Energy-Based Transformers are Scalable Learners and Thinkers Paper • 2507.02092 • Published 3 days ago • 14
Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers Paper • 2507.02694 • Published 2 days ago • 12
NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes Paper • 2504.11544 • Published Apr 15 • 42
Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model? Paper • 2504.13837 • Published Apr 18 • 129
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems Paper • 2504.01990 • Published Mar 31 • 293
Beyond RAG: Task-Aware KV Cache Compression for Comprehensive Knowledge Reasoning Paper • 2503.04973 • Published Mar 6 • 24
Feature-Level Insights into Artificial Text Detection with Sparse Autoencoders Paper • 2503.03601 • Published Mar 5 • 233
Fine-Tuning Small Language Models for Domain-Specific AI: An Edge AI Perspective Paper • 2503.01933 • Published Mar 3 • 12
You Do Not Fully Utilize Transformer's Representation Capacity Paper • 2502.09245 • Published Feb 13 • 38
Train Small, Infer Large: Memory-Efficient LoRA Training for Large Language Models Paper • 2502.13533 • Published Feb 19 • 11
Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data Paper • 2502.14044 • Published Feb 19 • 8
From RAG to Memory: Non-Parametric Continual Learning for Large Language Models Paper • 2502.14802 • Published Feb 20 • 13
Discovering highly efficient low-weight quantum error-correcting codes with reinforcement learning Paper • 2502.14372 • Published Feb 20 • 36
How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM? Paper • 2502.14502 • Published Feb 20 • 91
TAG: A Decentralized Framework for Multi-Agent Hierarchical Reinforcement Learning Paper • 2502.15425 • Published Feb 21 • 9