KUPAS MASTER: Distilling the Tacit Expertise of Master Practitioners into Agent-Ready Experience Corpora

arXiv:2609.37673 · cs.AI, cs.CL · Submitted 2026-09-29 · Read on arXiv

cs.AI, cs.CL

Submitted: 2026-09-29

Updated: 2026-09-29

Comments: Technical Report. Official website: https://lsf.kupasai.com/ Report homepage: https://tongjiai4e.github.io/KUPAS-MASTER-Report/

Project page: https://lsf.kupasai.com

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• RAG — 31× in this paper · explained in 3 episodes
• Accuracy — 16× in this paper · explained in 1 episode
• Verification — 10× in this paper · explained in 2 episodes
• Trace — 9× in this paper · explained in 2 episodes
• Alignment — 8× in this paper · explained in 1 episode
• Actionability — 6× in this paper · explained in 3 episodes
• Calibration — 5× in this paper · explained in 7 episodes
• Core — 5× in this paper · explained in 2 episodes
• Semantic Alignment — 5× in this paper · explained in 3 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Distillation — 4× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• AI Agents — 2× in this paper · explained in 2 episodes
• Continual Learning — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Reward Function — 2× in this paper · explained in 2 episodes
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Agent Memory — 1× in this paper · explained in 1 episode
• Agent Skills — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Multi-agent Collaborative Framework — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 16 episodes
• Verifiability — 1× in this paper · explained in 2 episodes

Sources

• From Local to Global: A Graph RAG Approach to Query-Focused Summarization
• ReAct: Synergizing Reasoning and Acting in Language Models
• AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications
• WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
• $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
• $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment
• From RAG to Memory: Non-Parametric Continual Learning for Large Language Models
• Cognitive Architectures for Language Agents
• Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• Agent Workflow Memory
• AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

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