Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers
Paper
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2506.14702
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Published
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3
Large Language Models
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MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
Paper • 2506.13585 • Published • 273 -
Scaling Test-time Compute for LLM Agents
Paper • 2506.12928 • Published • 63 -
A Survey on Latent Reasoning
Paper • 2507.06203 • Published • 93 -
Pre-Trained Policy Discriminators are General Reward Models
Paper • 2507.05197 • Published • 39 -
First Return, Entropy-Eliciting Explore
Paper • 2507.07017 • Published • 23 -
Jan-nano Technical Report
Paper • 2506.22760 • Published • 9 -
Test-Time Scaling with Reflective Generative Model
Paper • 2507.01951 • Published • 107 -
Geometric-Mean Policy Optimization
Paper • 2507.20673 • Published • 31 -
RecGPT Technical Report
Paper • 2507.22879 • Published • 37 -
CLEAR: Error Analysis via LLM-as-a-Judge Made Easy
Paper • 2507.18392 • Published • 19 -
SAND-Math: Using LLMs to Generate Novel, Difficult and Useful Mathematics Questions and Answers
Paper • 2507.20527 • Published • 6 -
A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence
Paper • 2507.21046 • Published • 82 -
UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning
Paper • 2508.18756 • Published • 36 -
LLaMAX2: Your Translation-Enhanced Model also Performs Well in Reasoning
Paper • 2510.09189 • Published • 3 -
Cache-to-Cache: Direct Semantic Communication Between Large Language Models
Paper • 2510.03215 • Published • 97 -
Scaling Latent Reasoning via Looped Language Models
Paper • 2510.25741 • Published • 221 -
The End of Manual Decoding: Towards Truly End-to-End Language Models
Paper • 2510.26697 • Published • 116 -
Kimi Linear: An Expressive, Efficient Attention Architecture
Paper • 2510.26692 • Published • 119