-interactive settings.
HR Simulator: Game-based evaluation of LLMs navigating complex workplace social norms like giving feedback and rejecting requests appropriately.
Identifies first-mover bias in SHAP explanations from gradient boosting's sequential fitting causing attribution instability under multicollinearity.
Step-level faithfulness evaluation shows chain-of-thought reasoning in frontier LLMs is often decorative, post-hoc narrative rather than genuine reasoning.
Code Review Agent Benchmark: Dataset and evaluation framework for assessing AI agents' ability to review code quality in generated codebases.
DiffAttn: Diffusion-based framework for predicting drivers' visual attention using LLM-enhanced semantic reasoning for intelligent vehicles.
SABLE: Semantics-aware backdoor attack on federated learning using realistic, in-distribution visual triggers instead of synthetic patterns.
MemFactory: Unified framework for training and inference of memory-augmented LLM agents with reinforcement learning optimization of memory operations.
Compares GraphRAG with VectorRAG for retrieval-augmented generation, showing simpler vector-based approaches handle chunk relationships effectively.