arXiv (All)
open access
TabAgent: Distilling LLM Agent Components into Cheap Classifiers
Agentic systems, AI architectures that autonomously execute multi-step workflows to achieve complex goals, are often built using repeated large language model (LLM) calls for closed-set decision tasks such as routing, shortlisting, gating, and verification. While convenient, this design makes deploy
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Towards Agentic Agent-Based Models: Feasibility, Performance, and Statistical Model Checking#1360659
- AgenTest: Mining Business Rules to Test LLM Agents for Business Logic Bugs#1412091
- AgenTest: Mining Business Rules to Test LLM Agents for Business Logic Bugs#1406552
- Exploring Autonomous Agentic Data Engineering for Model Specialization#800049
- LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios#1156205
Record · ID 1413606
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive