PAPER NOTES: WHY DO MULTI-AGENT LLM SYSTEM FAIL?

MAST-Data: The first multi-agent system dataset to characterize MAS failure dynamics, guiding the development of better future systems. Build the first Multi-Agent System Failure Taxonomy (MAST). The entire process is divided into 14 distinct failure modes, grouped into 3 categories: System Design Issues Inter-Agent Misalignment Task Verification Develop an LLM-as-a-Judge pipeline with high consistency with human annotations. Found that the primary cause of MAS task failures is system design issues, which cannot be simply summarized as prompt and LLM limitations.

PAPER NOTES: PARAMETER-EFFICIENT FINE-TUNING

A collection of lightweight fine-tuning methods.

PAPER NOTES: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS

LoRA is a lightweight fine-tuning technique for large models.

PAPER NOTES: ATTENTION IS ALL YOU NEED

Notes on this classic masterpiece: Attention is All You Need.

DEFINITION AND ALGORITHMS OF TRIE

Trie Algorithm