Radicle 1.3.0
radicle.xyz·4d·
Discuss: Lobsters, Hacker News
📁File Systems
Onsager Principle-Based Domain Embedding for Thermodynamically Consistent Cahn-Hilliard Model in Arbitrary Domain
arxiv.org·5d
🎨Computer Graphics
"Pull or Not to Pull?'': Investigating Moral Biases in Leading Large Language Models Across Ethical Dilemmas
arxiv.org·5d
🦾Rust ML
Positional Biases Shift as Inputs Approach Context Window Limits
arxiv.org·5d
🤖AI
ActivityDiff: A diffusion model with Positive and Negative Activity Guidance for De Novo Drug Design
arxiv.org·6d
🤖AI
Enhancing Systematic Interoperability: Convergences and Mismatches between Web 3.0 and the EU Data Act
arxiv.org·5d
📦Binary Formats
Do AI Companies Make Good on Voluntary Commitments to the White House?
arxiv.org·4d
🤖AI
Based AI improves human decision-making but reduces trust
arxiv.org·3d
🤖AI
Against Epistemic Democracy: A Epistemic Tier List of What Actually Works
lesswrong.com·3d
🎮Game Engines
Reconstruction of Solar EUV Irradiance Using CaII K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
arxiv.org·5d
🎨Computer Graphics
Towards MR-Based Trochleoplasty Planning
arxiv.org·6d
🎨Computer Graphics
Overconfidence in LLM-as-a-Judge: Diagnosis and Confidence-Driven Solution
arxiv.org·6d
🤖AI
G-UBS: Towards Robust Understanding of Implicit Feedback via Group-Aware User Behavior Simulation
arxiv.org·6d
🎮Game Engines
Fully-Fluctuating Participation in Sleepy Consensus
arxiv.org·5d
🌍Distributed Systems
Levarging Learning Bias for Noisy Anomaly Detection
arxiv.org·5d
🤖AI
Dual Information Speech Language Models for Emotional Conversations
arxiv.org·5d
🏗️LLVM
Can LLMs Detect Their Confabulations? Estimating Reliability in Uncertainty-Aware Language Models
arxiv.org·5d
🦾Rust ML
Word Clouds as Common Voices: LLM-Assisted Visualization of Participant-Weighted Themes in Qualitative Interviews
arxiv.org·5d
🦾Rust ML
First Ask Then Answer: A Framework Design for AI Dialogue Based on Supplementary Questioning with Large Language Models
arxiv.org·4d
🤖AI
Using LLMs to Capture Users' Temporal Context for Recommendation
arxiv.org·4d
🗣️Language Servers