新闻
Token-Efficient Data Reasoning Agents via Adaptive Structuring of Unstructured Data
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers propose agentic data cracking, which structures unstructured documents adaptively during agent reasoning to reduce token consumption by 53% on multi-question tasks.
- 为何重要
- Matters for engineers building LLM agents over documents, where token costs from repeatedly opening large files make deployments prohibitively expensive today.
- 注意
- Method tested on extended FanOutQA benchmark with one related question per test case; real-world effectiveness across diverse document types and query patterns remains unproven.
- agent
- llm
- reasoning
- token
- edge
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