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T02. Recursive language models and long-context handling
Research on reading documents that do not fit in one context window.
- T02-1Structural parallelSince 2025
Recursive Language Models
- T02-2Structural parallel
RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
- T02-3Similar problem
Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading (MemWalker)
- T02-4Candidate approach
MemGPT: Towards LLMs as Operating Systems
- T02-5Structural parallel
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks
- T02-6Similar problem
Lost in the Middle: How Language Models Use Long Contexts
- T02-7Similar problem
RULER: What's the Real Context Size of Your Long-Context Language Models?
- T02-8Candidate approach
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression
- T02-9Structural parallel
Recursively Summarizing Books with Human Feedback
- T02-10Candidate approach
Efficient Streaming Language Models with Attention Sinks (StreamingLLM)
- T02-11Similar problem
LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding
- T02-12Candidate approach
Ring Attention with Blockwise Transformers for Near-Infinite Context
- T02-13Candidate approach
Extending Context Window of Large Language Models via Positional Interpolation
- T02-14Candidate approach
Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention
- T02-15Candidate approach
H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models
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