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Beyond Single-Agent Safety: A Taxonomy of Risks in LLM-to-LLM Interactions

Beyond Single-Agent Safety: A Taxonomy of Risks in LLM-to-LLM Interactions

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ œ๊ธฐ ๋‹จ์ผโ€‘์—์ด์ „ํŠธ ์•ˆ์ „ ํŒจ๋Ÿฌ๋‹ค์ž„์˜ ํ•œ๊ณ„ ๊ธฐ์กด ์•ˆ์ „ ๊ธฐ์ˆ (RLHF, ํ”„๋กฌํ”„ํŠธ ์—”์ง€๋‹ˆ์–ด๋ง, ์ถœ๋ ฅ ๋ชจ๋”๋ ˆ์ด์…˜ ๋“ฑ)์€ ์ ๋ณ„ (pointwise) ์ œ์–ด์— ์ดˆ์ ์„ ๋งž์ถ˜๋‹ค. ์ด๋Š” โ€œํ•˜๋‚˜์˜ ๋ชจ๋ธ โ†” ํ•˜๋‚˜์˜ ์‚ฌ์šฉ์žโ€๋ผ๋Š” ์ด์›์ (dyadic) ์ƒํ™ฉ์„ ์ „์ œ๋กœ ํ•˜๋ฉฐ, ๋ชจ๋ธ์˜ ์ถœ๋ ฅ์ด ์™ธ๋ถ€ ์‹œ์Šคํ…œ์— ์žฌํˆฌ์ž…๋˜๋Š” ๊ฒฝ์šฐ๋ฅผ ๊ณ ๋ คํ•˜์ง€ ์•Š๋Š”๋‹ค. LLMโ€‘toโ€‘LLM ์ƒํƒœ๊ณ„์˜ ๊ธ‰์„ฑ์žฅ AutoGen, CAMEL, SWEโ€‘agent, Voyager ๋“ฑ์—์„œ ๋ณด๋“ฏ, LLM์ด ๋„๊ตฌ, ๋ฉ”๋ชจ๋ฆฌ, ๋‹ค๋ฅธ LLM๊ณผ ์—ฐ๊ณ„๋˜๋Š” ๋ฉ€ํ‹ฐโ€‘์—์ด์ „ํŠธ ๊ตฌ์กฐ๊ฐ€ ์‹ค๋ฌด์™€ ์—ฐ๊ตฌ ๋ชจ๋‘

Dynamical modeling of nonlinear latent factors in multiscale neural activity with real-time inference

Dynamical modeling of nonlinear latent factors in multiscale neural activity with real-time inference

์ด ๋…ผ๋ฌธ์˜ ํ•ต์‹ฌ ์•„์ด๋””์–ด๋Š” ์‹ ๊ฒฝ ๋ฐ์ดํ„ฐ๋ฅผ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๋””์ฝ”๋”ฉํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์‹œํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ํŠนํžˆ, ์ด ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ๋‹ค์–‘ํ•œ ์‹œ๊ฐ„ ์ฒ™๋„์™€ ํ™•๋ฅ  ๋ถ„ํฌ๋ฅผ ๊ฐ€์ง€๋ฉฐ ๋•Œ๋กœ๋Š” ๋ˆ„๋ฝ๋œ ์ƒ˜ํ”Œ์ด ์žˆ๋Š” ์—ฌ๋Ÿฌ ๋ชจ๋‹ฌ๋ฆฌํ‹ฐ์˜ ์ •๋ณด๋ฅผ ํ†ตํ•ฉํ•˜์—ฌ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๋””์ฝ”๋”ฉํ•  ์ˆ˜ ์žˆ๋„๋ก ์„ค๊ณ„๋˜์—ˆ๋‹ค. ๊ธฐ์กด์˜ ๋น„์„ ํ˜• ๋‹ค์ค‘ ๋ชจ๋‹ฌ ์‹ ๊ฒฝ ํ™œ๋™ ๋ชจ๋ธ๋“ค์€ ์ด๋Ÿฌํ•œ ๋ณต์žกํ•œ ๋ฌธ์ œ๋“ค์„ ํ•ด๊ฒฐํ•˜์ง€ ๋ชปํ•˜์˜€์œผ๋ฉฐ, ์ผ๋ถ€๋Š” ์‹ค์‹œ๊ฐ„ ๋””์ฝ”๋”ฉ์ด ๋ถˆ๊ฐ€๋Šฅํ–ˆ๋‹ค. ํ”„๋ ˆ์ž„์›Œํฌ์˜ ํ•ต์‹ฌ ๊ตฌ์„ฑ ์š”์†Œ๋กœ๋Š” ๋‹ค์ค‘ ์Šค์ผ€์ผ ์ธ์ฝ”๋”์™€ ๋ฐฑ๋ณธ, ๊ทธ๋ฆฌ๊ณ  ๋ชจ๋‹ฌ๋ฆฌํ‹ฐ๋ณ„ ๋””์ฝ”๋”๊ฐ€ ์žˆ๋‹ค. ์ด๋“ค ๊ตฌ์„ฑ ์š”์†Œ๋Š” ๊ฐ๊ฐ ๋‹ค๋ฅธ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•œ๋‹ค:

Model
Exploration vs. Fixation: Scaffolding Divergent and Convergent Thinking for Human-AI Co-Creation with Generative Models

Exploration vs. Fixation: Scaffolding Divergent and Convergent Thinking for Human-AI Co-Creation with Generative Models

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์ƒ์„ฑํ˜• AI์˜ ๋ฏผ์ฃผํ™” ๋Š” ์ดˆ๋ณด์ž๋„ ๊ณ ํ’ˆ์งˆ ๊ฒฐ๊ณผ๋ฌผ์„ ๋งŒ๋“ค ์ˆ˜ ์žˆ๊ฒŒ ํ–ˆ์ง€๋งŒ, โ€œ์Šฌ๋กฏ ๋จธ์‹ โ€ํ˜• ์›Œํฌํ”Œ๋กœ์šฐ (ํ”„๋กฌํ”„ํŠธ โ†’ ๊ฒฐ๊ณผ โ†’ ๋ฐ˜๋ณต) ๋•Œ๋ฌธ์— ์‚ฌ์šฉ์ž๋Š” ์ดˆ๊ธฐ ๊ฒฐ๊ณผ์— ๊ธ‰์†ํžˆ ์ˆ˜๋ ดํ•˜๊ณ , ๋Œ€์•ˆ ํƒ์ƒ‰์„ ํฌ๊ธฐํ•œ๋‹ค. ์ด ํ˜„์ƒ์€ ๋ฐœ์‚ฐ ์‚ฌ๊ณ  ๊ฐ€ ์–ต์ œ๋˜๊ณ  ๋””์ž์ธ ๊ณ ์ฐฉ ์ด ๋ฐœ์ƒํ•œ๋‹ค๋Š” ๊ธฐ์กด ๋ฌธํ—Œ(์˜ˆ: 42, 47)๊ณผ ์ผ์น˜ํ•œ๋‹ค. ๋˜ํ•œ, ์‚ฌ์šฉ์ž๋Š” ๊ณ ์ˆ˜์ค€ ์˜๋„ ๋ฅผ ๊ตฌ์ฒด์ ์ธ ํ”„๋กฌํ”„ํŠธ๋กœ ๋ณ€ํ™˜ํ•˜๋Š” ๊ณผ์ •์—์„œ โ€œ์‹œ๊ฐํ™”์˜ ๊ฐ„๊ทน(gulf of envisioning)โ€ ์— ๋ถ€๋”ชํ˜€, ๋ฏธ์„ธ ์กฐ์ •์— ๋จธ๋ฌด๋ฅด๊ฒŒ ๋œ๋‹ค. 2. ์ด๋ก ์  ํ† ๋Œ€ โ€“ Wallas ๋ชจ

Model
No Image

LoopBench: Discovering Emergent Symmetry Breaking Strategies with LLM Swarms

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ LLM์˜ ๋‹ค์ค‘ ์—์ด์ „ํŠธํ™” : ์ตœ๊ทผ LLM์ด ๋‹จ์ผ ์งˆ์˜์‘๋‹ต์„ ๋„˜์–ด ๋ณตํ•ฉ์ ์ธ ์ž‘์—…์„ ์ˆ˜ํ–‰ํ•˜๋„๋ก ์„ค๊ณ„๋˜๋Š” ์ถ”์„ธ์ด๋ฉฐ, โ€œAI ์Šค์›œโ€์ด๋ผ๋Š” ๊ฐœ๋…์ด ๋“ฑ์žฅํ•˜๊ณ  ์žˆ๋‹ค. ๋ถ„์‚ฐ ํ˜‘์—…์˜ ํ•ต์‹ฌ ๊ณผ์ œ : ์ค‘์•™ ์กฐ์ •์ž๊ฐ€ ์—†๋Š” ํ™˜๊ฒฝ์—์„œ ์—์ด์ „ํŠธ๊ฐ€ ๋Œ€์นญ ๊นจ๊ธฐ(symmetry breaking) ๋ฅผ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š”์ง€ ์—ฌ๋ถ€๋Š” ๋ฉ”ํƒ€์ธ์ง€์  ์‚ฌ๊ณ  ๋Šฅ๋ ฅ๊ณผ ์ง๊ฒฐ๋œ๋‹ค. ํ™€์ˆ˜ ์‚ฌ์ดํด ๊ทธ๋ž˜ํ”„ : ์ƒ‰์ƒ์ด ๋ถ€์กฑํ•œ(2์ƒ‰) ํ™€์ˆ˜ ์‚ฌ์ดํด์€ ์™„์ „ ๋Œ€์นญ ์„ ์ด๋ฃจ๋ฉฐ, ๊ฒฐ์ •๋ก ์  ๋ฌดํ†ต์‹  ์—์ด์ „ํŠธ๋Š” ๋ฐ˜๋“œ์‹œ ๋ฌดํ•œ ์ง„๋™์— ๋น ์ง„๋‹ค. ๋”ฐ๋ผ์„œ ์ด ๋ฌธ์ œ๋Š” โ€œ์ข์€ ์‹œ๊ฐโ€์ด ๊ต์ฐฉ์„ ์ดˆ๋ž˜ํ•˜

A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice

A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice

์ด ๋…ผ๋ฌธ์€ Janus Pro CXR์ด๋ผ๋Š” ๊ฐ€์Šด ์—‘์Šค๋ ˆ์ด ํ•ด์„ ์‹œ์Šคํ…œ์— ๋Œ€ํ•œ ์ฒ ์ €ํ•œ ํ‰๊ฐ€๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ์ด ์‹œ์Šคํ…œ์€ DeepSeek์˜ Janus Pro ๋ชจ๋ธ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ฐœ๋ฐœ๋˜์—ˆ์œผ๋ฉฐ, ๋‹ค์ค‘ ์„ผํ„ฐ ์ „ํ–ฅ์  ์—ฐ๊ตฌ(NCT07117266)๋ฅผ ํ†ตํ•ด ๊ฒ€์ฆ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ๋…ผ๋ฌธ์—์„œ ์ œ์‹œ๋œ ๊ฒฐ๊ณผ๋Š” AI ์ง€์› ๋ฐฉ์‚ฌ์„  ํ•ด์„ ์‹œ์Šคํ…œ์ด ์ž„์ƒ ํ™˜๊ฒฝ์—์„œ ์‹ค์ œ์ ์ธ ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ์Œ์„ ์ž…์ฆํ•ฉ๋‹ˆ๋‹ค. Janus Pro CXR์€ ์ž๋™ ๋ณด๊ณ ์„œ ์ƒ์„ฑ์—์„œ ์ตœ์‹  X ray ๋ณด๊ณ ์„œ ์ƒ์„ฑ ๋ชจ๋ธ๋“ค์„ ๋Šฅ๊ฐ€ํ•˜๋ฉฐ, ํŠนํžˆ 200B ๋งค๊ฐœ๋ณ€์ˆ˜์˜ ChatGPT 4o๋ณด๋‹ค๋„ ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ์—ˆ์Šต๋‹ˆ๋‹ค

System
Statistical Arbitrage in Polish Equities Market Using Deep Learning Techniques

Statistical Arbitrage in Polish Equities Market Using Deep Learning Techniques

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ํ†ต๊ณ„์  ์ฐจ์ต๊ฑฐ๋ž˜(Statistical Arbitrage) ๋Š” ๋™์ผ ์‹œ์žฅ ๋‚ด ์ž์‚ฐ ๊ฐ„ ๊ฐ€๊ฒฉ ๋ถˆ์ผ์น˜๋ฅผ ์ด์šฉํ•˜๋Š” ์ „๋žต์œผ๋กœ, ํšจ์œจ์  ์‹œ์žฅ ๊ฐ€์„ค์„ ๋ถ€๋ถ„์ ์œผ๋กœ ์œ„๋ฐ˜ํ•œ๋‹ค๋Š” ์ ์—์„œ ํ•™๊ณ„ยท์‹ค๋ฌด ๋ชจ๋‘ ๊ด€์‹ฌ์ด ์ง‘์ค‘๋œ๋‹ค. ๊ธฐ์กด ํŽ˜์–ด ํŠธ๋ ˆ์ด๋”ฉ์€ ๊ณ ์ƒ๊ด€ ๋‘ ์ข…๋ชฉ์„ ์ง์ ‘ ๋งค์นญํ•˜์ง€๋งŒ, ๋ฆฌ์Šคํฌ ํŒฉํ„ฐ ๋ณต์ œ ๋Š” ๋ณด๋‹ค ์œ ์—ฐํ•œ โ€œ๊ฐ€์ƒ ํŽ˜์–ดโ€๋ฅผ ๋งŒ๋“ ๋‹ค. ์ด๋Š” ํŠนํžˆ ์‹œ์žฅ ๊ทœ๋ชจ๊ฐ€ ์ž‘๊ณ  ์ƒ์žฅ ETF๊ฐ€ ์ œํ•œ์ ์ธ ํด๋ž€๋“œ ์™€ ๊ฐ™์€ ์‹ ํฅ ์‹œ์žฅ์—์„œ ์œ ์šฉํ•˜๋‹ค. 2. ๋ฐฉ๋ฒ•๋ก  | ๋‹จ๊ณ„ | ๋‚ด์šฉ | ํ•ต์‹ฌ ๊ธฐ์ˆ  | | | | | | ๋ฆฌ์Šคํฌ ํŒฉํ„ฐ ๊ตฌ์„ฑ | PCA : ์ „์ฒด

Learning
AI-Driven Expansion and Application of the Alexandria Database

AI-Driven Expansion and Application of the Alexandria Database

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉํ‘œ ์•Œ๋ ‰์‚ฐ๋“œ๋ฆฌ์•„ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๋Š” ํ˜„์žฌ ์ „ ์„ธ๊ณ„์—์„œ ๊ฐ€์žฅ ํฐ ๊ณต๊ฐœ DFT ๊ธฐ๋ฐ˜ ์—ด์—ญํ•™ ์•ˆ์ • ๋ฌผ์งˆ ์ปฌ๋ ‰์…˜์ด๋ฉฐ, ๊ธฐ์กด ๋ฒ„์ „์€ ์•ฝ 5.8 M ๊ตฌ์กฐยท17.5 ร— 10โด ์•ˆ์ • ๋ฌผ์งˆ์„ ํฌํ•จํ•œ๋‹ค. ๊ธฐ์กด ๊ณ ์† ํƒ์ƒ‰(HT) ๋ฐฉ์‹์€ ์„ฑ๊ณต๋ฅ  โ‰ˆ 0.1 % ์— ๋จธ๋ฌผ๋Ÿฌ, ์‹ค์ œ ์‹คํ—˜ ๊ฐ€๋Šฅํ•œ ํ›„๋ณด๋ฅผ ์ฐพ๋Š” ๋ฐ ๋น„ํšจ์œจ์ ์ด์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์„ฑ๊ณต๋ฅ  99 % (100 meV/atom ์ด๋‚ด)์™€ 3๋ฐฐ ํ–ฅ์ƒ๋œ ํšจ์œจ ์„ ๋ชฉํ‘œ๋กœ, ์ตœ์‹  ์ƒ์„ฑ ๋ชจ๋ธยท๋ณดํŽธ MLIPยท๊ทธ๋ž˜ํ”„ NN์„ ํ†ตํ•ฉํ•œ ํŒŒ์ดํ”„๋ผ์ธ์„ ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์ˆ  ๋ฐ ์›Œํฌํ”Œ๋กœ์šฐ | ๋‹จ๊ณ„ | ์‚ฌ์šฉ ๊ธฐ

Data
Towards Efficient Hypergraph and Multi-LLM Agent Recommender Systems

Towards Efficient Hypergraph and Multi-LLM Agent Recommender Systems

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ LLM ๊ธฐ๋ฐ˜ ์ƒ์„ฑํ˜• ์ถ”์ฒœ ์€ ์˜๋ฏธ๋ก ์  ์ดํ•ด์™€ ๋Œ€ํ™”ํ˜• ์ถ”๋ก ์„ ์ œ๊ณตํ•˜์ง€๋งŒ, ํ™˜๊ฐ(Hallucination) ๋ฌธ์ œ์™€ ๊ณ ๋น„์šฉ ์—ฐ์‚ฐ ์ด ์‹ค์šฉ์„ฑ์„ ์ €ํ•ดํ•œ๋‹ค. ๊ธฐ์กด GNN ๊ธฐ๋ฐ˜ ์ถ”์ฒœ์€ ์ •์ ยท์Œ๋ฐฉํ–ฅ ๊ด€๊ณ„๋งŒ์„ ๋‹ค๋ฃจ์–ด, ์‹œ๊ฐ„์— ๋”ฐ๋ผ ๋ณ€ํ•˜๋Š” ๋‹ค์ค‘ ํ–‰๋™(์กฐํšŒ, ์žฅ๋ฐ”๊ตฌ๋‹ˆ, ๊ตฌ๋งค ๋“ฑ)์„ ์ถฉ๋ถ„ํžˆ ํฌ์ฐฉํ•˜์ง€ ๋ชปํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด | ๊ตฌ์„ฑ ์š”์†Œ | ์—ญํ•  | ์ฃผ์š” ๊ธฐ๋ฒ• | | | | | | ํ•˜์ดํผ๊ทธ๋ž˜ํ”„ ์ธ์ฝ”๋” (HGNN) | ์‚ฌ์šฉ์žยท์•„์ดํ…œยทํ–‰๋™์„ ํ•˜๋‚˜์˜ ํ•˜์ดํผ์—ฃ์ง€๋กœ ์—ฐ๊ฒฐ, ๊ณ ์ฐจ์› ์ƒํ˜ธ์ž‘์šฉ ํ•™์Šต | ๋‘ ๋‹จ๊ณ„ ํ•˜์ดํผ๊ทธ๋ž˜ํ”„ ์ปจ๋ณผ๋ฃจ์…˜ + A

System
์ž…๋ ฅ ํฌ๊ธฐ์™€ ๋ฌด๊ด€ํ•œ ์‹œ๊ฐ ์ธ์ฝ”๋” MambaEye

์ž…๋ ฅ ํฌ๊ธฐ์™€ ๋ฌด๊ด€ํ•œ ์‹œ๊ฐ ์ธ์ฝ”๋” MambaEye

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ž…๋ ฅ ํฌ๊ธฐ ๋ถˆ๋ณ€์„ฑ ์€ ์ธ๊ฐ„ ์‹œ๊ฐ์˜ ํ•ต์‹ฌ ํŠน์„ฑ์œผ๋กœ, ํ˜„์žฌ ๋Œ€๋ถ€๋ถ„์˜ ๋น„์ „ ๋ชจ๋ธ์€ ๊ณ ์ •๋œ ์ž…๋ ฅ ํ•ด์ƒ๋„์— ๋งž์ถฐ ์„ค๊ณ„๋ผ ์ด๋ฏธ์ง€ ๋ฆฌ์‚ฌ์ด์ง•ยทํฌ๋กญ์œผ๋กœ ์ธํ•œ ์ •๋ณด ์†์‹ค์ด ๋ฐœ์ƒํ•œ๋‹ค. CNN์€ ๊ณ ์ • ์ปค๋„๋กœ ์ธํ•ด ์ „์—ญ ์˜์กด์„ฑ์„ ํ™•๋ณดํ•˜๊ธฐ ์–ด๋ ต๊ณ , ViT๋Š” ํŒจ์น˜ ์ˆ˜์— ๋Œ€ํ•ด ์ œ๊ณฑ ๋ณต์žก๋„ ๋ฅผ ๊ฐ€์ง€๋ฉฐ ํ•ด์ƒ๋„ ๋ณ€ํ™”์— ์ทจ์•ฝํ•˜๋‹ค. ์ตœ๊ทผ SSM ๊ธฐ๋ฐ˜ ๋ชจ๋ธ(Mamba ๋“ฑ)์€ ์„ ํ˜• ์‹œ๊ฐ„ยท์ƒ์ˆ˜ ๋ฉ”๋ชจ๋ฆฌ ์žฅ์ ์„ ์ œ๊ณตํ•˜์ง€๋งŒ, ๋Œ€๋ถ€๋ถ„์€ ์–‘๋ฐฉํ–ฅ ์ฒ˜๋ฆฌ์™€ ๊ณ ์ • ์Šค์บ” ๊ฒฝ๋กœ (์˜ˆ: raster) ์˜์กด์œผ๋กœ ์ง„์ •ํ•œ ํฌ๊ธฐ ๋ถˆ๋ณ€์„ฑ์„ ๋‹ฌ์„ฑํ•˜์ง€ ๋ชปํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด |

AgriRegion: Region-Aware Retrieval for High-Fidelity Agricultural Advice

AgriRegion: Region-Aware Retrieval for High-Fidelity Agricultural Advice

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ LLM์˜ ํ•œ๊ณ„ : ์ผ๋ฐ˜ ๋ชฉ์  ๋ชจ๋ธ์€ ๋ฐฉ๋Œ€ํ•œ ์ผ๋ฐ˜ ์ง€์‹์„ ๊ฐ–์ง€๋งŒ, ๋†์—…์ฒ˜๋Ÿผ ์ง€์—ญยท์‹œ์ฆŒยท๋ฒ•๊ทœ์— ๋ฏผ๊ฐํ•œ ๋„๋ฉ”์ธ์—์„œ๋Š” โ€œ๋งฅ๋ฝ์  ํ™˜๊ฐโ€ ์œ„ํ—˜์ด ๋†’๋‹ค. RAG์˜ ๊ธฐ์กด ํ™œ์šฉ : ๊ธฐ์กด RAG๋Š” ์˜๋ฏธ์  ์œ ์‚ฌ๋„ ๊ธฐ๋ฐ˜ ๊ฒ€์ƒ‰์— ๋จธ๋ฌผ๋Ÿฌ, ์ง€๋ฆฌยท๊ธฐํ›„์™€ ๊ฐ™์€ ๋น„ํ…์ŠคํŠธ์  ์ปจํ…์ŠคํŠธ๋ฅผ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๊ธฐ์—ฌ ๋‚ด์šฉ | ์˜์˜ | | | | | |โ‘ | Regionโ€‘Aware Retrieval : ์ง€๋ฆฌ ๋ฉ”ํƒ€๋ฐ์ดํ„ฐ๋ฅผ ๋ฌธ์„œ์— ์ฃผ์ž…ํ•˜๊ณ , ์งˆ์˜ ์‹œ ์œ„์น˜ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•ด ๊ฒ€์ƒ‰ยท์žฌ๋žญํ‚น ์ˆ˜ํ–‰|์ง€์—ญ ๋งž์ถคํ˜• ์กฐ์–ธ ์ œ๊ณต, ํ™˜๊ฐ ๊ฐ์†Œ |

AutoICE: Automatically Synthesizing Verifiable C Code via LLM-driven Evolution

AutoICE: Automatically Synthesizing Verifiable C Code via LLM-driven Evolution

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ž๋™ ํ˜•์‹ํ™”์˜ ํ•„์š”์„ฑ : C ์ฝ”๋“œ์™€ ACSL ์ฃผ์„์„ ๋™์‹œ์— ์ƒ์„ฑํ•ด์•ผ ํ•˜๋Š” ์ž๋™ ํ˜•์‹ํ™”๋Š” ์•ˆ์ „ยท์˜๋ฃŒยท๊ธˆ์œต ๋“ฑ ์•ˆ์ „โ€‘์ค‘์š” ๋ถ„์•ผ์—์„œ ํ˜•์‹ ๊ฒ€์ฆ ๋„์ž…์„ ์ด‰์ง„ํ•œ๋‹ค. LLM ํ™œ์šฉ์˜ ํ•œ๊ณ„ : ๊ธฐ์กด LLM ๊ธฐ๋ฐ˜ ์ ‘๊ทผ๋ฒ•์€ (1) ๋ฐ์ดํ„ฐ ํฌ์†Œ์„ฑ์œผ๋กœ ์ธํ•œ ๊ตฌ๋ฌธยท์˜๋ฏธ ์˜ค๋ฅ˜, (2) ํฌ๋ฐ•ํ•œ ๊ฒ€์ฆ ํ”ผ๋“œ๋ฐฑ์„ ํ†ตํ•œ ์ž์ฒด ์ˆ˜์ • ์‹œ ์˜ค๋ฅ˜ ๋ˆ„์ , (3) ์•”๋ฌต ์ง€์‹(์ „์ œ, ๋ฃจํ”„ ๋ถˆ๋ณ€์‹ ๋“ฑ) ์ถ”์ถœ ๋ถ€์กฑ์ด๋ผ๋Š” ์„ธ ๊ฐ€์ง€ ์ฃผ์š” ๋ฌธ์ œ์— ์ง๋ฉดํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ๋ฐ ๋ฐฉ๋ฒ•๋ก  | ๊ตฌ์„ฑ ์š”์†Œ | ์„ค๋ช… | ์—ญํ•  | | | | | | ๋‹ค์–‘ํ•œ ์ดˆ๊ธฐ ๊ฐœ์ฒด(Di

Automated Risk-of-Bias Assessment of Randomized Controlled Trials: A First Look at a GEPA-trained Programmatic Prompting Framework

Automated Risk-of-Bias Assessment of Randomized Controlled Trials: A First Look at a GEPA-trained Programmatic Prompting Framework

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ RoB ํ‰๊ฐ€์˜ ํ•ต์‹ฌ์„ฑ : Cochrane RoB 1 ๋„๊ตฌ๋Š” ๋ฉ”ํƒ€๋ถ„์„ ์‹ ๋ขฐ์„ฑ์˜ ๊ธฐ์ดˆ์ด๋ฉฐ, 7๊ฐœ ๋„๋ฉ”์ธ ๊ฐ๊ฐ์— ๋Œ€ํ•ด โ€˜Low/High/Unclearโ€™ ํŒ๋‹จ์„ ์š”๊ตฌํ•œ๋‹ค. ์ž๋™ํ™”์˜ ์žฅ์• ๋ฌผ : ๊ธฐ์กด LLM ๊ธฐ๋ฐ˜ ์ž๋™ํ™”๋Š” ์ˆ˜์ž‘์—… ํ”„๋กฌํ”„ํŠธ ์— ํฌ๊ฒŒ ์˜์กดํ•ด, ํ”„๋กฌํ”„ํŠธ ์žฌํ˜„ยท๋‹ค๋ฅธ ๋ฐ์ดํ„ฐ์…‹ ์ ์šฉ์ด ์–ด๋ ค์› ๋‹ค. ๋˜ํ•œ, ์ธ๊ฐ„ ์ „๋ฌธ๊ฐ€์™€์˜ ์ผ๊ด€์„ฑ ํ™•๋ณด๊ฐ€ ๋ฏธํกํ–ˆ๋‹ค. ํ”„๋กœ๊ทธ๋ž˜๋ฐ๋œ ํ”„๋กฌํ”„ํŠธ : DSPy๋Š” โ€œ์ฝ”๋“œ๋กœ ํ”„๋กฌํ”„ํŠธ๋ฅผ ์ •์˜โ€ํ•จ์œผ๋กœ์จ ๋ชจ๋“ˆํ™”ยท๋ฒ„์ „ ๊ด€๋ฆฌยท๋””๋ฒ„๊น… ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๊ณ , GEPA๋Š” ์ง„ํ™”์ (Genetic)ยท๋‹ค๋ชฉ์ (Pareto)

Framework
ReactorFold: Generative discovery of nuclear reactor cores via emergent physical reasoning

ReactorFold: Generative discovery of nuclear reactor cores via emergent physical reasoning

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ํƒ„์†Œ ์ค‘๋ฆฝ ๋ชฉํ‘œ์™€ SMR : ์ „ ์„ธ๊ณ„์ ์ธ ํƒ„์†Œ ์ค‘๋ฆฝ ์ถ”์ง„ ์†์—์„œ ์†Œํ˜• ๋ชจ๋“ˆ ์›์ž๋กœ(SMR)์˜ ๋น ๋ฅธ ์ƒ์šฉํ™”๊ฐ€ ์š”๊ตฌ๋˜๋ฉฐ, ํ•ต์‹ฌ ์„ค๊ณ„์ธ ์—ฐ๋ฃŒ ์–ด์…ˆ๋ธ”๋ฆฌ ์ตœ์ ํ™”๊ฐ€ ํ•ต์‹ฌ ๊ณผ์ œ๋กœ ๋ถ€๊ฐ๋œ๋‹ค. ๊ธฐ์กด ์ตœ์ ํ™” ํ•œ๊ณ„ : ๊ฒฐ์ •๋ก ์ ยท๋ฉ”ํƒ€ํœด๋ฆฌ์Šคํ‹ฑยทML ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•๋“ค์€ ๋ชจ๋‘ ๊ณ ์ •๋œ ์„ค๊ณ„ ํŒŒ๋ผ๋ฏธํ„ฐ(์˜ˆ: Gd ํก์ˆ˜๋ด‰ ์ˆ˜) ๋ฅผ ์ „์ œ๋กœ ํ•˜์—ฌ ํƒ์ƒ‰ ๋ฒ”์œ„๋ฅผ ์ œํ•œํ•œ๋‹ค. ์ด๋Š” ์ƒˆ๋กœ์šด ํ† ํด๋กœ์ง€๋ฅผ ๋ฐœ๊ฒฌํ•˜๊ธฐ ์–ด๋ ค์šด ๊ตฌ์กฐ์  ์ œ์•ฝ์„ ๋งŒ๋“ ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ์‹œํ€€์Šค ๋ชจ๋ธ๋ง ์ „ํ™˜ : 17ร—17 2D ๊ฒฉ์ž๋ฅผ 289๊ฐœ์˜ ํ† ํฐ ์‹œํ€€์Šค๋กœ ์ง๋ ฌํ™”ํ•˜์—ฌ, ์–ธ์–ด ๋ชจ๋ธ์ด โ€œ๋ฌธ

No Image

An Optimal Policy for Learning Controllable Dynamics by Exploration

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ œ์–ด ๊ฐ€๋Šฅํ•œ ๋งˆ์ฝ”ํ”„ ์ฒด์ธ ์€ ์ˆœ์ฐจ์  ์˜์‚ฌ๊ฒฐ์ • ๋ฌธ์ œ์™€ ๊ฐ•ํ™”ํ•™์Šต(RL)์—์„œ ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ์ œ์–ด์˜ ํ•ต์‹ฌ์ด๋‹ค. ํ™˜๊ฒฝ ๋ชจ๋ธ์ด ์‚ฌ์ „์— ์ฃผ์–ด์ง€์ง€ ์•Š์„ ๋•Œ ํƒ์ƒ‰์„ ํ†ตํ•œ ๋ชจ๋ธ ํ•™์Šต ์ด ํ•„์ˆ˜์ด๋ฉฐ, ์ด๋Š” ๋™๋ฌผ์˜ ํ˜ธ๊ธฐ์‹ฌ ํ–‰๋™์ด๋‚˜ ๋Šฅ๋™ ํ•™์Šต(active learning)๊ณผ๋„ ์—ฐ๊ด€๋œ๋‹ค. ๊ธฐ์กด RL ์•Œ๊ณ ๋ฆฌ์ฆ˜(Qโ€‘Learning, Dynaโ€‘Q ๋“ฑ)์€ ๋ฌดํ•œ ์‹œ๊ฐ„ ์ˆ˜ํ‰ ์„ ์ „์ œ๋กœ ํ•˜์—ฌ ์ •์ƒ(stationary) ์ •์ฑ… ์„ ๋„์ถœํ•œ๋‹ค. ํ•˜์ง€๋งŒ ์ œํ•œ๋œ ํƒ์ƒ‰ ๋‹จ๊ณ„(์œ ํ•œ horizon)์—์„œ๋Š” ์ƒํƒœ์— ๋”ฐ๋ผ ์ •์ฑ…์ด ๋‹ฌ๋ผ์ ธ์•ผ ํ•จ์„ ์ €์ž๋Š” ๊ฐ•์กฐํ•œ๋‹ค. 2. ํ•ต์‹ฌ

Learning
Remoe: Towards Efficient and Low-Cost MoE Inference in Serverless Computing

Remoe: Towards Efficient and Low-Cost MoE Inference in Serverless Computing

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ MoE์™€ ์„œ๋ฒ„๋ฆฌ์Šค์˜ ๊ถํ•ฉ : MoE๋Š” ํ† ํฐ๋‹น ๋ช‡ ๊ฐœ์˜ ์ „๋ฌธ๊ฐ€๋งŒ ํ™œ์„ฑํ™”๋˜๋ฏ€๋กœ ์—ฐ์‚ฐ๋Ÿ‰์€ ์ ˆ๊ฐ๋˜์ง€๋งŒ, ์ „์ฒด ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ๋ฉ”๋ชจ๋ฆฌ์— ๋กœ๋“œํ•ด์•ผ ํ•˜๋Š” ๊ตฌ์กฐ์  ํŠน์„ฑ ๋•Œ๋ฌธ์— ์„œ๋ฒ„๋ฆฌ์Šค์˜ payโ€‘perโ€‘use ๋ชจ๋ธ์—์„œ ๋ฉ”๋ชจ๋ฆฌ ๋น„์šฉ์ด ๊ธ‰์ฆํ•œ๋‹ค. ๊ธฐ์กด ์˜คํ”„๋กœ๋“œ ํ•œ๊ณ„ : fMoE, HOBBIT ๋“ฑ์€ CPUโ€‘GPU ๊ฐ„ ๋™์  ์Šค์™€ํ•‘์„ ์‚ฌ์šฉํ•˜์ง€๋งŒ, CPU ๋ฉ”๋ชจ๋ฆฌ ํ’€์„ ์ง€์†์ ์œผ๋กœ ์œ ์ง€ํ•ด์•ผ ํ•˜๋ฏ€๋กœ ์„œ๋ฒ„๋ฆฌ์Šค ํ™˜๊ฒฝ์—์„œ ๋น„์šฉ ์ ˆ๊ฐ ํšจ๊ณผ๊ฐ€ ์ œํ•œ์ ์ด๋‹ค. ์ „๋ฌธ๊ฐ€ ํ™œ์„ฑํ™” ์˜ˆ์ธก์˜ ์–ด๋ ค์›€ : ๊ฒŒ์ดํŒ… ๋„คํŠธ์›Œํฌ๊ฐ€ ์ž…๋ ฅ์— ๊ฐ•ํ•˜๊ฒŒ ์˜์กดํ•ด ์ „๋ฌธ๊ฐ€ ํ™œ์„ฑํ™”๊ฐ€ ๋น„์ •ํ˜•์ ์ด๋ฉฐ

The Wisdom of Deliberating AI Crowds: Does Deliberation Improve LLM-Based Forecasting?

The Wisdom of Deliberating AI Crowds: Does Deliberation Improve LLM-Based Forecasting?

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์ธ๊ฐ„โ€‘AI ๋น„๊ต : ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ๋Š” ์ธ๊ฐ„ ์ „๋ฌธ๊ฐ€๊ฐ€ ์—ฌ์ „ํžˆ LLM ๊ธฐ๋ฐ˜ ์‹œ์Šคํ…œ์„ ์•ž์„ ๋‹ค๊ณ  ๋ณด๊ณ ํ–ˆ์œผ๋ฉฐ, ๋ชจ๋ธ ๊ทœ๋ชจยทํ”„๋กฌํ”„ํŠธ ์„ค๊ณ„ ๋“ฑ์ด ์„ฑ๋Šฅ์— ํฐ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค๊ณ  ์ฃผ์žฅํ•œ๋‹ค. ํ† ๋ก (Deliberation) ๋„์ž… : ์ธ๊ฐ„ ์ง‘๋‹จ ํ† ๋ก ์ด ์˜ˆ์ธก ์ •ํ™•๋„๋ฅผ ๋†’์ธ ์‚ฌ๋ก€๋ฅผ LLM์— ์ ์šฉํ•œ ์—ฐ๊ตฌ๋Š” ์•„์ง ๋ถ€์กฑํ–ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ โ€œ๋‹ค์ค‘โ€‘์—์ด์ „ํŠธ ํ† ๋ก โ€์„ LLM์— ์ง์ ‘ ์ ์šฉํ•ด, ๋ชจ๋ธ ๊ฐ„ ์ƒํ˜ธ ๊ฒ€ํ† ๊ฐ€ ์‹ค์ œ๋กœ ์„ฑ๋Šฅ์„ ๊ฐœ์„ ํ•˜๋Š”์ง€๋ฅผ ์ตœ์ดˆ๋กœ ์‹ค์ฆํ•œ๋‹ค. 2. ์‹คํ—˜ ์„ค๊ณ„ | ์š”์†Œ | ์„ค์ • | ์„ค๋ช… | | | | | | ๋ชจ๋ธ ๋‹ค์–‘์„ฑ | ๋‹ค์–‘ (GPTโ€‘

Distill, Forget, Repeat: A Framework for Continual Unlearning in Text-to-Image Diffusion Models

Distill, Forget, Repeat: A Framework for Continual Unlearning in Text-to-Image Diffusion Models

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฒ•์ ยท์œค๋ฆฌ์  ์••๋ ฅ : GDPRยทCCPA ๋“ฑ์—์„œ โ€œ์žŠํž ๊ถŒ๋ฆฌโ€๋ฅผ ๋ช…์‹œ, ๋Œ€๊ทœ๋ชจ ์ด๋ฏธ์ง€ ์ƒ์„ฑ ๋ชจ๋ธ์€ ๋ฐ์ดํ„ฐ ์‚ญ์ œ ์š”๊ตฌ์— ์ฆ‰๊ฐ ๋Œ€์‘ํ•ด์•ผ ํ•จ. ์žฌํ•™์Šต ๋น„์šฉ : ์ˆ˜์‹ญ์–ต ํŒŒ๋ผ๋ฏธํ„ฐ์™€ ์ˆ˜๋ฐฑ TB ๋ฐ์ดํ„ฐ๋กœ ํ•™์Šต๋œ ํ™•์‚ฐ ๋ชจ๋ธ์„ ์ฒ˜์Œ๋ถ€ํ„ฐ ๋‹ค์‹œ ํ•™์Šตํ•˜๋Š” ๋น„์šฉ์€ ํ˜„์‹ค์ ์œผ๋กœ ๋ถˆ๊ฐ€๋Šฅ. ์—ฐ์†์  ์‚ญ์ œ ์ƒํ™ฉ : ์‹ค์ œ ์„œ๋น„์Šค์—์„œ๋Š” ์‚ฌ์šฉ์ž๊ฐ€ ์–ธ์ œ๋“  ์ƒˆ๋กœ์šด ์‚ญ์ œ ์š”์ฒญ์„ ํ•  ์ˆ˜ ์žˆ์–ด, ๋‹จ์ผ ๋‹จ๊ณ„ ์–ธ๋Ÿฌ๋‹๋งŒ์œผ๋กœ๋Š” ์ถฉ๋ถ„์น˜ ์•Š์Œ. 2. ๊ธฐ์กด ๋ฐฉ๋ฒ•์˜ ํ•œ๊ณ„ (Failure Modes) | Failure Mode | ์›์ธ | ๊ธฐ์กด ๋ฐฉ๋ฒ•์—์„œ ๊ด€์ฐฐ๋œ ํ˜„์ƒ |

Learning Framework Model
Magnification-Aware Distillation (MAD): A Self-Supervised Framework for Unified Representation Learning in Gigapixel Whole-Slide Images

Magnification-Aware Distillation (MAD): A Self-Supervised Framework for Unified Representation Learning in Gigapixel Whole-Slide Images

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฉ€ํ‹ฐโ€‘์Šค์ผ€์ผ ํŠน์„ฑ : ๋ณ‘๋ฆฌํ•™์ž๋Š” ์ €๋ฐฐ์œจ์—์„œ ์กฐ์ง ์ „์ฒด ๊ตฌ์กฐ๋ฅผ ํŒŒ์•…ํ•˜๊ณ , ๊ณ ๋ฐฐ์œจ์—์„œ ์„ธํฌ ์ˆ˜์ค€์˜ ์„ธ๋ถ€ ์ •๋ณด๋ฅผ ํ™•์ธํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ชจ๋ธ๋„ ๋‘ ๋ฐฐ์œจ ์‚ฌ์ด์˜ ์—ฐ๊ด€์„ฑ์„ ์ดํ•ดํ•ด์•ผ ์‹ค์ œ ์ง„๋‹จ ํ๋ฆ„์— ์ ์šฉ ๊ฐ€๋Šฅํ•˜๋‹ค. ๊ธฐ์กด SSL ํ•œ๊ณ„ : ๋Œ€๋ถ€๋ถ„์˜ ์ž๊ฐ€์ง€๋„ ๊ธฐ๋ฐ˜ ๋ณ‘๋ฆฌ ๋ชจ๋ธ(UNI, UNI2, Provโ€‘GigaPath ๋“ฑ)์€ ๋‹จ์ผ ๋ฐฐ์œจ(์ฃผ๋กœ 20ร—)์—๋งŒ ์ดˆ์ ์„ ๋งž์ถ”์–ด, ๋ฐฐ์œจ ์ „์ด ์‹œ ์„ฑ๋Šฅ์ด ๊ธ‰๊ฒฉํžˆ ์ €ํ•˜๋œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ Magnificationโ€‘Aware Distillation (MAD) | ์š”์†Œ | ๊ธฐ์กด DINO

Learning Framework
No Image

Safe Path Planning and Observation Quality Enhancement Strategy for Unmanned Aerial Vehicles in Water Quality Monitoring Tasks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ UAV ๊ธฐ๋ฐ˜ ๊ณ ํ•ด์ƒ๋„ ์ŠคํŽ™ํŠธ๋Ÿผ ์›๊ฒฉํƒ์‚ฌ๋Š” ๊ธฐ์กด ์ˆ˜์งˆ ์กฐ์‚ฌ ๋ฐฉ์‹๋ณด๋‹ค ๋น ๋ฅด๊ณ  ๋„“์€ ์˜์—ญ์„ ์ปค๋ฒ„ํ•  ์ˆ˜ ์žˆ์–ด ํ˜„์žฌ ๊ธ‰์ฆํ•˜๋Š” ์ˆ˜์งˆ ๋ชจ๋‹ˆํ„ฐ๋ง ์ˆ˜์š”์— ๋ถ€ํ•ฉํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ฌผ๋ฉด์˜ ๋™์  ๊ทธ๋ฆผ์žยท๊ธ€๋ฆฐํŠธ๋Š” ์ŠคํŽ™ํŠธ๋Ÿผ ์™œ๊ณก์„ ์ผ์œผ์ผœ ๋ฐ์ดํ„ฐ ๊ฐ€์šฉ์„ฑ์„ ํฌ๊ฒŒ ์ €ํ•˜์‹œํ‚จ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์‚ฌํ›„ ๋ณด์ •(๋ผ๋””๋ฉ”ํŠธ๋ฆฌ ๋ณด์ •, ๊ทธ๋ฆผ์ž ๋งˆ์Šคํฌ ๋“ฑ)์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์œผ๋ฉฐ, ๋น„ํ–‰ ์ค‘ ์‹ค์‹œ๊ฐ„ ํšŒํ”ผ ์ „๋žต์€ ๋ฏธ๋น„ํ–ˆ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ ๋™์  ๊ต๋ž€ ์˜ˆ์ธก ๋ชจ๋ธ : ์‹ค์‹œ๊ฐ„ ๊ธฐ์ƒยทํƒœ์–‘ ๊ณ ๋„ยท์œ„์น˜ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•ด ์กฐ๋ช… ๊ต๋ž€์„ 3D ๊ฐ€์ƒ ์žฅ์• ๋ฌผ๋กœ ๋ณ€ํ™˜, โ€œ์†Œํ”„ํŠธ ์žฅ์• ๋ฌผโ€

Scalable Decision Focused Learning via Online Trainable Surrogates

Scalable Decision Focused Learning via Online Trainable Surrogates

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ Predictionโ€‘Focused Learning (PFL) ์€ ์˜ˆ์ธก ์ •ํ™•๋„(์˜ˆ: ๋กœ๊ทธ์šฐ๋„)๋งŒ์„ ์ตœ์ ํ™”ํ•ด, ์˜์‚ฌ๊ฒฐ์ • ๋‹จ๊ณ„์—์„œ ๋ฐœ์ƒํ•˜๋Š” regret ์„ ๋ฌด์‹œํ•œ๋‹ค. Decisionโ€‘Focused Learning (DFL) ์€ ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋ ค ์‹ค์ œ ์˜์‚ฌ๊ฒฐ์ • ๋น„์šฉ์„ ์†์‹ค๋กœ ์‚ฌ์šฉํ•˜์ง€๋งŒ, NPโ€‘hard ์ˆ˜์ค€์˜ ์ตœ์ ํ™” ๋ฌธ์ œ๋ฅผ ๋งค ํ•™์Šต ์Šคํ…๋งˆ๋‹ค ํ’€์–ด์•ผ ํ•˜๋ฏ€๋กœ ํ›ˆ๋ จ ์‹œ๊ฐ„ ๋ณต์žก๋„ ๊ฐ€ ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€ํ•œ๋‹ค. ๊ธฐ์กด DFL ๋ฐฉ๋ฒ•๋“ค์€ (i) ์„ ํ˜• ๋น„์šฉยท์ œ์•ฝ ๊ฐ€์ •, (ii) ์†”๋ฒ„ ๋‚ด๋ถ€ ์ƒํƒœ ์ ‘๊ทผ ํ•„์š”, (iii) ํŽธํ–ฅ๋œ ๋Œ€๋ฆฌ ์†์‹ค ์‚ฌ์šฉ

Learning
Software Vulnerability Management in the Era of Artificial Intelligence: An Industry Perspective

Software Vulnerability Management in the Era of Artificial Intelligence: An Industry Perspective

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ทจ์•ฝ์  ์ฆ๊ฐ€์™€ ๋Œ€์‘ ๋น„์šฉ : ๋…ผ๋ฌธ์€ SV(Software Vulnerabilities)์˜ ๊ธ‰์ฆ๊ณผ ํŒจ์น˜ ์ง€์—ฐ(์ ˆ๋ฐ˜ ์ด์ƒ์ด 1๋…„ ์ด์ƒ ์†Œ์š”)์ด๋ผ๋Š” ํ˜„์ƒ์„ ์ง€์ ํ•˜๋ฉฐ, ์ž๋™ํ™”๋œ SVM์ด ํ•„์ˆ˜์ž„์„ ๊ฐ•์กฐํ•œ๋‹ค. AIโ€‘๊ธฐ๋ฐ˜ ๋„๊ตฌ์˜ ๊ธฐ๋Œ€์™€ ํ˜„์‹ค ๊ฒฉ์ฐจ : ๋”ฅ๋Ÿฌ๋‹ยทLLM ๋“ฑ ์ตœ์‹  AI ๊ธฐ์ˆ ์ด ํ•™์ˆ ์ ์œผ๋กœ๋Š” ๋†’์€ ํƒ์ง€ยท์ˆ˜์ • ์„ฑ๋Šฅ์„ ๋ณด์ด์ง€๋งŒ, ์‹ค์ œ ํ˜„์žฅ์—์„œ๋Š” ์„ฑ๋Šฅ ๊ธ‰๋ฝ(์ตœ๋Œ€ 95 %p) , ๋ฐ์ดํ„ฐ ๋ถˆ๊ท ํ˜•ยท์˜ค๋ฒ„ํ”ผํŒ… ๋“ฑ ์‹ค์šฉ์„ฑ ๋ฌธ์ œ์— ์ง๋ฉดํ•œ๋‹ค. 2. ์—ฐ๊ตฌ ์„ค๊ณ„ | ์š”์†Œ | ๋‚ด์šฉ | | | | | ๋ชฉํ‘œ | AIโ€‘SVM ๋„๊ตฌ์˜ ์‚ฐ์—… ํ˜„์žฅ

Unavoidable patterns and plane paths in dense topological graphs

Unavoidable patterns and plane paths in dense topological graphs

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋‹จ์ˆœ ์œ„์ƒ ๊ทธ๋ž˜ํ”„ (simple topological graph)๋Š” ๊ฐ ๊ฐ„์„ ์ด ์„œ๋กœ ์ตœ๋Œ€ ํ•œ ๋ฒˆ๋งŒ ๊ต์ฐจํ•˜๋„๋ก ๊ทธ๋ฆฐ ๊ทธ๋ž˜ํ”„์ด๋ฉฐ, ๊ธฐํ•˜ํ•™์ ยท์กฐํ•ฉ๋ก ์  ๋ฌธ์ œ์—์„œ ํ•ต์‹ฌ ๋ชจ๋ธ์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ(Pachโ€‘Solymosiโ€‘Tรณth, 2003 ๋“ฑ)๋Š” ์™„์ „ ์œ„์ƒ ๊ทธ๋ž˜ํ”„ ์—์„œ ๋ณผ๋ก ๊ธฐํ•˜ํ•™์  ๊ทธ๋ž˜ํ”„ ํ˜น์€ twisted ๊ทธ๋ž˜ํ”„ ์™€ ๊ฐ™์€ ํฐ ๊ตฌ์กฐ๊ฐ€ ๋ฐ˜๋“œ์‹œ ์กด์žฌํ•จ์„ ๋ณด์˜€์ง€๋งŒ, ์ •๋Ÿ‰์  ์ƒ์ˆ˜๊ฐ€ ๋งค์šฐ ์•ฝํ–ˆ๋‹ค. Negami(1998) ๋Š” ์ด๋ถ„ ๊ทธ๋ž˜ํ”„ ๋ฒ„์ „์„ ์ œ์‹œํ–ˆ์œผ๋‚˜, ๊ตฌ์ฒด์ ์ธ ์ •์  ์ˆ˜์— ๋Œ€ํ•œ ๋ช…์‹œ์  ์ƒํ•œ์„ ์ œ๊ณตํ•˜์ง€ ๋ชปํ–ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ด๋Ÿฌํ•œ

CORE: Concept-Oriented Reinforcement for Bridging the Definition-Application Gap in Mathematical Reasoning

CORE: Concept-Oriented Reinforcement for Bridging the Definition-Application Gap in Mathematical Reasoning

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์ •์˜โ€‘์‘์šฉ ๊ฒฉ์ฐจ : ์ตœ์‹  LLM์€ ์ •์˜๋ฅผ ์ •ํ™•ํžˆ ์„œ์ˆ ํ•˜์ง€๋งŒ, ํ•ด๋‹น ์ •์˜๋ฅผ ์‹ค์ œ ๋ฌธ์ œ์— ์ ์šฉํ•˜๋Š” ๋ฐ ์‹คํŒจํ•œ๋‹ค๋Š” ์ง„๋‹จ์ด ์—ฌ๋Ÿฌ ์—ฐ๊ตฌ(Yang 2024b, Guo 2025a ๋“ฑ)์—์„œ ์ œ์‹œ๋จ. RLVR์˜ ํ•œ๊ณ„ : ๊ธฐ์กด ๊ฐ•ํ™”ํ•™์Šต ํŒŒ์ดํ”„๋ผ์ธ์€ ์ตœ์ข… ์ •๋‹ต์— ๋Œ€ํ•œ scalar reward๋งŒ์„ ์‚ฌ์šฉํ•ด, โ€œ์–ด๋–ค ๊ฐœ๋…์„ ์–ธ์ œ, ์–ด๋–ป๊ฒŒ ์“ฐ๋Š”๊ฐ€โ€๋ผ๋Š” ์„ธ๋ถ€ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์ด๋Š” ๋ชจ๋ธ์ด ํŒจํ„ด ๋งค์นญ ์— ๋จธ๋ฌด๋ฅด๊ฒŒ ๋งŒ๋“ ๋‹ค. 2. CORE ํ”„๋ ˆ์ž„์›Œํฌ ํ•ต์‹ฌ ๊ตฌ์„ฑ | ๊ตฌ์„ฑ ์š”์†Œ | ๊ตฌํ˜„ ๋ฐฉ์‹ | ์—ญํ•  | | | | | | ๋ฐ์ดํ„ฐ |

Deconstructing Generative Diversity: An Information Bottleneck Analysis of Discrete Latent Generative Models

Deconstructing Generative Diversity: An Information Bottleneck Analysis of Discrete Latent Generative Models

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ด์‚ฐ ์ž ์žฌ ๋ชจ๋ธ์˜ ๋ถ€์ƒ : VQโ€‘VAE, VQโ€‘GAN ๋“ฑ ๋ฒกํ„ฐ ์–‘์žํ™”๋ฅผ ์ด์šฉํ•œ ๋ชจ๋ธ๋“ค์€ ํ† ํฐ ๊ธฐ๋ฐ˜ ํ•™์Šต๊ณผ ๋Œ€๊ทœ๋ชจ ํ•™์Šต ํŒŒ์ดํ”„๋ผ์ธ์— ์ ํ•ฉํ•ด ์ด๋ฏธ์ง€ ํ•ฉ์„ฑ ๋ถ„์•ผ์—์„œ ๊ธ‰๋ถ€์ƒํ•˜๊ณ  ์žˆ๋‹ค. ๋‹ค์–‘์„ฑ ์ธก์ •์˜ ํ•œ๊ณ„ : ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ๋‹ค์–‘์„ฑ์„ ์ •๋Ÿ‰ํ™”ํ•˜๋Š” ๋ฉ”ํŠธ๋ฆญ(์˜ˆ: FIDโ€‘๋‹ค์–‘์„ฑ, LPIPS ๋“ฑ)์— ์ง‘์ค‘ํ–ˆ์ง€๋งŒ, ์™œ ๋ชจ๋ธ๋งˆ๋‹ค ๋‹ค์–‘์„ฑ ์ˆ˜์ค€์ด ๋‹ค๋ฅธ์ง€์— ๋Œ€ํ•œ ๋ฉ”์ปค๋‹ˆ์ฆ˜์  ์ดํ•ด๋Š” ๋ถ€์กฑํ–ˆ๋‹ค. ์ •๋ณด ๋ณ‘๋ชฉ(IB) ์ ‘๊ทผ : IB๋Š” ์••์ถ•(์ •๋ณด ๊ฐ์†Œ)๊ณผ ํ‘œํ˜„๋ ฅ(์ •๋ณด ์œ ์ง€) ์‚ฌ์ด์˜ ํŠธ๋ ˆ์ด๋“œ์˜คํ”„๋ฅผ ์ •๋Ÿ‰ํ™”ํ•œ๋‹ค. ์ด๋ฅผ ์ƒ์„ฑ ๋ชจ๋ธ์— ์ ์šฉํ•˜๋ฉด โ€œ์••์ถ• ์••๋ ฅโ€๊ณผ

Analysis Model
DUET: Agentic Design Understanding via Experimentation and Testing

DUET: Agentic Design Understanding via Experimentation and Testing

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ LLM์˜ ํ•œ๊ณ„ : ๊ธฐ์กด LLM์€ ํ…์ŠคํŠธ ๊ธฐ๋ฐ˜ ์–ธ์–ด(ํŒŒ์ด์ฌ, ์ž๋ฐ” ๋“ฑ)์˜ ๊ตฌ์กฐ์  ํŠน์„ฑ์„ ํ™œ์šฉํ•ด ์ฝ”๋“œ๋ฅผ ์ดํ•ดํ•˜์ง€๋งŒ, RTL์€ ์‹œ๊ฐ„ ํ๋ฆ„๊ณผ ์‹ ํ˜ธ ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ์„ ์•”์‹œ์ ์œผ๋กœ ํ‘œํ˜„ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๊ตฌ๋ฌธ๋งŒ์œผ๋กœ๋Š” ์„ค๊ณ„ ์˜๋„๋ฅผ ํŒŒ์•…ํ•˜๊ธฐ ํž˜๋“ค๋‹ค. ์ธ๊ฐ„ ์„ค๊ณ„์ž์˜ ์ ‘๊ทผ๋ฒ• : ์„ค๊ณ„์ž๋Š” RTL์„ ์ฝ๋Š” ๊ฒƒ์— ๊ทธ์น˜์ง€ ์•Š๊ณ , ์‹œ๋ฎฌ๋ ˆ์ด์…˜, ํŒŒํ˜• ๋ทฐ์–ด, ํ˜•์‹ ๊ฒ€์ฆ ๋“ฑ์„ ํ†ตํ•ด ์‹คํ—˜์ ์œผ๋กœ ์„ค๊ณ„ ๋™์ž‘์„ ๊ฒ€์ฆํ•œ๋‹ค. ์ด ๊ณผ์ •์ด ์„ค๊ณ„ ์ดํ•ด์˜ ํ•ต์‹ฌ์ด๋‹ค. 2. DUET ๋ฐฉ๋ฒ•๋ก  ํ•ต์‹ฌ ์•„์ด๋””์–ด | ๋‹จ๊ณ„ | ์„ค๋ช… | | | | | ๊ฐ€์„ค ์ƒ์„ฑ | LLM์ด ํ˜„์žฌ RT

Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection

Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ | ๋ฌธ์ œ์  | ๊ธฐ์กด ์ ‘๊ทผ๋ฒ• | ํ•œ๊ณ„ | | | | | | ํ”Œ๋ž˜ํ†ค ๋‚ด๋ถ€ ์ฐจ๋Ÿ‰์ด ์œ„์กฐ ๋ฐ์ดํ„ฐ ์ „์†ก | ํ”Œ๋ผ์‹œ๋นŒ๋ฆฌํ‹ฐ ์ฒดํฌ, ํ†ต๊ณ„์  ์ด์ƒ์น˜ ํƒ์ง€ | ๋†’์€ FP, ๋ณต์žกํ•œ ์‹œ๊ณต๊ฐ„ ์˜์กด์„ฑ ๋ฏธ๋ฐ˜์˜ | | ์‹ค์‹œ๊ฐ„ ์š”๊ตฌ (โ‰ค100 ms) | ์ „ํ†ต์ ์ธ RNN/LSTM ๊ธฐ๋ฐ˜ ๋ชจ๋ธ | ์ˆœ์ฐจ ์ฒ˜๋ฆฌ๋กœ ์ธํ•œ ๋†’์€ ์ง€์—ฐ | | ๋‹ค์–‘ํ•œ ํ”Œ๋ž˜ํ†ค ํ† ํด๋กœ์ง€์™€ ๋™์  ํ•ฉ๋ฅ˜ยท์ดํƒˆ | ์ˆ˜๋™ ํ”ผ์ฒ˜ ์—”์ง€๋‹ˆ์–ด๋ง | ํ† ํด๋กœ์ง€ ๋ณ€ํ™”์— ์ทจ์•ฝ | AIMFORMER๋Š” ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋ฅผ ํŠธ๋žœ์Šคํฌ๋จธ์˜ ๋ณ‘๋ ฌ ์‹œํ€€์Šค ์ฒ˜๋ฆฌ ์™€ ์…€ํ”„โ€‘์–ดํ…์…˜ ์„ ํ™œ์šฉํ•ด ๋™์‹œ์— ์‹œ๊ฐ„์  ยท ๊ณต๊ฐ„์ 

Detection
Cognitive Control Architecture (CCA): A Lifecycle Supervision Framework for Robustly Aligned AI Agents

Cognitive Control Architecture (CCA): A Lifecycle Supervision Framework for Robustly Aligned AI Agents

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ IPI ๊ณต๊ฒฉ ์€ ์™ธ๋ถ€ ๋ฐ์ดํ„ฐ(์›น ํŽ˜์ด์ง€, ํŒŒ์ผ ๋“ฑ)์— ์•…์˜์  ํ”„๋กฌํ”„ํŠธ๋ฅผ ์‚ฝ์ž…ํ•ด LLM ์—์ด์ „ํŠธ๋ฅผ ์˜ค๋„ํ•œ๋‹ค. ๊ธฐ์กด ๋ฐฉ์–ด๋Š” ๋Ÿฐํƒ€์ž„ ๊ฒ€์ฆ , ์•„ํ‚คํ…์ฒ˜ ์ƒŒ๋“œ๋ฐ•์‹ฑ , ํ•™์Šต ๋‹จ๊ณ„ ์ •๋ ฌ ๋“ฑ์œผ๋กœ ๋‚˜๋‰˜์ง€๋งŒ, ๊ฐ๊ฐ (a) ๋‹จ์ผ ํ–‰๋™๋งŒ ๊ฒ€์‚ฌ , (b) ๋†’์€ ์˜ค๋ฒ„ํ—ค๋“œ , (c) ์ผ๋ฐ˜ํ™” ํ•œ๊ณ„ ๋ผ๋Š” ๊ณตํ†ต์ ์ธ ์•ฝ์ ์„ ๊ฐ€์ง„๋‹ค. ํŠนํžˆ ์กฐ๊ฑด๋ถ€ยท์ง€์—ฐํ˜• IPI (์˜ˆ: ํŠน์ • ์ƒํƒœ์—์„œ๋งŒ ๋ฐœ๋™)์™€ ์œ„์žฅ๋œ ์ •๋‹น์„ฑ (์•…์˜์  ํ–‰๋™์ด ํ•ฉ๋ฆฌ์ ์ธ ์ด์œ ์™€ ํ•จ๊ป˜ ์ œ์‹œ)์—๋Š” ๊ธฐ์กด ๋ฐฉ์–ด๊ฐ€ ๊ฑฐ์˜ ๋ฌด๋ ฅํ™”๋œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด์™€ ์„ค๊ณ„ ์›์น™ | ์›์น™ | ๋‚ด์šฉ |

Framework
Multi-Intent Spoken Language Understanding: Methods, Trends, and Challenges

Multi-Intent Spoken Language Understanding: Methods, Trends, and Challenges

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์‹ค์ œ ๋Œ€ํ™” ์‹œ์Šคํ…œ ์—์„œ๋Š” ํ•œ ๋ฐœํ™”์— ์—ฌ๋Ÿฌ ๋ชฉํ‘œ๊ฐ€ ๋™์‹œ์— ํฌํ•จ๋˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ํ”ํ•˜๋ฉฐ, Amazon ๋‚ด๋ถ€ ๋ฐ์ดํ„ฐ์…‹์—์„œ๋Š” 52%๊ฐ€ ๋‹ค์ค‘ ์˜๋„์ž„์„ ๋ณด๊ณ ํ•œ๋‹ค. ๊ธฐ์กด ๋‹จ์ผ ์˜๋„ SLU ๋ชจ๋ธ์€ ์ „์ฒด ๋ฐœํ™”๋ฅผ ํ•˜๋‚˜์˜ ๋ฌธ์žฅ ํ‘œํ˜„์œผ๋กœ ์••์ถ•ํ•ด ์˜๋„์™€ ์Šฌ๋กฏ์„ ์˜ˆ์ธกํ•˜๋ฏ€๋กœ, ๋‹ค์ค‘ ์˜๋„ ์ƒํ™ฉ์—์„œ ์˜๋„ ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ ๊ณผ ํด๋ผ์šฐ์ฆˆ ๊ฐ„ ์˜์กด์„ฑ ์„ ์ถฉ๋ถ„ํžˆ ํฌ์ฐฉํ•˜์ง€ ๋ชปํ•œ๋‹ค. 2. ๋…ผ๋ฌธ์˜ ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | | | | | โ‘  | ๋””์ฝ”๋”ฉ ํŒจ๋Ÿฌ๋‹ค์ž„ ์„ ๋ถ„๋ฅ˜ ๊ธฐ๋ฐ˜ (thresholdโ€‘based multiโ€‘label, token voting ๋“ฑ

No Image

PHANTOM: Progressive High-fidelity Adversarial Network for Threat Object Modeling

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋ฐ์ดํ„ฐ ๋ถ€์กฑ ๋ฌธ์ œ : ์‚ฌ์ด๋ฒ„ ๊ณต๊ฒฉ ๋ฐ์ดํ„ฐ๋Š” ๋ฒ•์ ยท์œค๋ฆฌ์  ์ œ์•ฝ๊ณผ ๊ธฐ์—…์˜ ๋ณด์•ˆ ์šฐ๋ ค๋กœ ๊ณต๊ฐœ๊ฐ€ ์ œํ•œ์ ์ด๋ฉฐ, ๊ธฐ์กด ๊ณต๊ฐœ ๋ฐ์ดํ„ฐ์…‹์€ ์˜ค๋ž˜๋˜๊ฑฐ๋‚˜ ํด๋ž˜์Šค ๋ถˆ๊ท ํ˜•์ด ์‹ฌ๊ฐํ•จ. ํ•ฉ์„ฑ ๋ฐ์ดํ„ฐ ํ•„์š”์„ฑ : ๋ฐ์ดํ„ฐ ์ฆ๊ฐ•ยทํด๋ž˜์Šค ๊ท ํ˜•ยทํฌ๊ท€ ๊ณต๊ฒฉ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์œ„ํ•ด ๊ณ ํ’ˆ์งˆ ํ•ฉ์„ฑ ๋ฐ์ดํ„ฐ ์ƒ์„ฑ์ด ํ•„์ˆ˜์ ์ž„. ๊ธฐ์กด ๊ทœ์น™ ๊ธฐ๋ฐ˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์€ ๋ณต์žกํ•œ ๊ณต๊ฒฉ ํ–‰๋™์„ ์ถฉ๋ถ„ํžˆ ์žฌํ˜„ํ•˜์ง€ ๋ชปํ•จ. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๊ธฐ์—ฌ ๋‚ด์šฉ | ์˜์˜ | | | | | | 1 | Progressive Training : ๋‹ค์ค‘ ํ•ด์ƒ๋„(๊ฑฐ์นœ โ†’ ์„ธ๋ฐ€) ํ•™์Šต์œผ๋กœ ๊ณต๊ฒฉ์˜ ๊ณ„์ธต

Network Model
Visual Funnel: Resolving Contextual Blindness in Multimodal Large Language Models

Visual Funnel: Resolving Contextual Blindness in Multimodal Large Language Models

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ๋งฅ๋ฝ ๋งน์ (Contextual Blindness) : ๊ธฐ์กด ๋‹จ์ผ ํฌ๋กญ ๋ฐฉ์‹์€ ๊ณ ํ•ด์ƒ๋„ ๋””ํ…Œ์ผ์„ ์ œ๊ณตํ•˜์ง€๋งŒ, ํ•ด๋‹น ๋””ํ…Œ์ผ์„ ํ•ด์„ํ•˜๊ธฐ ์œ„ํ•œ ์ฃผ๋ณ€ ๋งฅ๋ฝ์ด ์‚ฌ๋ผ์ง„๋‹ค. ์ด๋Š” โ€œ์ •๋ณด๊ฐ€ ์ถฉ๋ถ„ํžˆ ์žˆ๋‹คโ€๋Š” ์ „์ œ์™€ ๋‹ฌ๋ฆฌ, ๊ตฌ์กฐ์  ์—ฐ๊ฒฐ ๊ณ ๋ฆฌ ๊ฐ€ ๋ถ€์žฌํ•ด ๋ฐœ์ƒํ•œ๋‹ค. ๊ตฌ์กฐ์  ๋‹ค์–‘์„ฑ(Structural Diversity) ๋ถ€์กฑ์ด ํ•ต์‹ฌ ์›์ธ์ด๋ผ๋Š” ๊ฐ€์„ค์€, ๋‹จ์ˆœํžˆ ๋” ๋งŽ์€ ํ”ฝ์…€์„ ์ œ๊ณตํ•˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ๋‹ค์ค‘ ์Šค์ผ€์ผ ์˜ ์‹œ๊ฐ ์ •๋ณด๋ฅผ ๋™์‹œ์— ์ œ๊ณตํ•ด์•ผ ํ•จ์„ ์‹œ์‚ฌํ•œ๋‹ค. 2. ์ œ์•ˆ ๋ฐฉ๋ฒ• โ€“ Visual Funnel | ๋‹จ๊ณ„ | ํ•ต์‹ฌ ์•„์ด๋””์–ด

Model
Improving Local Fidelity Through Sampling and Modeling Nonlinearity

Improving Local Fidelity Through Sampling and Modeling Nonlinearity

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์„ค๋ช… ๊ฐ€๋Šฅ ์ธ๊ณต์ง€๋Šฅ(XAI) ์€ ๊ณ ์œ„ํ—˜ ๋ถ„์•ผ์—์„œ ๋ชจ๋ธ ํˆฌ๋ช…์„ฑยท์ฑ…์ž„์„ฑ์„ ํ™•๋ณดํ•˜๊ธฐ ์œ„ํ•œ ํ•ต์‹ฌ ๊ธฐ์ˆ ์ด๋‹ค. LIME ์€ ๋ชจ๋ธโ€‘๋ถˆ๊ฐ€์ง€๋ก ์  ๋กœ์ปฌ ์„œํ”„๋ผ์ด์ฆˆ ๋ฐฉ์‹์˜ ๋Œ€ํ‘œ์ฃผ์ž์ด์ง€๋งŒ, (i) ์„ ํ˜• ๊ฐ€์ • , (ii) ์ƒ˜ํ”Œ ์žฌ๊ฐ€์ค‘์น˜ )๋ผ๋Š” ๋‘ ๊ฐ€์ง€ ๊ตฌ์กฐ์  ํ•œ๊ณ„ ๋•Œ๋ฌธ์— ๋น„์„ ํ˜• ๊ฒฐ์ • ๊ฒฝ๊ณ„์—์„œ ๋‚ฎ์€ local fidelity ๋ฅผ ๋ณด์ธ๋‹ค. ์ตœ๊ทผ ์—ฐ๊ตฌ๋“ค์€ ์ƒ˜ํ”Œ๋ง ๊ฐœ์„  (LSโ€‘LIME, Nโ€‘ball ๋“ฑ)๊ณผ ๋น„์„ ํ˜• ์„œํ”„๋ผ์ด์ฆˆ ๋„์ž… (MARS, GMM, ํŠธ๋ฆฌ ๊ธฐ๋ฐ˜) ๋‘ ์ถ•์œผ๋กœ LIME์„ ๋ณด์™„ํ•˜๋ ค ํ–ˆ์ง€๋งŒ, ๋Œ€๋ถ€๋ถ„์€ ํ•˜๋‚˜์˜ ์ถ•๋งŒ ์ ์šฉํ•˜๊ฑฐ๋‚˜ ๋น„์„ 

Model
Reusability in MLOps: Leveraging Ports and Adapters to Build a Microservices Architecture for the Maritime Domain

Reusability in MLOps: Leveraging Ports and Adapters to Build a Microservices Architecture for the Maritime Domain

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ํ•ด์–‘ ๋„๋ฉ”์ธ์˜ ํŠน์ˆ˜์„ฑ : ๊ตญ์ œ ๊ฒฝ๊ณ„, ๊ธฐํ›„ ๋ณ€ํ™”, ๊ทœ์ œ ๋‹ค์–‘์„ฑ ๋“ฑ์œผ๋กœ ๋ฐ์ดํ„ฐ ์†Œ์Šค์™€ ์šด์˜ ํ™˜๊ฒฝ์ด ๋ณต์žกํ•จ. MLES์˜ ๋ณตํ•ฉ์„ฑ : ๋จธ์‹ ๋Ÿฌ๋‹ ๋ชจ๋ธ, ๋ฐ์ดํ„ฐ ํŒŒ์ดํ”„๋ผ์ธ, ์‹ค์‹œ๊ฐ„ ์„œ๋น™, ๋ชจ๋‹ˆํ„ฐ๋ง ๋“ฑ ๋‹ค์ค‘ ์ปดํฌ๋„ŒํŠธ๊ฐ€ ์–ฝํ˜€ ์žˆ์–ด ์œ ์ง€๋ณด์ˆ˜์™€ ํ™•์žฅ์ด ์–ด๋ ค์›€. ๋ชฉํ‘œ : ํ•˜๋‚˜์˜ ์ฝ”๋“œ๋ฒ ์ด์Šค์—์„œ ์—ฌ๋Ÿฌ ๋งˆ์ดํฌ๋กœ์„œ๋น„์Šค๋ฅผ ์žฌ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•˜๋„๋ก ์„ค๊ณ„ํ•จ์œผ๋กœ์จ ๊ฐœ๋ฐœ ํšจ์œจ์„ฑยท๋ฐฐํฌ ์†๋„ยทํŒ€ ๊ฐ„ ํ˜‘์—…์„ ๊ฐœ์„ ํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ํ•ด์–‘ ์ด์ƒ ํƒ์ง€ ์‹œ์Šคํ…œ์„ ๋น ๋ฅด๊ฒŒ ์ง„ํ™”์‹œํ‚ค๊ณ ์ž ํ•จ. 2. ํ•ต์‹ฌ ๊ธฐ๋ฒ• | ๊ธฐ๋ฒ• | ์„ค๋ช… | ์ ์šฉ ์‚ฌ๋ก€ | | | | | | ํ—ฅ์‚ฌ๊ณ ๋‚ 

Assignment-Routing Optimization: Solvers for Problems Under Constraints

Assignment-Routing Optimization: Solvers for Problems Under Constraints

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ JRA ๋ฌธ์ œ ๋Š” ์•„์ดํ…œ โ†” ํ”Œ๋ ˆ์ด์Šคํ™€๋” 1:1 ๋งค์นญ๊ณผ ๋™์‹œ์— ๋ชจ๋“  ๋…ธ๋“œ๋ฅผ ํ•œ ๋ฒˆ์”ฉ ๋ฐฉ๋ฌธํ•˜๋Š” ํ•ด๋ฐ€ํ„ด ์ˆœํšŒ๋ฅผ ์š”๊ตฌํ•œ๋‹ค๋Š” ์ ์—์„œ NPโ€‘hard ์ด๋‹ค. ์‹ค์ œ ๋กœ๋ด‡ ํฌ์žฅยท๋ฌผ๋ฅ˜ ํ˜„์žฅ์—์„œ๋Š” ์‹œ๊ฐ„โ€‘์ฐฝ(timeโ€‘window) , ๋‹ค์ค‘ ํด๋ž˜์Šค(item class) , ํ”Œ๋ ˆ์ด์Šคํ™€๋” ๋‹ค์ค‘ ์„ ํƒ ๋“ฑ ๋ณตํ•ฉ ์ œ์•ฝ์ด ํ•„์ˆ˜์ ์ด๋ฉฐ, ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ด๋Ÿฌํ•œ ์ œ์•ฝ์„ ์ถฉ๋ถ„ํžˆ ๋‹ค๋ฃจ์ง€ ๋ชปํ–ˆ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | | | | | โ‘  | JRA์— ์‹œ๊ฐ„โ€‘ํ”„๋ ˆ์ž„ ์ œ์•ฝ ์„ ๋ช…์‹œ์ ์œผ๋กœ ๋ชจ๋ธ๋ง (์„น์…˜ ๊ธฐ๋ฐ˜ ์ˆœ์„œ ์ œ์•ฝ C1โ€‘C3) |

Earth radius from a single sunrise image: a classroom-ready activity

Earth radius from a single sunrise image: a classroom-ready activity

1. ์—ฐ๊ตฌ์˜ ๊ต์œก์  ์˜์˜ ๊ณผํ•™์  ์‚ฌ๊ณ  ๊ณผ์ • (๊ด€์ฐฐ โ†’ ๊ฐ€์„ค โ†’ ๋ชจ๋ธ๋ง โ†’ ๊ณ„์‚ฐ โ†’ ๊ฒ€์ฆ)์„ ํ•œ ๋ฒˆ์— ๊ฒฝํ—˜ํ•˜๊ฒŒ ํ•ด์ค€๋‹ค. ์‹ค์ œ ์‚ฌ์ง„ ๊ณผ ์ผ์ƒ์ ์ธ ์žฅ๋น„ (๋””์ง€ํ„ธ ์นด๋ฉ”๋ผ)๋งŒ์œผ๋กœ๋„ ์ •๋Ÿ‰์  ์ถ”์ •์ด ๊ฐ€๋Šฅํ•˜๋ฏ€๋กœ, ํ•™์ƒ๋“ค์˜ ํฅ๋ฏธ๋ฅผ ํฌ๊ฒŒ ๋Œ ์ˆ˜ ์žˆ๋‹ค. ๊ฒฐ๊ณผ๊ฐ€ ์‹ค์ œ๊ฐ’๋ณด๋‹ค ํฌ๊ฒŒ ๋‚˜์˜ค๊ฒŒ ์„ค๊ณ„(์ƒํ•œ๊ฐ’)ํ•จ์œผ๋กœ์จ ์˜ค์ฐจ์™€ ํŽธํ–ฅ ์„ ํ† ๋ก ํ•  ์ˆ˜ ์žˆ๋Š” ์ข‹์€ ์‚ฌ๋ก€๊ฐ€ ๋œ๋‹ค. 2. ํ•ต์‹ฌ ๋ฌผ๋ฆฌยท์ˆ˜ํ•™ ๋ชจ๋ธ | ๋‹จ๊ณ„ | ์‚ฌ์šฉ๋œ ๊ณต์‹/๊ฐ€์ • | ํ•ต์‹ฌ ๋ณ€์ˆ˜ | | | | | | ์ด๋ฏธ์ง€ โ†’ ๊ฐ๋„ ๋ณ€ํ™˜ | (delta arcsin(Delta d {text{photo}}/f))

Memory as Resonance: A Biomimetic Architecture for Infinite Context Memory on Ergodic Phonetic Manifolds

Memory as Resonance: A Biomimetic Architecture for Infinite Context Memory on Ergodic Phonetic Manifolds

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ Memory Wall : ํ˜„์žฌ LLM์€ KV ์บ์‹œ๊ฐ€ ํ† ํฐ๋‹น ๋ฉ”๋ชจ๋ฆฌ๋ฅผ ์„ ํ˜•์ ์œผ๋กœ ์ฆ๊ฐ€์‹œ์ผœ VRAMยท์ „๋ ฅ ํ•œ๊ณ„์— ์ง๋ฉดํ•œ๋‹ค. ์ƒ๋ฌผํ•™์  ์˜๊ฐ : ์ธ๊ฐ„ ๋‘๋‡Œ๋Š” ํ…์ŠคํŠธ๋ฅผ ํŒŒ์ผ ํ˜•ํƒœ๋กœ ์ €์žฅํ•˜์ง€ ์•Š๊ณ , ๋ฆฌ๋“ฌยท์Œ์„ฑยทํฌ์†Œ ์˜๋ฏธ ์•ต์ปค๋ฅผ ํ†ตํ•ด ๊ถค์  ํ˜•ํƒœ๋กœ ์žฌ๊ตฌ์„ฑํ•œ๋‹ค๋Š” ์ ์„ ์ฐจ์šฉ. ๊ธฐ์กด ์ ‘๊ทผ๋ฒ•(์ปจํ…์ŠคํŠธ ํ™•์žฅ, RAG, ์ƒํƒœโ€‘๊ณต๊ฐ„ ์••์ถ•)์€ ๊ฐ๊ฐ ํ•˜๋“œ์›จ์–ด ํ™•์žฅ , ๋ฐ์ดํ„ฐ ํŒŒํŽธํ™” , ํ•ด์ƒ๋„ ์†์‹ค ์ด๋ผ๋Š” ๊ทผ๋ณธ์  ํ•œ๊ณ„๋ฅผ ๊ฐ–๋Š”๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ๋ฐ ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ํ•ต์‹ฌ ์•„์ด๋””์–ด | ๊ตฌ์ฒด์  ๊ตฌํ˜„ | | | | | | 1 | ๋ฌดํ•œ ๊ถค์ 

Tractatus Quanticum

Tractatus Quanticum

1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ๊ณผ ๋ชฉํ‘œ ๋น„ํŠธ๊ฒ์Šˆํƒ€์ธ๊ณผ ํŠธ๋ž™ํƒ€ํˆฌ์Šค : ์›๋ณธ Tractatus ๋Š” ์–ธ์–ด์™€ ์„ธ๊ณ„์˜ ๋…ผ๋ฆฌ์  ๊ตฌ์กฐ๋ฅผ โ€œ์‚ฌ์‹คโ€์ด๋ผ๋Š” ๊ธฐ๋ณธ ๋‹จ์œ„๋กœ ์„ค๋ช…ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ 20์„ธ๊ธฐ ํ›„๋ฐ˜ ์–‘์ž์—ญํ•™์˜ ๋“ฑ์žฅ์œผ๋กœ โ€œ๊ด€์ธก์žโ€‘๊ด€์ธก ๋Œ€์ƒโ€์˜ ๊ตฌ๋ถ„์ด ํ๋ ค์กŒ๊ณ , ๋น„ํŠธ๊ฒ์Šˆํƒ€์ธ์˜ ๊ด€์ โ€‘์ค‘์‹ฌ์  ๊ตฌ์กฐ๋Š” ํ•œ๊ณ„์— ๋ด‰์ฐฉํ•œ๋‹ค. ๊ด€๊ณ„์  ์–‘์ž์—ญํ•™(RQM) : ์นด๋ฅผ๋กœ ๋กœ๋ฒจ๋ฆฌ์™€ ๋™๋ฃŒ๋“ค์ด ์ œ์•ˆํ•œ RQM์€ ๋ฌผ๋ฆฌ์  ์ƒํƒœ๊ฐ€ ์ ˆ๋Œ€์ ์ธ ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ๊ด€๊ณ„ (๊ด€์ ) ์•ˆ์—์„œ ์ •์˜๋œ๋‹ค๊ณ  ์ฃผ์žฅํ•œ๋‹ค. ์ด๋Š” โ€œ๊ด€์ธก์žโ€๋ฅผ ๋ฌผ๋ฆฌ์  ์‹œ์Šคํ…œ์œผ๋กœ ํ™˜์›ํ•˜๊ณ , ์ƒํƒœ์˜ ๊ฐ๊ด€์„ฑ์„ ๊ด€์ โ€‘์˜์กด์ ์œผ๋กœ ์žฌํ•ด์„ํ•œ๋‹ค. ๋…ผ๋ฌธ์˜ ๋ชฉํ‘œ :

AdaptVision: Efficient Vision-Language Models via Adaptive Visual Acquisition

AdaptVision: Efficient Vision-Language Models via Adaptive Visual Acquisition

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์‹œ๊ฐ ํ† ํฐ ๋น„์šฉ : ์ตœ์‹  VLM(์˜ˆ: Qwen2.5โ€‘VL)์—์„œ๋Š” 2048ร—1024 ํ•ด์ƒ๋„ ์ด๋ฏธ์ง€๋‹น 2,678๊ฐœ์˜ ์‹œ๊ฐ ํ† ํฐ์ด ํ•„์š”ํ•ด GPU ๋ฉ”๋ชจ๋ฆฌยท์—ฐ์‚ฐ๋Ÿ‰์ด ๊ธ‰์ฆํ•œ๋‹ค. ๊ธฐ์กด ์••์ถ• ๋ฐฉ์‹์˜ ํ•œ๊ณ„ : ๊ณ ์ • ๋น„์œจ ํ”„๋ฃจ๋‹ยท๋จธ์ง•, ํ˜น์€ ์ €ํ•ด์ƒ๋„/๊ณ ํ•ด์ƒ๋„ ์ „ํ™˜ ๋“ฑ์€ ์ˆ˜๋™์  ์ด๋ฉฐ, ์งˆ๋ฌธยท์ด๋ฏธ์ง€ ๋ณต์žก๋„์— ๋”ฐ๋ผ ์ตœ์  ํ† ํฐ ์ˆ˜๋ฅผ ์Šค์Šค๋กœ ํŒ๋‹จํ•˜์ง€ ๋ชปํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ๋Šฅ๋™ ์‹œ๊ฐ ๋ฉ”์ปค๋‹ˆ์ฆ˜ ์ฐจ์šฉ : ์ธ๊ฐ„์ด ๋จผ์ € ๊ฑฐ์นœ ์ „๋ฐ˜์  ์ •๋ณด๋ฅผ ํŒŒ์•…ํ•˜๊ณ , ๊ด€์‹ฌ ์˜์—ญ์— ์ง‘์ค‘ํ•ด ์„ธ๋ถ€ ์ •๋ณด๋ฅผ ํš๋“ํ•˜๋“ฏ, ๋ชจ๋ธ๋„ ์ €ํ•ด์ƒ๋„ ํ† ํฐ์œผ๋กœ โ€œ๊ฑฐ์นœโ€ ์ดํ•ด๋ฅผ ์ˆ˜

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JMMMU-Pro: Image-based Japanese Multi-discipline Multimodal Understanding Benchmark via Vibe Benchmark Construction

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ ๋ชจ๋ธ์˜ ์–ธ์–ด ํŽธํ–ฅ : ์˜์–ด ์ค‘์‹ฌ LMM์ด ๊ธ‰์„ฑ์žฅํ•œ ๋ฐ˜๋ฉด, ์ผ๋ณธ์–ด์™€ ๊ฐ™์€ ๋น„์˜์–ด๊ถŒ ์–ธ์–ด์— ๋Œ€ํ•œ ํ‰๊ฐ€ ์ธํ”„๋ผ๊ฐ€ ๋ถ€์กฑํ–ˆ๋‹ค. ๊ธฐ์กด JMMMU๋Š” ํ…์ŠคํŠธ์™€ ์ด๋ฏธ์ง€๋ฅผ ๋ณ„๋„ ์ž…๋ ฅ์œผ๋กœ ์ œ๊ณตํ•ด ์‹ค์ œ ์‚ฌ์šฉ์ž ํ™˜๊ฒฝ(์Šคํฌ๋ฆฐ์ƒท, UI ๋“ฑ)๊ณผ ๊ดด๋ฆฌ๊ฐ์ด ์žˆ์—ˆ๋‹ค. ์‹œ๊ฐโ€‘ํ…์ŠคํŠธ ํ†ตํ•ฉ ์ธ์ง€ : ์ธ๊ฐ„์€ ์ด๋ฏธ์ง€ ์•ˆ์— ํฌํ•จ๋œ ํ…์ŠคํŠธ๋ฅผ ๋™์‹œ์— โ€œ๋ณด๊ณ โ€‘์ฝ๋Š”๋‹คโ€. ์ด๋ฅผ ๋ชจ๋ธ์— ๊ฐ•์ œํ•˜๋ ค๋ฉด ์งˆ๋ฌธ๊ณผ ์ •๋‹ต์ด ๊ฐ™์€ ์‹œ๊ฐ์  ์žฅ๋ฉด์— ์กด์žฌํ•ด์•ผ ํ•œ๋‹ค๋Š” ์ ์„ ๊ฐ•์กฐํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๊ตฌ๋ถ„ | ๋‚ด์šฉ | ์˜์˜ | | | | | | ๋ฒค์น˜๋งˆํฌ ์„ค๊ณ„ | JMMM

Language Models as Semantic Teachers: Post-Training Alignment for Medical Audio Understanding

Language Models as Semantic Teachers: Post-Training Alignment for Medical Audio Understanding

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์Œํ–ฅโ€‘์ž„์ƒ ์˜๋ฏธ ๊ฒฉ์ฐจ : ๊ธฐ์กด ์ฒญ์ง„์Œ ๋ถ„์„ ๋ชจ๋ธ์€ ์ŠคํŽ™ํŠธ๋Ÿผยท์‹œ๊ฐ„ ํŒจํ„ด์„ ์ž˜ ํฌ์ฐฉํ•˜์ง€๋งŒ, โ€œ์ˆ˜์ถ•๊ธฐ ์žก์Œโ€, โ€œ์ฒœ๋ช…์Œโ€ ๋“ฑ ์˜๋ฃŒ ์šฉ์–ด์™€ ์—ฐ๊ฒฐ๋˜์ง€ ์•Š์•„ โ€˜์‹œ๋งจํ‹ฑ ๋ธ”๋ผ์ธ๋“œโ€™ ์ƒํƒœ์— ์žˆ๋‹ค. LLM์˜ ํ’๋ถ€ํ•œ ์˜๋ฃŒ ์ง€์‹ : ์ตœ์‹  ์˜๋ฃŒ ์ „์šฉ LLM์€ ๋ฐฉ๋Œ€ํ•œ ํ…์ŠคํŠธ ์ฝ”ํผ์Šค๋ฅผ ํ†ตํ•ด ์ž„์ƒ ๊ฐœ๋…์„ ๋‚ด์žฌํ•˜๊ณ  ์žˆ์œผ๋‚˜, ์ด๋ฅผ ์ง์ ‘ ์˜ค๋””์˜ค ์‹ ํ˜ธ์™€ ์—ฐ๊ฒฐํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ๋ถ€์กฑํ–ˆ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ๋น„๋Œ€์นญ ์ง€์‹ ์ „๋‹ฌ : ๊ธฐ์กด์˜ ์–‘๋ฐฉํ–ฅ ์ •๋ ฌ(์˜ˆ: CLIP)๊ณผ ๋‹ฌ๋ฆฌ, LLM์„ ๊ณ ์ •๋œ ๊ต์‚ฌ (teacher)๋กœ ๋‘๊ณ  ์˜ค๋””์˜ค ์ธ์ฝ”๋”๋ฅผ ํ•™์ƒ (stude

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PyBangla at BLP-2025 Task 2: Enhancing Bangla-to-Python Code Generation with Iterative Self-Correction and Multilingual Agents

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์–ธ์–ด ํŽธํ–ฅ ๋ฌธ์ œ : ํ˜„์žฌ ๋Œ€๋ถ€๋ถ„์˜ ์ฝ”๋“œ ์ƒ์„ฑ ๋ชจ๋ธ์€ ์˜์–ด ๋ฐ์ดํ„ฐ์— ์ตœ์ ํ™”๋ผ ์žˆ์–ด, ๋ฐฉ๊ธ€๋ผ์–ด์™€ ๊ฐ™์€ ์ €์ž์› ์–ธ์–ด ์‚ฌ์šฉ์ž๋Š” ํ˜œํƒ์„ ๋ฐ›์ง€ ๋ชปํ•œ๋‹ค. ๋ฐฉ๊ธ€๋ผ์–ด๋Š” ์ „ ์„ธ๊ณ„ 2์–ต ๋ช… ์ด์ƒ์ด ์‚ฌ์šฉํ•˜๋Š” ์ฃผ์š” ์–ธ์–ด์ด์ง€๋งŒ, ๋ฐฉ๋Œ€ํ•œ ๋ณ‘๋ ฌ NLโ€‘Code ์ฝ”ํผ์Šค๊ฐ€ ๋ถ€์กฑํ•˜๋‹ค. ๊ธฐ์กด ์ ‘๊ทผ๋ฒ•์˜ ํ•œ๊ณ„ : ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์˜์–ดโ€‘centric fineโ€‘tuning ํ˜น์€ ๋Œ€๊ทœ๋ชจ ๋ฉ€ํ‹ฐ๋ง๊ตฌ์–ผ ์‚ฌ์ „ํ•™์Šต์— ์˜์กดํ•œ๋‹ค. ์ด๋Š” ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ๋น„์šฉ์ด ๋†’๊ณ , ๋ฐฉ๊ธ€๋ผ์–ด ํŠน์œ ์˜ ํ˜•ํƒœ์†Œยท๋ฌธ๋ฒ• ๋ณต์žก์„ฑ์„ ์ถฉ๋ถ„ํžˆ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | | |

Computer Science NLP
Audited Skill-Graph Self-Improvement for Agentic LLMs via Verifiable Rewards, Experience Synthesis, and Continual Memory

Audited Skill-Graph Self-Improvement for Agentic LLMs via Verifiable Rewards, Experience Synthesis, and Continual Memory

1. ์—ฐ๊ตฌ ๋™๊ธฐ์™€ ๋ฌธ์ œ ์ •์˜ ์šด์˜ ๋ณด์•ˆ ๊ฒฉ์ฐจ : ๊ธฐ์กด ์ž๊ธฐ ๊ฐœ์„  ์—์ด์ „ํŠธ๋Š” ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ ๋ณ€ํ™”๋งŒ์„ ๊ฐœ์„  ๋‹จ์œ„๋กœ ๋ณด๋ฉฐ, ๋ณด์ƒ ํ•ดํ‚นยทํ–‰๋™ ๋“œ๋ฆฌํ”„ํŠธ๋ฅผ ์‹ค์‹œ๊ฐ„์œผ๋กœ ํƒ์ง€ยท์ œ์–ดํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ์ฆ๊ฑฐ ๊ธฐ๋ฐ˜ ๊ฑฐ๋ฒ„๋„Œ์Šค ํ•„์š”์„ฑ : ์ •์ฑ… ๋ชจ๋ธ์ด ์ƒ์„ฑํ•œ ํ–‰๋™์„ ๊ฒ€์ฆ ๊ฐ€๋Šฅํ•œ ์•„ํ‹ฐํŒฉํŠธ(ํˆด ํ˜ธ์ถœ ๋กœ๊ทธ, ์Šคํ‚ค๋งˆ ๊ฒ€์ฆ, ๋ฉ”๋ชจ๋ฆฌ ์—…๋ฐ์ดํŠธ ๋“ฑ)์™€ ์—ฐ๊ฒฐํ•จ์œผ๋กœ์จ, ์Šน๊ฒฉ ๊ฒฐ์ •๊ณผ ๋ณด์ƒ ํ• ๋‹น์„ ๋…๋ฆฝ์ ์ธ ๊ฒ€์ฆ ๋‹จ๊ณ„์—์„œ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด์™€ ์„ค๊ณ„ | ๊ตฌ์„ฑ ์š”์†Œ | ์„ค๋ช… | ์ฃผ์š” ์žฅ์  | | | | | | Skill Compiler | ์„ฑ๊ณต์ ์ธ ๋กค์•„์›ƒ์—์„œ ์žฌ์‚ฌ์šฉ ๊ฐ€

Computer Science Cryptography and Security
CODE ACROSTIC: Robust Watermarking for Code Generation

CODE ACROSTIC: Robust Watermarking for Code Generation

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์ฝ”๋“œ ์›Œํ„ฐ๋งˆํ‚น์˜ ํ•„์š”์„ฑ : ์ฝ”๋“œ์—๋Š” ๊ธฐ์—…ยท์—ฐ๊ตฌ๊ธฐ๊ด€์˜ ํ•ต์‹ฌ ๋กœ์ง์ด ํฌํ•จ๋ผ ์žˆ์–ด ๋ฌด๋‹จ ์‚ฌ์šฉยทํ‘œ์ ˆ ๋ฐฉ์ง€๋ฅผ ์œ„ํ•œ ์ถ”์ ์ด ํ•„์ˆ˜. ๊ธฐ์กด ๋ฐฉ๋ฒ•์˜ ํ•œ๊ณ„ : KGW, SWEET, EWD ๋“ฑ์€ ์—”ํŠธ๋กœํ”ผ ๊ธฐ๋ฐ˜ ์œผ๋กœ ์›Œํ„ฐ๋งˆํฌ๋ฅผ ์‚ฝ์ž…ํ•˜์ง€๋งŒ, ์ฃผ์„๊ณผ ๊ฐ™์€ ์ €์—”ํŠธ๋กœํ”ผ ์˜์—ญ ์— ์˜์กดํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์•„ ์ฃผ์„ ์ œ๊ฑฐ๋งŒ์œผ๋กœ๋„ ์›Œํ„ฐ๋งˆํฌ๊ฐ€ ์‚ฌ๋ผ์ง„๋‹ค. ์ƒˆ๋กœ์šด ๊ณต๊ฒฉ ์‹œ๋‚˜๋ฆฌ์˜ค : ๋…ผ๋ฌธ์€ โ€œ์ฃผ์„ ์ œ๊ฑฐ ๊ณต๊ฒฉโ€์„ ์ตœ์ดˆ๋กœ ์ •์˜ํ•˜๊ณ , ์‹ค์ œ HumanEval ๊ฒฐ๊ณผ์—์„œ ๊ธฐ์กด ๊ธฐ๋ฒ•๋“ค์˜ True Positive Rate๊ฐ€ ๊ธ‰๊ฒฉํžˆ ๊ฐ์†Œํ•จ์„ ์‹œ๊ฐํ™”(Fig. 1)ํ•œ๋‹ค.

LabelFusion: Learning to Fuse LLMs and Transformer Classifiers for Robust Text Classification

LabelFusion: Learning to Fuse LLMs and Transformer Classifiers for Robust Text Classification

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํŠธ๋žœ์Šคํฌ๋จธ vs. LLM : ํŠธ๋žœ์Šคํฌ๋จธ๋Š” ๋ผ๋ฒจ์ด ์ถฉ๋ถ„ํžˆ ์žˆ๋Š” ์ƒํ™ฉ์—์„œ ๋น ๋ฅด๊ณ  ๋น„์šฉ ํšจ์œจ์ ์ด์ง€๋งŒ, ๋ฐ์ดํ„ฐ๊ฐ€ ๋ถ€์กฑํ•˜๊ฑฐ๋‚˜ ๋„๋ฉ”์ธ ๋ณ€์ด๊ฐ€ ํฐ ๊ฒฝ์šฐ ์„ฑ๋Šฅ์ด ๊ธ‰๋ฝํ•œ๋‹ค. ๋ฐ˜๋ฉด LLM์€ fewโ€‘shotยทzeroโ€‘shot ๋Šฅ๋ ฅ์ด ๋›ฐ์–ด๋‚˜์ง€๋งŒ API ํ˜ธ์ถœ ๋น„์šฉยท์ง€์—ฐ์ด ํฌ๋‹ค. ์œตํ•ฉ์˜ ๊ธฐ๋Œ€ํšจ๊ณผ : ๋‘ ๋ชจ๋ธ์˜ ์žฅ์ ์„ ๋ณด์™„ํ•ด โ€œ์–ธ์ œ๋Š” ๋น ๋ฅธ ํŠธ๋žœ์Šคํฌ๋จธ, ์–ธ์ œ๋Š” ๊ณ ๋น„์šฉ LLMโ€์„ ์ž๋™์œผ๋กœ ์„ ํƒํ•˜๋„๋ก ํ•™์Šต์‹œํ‚ค๋Š” ๊ฒƒ์ด ํ•ต์‹ฌ ๋ชฉํ‘œ๋‹ค. 2. ํ•ต์‹ฌ ์„ค๊ณ„ | ๊ตฌ์„ฑ ์š”์†Œ | ์—ญํ•  | ๊ตฌํ˜„ ํฌ์ธํŠธ | | | | | | ML Backbone | RoB

Learning
Information-Dense Reasoning for Efficient and Auditable Security Alert Triage

Information-Dense Reasoning for Efficient and Auditable Security Alert Triage

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ Alert Triage Latency Paradox ๋ฅผ ๋ช…ํ™•ํžˆ ์ •์˜ํ•˜๊ณ , SOC ํ˜„์žฅ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋Œ€๊ทœ๋ชจยท๋‹ค์–‘ํ•œ ์•Œ๋ฆผ ๊ณผ ๋ถ„๋‹น 1~5๋ถ„ ์ด๋ผ๋Š” ์„œ๋น„์Šค ์œˆ๋„์šฐ ์‚ฌ์ด์˜ ๊ธด์žฅ ๊ด€๊ณ„๋ฅผ ์ˆ˜ํ•™์ ์œผ๋กœ ๋ชจ๋ธ๋ง(์‹ (1)ยท(2)). ๊ธฐ์กด ์†”๋ฃจ์…˜(์‹œ๊ทธ๋‹ˆ์ฒ˜, ์ด์ƒ ํƒ์ง€, ์ „๋ฉด ํด๋ผ์šฐ๋“œ LLM)์˜ ํ•œ๊ณ„ ๋ฅผ ์ฒด๊ณ„์ ์œผ๋กœ ์ •๋ฆฌํ•˜๊ณ , ํŠนํžˆ ํ”„๋ผ์ด๋ฒ„์‹œยท๊ทœ์ œ(์˜ˆ: GDPR, SOC 2) ์™€ ๋น„์šฉ(ํ† ํฐ๋‹น ๊ณผ๊ธˆ) ๋ฌธ์ œ๋ฅผ ๊ฐ•์กฐํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | | | | |โ‘ | Constrained Informatio

SIMA 2: A Generalist Embodied Agent for Virtual Worlds

SIMA 2: A Generalist Embodied Agent for Virtual Worlds

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๊ธฐ์ดˆ ๋ชจ๋ธ์˜ ํ•œ๊ณ„ : ๊ธฐ์กด ๋Œ€ํ˜• ์–ธ์–ดยท์‹œ๊ฐ ๋ชจ๋ธ์€ ๋ฐฉ๋Œ€ํ•œ ์ •์  ๋ฐ์ดํ„ฐ๋กœ ํ•™์Šต๋ผ โ€œ๋น„๊ตฌํ˜„๋œ ์ง€๋Šฅโ€์— ๋จธ๋ฌผ๋Ÿฌ, ์‹ค์ œ ๋ฌผ๋ฆฌยท๊ณต๊ฐ„์  ํ–‰๋™์„ ์ˆ˜ํ–‰ํ•˜์ง€ ๋ชปํ•œ๋‹ค(โ€˜Moravecโ€™s paradoxโ€™). ๊ตฌํ˜„ํ˜• ์—์ด์ „ํŠธ ํ•„์š”์„ฑ : ์ธ๊ฐ„ ์ˆ˜์ค€์˜ ๋ชฉํ‘œโ€‘์ง€ํ–ฅ ํ–‰๋™, ์žฅ๊ธฐ ๊ณ„ํš, ํ™˜๊ฒฝ ์ƒํ˜ธ์ž‘์šฉ์„ ์œ„ํ•ด์„œ๋Š” ๊ฐ๊ฐยทํ–‰๋™ ๋ฃจํ”„ ๊ฐ€ ํ•„์ˆ˜์ ์ด๋‹ค. SIMA 1์˜ ํ•œ๊ณ„ : ์งง๊ณ  ์ง์ ‘์ ์ธ ๋ช…๋ น์—๋งŒ ๋ฐ˜์‘, ๋Œ€ํ™”ยท์ถ”๋ก  ๋ถ€์žฌ, ์ƒˆ๋กœ์šด ์ƒํ™ฉ์— ๋Œ€ํ•œ ์ผ๋ฐ˜ํ™” ์•ฝํ•จ. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | | | | | โ‘  | Gemini ๊ธฐ๋ฐ˜ V

AI๋กœ ๋ณด๋Š” ์ €๋น„์šฉ ์ฒด์ง€๋ฐฉ๋ฅ  ์ถ”์ • ์ด๋ฏธ์ง€์™€ ์ธ์ฒด๊ณ„์ธก ๋ฐ์ดํ„ฐ ํ™œ์šฉ

AI๋กœ ๋ณด๋Š” ์ €๋น„์šฉ ์ฒด์ง€๋ฐฉ๋ฅ  ์ถ”์ • ์ด๋ฏธ์ง€์™€ ์ธ์ฒด๊ณ„์ธก ๋ฐ์ดํ„ฐ ํ™œ์šฉ

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ DEXA์˜ ํ•œ๊ณ„ : ์ •ํ™•๋„๋Š” ๋†’์ง€๋งŒ ์žฅ๋น„ ๋น„์šฉยท์‹œ๊ฐ„ยท๋ฐฉ์‚ฌ์„  ๋…ธ์ถœ ๋“ฑ์œผ๋กœ ์ผ์ƒ์ ์ธ ์ฒด์„ฑ๋ถ„ ๊ด€๋ฆฌ์— ๋ถ€์ ํ•ฉ. ๊ธฐ์กด ์ €๋น„์šฉ ๋ฐฉ๋ฒ• : BMI, ๋ฏธ๊ตฐ ํ•ด๊ตฐ์‹ ๋“ฑ์€ ์ธก์ •์ด ๊ฐ„ํŽธํ•˜์ง€๋งŒ ์ •ํ™•๋„๊ฐ€ ๋‚ฎ์•„ ์ฒด์ง€๋ฐฉ ๋ณ€ํ™”๋ฅผ ๋ฏผ๊ฐํ•˜๊ฒŒ ํฌ์ฐฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค. AIยท์ปดํ“จํ„ฐ ๋น„์ „์˜ ๋ถ€์ƒ : ์ด๋ฏธ์ง€ ๊ธฐ๋ฐ˜ ์ฒดํ˜• ๋ถ„์„, ์ž์„ธ ์ถ”์ •, ๋น„๋งŒ ํƒ์ง€ ๋“ฑ์—์„œ ๋”ฅ๋Ÿฌ๋‹์ด ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์ด๋ฉฐ, ์ฒด์ง€๋ฐฉ๋ฅ  ์ถ”์ •์—๋„ ์ ์šฉ ๊ฐ€๋Šฅ์„ฑ์ด ์ œ๊ธฐ๋˜๊ณ  ์žˆ๋‹ค. 2. ๋ฐ์ดํ„ฐ์…‹ ๊ตฌ์ถ• | ๊ตฌ๋ถ„ | ์ƒ˜ํ”Œ ์ˆ˜ | ์ฃผ์š” ํŠน์„ฑ | | | | | | ์ธ์ฒด๊ณ„์ธก | 253 | ๋‚จ์„ฑ ์ „์šฉ, ์ฒด์ค‘ยทํ‚ค

ForCM: Forest Cover Mapping from Multispectral Sentinel-2 Image by Integrating Deep Learning with Object-Based Image Analysis

ForCM: Forest Cover Mapping from Multispectral Sentinel-2 Image by Integrating Deep Learning with Object-Based Image Analysis

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์‚ฐ๋ฆผ์˜ ์—ญํ•  : ํƒ„์†Œ ๊ฒฉ๋ฆฌ, ์ƒ๋ฌผ๋‹ค์–‘์„ฑ ๋ณด์ „, ๊ธฐํ›„ ์กฐ์ ˆ ๋“ฑ ์ „ ์ง€๊ตฌ์  ์ƒํƒœ์„œ๋น„์Šค ์ œ๊ณต. ํ˜„ํ–‰ ๋งคํ•‘ ํ•œ๊ณ„ : ํ”ฝ์…€โ€‘๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•์€ ๊ฒฝ๊ณ„์„ (Edge) ์ถ”์ถœ์— ์•ฝํ•˜๊ณ , OBIA๋Š” ๊ณ ํ’ˆ์งˆ ์ž…๋ ฅ ์ด๋ฏธ์ง€์™€ ์„ธ๋ถ„ํ™” ํŒŒ๋ผ๋ฏธํ„ฐ์— ํฌ๊ฒŒ ์˜์กดํ•œ๋‹ค. ๋‘ ์ ‘๊ทผ๋ฒ• ๋ชจ๋‘ ์ „๋ฌธ๊ฐ€ ์˜์กด๋„๊ฐ€ ๋†’๊ณ  ๋น„์šฉ์ด ๋งŽ์ด ๋“ ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ๋ฐ ๊ธฐ์—ฌ | ๊ตฌ๋ถ„ | ๊ธฐ์กด ์—ฐ๊ตฌ | ๋ณธ ์—ฐ๊ตฌ์˜ ์ฐจ๋ณ„์  | | | | | | ๋ชจ๋ธ ์œตํ•ฉ | DL โ†” OBIA ๊ฒฐํ•ฉ ์‚ฌ๋ก€๋Š” ์ œํ•œ์ (์˜ˆ: ๊ฑด๋ฌผ ์ถ”์ถœ, ํ† ์‚ฌ๋ฅ˜ ํƒ์ง€) | ๋‹ค์ค‘ DL ๋ชจ๋ธ(ResUNet, Attent

Computer Science Learning Computer Vision Analysis
PCIA: A Path Construction Imitation Algorithm for Global Optimization

PCIA: A Path Construction Imitation Algorithm for Global Optimization

1. ์—ฐ๊ตฌ ๋™๊ธฐ์™€ ์œ„์น˜ไป˜ใ‘ ๋ฉ”ํƒ€ํœด๋ฆฌ์Šคํ‹ฑ์€ ํฌ๊ฒŒ ์ž์—ฐ ์ง„ํ™”, ๋ฌผ๋ฆฌ ํ˜„์ƒ, ๋™๋ฌผ ๊ตฐ์ง‘, ์ƒ๋ฌผํ•™์  ๊ณผ์ •, ์ธ๊ฐ„ ์‚ฌํšŒ ๋“ฑ 5๊ฐ€์ง€ ์˜๊ฐ์›์ฒœ์œผ๋กœ ๋ถ„๋ฅ˜๋œ๋‹ค(๊ทธ๋ฆผ 1). ๊ธฐ์กด ์—ฐ๊ตฌ ๋Œ€๋ถ€๋ถ„์ด ์•ž 4๊ฐ€์ง€์— ์ง‘์ค‘ํ–ˆ์œผ๋ฉฐ, ์ธ๊ฐ„ ํ–‰๋™์„ ์ง์ ‘ ๋ชจ๋ฐฉํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ƒ๋Œ€์ ์œผ๋กœ ๋“œ๋ฌผ๋‹ค. PCIA๋Š” โ€˜๊ธธ ์ฐพ๊ธฐโ€™๋ผ๋Š” ๊ตฌ์ฒด์  ์ธ๊ฐ„ ํ–‰๋™ ์„ ์„ ํƒํ•จ์œผ๋กœ์จ, ๊ธฐ์กด ์ธ๊ฐ„โ€‘์˜๊ฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜(ICA, TLBO, HS, TS)๊ณผ ์ฐจ๋ณ„ํ™”ํ•œ๋‹ค. ํŠนํžˆ โ€œ์ธ๊ธฐ ๊ฒฝ๋กœ ์œ ์ง€ยทํ์‡„ ์‹œ ๋ถ€๋ถ„ ๊ฒฝ๋กœ ์žฌ์กฐํ•ฉโ€์ด๋ผ๋Š” ๋ฉ”์ปค๋‹ˆ์ฆ˜์€ ์‹ค์ œ ๊ตํ†ตยท๋„คํŠธ์›Œํฌ ์„ค๊ณ„ ์™€๋„ ์—ฐ๊ด€์„ฑ์ด ๋†’์•„ ์‹ค์šฉ์  ํ™•์žฅ ๊ฐ€๋Šฅ์„ฑ์„ ์‹œ์‚ฌํ•œ๋‹ค.

Post-Cold War Diaspora of Russian Particle Physicists

Post-Cold War Diaspora of Russian Particle Physicists

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ๋‡Œ์œ ์ถœ์˜ ์—ญ์‚ฌ์  ๋งฅ๋ฝ : 1970โ€‘โ€‘80๋…„๋Œ€๋Š” ์†Œ๋ จ ๋‚ด๋ถ€ ์ด๋™์ด ์ฃผ๋ฅผ ์ด๋ฃจ์—ˆ๊ณ , ํ•ด์™ธ ์ด์ฃผ๋Š” ๊ตญ๊ฐ€ ์ฐจ์›์—์„œ ์—„๊ฒฉํžˆ ํ†ต์ œ๋˜์—ˆ๋‹ค. 1991๋…„ ์ดํ›„ ๊ธ‰๊ฒฉํ•œ ์ •์ฑ… ์™„ํ™”์™€ ๊ฒฝ์ œ ๋ถ•๊ดด๊ฐ€ โ€˜๋Œ€๊ทœ๋ชจ ๊ณผํ•™ ์ธ์žฌ ์œ ์ถœโ€™์ด๋ผ๋Š” ์ƒˆ๋กœ์šด ํ˜„์ƒ์„ ๋งŒ๋“ ๋‹ค. ํ•ต์‹ฌ ์งˆ๋ฌธ 1. ๋””์•„์Šคํฌ๋ผ๊ฐ€ ํ˜•์„ฑ๋œ ๊ตฌ์กฐ์ ยท๋ฌธํ™”์  ์š”์ธ์€ ๋ฌด์—‡์ธ๊ฐ€? 2. ๋Ÿฌ์‹œ์•„ ๋ฌผ๋ฆฌํ•™ ์—ฐ๊ตฌ ๊ธฐ๋ฐ˜์— ์–ด๋–ค ์†์‹คยท์ด๋“์ด ์žˆ์—ˆ๋Š”๊ฐ€? 3. ๊ตญ์ œ ์ž…์ž๋ฌผ๋ฆฌํ•™ ๊ณต๋™์—ฐ๊ตฌ์— ๋Œ€ํ•œ ๊ตฌ์ฒด์  ๊ธฐ์—ฌ๋Š” ์–ด๋–ป๊ฒŒ ์ธก์ •๋˜๋Š”๊ฐ€? 2. ๋ฐ์ดํ„ฐ์™€ ๋ฐฉ๋ฒ•๋ก  ์ •๋Ÿ‰์  ์ž๋ฃŒ : OECD R&D ์ง€์ถœ ํ†ต๊ณ„, APS ํŽ ๋กœ์šฐ

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๊ฒฝ๋Ÿ‰ ์—์ด์ „ํŠธ ์ฝ”์–ด Xmodelโ€‘2.5: ยตP ๊ธฐ๋ฐ˜ ํŒŒ๋ผ๋ฏธํ„ฐ ์ „์ด์™€ FP8 ํ˜ผํ•ฉ ์ •๋ฐ€๋„ ํ•™์Šต

1. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๊ธฐ์—ฌ ๋‚ด์šฉ | ์˜์˜ | | | | | |โ‘ | ยตP ๊ธฐ๋ฐ˜ ํ•˜์ดํผํŒŒ๋ผ๋ฏธํ„ฐ ์ „์ด โ€“ 20 M ํ”„๋ก์‹œ โ†’ 1.3 B ์ „์ฒด ๋ชจ๋ธ | ๋ชจ๋ธ ๊ทœ๋ชจ ํ™•๋Œ€ ์‹œ ์žฌํŠœ๋‹ ๋น„์šฉ์„ ๊ฑฐ์˜ 0์œผ๋กœ ๊ฐ์†Œ์‹œ์ผœ, ์—ฐ๊ตฌยท์‚ฐ์—… ํ˜„์žฅ์—์„œ ๋น ๋ฅธ ํ”„๋กœํ† ํƒ€์ดํ•‘ ๊ฐ€๋Šฅ | |โ‘ก| WSD ์ปค๋ฆฌํ˜๋Ÿผ + Optimizer Switch (AdamW โ†’ Muon) | ์ดˆ๊ธฐ ํ•™์Šต ์•ˆ์ •์„ฑ(AdamW) + ํ›„๋ฐ˜ ์ƒคํ”„๋‹(Muon) ์กฐํ•ฉ์ด โ€œํ•™์Šต ์Šค์ผ€์ค„ ์—†์ด๋„โ€ ์„ฑ๋Šฅ์„ 4.58 %p ์ƒ์Šน์‹œํ‚ด | |โ‘ข| FP8โ€‘mixedโ€‘precision (E4M3 forward /

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