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StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization
Paper • 2410.08815 • Published • 47 -
A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning
Paper • 2304.14856 • Published • 1 -
xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token
Paper • 2405.13792 • Published • 1 -
Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models
Paper • 2404.04522 • Published
Collections
Discover the best community collections!
Collections including paper arxiv:2510.18866
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TradingAgents: Multi-Agents LLM Financial Trading Framework
Paper • 2412.20138 • Published • 47 -
Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing
Paper • 2509.08721 • Published • 665 -
From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence
Paper • 2511.18538 • Published • 304 -
Memory in the Age of AI Agents
Paper • 2512.13564 • Published • 157
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O-Mem: Omni Memory System for Personalized, Long Horizon, Self-Evolving Agents
Paper • 2511.13593 • Published • 28 -
OmniScientist: Toward a Co-evolving Ecosystem of Human and AI Scientists
Paper • 2511.16931 • Published • 8 -
General Agentic Memory Via Deep Research
Paper • 2511.18423 • Published • 170 -
MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling
Paper • 2511.11793 • Published • 195
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LightMem: Lightweight and Efficient Memory-Augmented Generation
Paper • 2510.18866 • Published • 115 -
Memory Augmented Language Models through Mixture of Word Experts
Paper • 2311.10768 • Published • 19 -
Cramming 1568 Tokens into a Single Vector and Back Again: Exploring the Limits of Embedding Space Capacity
Paper • 2502.13063 • Published • 74 -
General Agentic Memory Via Deep Research
Paper • 2511.18423 • Published • 170
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Latent Zoning Network: A Unified Principle for Generative Modeling, Representation Learning, and Classification
Paper • 2509.15591 • Published • 45 -
A Survey on Latent Reasoning
Paper • 2507.06203 • Published • 94 -
Quantized Evolution Strategies: High-precision Fine-tuning of Quantized LLMs at Low-precision Cost
Paper • 2602.03120 • Published • 1 -
TADA! Tuning Audio Diffusion Models through Activation Steering
Paper • 2602.11910 • Published • 2
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TradingAgents: Multi-Agents LLM Financial Trading Framework
Paper • 2412.20138 • Published • 47 -
MinerU: An Open-Source Solution for Precise Document Content Extraction
Paper • 2409.18839 • Published • 41 -
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing
Paper • 2509.22186 • Published • 160 -
Agent Lightning: Train ANY AI Agents with Reinforcement Learning
Paper • 2508.03680 • Published • 140
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LightMem: Lightweight and Efficient Memory-Augmented Generation
Paper • 2510.18866 • Published • 115 -
AdaSPEC: Selective Knowledge Distillation for Efficient Speculative Decoders
Paper • 2510.19779 • Published • 62 -
Emu3.5: Native Multimodal Models are World Learners
Paper • 2510.26583 • Published • 114
-
StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization
Paper • 2410.08815 • Published • 47 -
A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning
Paper • 2304.14856 • Published • 1 -
xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token
Paper • 2405.13792 • Published • 1 -
Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models
Paper • 2404.04522 • Published
-
Latent Zoning Network: A Unified Principle for Generative Modeling, Representation Learning, and Classification
Paper • 2509.15591 • Published • 45 -
A Survey on Latent Reasoning
Paper • 2507.06203 • Published • 94 -
Quantized Evolution Strategies: High-precision Fine-tuning of Quantized LLMs at Low-precision Cost
Paper • 2602.03120 • Published • 1 -
TADA! Tuning Audio Diffusion Models through Activation Steering
Paper • 2602.11910 • Published • 2
-
TradingAgents: Multi-Agents LLM Financial Trading Framework
Paper • 2412.20138 • Published • 47 -
Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing
Paper • 2509.08721 • Published • 665 -
From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence
Paper • 2511.18538 • Published • 304 -
Memory in the Age of AI Agents
Paper • 2512.13564 • Published • 157
-
O-Mem: Omni Memory System for Personalized, Long Horizon, Self-Evolving Agents
Paper • 2511.13593 • Published • 28 -
OmniScientist: Toward a Co-evolving Ecosystem of Human and AI Scientists
Paper • 2511.16931 • Published • 8 -
General Agentic Memory Via Deep Research
Paper • 2511.18423 • Published • 170 -
MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling
Paper • 2511.11793 • Published • 195
-
LightMem: Lightweight and Efficient Memory-Augmented Generation
Paper • 2510.18866 • Published • 115 -
Memory Augmented Language Models through Mixture of Word Experts
Paper • 2311.10768 • Published • 19 -
Cramming 1568 Tokens into a Single Vector and Back Again: Exploring the Limits of Embedding Space Capacity
Paper • 2502.13063 • Published • 74 -
General Agentic Memory Via Deep Research
Paper • 2511.18423 • Published • 170
-
TradingAgents: Multi-Agents LLM Financial Trading Framework
Paper • 2412.20138 • Published • 47 -
MinerU: An Open-Source Solution for Precise Document Content Extraction
Paper • 2409.18839 • Published • 41 -
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing
Paper • 2509.22186 • Published • 160 -
Agent Lightning: Train ANY AI Agents with Reinforcement Learning
Paper • 2508.03680 • Published • 140
-
LightMem: Lightweight and Efficient Memory-Augmented Generation
Paper • 2510.18866 • Published • 115 -
AdaSPEC: Selective Knowledge Distillation for Efficient Speculative Decoders
Paper • 2510.19779 • Published • 62 -
Emu3.5: Native Multimodal Models are World Learners
Paper • 2510.26583 • Published • 114