AGI-Papers
A curated archive of breakthroughs in Agents, Architecture, Training, RAG, and On-Device AI.
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This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**
README:
Toward Artificial General Intelligence (AGI) in 2026.
A curated archive of breakthroughs in Agents, Architecture, Training, RAG, and On-Device AI.
2026๋
, AGI์ ๊ทธ ์ด๋ ๋๋ณด๋ค ๊ฐ๊น์ด ์๋๊ฐ ๋๋ํ์ต๋๋ค.
์ด ์ ์ฅ์๋ AGI(Artificial General Intelligence) ๋ก ํฅํ๋ ์ฌ์ ์์ ์ค์ํ ๋
ผ๋ฌธ๋ค์ ๋ฆฌ๋ทฐํ๊ณ ์์นด์ด๋นํ๋ ๊ณต๊ฐ์
๋๋ค.
์ฃผ๋ก ์ LinkedIn ์์ ๋ค๋ฃฌ ๋ ผ๋ฌธ๋ค์ ๋ํ ์ฌ๋ ์๋ ๋ฆฌ๋ทฐ๊ฐ ์ ๋ก๋๋๋ฉฐ, ๋๋ก๋ ์์ ๋ฏธ๋์ด์ ๊ณต์ ํ๊ธฐ ์ ์ Pre-release ์ธ์ฌ์ดํธ๋ ๋ ๊ฒ์ ์๊ฐ๋ค์ด ์ด๊ณณ์ ๋จผ์ ๊ธฐ๋ก๋ ์์ ์ ๋๋ค.
์์ฑ ์ค์ธ ์๋ก์ด ๊ธ๋ค์ ์๋ ๋งํฌ์์ ํ์ธํ์ค ์ ์์ต๋๋ค.
๊ณผ๊ฑฐ์ ์ ๋ฆฌํ๋ ๋ ผ๋ฌธ ๋ฆฌ์คํธ๋ ์๋ ๋งํฌ์์ ํ์ธํ์ค ์ ์์ต๋๋ค.
์ด ์ ์ฅ์๋ AGI๋ฅผ ํฅํ ์ฌ์ ์ ๋ค์ 8๊ฐ์ง ํต์ฌ ์ฃผ์ ๋ก ๋ถ๋ฅํ์ฌ ์ ๋ฆฌํฉ๋๋ค.
- ๐ค Agents : ์์จ ์์ด์ ํธ, ํ๋/๊ณํ(Planning) ๋ชจ๋ธ, ํ๋ ์์ํฌ
- ๐ง Architecture : LLM ์ํคํ ์ฒ ํ์ (Transformer, Mamba, MoE)
- ๐ Pre-Training : ํ์ต ๋ฐ์ดํฐ, ์ค์ผ์ผ๋ง ๋ฒ์น, ํ์ด๋ฐ์ด์ ๋ชจ๋ธ
- ๐ฏ Post-Training : RLHF, DPO, GRPO, ์ ๋ ฌ(Alignment)
- โ๏ธ Evaluation : ๋ฒค์น๋งํฌ, ํ๊ฐ ๋ฐฉ๋ฒ๋ก , ๋นํ
- ๐๏ธ RAG & Knowledge : ๊ฒ์ ์ฆ๊ฐ ์์ฑ, ์ง์ ๊ทธ๋ํ, ๋ฉ๋ชจ๋ฆฌ
- ๐ป On-Device AI : ๋ก์ปฌ ๊ตฌ๋, ์ฃ์ง ์ปดํจํ , ์ต์ ํ
- ๐ Projects : ์ง์ ๊ตฌํํ ํ๋ก์ ํธ ๋ฐ ์คํ ๊ฒฐ๊ณผ
- ๐ฅ Trends & Industry : AI ์ฐ์ ์ ๋ํฅ, ์ธ์ฌ์ดํธ, ์ฃผ์ ๋ด์ค
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Adaptation of Agentic AI
๊ฑฐ๋ ๋ชจ๋ธ ํ๋๋ณด๋ค ๋๊ตฌ ํ๋์ด ํจ์จ์ ์ธ ์ด์ (T2 > A2). -
Memory in the Age of AI Agents
์์ด์ ํธ ๊ธฐ์ต์ ํํ, ๊ธฐ๋ฅ, ์ญ๋์ฑ์ ๋ํ ๊ณ ์ฐฐ. -
World Models Research
World Knowledge Injection vs Specific Tasks. -
Mixture-of-Models
Unifying Heterogeneous Agents via N-Way Self-Evaluating Deliberation. -
AIRS-Bench
Frontier AI Research Science Agents๋ฅผ ์ํ ํ์คํฌ. -
OctoTools
Training-free LLM Agent Framework. -
Chain-of-Draft(CoD)
CoT์ ์ฅ์ ์ ์ ์งํ๋ฉด์ ํ ํฐ ์ฌ์ฉ๋๊ณผ ๊ณ์ฐ ๋น์ฉ์ ์ค์ด๋ ํ๊ธฐ์ ์ธ ์ ๊ทผ๋ฒ. -
Scaling Agent Systems: ๋ค๋ค์ต์ ์ ํจ์
๊ตฌ๊ธ๊ณผ MIT๊ฐ ๋ฐํ๋ธ ๋ฉํฐ ์์ด์ ํธ์ ๊ณผํ. -
LOTaD: Optimal Task Decomposition
์์ด์ ํธ๋ ์ด๋ป๊ฒ ์ผ์ ๋๋ ์ผ ํ ๊น? -
ADGR: Agentic Deep Graph Reasoning
์ค์ค๋ก ์ง๋๋ฅผ ๊ทธ๋ฆฌ๋ ์์ด์ ํธ. -
Agentic Reasoning
์๊ฐ์ ๋๊ตฌ๋ฅผ ์ฐ๋ ์์ด์ ํธ. -
MetaChain
Zero-code Framework: ๋ง๋ง ํ๋ฉด ๋ง๋ค์ด์ง๋ ์์ด์ ํธ. -
LoRASA: Agent Adaption
๋ฐ๋ก ๋ ๊ฐ์ด, ์์ด์ ํธ์ ๊ฐ์ธ๊ธฐ. -
AgentArcEval
์์ด์ ํธ ์ํคํ ์ฒ, ์ ์ ๋งค๊ฒจ๋๋ฆฝ๋๋ค. -
SciAgents
AI ๊ณผํ์์ ํ์. -
Agent Workflows (Anthropic)
์คํธ๋กํฝ์ด ์ ์ํ๋ 5๊ฐ์ง ํต์ฌ ํจํด. -
ASA: Training-Free Tool Calling
๊ฒ์ผ๋ฅธ ์์ด์ ํธ(Lazy Agent)๋ฅผ ๊นจ์ฐ๋ ๊ฐ์ฅ ๊ฐ๋ฒผ์ด ๋ฐฉ๋ฒ. -
HUMANLM: State Alignment for User Simulation
์ง์ ํ ํ๋ฅด์๋๋ '๋ง์'์์ ๋์จ๋ค. -
SKILLRL: ์์ด์ ํธ๋ '์คํจ'๋ฅผ ๋จน๊ณ ์๋๋ค
์์ด์ ํธ์๊ฒ ๊ฒฝํ์ '์คํฌ'๋ก ์ฆ๋ฅ(Distill)ํ์ฌ ํ์ ํ์ต์ ๊ธธ์ ์ด์ด์ฃผ๋ค.
-
LLaDA2.0: Scaling Up Diffusion Language Models to 100B
100B Diffusion ๋ชจ๋ธ์ ๋ฑ์ฅ: ๊ธฐ์กด AR ๋ชจ๋ธ์ ๊ฐ์กฐํ์ฌ ํจ์จ์ฑ์ 2๋ฐฐ ๋์ธ ๋น๊ฒฐ. -
TEON vs Muon: ์ตํฐ๋ง์ด์ ์ ์
AdamW์ ์๋๋ ๊ฐ๋๊ฐ? ๋ ์ด์ด(Layer)๋ฅผ ๋์ด ํ ์(Tensor) ์ฐจ์์ ์ต์ ํ๋ก. -
EinFields: ์์ธ์ํ์ธ์ ์ํ ์ ๊ฒฝ๋ง
์ฐ์ฃผ์ ์๊ณต๊ฐ(Spacetime)์ ์ ๊ฒฝ๋ง์ ๊ฐ์ค์น ์์ ์์ถํ๋ค. -
Micro GPT: LLM์ ๋ฐ๋ฅ์ ๋ณด๋ค
Andrej Karpathy์ ์ ๋ฌผ. ์ค์ง ํ๋ ฌ ๊ณฑ์ ๊ณผ ๋ฏธ๋ถ์ด ์์ ๋ฟ. -
QED-Nano: ๋ค์์ด ๊ณจ๋ฆฌ์์ ์ด๊ธฐ๋ ๋ฒ
์ํ ์ฆ๋ช ์์ 4B ๋ชจ๋ธ์ด 100B ๋ชจ๋ธ์ ์๋ํ ๋น๊ฒฐ. -
Moonshine: ๋ฌ๋น์ฒ๋ผ ๊ฐ๋ฒผ์ด ์์ฑ ์ธ์
OpenAI Whisper์ ๋ํญ๋ง? ์ฃ์ง(Edge) ๋๋ฐ์ด์ค๋ฅผ ์ํ ๊ตฌ์ธ์ฃผ. -
Nested Learning
๋ฅ๋ฌ๋์ '๊น์ด'๊ฐ ์๋๋ผ '์ค์ฒฉ'์ด๋ค. -
Diffusion LLM (100B Parameters)
30B ๋ชจ๋ธ๋ณด๋ค 2๋ฐฐ ๋น ๋ฅธ ๋ณ๋ ฌ ์์ฑ ๋ชจ๋ธ์ ๋ฑ์ฅ. -
RNN is all you need
Transformer์ ์๋๋ฅผ ์ก์ ๋ณ๋ ฌ ํ์ต RNN (minLSTM, minGRU)์ ๋ถํ. -
Titans: Learning to Memorize at Test Time
Transformer์ ๊ธฐ์ต๋ ฅ์ ๋์ด์๋ ์๋ก์ด ๋ฉ๋ชจ๋ฆฌ ์ค์ฌ ์ํคํ ์ฒ. -
LLM์ "์ ๋ ฅ ๊ธธ์ด ์ ๊ณฑ(N^2)"์ ์ ์ฃผ
๋๊ฐ ๋จผ์ ๋์ด๋ผ ๊ฒ์ธ๊ฐ? -
Mistral Large 3: ํจ์จ์ฑ์ ๊ทน๋ํ
Mistral Large 3 vs Kimi K2: Efficiency vs Scale. -
ํ์ค์ด ๋ V3 ์ํคํ ์ฒ
Mistral Large 3, Kimi K2 ๊ทธ๋ฆฌ๊ณ DeepSeek V3.2 ๋ถ์. -
Ai2 Olmo 3
์ฑ๋ฅ๋ณด๋ค๋ ๊ณผ์ ์ ํฌ๋ช ์ฑ์ ์ง์คํ LLM ์ฐ๊ตฌ์ ๊ต๊ณผ์. -
Nemotron-3-Nano-30B-A3B
Qwen3๋ณด๋ค ๋น ๋ฅด๊ณ ๊ฐ๋ ฅํ Mamba-2 ํ์ด๋ธ๋ฆฌ๋ ๋ชจ๋ธ. -
DeepSeek Engram
๊ธฐ์ต์ ํจ์จํํ์ฌ ์ฐ์ฐ ๋ญ๋น๋ฅผ ์ค์ด๋ ์๋ก์ด ํฌ์์ฑ ์ถ. -
2026๋ ์ ํต๋ ํ๊ดด: 90M, 600M ๋ชจ๋ธ
์ด์ํ ๋ชจ๋ธ๋ค์ ๋๋ผ์ด ์ง์ ์ดํ ๋ฅ๋ ฅ. -
Sakana AI: DroPE
์์น ์ ๋ณด(Positional Embeddings)๋ ํ์ตํ ๋๋ง ์ฐ๊ณ ์ค์ ์์๋ ๋ฒ๋ ค๋ผ. -
Sakana AI RePo
์์น ์ ๋ณด๋ฅผ ์ฌ์ค๊ณ(Re-position)ํ๋ผ. -
DeepSeek vs Qwen (A3B MoE)
์ ๋ฐ๋์ ์ค๊ณ ์ฒ ํ ๋ถ์. -
Generative Modeling via Drifting
ํ์ฐ ๋ชจ๋ธ์ 250๋จ๊ณ๋ฅผ ๋จ 1๋จ๊ณ(1-step)๋ก ์ค์ฌ ์๋์ ํ์ง์ ๋์์ ์ก์ ํ์ . -
Beyond Transformers 2
๋ฉ์น ๊ฒฝ์์ ๋์ด ์๊ฐ๊ณผ ๋ณธ์ง๋ก. -
DeepSeek-V3 vs V3.2: ์ํคํ ์ฒ์ ์งํ
์ํคํ ์ฒ์ ์งํ์ ๊ธฐ์ ์ ๋ชฉํ์ . -
Gemma 3 ๋ชจ๋ธ์ ํต์ฌ ๋ชฉํ ๋ฐ ํน์ง
๊ตฌ๊ธ ๋ฅ๋ง์ธ๋์ ์ต์ ๋ฉํฐ๋ชจ๋ฌ ๋ชจ๋ธ ๋ถ์. -
Python ์ฌ๊ท๋ก ์์ํ๋ 1,000๋ง ํ ํฐ ์๋
Recursive Language Models: Python ์ฌ๊ท๋ก 1,000๋ง ํ ํฐ ์ฒ๋ฆฌํ๊ธฐ.
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LLM ํ์ต ํจ์จํ ๋ฐฉ์: ์ธ๊ฐ์ ์ธ์ด ์ต๋ ๋ฐฉ์
์ธ๊ฐ์ ์ธ์ด ์ต๋ ๋ฐฉ์์ ๋ชจ๋ฐฉํ ์ ์ง์ ์ดํ ํ์ต๋ฒ(Vocabulary Curriculum Learning). -
RoPE๊ฐ ์ ๋ณด๋ฅผ ์ ์คํ๊ณ ์๋ค?
ํธ๋จ๋ ์ฐ๊ตฌ์ง์ ์ถฉ๊ฒฉ์ ์ธ ๋ฐ๊ฒฌ๊ณผ ํด๊ฒฐ์ฑ . -
CALM: Continuous Autoregressive Language Models
ํ ๊ธ์์ฉ ํ์ดํํ๋ LLM์ ๋์ด, 4๊ฐ์ฉ ์์ฑํ๋ ์ฐ์ ๋ฒกํฐ ์์ธก.
-
Parameter-Efficient Fine-Tuning for Foundation Models
๊ฑฐ๋ ๋ชจ๋ธ์ ํจ์จ์ ์ผ๋ก ํ๋ํ๋ 5๊ฐ์ง ํต์ฌ ๊ธฐ๋ฒ(PEFT) ์ด์ ๋ฆฌ. -
When Reasoning Meets its Laws
๋จ 3,900๊ฐ์ ๋ฐ์ดํฐ๋ก AI์๊ฒ '์ถ๋ก ์ ๋ฌผ๋ฆฌ ๋ฒ์น'์ ๊ฐ๋ฅด์น๋ ๋ฒ (LORE). -
LIE: ๊น๊ฒ ์๊ฐํ ์๋ก ๋ ๋๋ํด์ง๋ค
LLM์๊ฒ '์๊ฐ์ ๋ฉ์ถ์ง ์๋ ๋ฒ'์ ๊ฐ๋ฅด์น๋ ๊ฐํํ์ต ์ ๋ต. -
ProRL: Prolonged Reinforcement Learning
๊ฐํํ์ต, ์งง๊ฒ ํ์ง ๋ง๊ณ ๊ธธ๊ฒ ํ๋ผ. RL ์ค์ผ์ผ๋ง ๋ฒ์น์ ๋ฐ๊ฒฌ. -
DuPO: Self-Verification via Dual Preference Optimization
์ ๋ต์ง ์๋ ๋ฒ์ญ์ ์ค์ค๋ก ๊ฒ์ฆํ๋ '์ผ๋ฐํ๋ ์๋์ฑ' ๊ธฐ๋ฒ. -
From Code Foundation Models to Agents
Code Foundation Model์์ ์์จ ์ฝ๋ฉ ์์ด์ ํธ๋ก์ ์งํ ์ฒญ์ฌ์ง. -
Emergent Misalignment
์ทจ์ฝํ ์ฝ๋๋ฅผ ๋ฐฐ์ด AI์ ์ํํ ์ผํ. -
Stabilizing RL with LLMs
ํ๋ คํ ๊ธฐ๊ต๋ณด๋ค ์ํ์ ๊ธฐ๋ณธ๊ธฐ๊ฐ ์ค์ํ ์ด์ . -
Yann LeCun: World Model์ ์ค์์ฑ
LLM์ ๋ฌผ๋ฆฌ ์ธ๊ณ๋ฅผ ๋ฐฐ์ธ ์ ์๋ค? -
GDPO: Multi-reward RL
GRPO์ ์ฝ์ ์ ๊ทน๋ณตํ ์๋ก์ด ๊ฐํํ์ต ๊ธฐ๋ฒ. -
Detailed balance in LLM-driven agents
LLM์ด ๋ฌผ๋ฆฌํ์ '์ต์ ์์ฉ์ ์๋ฆฌ'๋ฅผ ๋ฐ๋ฅธ๋ค๋ ๊ฒ์ ์ฆ๋ช ํ ์ฐ๊ตฌ. -
iGRPO
Self-Feedback-Driven LLM Reasoning: ๋ชจ๋ธ์ด ์ค์ค๋ก ๋ง๋ ์ด์์ ๋ณด๊ณ ๋ฐฐ์ฐ๋ ์๊ฐ ๊ฐ์ ๊ฐํํ์ต.
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Preference Leakage: A Contamination Problem in LLM-as-a-Judge
LLM ํ๊ฐ์๊ฐ ์์ ์ ํจ๋ฐ๋ฆฌ ๋ชจ๋ธ์ ํธ์ ํ๋ '์ ํธ๋ ์ ์ถ' ๋ฌธ์ . -
ADR-Bench ์ ๋ฌธ๊ฐ ํ๊ฐ
DeepSeek-v3.2๋ฅผ ์๋ํ ํจ์จ์ ์ธ ์์ด์ ํธ ๋ชจ๋ธ.
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LIMRANK: Less is More
2๋ง ๊ฐ ๋ฐ์ดํฐ๋ก SOTA ๋ฆฌ๋ญ์ปค ๋ง๋ค๊ธฐ. -
HippoRAG 2
์ธ๊ฐ์ ๊ธฐ์ต ๋ฉ์ปค๋์ฆ์ ๋ชจ๋ฐฉํ ๋น๋ชจ์์ ์ฐ์ ํ์ต (Bio-inspired Continual Learning). -
vLLM์ ์น๋ฆฌ: ์๋์ ์ธ ์๋
ํ์ค์ด ๋๊ธฐ๊น์ง. -
RAG & Agent Memory 4์
GraphSearch, S-RAG, xMemory ๋ฑ ์ต์ ๋ ผ๋ฌธ ์๊ฐ. -
Beyond Naive RAG
4 Papers that Redefine Agent Memory. -
SEPAL: Scalable Feature Learning
9์ฒ๋ง ๊ฐ ์ง์ ๊ทธ๋ํ, V100 ํ ์ฅ์ผ๋ก ํ์ตํ๊ธฐ. -
GraphRAG Survey
RAG์ ๋ฏธ๋๋ ๊ทธ๋ํ๋ค (ACM TOIS). -
A-MEM: Agentic Memory
์์ด์ ํธ๋ฅผ ์ํ ์ด์์๋ ๊ธฐ์ต. -
PISCO: Compression for RAG
RAG๋ฅผ ์ํ ์ด๊ณ ํจ์จ ์์ถ. -
SymAgent: Symbolic Knowledge Graph
๊ธฐํธ ์ถ๋ก ์ผ๋ก ์์ฑํ๋ ์ง์ ๊ทธ๋ํ. -
VideoRAG
์์์ ์ฝ๋ RAG.
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Liquid AI 1.2B vs Google 4B
Pau Labarta Bajo's Local AI Insight. -
๊ตญ๊ฐ๋ํ AI ํ๋ฝ ๊ทธ ํ (On-Device Focus)
ํ์ค์ ์ธ ์ง๋จ๊ณผ ์ค๊ตญ ๋ชจ๋ธ๊ณผ์ ๋น๊ต. -
๋ก์ปฌ LLM ๊ตฌ๋์ 6๊ฐ์ง ํ์ค์ ๋ฐฉ๋ฒ
STEM: ๋จ์ํ ์ง์์ ๊บผ๋ด๊ธฐ ์ํด ๋น์ผ GPU๋ฅผ ์ฐ์ง ๋ง์. -
LLM ์ง๋ฅ์ ๋ฏผ๋ฏ๊ณผ ํ๊ณ
๋ฒค์น๋งํฌ๋ ์์์ด์ง๋ง ํ์ฅ(์ง๋ฃ)์์๋ ๋์ ์ธ ์ด์ ์ ํด๊ฒฐ์ฑ .
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Gemini-Claw ๊ฐ๋ฐ๊ธฐ
2์๊ฐ ๋ง์ ๋ง๋ , ์ค์ค๋ก ์ฝ๋๋ฅผ ์ง๊ณ ๋ด์ค๋ฅผ ๋ถ์ํ๋ ์์ด์ ํธ. -
์ค์ค๋ก ์นํ์ด์ง๋ฅผ ๋ง๋ค๊ณ ๊ฒ์ฆํ๋ AI
Gemini-Claw: ์ค์ค๋ก ์นํ์ด์ง๋ฅผ ๋ง๋ค๊ณ , ์คํํ๊ณ , ๊ฒ์ฆ๊น์ง ํ๋ ์์ด์ ํธ. -
Insight Agents
An LLM-Based Multi-Agent System for Data Insights. -
SEAL: ์ค์ค๋ก Fine-tuningํ๋ ์์ด์ ํธ
๊ฐ๋ฅ์ฑ๊ณผ ํ๊ณ.
-
Claube Vibe Coding
๋ณต์กํ ๋ฐฑ์๋๋ AI์๊ฒ ๋งก๊ธฐ๊ณ ๊ณต์์์ ๋ฌ๋ํ๊ธฐ. -
๋ฌดํ ๋ฃจํ ๋ฐ์ด๋ธ ์ฝ๋ฉ
"ํ ์คํธ ์ฑ๊ณตํ ๋๊น์ง ๊ณ์ํด" ํ๋ง๋๋ก ๊ฐ๋ฐ ๋๋ด๊ธฐ. -
Docling-Translate
CLI์ ๋ฒ๊ฑฐ๋ก์์ ํด๊ฒฐํ Streamlit ๊ธฐ๋ฐ ๋ฒ์ญ ๋๊ตฌ. -
LFM-Scholar
๋ ผ๋ฌธ Related Work ์๋ ์์ฑ์ ์ํ LLM ๋๊ตฌ. -
Gemini-Claw ํ์ผ ์กฐ์ ๊ธฐ๋ฅ
"ํฐ๋ฏธ๋ ์กฐ์ ๊ธฐ๋ฅ์ด๋ ๋ฃ์ด๋ณผ๊น?" -
Gemini-Claw ์คํผ์ค ์์ฑ
๋ก์ปฌ ํด๋๋ฅผ ๋ถ์ํด 94์ด ๋ง์ ํ ํจํค์ง ์์ฑ. -
Gemini 3 Pro + ๋ฏ์ API
๊ธฐ๋ ์ด์์ ์ฝ๋ ํ๋ฆฌํฐ์ ์ฌ๋ฏธ. -
Liquid AI LFM2-2.6B-Exp ํ๋๊ธฐ
๋ ผ๋ฌธ Related Work ์น์ ์ ํต์งธ๋ก ์์ฑํ๋ ๋๊ตฌ ์ ์.
-
Tiny MoA
์๊ฐ๋น $100 ํ์ฐ๋ AI vs CPU๋ก ๋๋ฆฌ๋ ๊ฐ์ฑ๋น ๋ฉํฐ ์์ด์ ํธ. -
Tiny MoA Tool Calling
16GB ๋ ธํธ๋ถ์์ ๊ตฌํํ ๋ก์ปฌ ์์ด์ ํธ์ ๋๊ณผ ์. -
Tiny MoA: ์ง์ ํ ์จ๋๋ฐ์ด์ค AI
Clawdbot is cool, but Tiny MoA runs on CPU. -
Clawdbot vs ๋ก์ปฌ AI
API ์๋ ์ง์ ํ ์จ๋๋ฐ์ด์ค AI๋ฅผ ํฅํ์ฌ. -
vLLM & SGLang in llama.cpp
CPU ์ถ๋ก ์๋ 1.8๋ฐฐ ํฅ์.
-
Open-Yaongi Project
52 Layers 4B(Active 0.6B) ๊ท๋ชจ์ ํจ์จ์ ์ธ sLLM ์คํ์์ค ํ๋ก์ ํธ (Mamba-2 + MoE). -
HybriKo: ํ์ด๋ธ๋ฆฌ๋ RNN+Attention
Google Griffin๊ณผ Liquid AI LFM2์์ ์๊ฐ์ ๋ฐ์ ์ํคํ ์ฒ. -
HybriKo-117M
A100 8์ฅ์ผ๋ก ๋ง๋ ๋ฆฌ๋ ์ค ๋ช ๋ น์ด Function Calling ๋ชจ๋ธ. -
HybriKo-117M-LinuxFC
ํ๊ตญ์ด๋ฅผ ๋ฆฌ๋ ์ค ๋ช ๋ น์ด๋ก ๋ฐ๊ฟ์ฃผ๋ ์ด๊ฒฝ๋ ๋ชจ๋ธ ๊ฐ๋ฐ๊ธฐ. -
52-Layer HybriKo-430M
T4 GPU ํ๋์ ์ต์ ์ํคํ ์ฒ๋ฅผ ์ฐ๊ฒจ๋ฃ์ ์คํ์. -
1.2B ๋ชจ๋ธ๋ก PPT ๋ง๋ค๊ธฐ
์ํ ๋ชจ๋ธ์ ๊ฐ๋ฅ์ฑ. -
GPT ๊ตฌ์กฐ์ ํ๊ณ๋ฅผ ๋์ด
Liquid AI, TII, NVIDIA์ ์๋ก์ด ์๋๋ค.
-
LFM2 1.2B ๊ธฐ๋ฐ ํ๊ตญ์ด-์์ด ๋ฒ์ญ๊ธฐ
LFM2 1.2B ๋ชจ๋ธ๋ก ๊ตฌ๊ธ๊ณผ ์๋ฆฌ๋ฐ๋ฐ์ 4B ๋ชจ๋ธ์ ์ด๊ธด ๋ฒ์ญ๊ธฐ ์ ์๊ธฐ. -
LFM2 ๋ฒ์ญ๊ธฐ ๊ฐ๋ฐ๊ธฐ: ํต์ฌ ๋ฐ๊ฒฌ ๋ฐ ์ฑ๊ณผ
SFT์ RL์ ์ฑ๋ฅ ์ฐจ์ด ๋ถ์ ๋ฐ Liquid AI ๊ณต์ ์ฟก๋ถ ๋ฑ์ฌ ์์. -
Small Language Model for Translation
Advice for AI engineers. -
Liquid AI LFM2-1.2B ํ๋ ์คํจ๊ธฐ
ํ๊ตญ์ด-์์ด ๋ฒ์ญ RL(GRPO) ํ์ต ์คํจ์ ๊ตํ. -
ํ๊ตญ์ด LLM ํ์ต ๋ฐ์ดํฐ์ ๋ถ์ฌ
Pre-training๋ถํฐ GRPO๊น์ง์ ํ๋ํ ์ฌ์ .
-
์ต๊ทผ ๊ตฌํํ AI ํ๋ก์ ํธ ๋ฐ ์ฑ๊ณผ
Gemini-Claw๋ก ๊ตฌํํ ๋งฅํจ์ง ์คํ์ผ ๋ณด๊ณ ์ ๋ฐ PPT ์๋ ์์ฑ. -
Gemini-Claw ์ฑ๋ฅ vs ๋ณด์
LLM ์์ด์ ํธ์ ์ํํ ์ ์ฌ๋ ฅ. -
AI์ ๋ํ ๋๋ ค์ vs ํฅ๋ฏธ
OpenClaw, ํ๊ฐ ์ธ์ฉ, Vibe Coding ํ์์ ๋ํ ๋จ์. -
Pau Labarta Bajo's Insight
๋ฉํฐ ์์ด์ ํธ ์์คํ ์ ๋ํ ์ธ์ฌ์ดํธ.
-
Andrej Karpathy: ์ฐ๋ฆฌ๋ ์ ๋ น์ ์ํํ๊ณ ์๋๊ฐ?
AGI์ ํจ์จ์ฑ๊ณผ ํต์ , ๊ทธ๋ฆฌ๊ณ ๋ณด์ ํดํน์ ๋ํ ๋จ์. -
AI Era Cognitive Surrender
AI์ ์์กดํ๋ ๋๊ฐ๋ '์ธ์ง์ ํญ๋ณต(Cognitive Surrender)'์ ๋๋ค. -
Open Claw: AI๊ฐ ๊ฐ๋ฐ์๋ฅผ ๊ณต๊ฒฉํ ๋
์คํ์์ค ๋ฉ์ธํ ์ด๋๊ฐ AI์๊ฒ ํ๋ฐ์ ๋นํ๋ค. -
Vibe Coding (๋ฐ์ด๋ธ ์ฝ๋ฉ)
์ฝ๋๋ ์์ด๋ผ. ๋ฌด๋(Vibe)๋ฅผ ๊ด๋ฆฌํด๋ผ. -
The Thinking Game (Demis Hassabis)
์ฒด์ค ๋ญํน 2์์ ์ฒ์ฌ ์๋ ์ ์ ๋น๊ฒํ ์น๋ถ์ ์ธ๊ณ๋ฅผ ๋ ๋ ์ธ๋ฅ๋ฅผ ๊ตฌ์ํ๋ฌ ๊ฐ๋๊ฐ? -
Sebastian Raschka, PhD: "Ahead of AI"
๊ธฐ๋ณธ๊ธฐ๋ถํฐ ์ต์ ํธ๋ ๋๊น์ง. -
Vibe Coding๊ณผ ์๊ตฌ์ ์ธ ์ฃผ๋์ด์ ํจ์
Karpathy๋ ํ๋ค์ดํ๋ ์๋์ ์์กด๋ฒ: ๋ฐ์ด๋ธ ์ฝ๋ฉ๊ณผ ๊ธฐ์ด์ ์ค์์ฑ. -
Hugging Face CEO์ ํ๊ตญ AI ๋ชจ๋ธ ์์
SKT A.X, LG AI, Upstage ๋ฑ ํ๊ตญ ๋ชจ๋ธ์ ์ ์ฑ์๋. -
Anthropic์ ์ํ๊ณ ์กฐ์ด๊ธฐ
OpenCode ์ฐจ๋จ๊ณผ Claude Code ์ฌ์ฉ๋ ์ ํ์ ์์ฌ์. -
CES 2026: AMD Lisa Su์ Liquid AI
AMD๊ฐ ์ ํํ ํํธ๋. -
๊ตญ๊ฐ๋ํ AI ํ๋ก์ ํธ 1์ฐจ ๊ฒฐ๊ณผ
LG, SKT, Upstage ์ ๋ฐ๊ณผ ํ๋ฝ ๊ธฐ์ ๋ค์ ํ๋ณด. -
Post-training์ ํ๊ณ
์ ๋ชจ๋ธ์ ํ์ต์ด ๋๋๋ฉด ๋ ์ด์ ๋๋ํด์ง์ง ์๋๊ฐ? -
LLM ๊ฐ๋ฐ๊ณผ ์ฌ๋ด ์ ์น
์ค๋ฌด์ vs ๊ฒฝ์์ง์ ๋ฆฌ์คํฌ ๊ด๋ฆฌ ๊ด์ ์ฐจ์ด. -
Solar Open์ GLM ํ์ ๋
ผ๋ ์ข
๊ฒฐ
From Scratch ๊ฐ๋ฐ์ ์น์ดํ ํ์ . -
What LLMs Think When You Don't Tell Them?
์๋ฌด๋ฐ ์ง์๋ ํ์ง ์์์ ๋ LLM์ ๋ฌด์์ ์๊ฐํ๋๊ฐ? ๋ชจ๋ธ ์ฑ๊ฒฉ ์ ํ ๋ถ์. -
AI ๊ฑฐํ๋ก ์ ๋ณธ์ง
์์ฅ ์ถ์๊ฐ ์๋ ์๊ธ ์์ ํ์ ์ฐ์ ์ ์ฑ์.
-
- ๐ง Contact: [email protected]
Disclaimer: The views and opinions expressed in these reviews are those of the author and do not necessarily reflect the official policy or position of any other agency, organization, employer or company.
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