arXiv AI
Token-Efficient Data Reasoning Agents via Adaptive Structuring of Unstructured Data
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arXiv AI
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Agentic Context Cracking: Token-Efficient Data Reasoning Agents via Adaptive Structuring of Unstructured Data
The paper introduces Agentic Context Cracking, a technique that adaptively and speculatively structures unstructured data during the reasoning process of large language model agents. By creating a sub-agent that extracts useful structure from documents as they are opened, the method reduces the need to repeatedly read large files, cutting token usage by 53% on the FanOutQA benchmark while maintaining accuracy. Over time, more queries are answered using the accumulated structured data, approaching the efficiency of a database lookup.
arXiv Computation and Language
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Benchmarking Hybrid Deep Research Across Database Querying and Web Search
arXiv:2609.09410v1 Announce Type: new Abstract: While autonomous agents have made significant strides in "deep research" by iteratively navigating the open web to synthesize information, real-world p...
arXiv AI
Jun 6
A2RAG: Adaptive Agentic Graph Retrieval for Cost-Aware and Reliable Reasoning
arXiv:2601. 21162v2 Announce Type: replace-cross Abstract: Graph Retrieval-Augmented Generation (Graph-RAG) enhances multihop question answering by organizing corpora into knowledge graphs and routing evidence through relational structure.
arXiv AI
Jul 23
Do Data Agents Need Semantic Metadata? A Comparative Study in Agentic Data Retrieval
arXiv:2605. 28787v2 Announce Type: replace-cross Abstract: In the era of autonomous agents, machine-actionable data is critical for data-driven workflows.
Hugging Face Trending Papers
Jun 23
AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning
Large language models are increasingly deployed as agents that reason over documents rather than answer from parametric knowledge. We study archive-grounded reasoning: locating sparse evidence across a large, messy collection of workplace files, reconciling inconsistent terminology, units, and time conventions, and computing an answer.
arXiv AI
Jul 24
Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
arXiv:2607. 21503v1 Announce Type: new Abstract: Production AI agents' failures are less often due to an inability to reason well and more often because they cannot manage what is in their reasoning context: conversation histories, large prompts, large tool definitions, and ballooning tool outputs.