New Text-to-Big SQL metrics show that LLM agents must balance accuracy with cost and speed at scale, where GPT-4o trades some accuracy for up to 12x speedup and GPT-5.2 proves more cost-effective than Gemini 3 Pro on large inputs.
Tool documentation enabl es zero-shot tool-usage with large language models
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
verdicts
UNVERDICTED 6roles
background 2polarities
background 2representative citing papers
First large-scale empirical analysis of MCP server construction shows predominant REST wrapping with low operation exposure, plus an AutoMCP pipeline that improves automated generation success and reduces tool complexity.
An automated environment construction pipeline plus verifiable rewards enables RL training that improves LLM tool-use performance across scales without harming general capabilities.
The survey structures agentic reasoning for LLMs into foundational, self-evolving, and collective multi-agent layers while distinguishing in-context orchestration from post-training optimization and reviewing applications across domains.
A survey synthesizing recent LLM research and assessing its applicability to financial data analysis.
A survey paper providing an overview of Large Language Models, their background, and recent advances in the field.
citing papers explorer
-
Both Ends Count! Just How Good are LLM Agents at "Text-to-Big SQL"?
New Text-to-Big SQL metrics show that LLM agents must balance accuracy with cost and speed at scale, where GPT-4o trades some accuracy for up to 12x speedup and GPT-5.2 proves more cost-effective than Gemini 3 Pro on large inputs.
-
From REST to MCP: An Empirical Study of API Wrapping and Automated Server Generation for LLM Agents
First large-scale empirical analysis of MCP server construction shows predominant REST wrapping with low operation exposure, plus an AutoMCP pipeline that improves automated generation success and reduces tool complexity.
-
Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments
An automated environment construction pipeline plus verifiable rewards enables RL training that improves LLM tool-use performance across scales without harming general capabilities.
-
Agentic Reasoning for Large Language Models
The survey structures agentic reasoning for LLMs into foundational, self-evolving, and collective multi-agent layers while distinguishing in-context orchestration from post-training optimization and reviewing applications across domains.
-
Bridging Language Models and Financial Analysis
A survey synthesizing recent LLM research and assessing its applicability to financial data analysis.
-
A Comprehensive Overview of Large Language Models
A survey paper providing an overview of Large Language Models, their background, and recent advances in the field.