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Token-Efficient Data Reasoning Agents via Adaptive Structuring of Unstructured Data

Paper recorded by Signals 4 on 2026-08-31 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-31 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.AI · 人工智能 · first seen 2026-09-01

Abstract

Valuable data remains embedded in unstructured sources: web pages, reports, contracts, filings, earnings calls, and PDFs. The big bet in enterprise AI is deploying LLM agents that reason over this data to answer complex questions for every knowledge worker. Agents can do this today, but at prohibitive cost. Each question repeatedly opens large documents to recover scattered evidence, consuming up

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#248 most recent of 300 cs.AI papers we have recorded · ↑ newer: Cross-Regional Grapevine Cold Hardiness Prediction via Learned Multimo · ↓ older: Reconciling Process Supervision with Outcome-Based Credit in Agentic P
Cite this page: Token-Efficient Data Reasoning Agents via Adaptive Structuring of Unstructured Data: the #248 most recent of 300 cs.AI papers we have recorded (as of 2026-08-31). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/token-efficient-data-reasoning-agents-via-adaptive-structuring-of-unstructured-d.html
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