Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedback
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Noticias de IA
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ScaleToT: Generalizing Structured LLM Reasoning for Billion-Scale Low-Activity User Model…
ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning
Quant Convergence: Bridging Classical Value Investing and Modern Factor Models for System…
Reinforcement Learning for Computer-Use Agents with Autonomous Evaluation
Can Scale Save Us From Plasticity Loss in Large Language Models?
MGI: Member vs Generated Inference
CompressKV: Semantic-Retrieval-Guided KV-Cache Compression for Resource-Efficient Long-Co…
FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning
An Introduction to Causal Reinforcement Learning
Random coloured digraphs defined by a Markov logic network
Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control
Critique of Agent Model
Neuro-Symbolic Drive: Rule-Grounded Faithful Reasoning for Driving VLAs
CrossPool: Efficient Multi-LLM Serving for Cold MoE Models through KV-Cache and Weight Di…
Promise and challenges of heart chamber segmentation from non-contrast CT scans using con…
Listening makes Vision Clear for VLMs
Neuromorphic Speech Enhancement with Dual-Branch Spiking Neural Networks
Engineering Reliable Autonomous Systems: Challenges and Solutions
Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Ri…
VeriPilot: An LLM-Powered Verilog Debugging Framework
Exploring Dualistic Meta-Learning to Enhance Domain Generalization in Open Set Scenarios
Structural Kolmogorov-Arnold Convolutions: Learnable Function on the Values or the Filter…
Self-Recognition Finetuning can Prevent and Reverse Emergent Misalignment
FP8 is All You Need (Part 2): Efficient Ozaki-Bailey Style FFT Through Tensor-core Garner…
SemChunk-C: Semantic Segmentation for C Code
Quantifying Prior Dominance in RAG Systems
Exploring Academic Influence of Algorithms by Co-occurrence Network Based on Full-text of…
Rapid FinFET Modelling Using an Autoencoder
Fast and Slow Variational Continual Learning