Large Language Model - Tag - DataSci Ocean

Large Language Model

2026

Claude Code Concepts: Agentic Loop, Tools, Skills, and Permissions 07-24

Cerebras's Enterprise RAG: Narrow-Waist Design, Decoupled Retrieval 07-23

SkillOpt: Treating an Agent's Skill File as a Trainable Weight 07-22

Adaptive Chunking: A Smarter, Per-Document Way to Split Text for RAG 07-22

Beyond Text-to-SQL: Implementing OpenAI’s 6-Layer Context Data Agent and the Hidden Challenges 04-25

AgentOpt: How to Optimize Client-Side LLM Agents and Cut API Costs by 67% 04-17

ERL: Teaching LLM Agents to Learn from a Single Attempt Without Fine-Tuning 04-10

Harness Engineering: Building Self-Evolving AI Agents with Ralph Loop and MemRL 04-05

WebResearcher: Redefining Deep Research through the Art of Iterative State Reconstruction 04-02

Takeaways of vLLM Semantic Router 03-29

Beyond Fine-Tuning: How MemRL Enables Self-Evolving AI Agents via Reinforcement Learning 03-29

UniversalRAG: Mastering Multimodal RAG with Intelligent Routing and Granularity Control 03-25

Beyond the Context Window: A Deep Dive into Context Engineering for AI Agents 03-22

Beyond Prompt Engineering: The System Engineering of AI Agents — Deconstructing Memory, Tool-Making, and Autonomy in OpenClaw 03-16

Why Your RAG Fails at Complex Tables—And How MixRAG Fixes It: A Deep Dive into Heterogeneous Document Retrieval 03-15

Deep Dive into MRAG: Solving Temporal Reasoning in RAG with Symbolic Logic (EMNLP 2025) 03-10

Selective Clarification: Overcoming LLM Hallucinations via the CLAM Framework 02-25

[EMNLP 2025] TableRAG Deep Dive: Text is for Reading, Tables are for Querying (via SQL) 02-13

Stop LLMs From Blindly Guessing: A Deep Dive into "Clarify When Necessary" (NAACL 2025) 02-08

Beyond HyDE: How ReDE-RF Makes RAG 10x Faster by "Judging" Instead of "Writing" 02-03

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