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Doc-to-Atom framework improves LLM long-document reasoning

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Doc-to-Atom (Doc2Atom), a new framework designed to improve how large language models handle long documents. Unlike previous methods that create a single adapter for an entire document, Doc2Atom breaks down documents into "knowledge atoms." Each atom is compiled into a small, independent adapter that can be selectively retrieved and combined at inference time. This approach aims to reduce memory usage and enhance reasoning capabilities for lengthy texts, outperforming existing Doc-to-LoRA methods in experiments. AI

IMPACT Enhances LLM efficiency and effectiveness in processing and reasoning over lengthy documents.

RANK_REASON This is a research paper describing a new method for LLMs.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Doc-to-Atom framework improves LLM long-document reasoning

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Xingjian Diao, Wenbo Li, Yashas Malur Saidutta, Avinash Amballa, Lazar Valkov, Srinivas Chappidi ·

    Doc-to-Atom: Learning to Compile and Compose Memory Atoms

    arXiv:2606.12400v1 Announce Type: new Abstract: Long input sequences are central to document understanding and multi-step reasoning in Large Language Models, yet the quadratic cost of attention makes inference both memory-intensive and slow. Context distillation mitigates this by…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Srinivas Chappidi ·

    Doc-to-Atom: Learning to Compile and Compose Memory Atoms

    Long input sequences are central to document understanding and multi-step reasoning in Large Language Models, yet the quadratic cost of attention makes inference both memory-intensive and slow. Context distillation mitigates this by compressing contextual information into model p…

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