World PulseNow
Newsletter

Probability-Biased Attention over Directed Bipartite Graphs for Long-Tail ICD Coding

arXiv — cs.LG•Friday, November 14, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The proposed method for ICD coding, which utilizes a Directed Bipartite Graph Encoder, reflects a broader trend in AI research focusing on complex multi-label classification tasks. Similar to the challenges in Fine-Grained Visual Classification (FGVC), where subtle inter-class differences complicate classification, the ICD coding task also grapples with a vast label space and the prevalence of rare codes. The integration of probability-based biases in attention mechanisms, as seen in the proposed method, aligns with emerging strategies in AI that enhance model performance by leveraging co-occurrence data, a theme echoed in the ReactionTeam study on divergent thinking in synthetic chemistry.
— via World Pulse Now AI Editorial System
Read Original

Was this article worth reading? Share it

Recommended Readings
Optimal Self-Consistency for Efficient Reasoning with Large Language Models
arXiv — stat.ML16 hours ago
Optimal Self-Consistency for Efficient Reasoning with Large Language Models
PositiveArtificial Intelligence
The paper titled 'Optimal Self-Consistency for Efficient Reasoning with Large Language Models' presents a comprehensive analysis of self-consistency (SC) as a technique for enhancing performance in chain-of-thought reasoning. SC involves generating multiple responses from a large language model (LLM) and selecting the most frequent answer. The study addresses the high costs associated with SC when applied at scale and introduces Blend-ASC, a novel variant aimed at improving sample efficiency and scaling behavior.
Read full article
via arXiv — stat.ML
iMAD: Intelligent Multi-Agent Debate for Efficient and Accurate LLM Inference
arXiv — cs.CL2 days ago
iMAD: Intelligent Multi-Agent Debate for Efficient and Accurate LLM Inference
PositiveArtificial Intelligence
The paper introduces Intelligent Multi-Agent Debate (iMAD), a framework designed to enhance the efficiency and accuracy of Large Language Model (LLM) inference. iMAD selectively triggers Multi-Agent Debate (MAD) only when beneficial, addressing the inefficiencies of triggering MAD for every query, which incurs high computational costs and may reduce accuracy. The framework learns to make informed debate decisions, improving reasoning on complex tasks while significantly reducing token usage by up to 92%.
Read full article
via arXiv — cs.CL
Short-Window Sliding Learning for Real-Time Violence Detection via LLM-based Auto-Labeling
arXiv — cs.CV2 days ago
Short-Window Sliding Learning for Real-Time Violence Detection via LLM-based Auto-Labeling
PositiveArtificial Intelligence
The paper presents a Short-Window Sliding Learning framework designed for real-time violence detection in CCTV footage. This innovative approach segments videos into 1-2 second clips, utilizing Large Language Model (LLM)-based auto-captioning to create detailed datasets. The method achieves a remarkable 95.25% accuracy on the RWF-2000 dataset and improves performance on longer videos, confirming its effectiveness and applicability in intelligent surveillance systems.
Read full article
via arXiv — cs.CV