PyraMathBench reveals LLMs' weaknesses in numerical computation for math tasks and SOLVE/IRPO training delivers a 5.0 point gain on Qwen-2.5.
Tool documentation enabl es zero-shot tool-usage with large language models
8 Pith papers cite this work, alongside 9 external citations. Polarity classification is still indexing.
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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.
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.
MoCA-Agent decomposes questions into typed atomic claims, clears them via trader-agent markets into confidence-weighted decisions, synthesizes and verifies executable Python code, and reports strong benchmark scores including 78.3% on FinQA.
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
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PyraMathBench: Evaluating and Improving Mathematical Capability in Large Language Models
PyraMathBench reveals LLMs' weaknesses in numerical computation for math tasks and SOLVE/IRPO training delivers a 5.0 point gain on Qwen-2.5.
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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.
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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.
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MoCA-Agent: A Market-of-Claims Code Agent for Financial and Numerical Reasoning
MoCA-Agent decomposes questions into typed atomic claims, clears them via trader-agent markets into confidence-weighted decisions, synthesizes and verifies executable Python code, and reports strong benchmark scores including 78.3% on FinQA.
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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.
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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.
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Bridging Language Models and Financial Analysis
A survey synthesizing recent LLM research and assessing its applicability to financial data analysis.
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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.