title: Self-Taught Agentic Long Context Understanding
summary: Answering complex, long-context questions remains a major challenge for large
  language models (LLMs) as it requires effective question clarifications and
  context retrieval. We propose Agentic Long-Context Understanding (AgenticLU), a
  framework designed to enhance an LLM's understanding of such queries by
  integrating targeted self-clarification with contextual grounding within an
  agentic workflow. At the core of AgenticLU is Chain-of-Clarifications (CoC),
  where models refine their understanding through self-generated clarification
  questions and corresponding contextual groundings. By scaling inference as a
  tree search where each node represents a CoC step, we achieve 97.8% answer
  recall on NarrativeQA with a search depth of up to three and a branching factor
  of eight. To amortize the high cost of this search process to training, we
  leverage the preference pairs for each step obtained by the CoC workflow and
  perform two-stage model finetuning: (1) supervised finetuning to learn
  effective decomposition strategies, and (2) direct preference optimization to
  enhance reasoning quality. This enables AgenticLU models to generate
  clarifications and retrieve relevant context effectively and efficiently in a
  single inference pass. Extensive experiments across seven long-context tasks
  demonstrate that AgenticLU significantly outperforms state-of-the-art prompting
  methods and specialized long-context LLMs, achieving robust multi-hop reasoning
  while sustaining consistent performance as context length grows.
paper_authors._id: 67bd1ae085a048f74fd1b8ec
paper_authors.name: Yufan Zhuang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-26T08:38:05.314Z
paper_authors._id: 6438ccbb3b46237de3d052e8
paper_authors.avatarUrl: /avatars/baa624d417b0b905e82127dc66346478.svg
paper_authors.fullname: Yufan Zhuang
paper_authors.type: user
paper_authors.user: yzhuang
paper_authors._id: 67bd1ae085a048f74fd1b8ed
paper_authors.name: Xiaodong Yu
paper_authors._id: 67bd1ae085a048f74fd1b8ee
paper_authors.name: Jialian Wu
paper_authors._id: 67bd1ae085a048f74fd1b8ef
paper_authors.name: Ximeng Sun
paper_authors._id: 67bd1ae085a048f74fd1b8f0
paper_authors.name: Ze Wang
paper_authors._id: 67bd1ae085a048f74fd1b8f1
paper_authors.name: Jiang Liu
paper_authors._id: 67bd1ae085a048f74fd1b8f2
paper_authors.name: Yusheng Su
paper_authors._id: 67bd1ae085a048f74fd1b8f3
paper_authors.name: Jingbo Shang
paper_authors._id: 67bd1ae085a048f74fd1b8f4
paper_authors.name: Zicheng Liu
paper_authors._id: 67bd1ae085a048f74fd1b8f5
paper_authors.name: Emad Barsoum
paper_title: Self-Taught Agentic Long Context Understanding
paper_summary: Answering complex, long-context questions remains a major challenge for large
  language models (LLMs) as it requires effective question clarifications and
  context retrieval. We propose Agentic Long-Context Understanding (AgenticLU), a
  framework designed to enhance an LLM's understanding of such queries by
  integrating targeted self-clarification with contextual grounding within an
  agentic workflow. At the core of AgenticLU is Chain-of-Clarifications (CoC),
  where models refine their understanding through self-generated clarification
  questions and corresponding contextual groundings. By scaling inference as a
  tree search where each node represents a CoC step, we achieve 97.8% answer
  recall on NarrativeQA with a search depth of up to three and a branching factor
  of eight. To amortize the high cost of this search process to training, we
  leverage the preference pairs for each step obtained by the CoC workflow and
  perform two-stage model finetuning: (1) supervised finetuning to learn
  effective decomposition strategies, and (2) direct preference optimization to
  enhance reasoning quality. This enables AgenticLU models to generate
  clarifications and retrieve relevant context effectively and efficiently in a
  single inference pass. Extensive experiments across seven long-context tasks
  demonstrate that AgenticLU significantly outperforms state-of-the-art prompting
  methods and specialized long-context LLMs, achieving robust multi-hop reasoning
  while sustaining consistent performance as context length grows.
paper_ai_summary: AgenticLU enhances LLMs by integrating self-clarification and contextual grounding, improving long-context understanding and multi-hop reasoning through a chain-of-clarifications workflow.
paper_ai_keywords: AgenticLU Chain-of-Clarifications CoC self-clarification contextual grounding tree search NarrativeQA answer recall preference pairs supervised finetuning direct preference optimization long-context tasks multi-hop reasoning