Paper Introduction - Category - DataSci Ocean

Paper Introduction

2026

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

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

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

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

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

VideoDR: Bridging the Gap Between Video Understanding and Agentic Search on the Open Web 01-25

Stop Using Giant LLMs for Everything: Why NVIDIA Research Says Small Language Models (SLMs) Are the Future of AI Agents 01-19

Google's "Free Lunch" for LLMs: How Prompt Repetition Fixes Attention Bottlenecks with Zero Latency 01-19

Is RLHF Killing Creativity? How Verbalized Sampling Mitigates Mode Collapse and Unlocks LLM Diversity 01-03

Beyond Self-Consistency: How CER Boosts LLM Reasoning by Leveraging "Process Confidence" (ACL 2025) 01-03

2025

Stop AI Hallucinations Early: How Meta's DeepConf Uses Token Confidence to Cut Inference Costs by 80% 12-28

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