Collections
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Collections including paper arxiv:2507.13334
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 627 -
MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 300 -
Group Sequence Policy Optimization
Paper • 2507.18071 • Published • 316 -
Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth
Paper • 2509.03867 • Published • 210
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Less is More: Recursive Reasoning with Tiny Networks
Paper • 2510.04871 • Published • 501 -
When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs
Paper • 2510.07499 • Published • 48 -
Improving Context Fidelity via Native Retrieval-Augmented Reasoning
Paper • 2509.13683 • Published • 8 -
Multimodal Iterative RAG for Knowledge-Intensive Visual Question Answering
Paper • 2509.00798 • Published • 1
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Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277 -
Reinforcement Pre-Training
Paper • 2506.08007 • Published • 263 -
GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
Paper • 2507.01006 • Published • 250 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 259
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Retrieval Augmented Generation for Domain-specific Question Answering
Paper • 2404.14760 • Published • 1 -
Question Answering on Patient Medical Records with Private Fine-Tuned LLMs
Paper • 2501.13687 • Published • 9 -
CFMatch: Aligning Automated Answer Equivalence Evaluation with Expert Judgments For Open-Domain Question Answering
Paper • 2401.13170 • Published • 4 -
KaPQA: Knowledge-Augmented Product Question-Answering
Paper • 2407.16073 • Published • 1
-
Less is More: Recursive Reasoning with Tiny Networks
Paper • 2510.04871 • Published • 501 -
When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs
Paper • 2510.07499 • Published • 48 -
Improving Context Fidelity via Native Retrieval-Augmented Reasoning
Paper • 2509.13683 • Published • 8 -
Multimodal Iterative RAG for Knowledge-Intensive Visual Question Answering
Paper • 2509.00798 • Published • 1
-
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277 -
Reinforcement Pre-Training
Paper • 2506.08007 • Published • 263 -
GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
Paper • 2507.01006 • Published • 250 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 259
-
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 627 -
MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 300 -
Group Sequence Policy Optimization
Paper • 2507.18071 • Published • 316 -
Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth
Paper • 2509.03867 • Published • 210
-
Retrieval Augmented Generation for Domain-specific Question Answering
Paper • 2404.14760 • Published • 1 -
Question Answering on Patient Medical Records with Private Fine-Tuned LLMs
Paper • 2501.13687 • Published • 9 -
CFMatch: Aligning Automated Answer Equivalence Evaluation with Expert Judgments For Open-Domain Question Answering
Paper • 2401.13170 • Published • 4 -
KaPQA: Knowledge-Augmented Product Question-Answering
Paper • 2407.16073 • Published • 1