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Re$^{text{2}}$MaP: Macro Placement by Recursively Prototyping and Packing Tree-based Relocating

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋งคํฌ๋กœ ๋ฐฐ์น˜ ๋Š” ๊ณ ์„ฑ๋Šฅ ASIC ์„ค๊ณ„์—์„œ ์ „๋ ฅยทํƒ€์ด๋ฐยทDRC ์ œ์•ฝ์„ ๋™์‹œ์— ๋งŒ์กฑ์‹œ์ผœ์•ผ ํ•˜๋Š” ๊ฐ€์žฅ ์–ด๋ ค์šด ๋‹จ๊ณ„ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๊ธฐ์กด ํ•™์ˆ  ๋ฐฐ์น˜๊ธฐ๋“ค์€ ์ฃผ๋กœ ์…€โ€‘๋‹จ์œ„ ์ตœ์ ํ™”์— ์ดˆ์ ์„ ๋งž์ถ”์–ด ๋งคํฌ๋กœ ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ์„ ์ถฉ๋ถ„ํžˆ ๋ชจ๋ธ๋งํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์ตœ๊ทผ ํ˜ผํ•ฉโ€‘ํฌ๊ธฐ ๋ฐฐ์น˜ ์™€ ํ•™์Šต ๊ธฐ๋ฐ˜ ์ ‘๊ทผ๋ฒ•์ด ๋“ฑ์žฅํ–ˆ์ง€๋งŒ, ๋งคํฌ๋กœ ๊ทธ๋ฃน ๊ฐ„ ์ „์—ญ์ ์ธ ๋ฐ์ดํ„ฐ ํ๋ฆ„ ๊ณผ ์™€์ด์–ด ๊ธธ์ด ๋ฅผ ๋™์‹œ์— ๊ณ ๋ คํ•˜๋Š” ์ฒด๊ณ„์ ์ธ ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ๋ถ€์กฑํ–ˆ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ๋ฐ ๊ธฐ๋ฒ• | ๋‹จ๊ณ„ | ์ฃผ์š” ๊ธฐ๋ฒ• | ๋ชฉ์  | | | | | | โ‘  ๋‹ค์ค‘ ๋ ˆ๋ฒจ ๋งคํฌ๋กœ ๊ทธ๋ฃนํ™” & PP

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Real time fault detection in 3D printers using Convolutional Neural Networks and acoustic signals

1. ์—ฐ๊ตฌ์˜ ์˜์˜ ๋ฐ ์ฐจ๋ณ„์  | ๊ธฐ์กด ๋ฐฉ๋ฒ• | ํ•œ๊ณ„ | ๋ณธ ๋…ผ๋ฌธ์˜ ํ˜์‹  | | | | | | ์‹œ๊ฐ ๊ฒ€์‚ฌ(์ธ๊ฐ„) | ์—ฐ์† ๋ชจ๋‹ˆํ„ฐ๋ง ๋ถˆ๊ฐ€, ์ฃผ๊ด€์ , ๋น„์šฉยท์‹œ๊ฐ„ ์†Œ๋ชจ | ์—ฐ์†ยท์ž๋™ ์Œํ–ฅ ๋ถ„์„ | | ์ ‘์ด‰ํ˜• ์„ผ์„œ(์ง„๋™ยท์ „๋ฅ˜) | ํ”„๋ฆฐํ„ฐ ๊ฐœ์กฐ ํ•„์š”, ๊ณ ๊ฐ€, ์ œํ•œ๋œ ๊ฐ์ง€ ๋ฒ”์œ„ | ๋น„์ ‘์ด‰ยท์ €๋น„์šฉ ๋งˆ์ดํฌ๋งŒ์œผ๋กœ ๊ตฌํ˜„ | | ๋‹จ์ผ ์นด๋ฉ”๋ผ | ์‹œ์•ผ ์ œํ•œ, ์กฐ๋ช… ์˜์กด | ์ „ํŒŒ(์Œํ–ฅ) ๊ธฐ๋ฐ˜ ํ™˜๊ฒฝ ๋…๋ฆฝ์  | ์Œํ–ฅ ์‹ ํ˜ธ๋Š” ํ”„๋ฆฐํ„ฐ ๋‚ด๋ถ€์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋ฏธ์„ธํ•œ ๊ธฐ๊ณ„์  ๋ณ€ํ™”๋ฅผ ํฌ์ฐฉํ•  ์ˆ˜ ์žˆ์–ด, ๋ฏธ์„ธ ๊ณ ์žฅ (์˜ˆ: ๋…ธ์ฆ ๋ถ€๋ถ„ ๋ง‰ํž˜)๊นŒ์ง€ ๊ฐ์ง€ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์ ์ด ํฐ ๊ฐ•์ ์ด๋‹ค.

Network Electrical Engineering and Systems Science Detection
Reflexive Evidence-Based Multimodal Learning for Clean Energy Transitions: Causal Insights on Cooking Fuel Access, Urbanization, and Carbon Emissions

Reflexive Evidence-Based Multimodal Learning for Clean Energy Transitions: Causal Insights on Cooking Fuel Access, Urbanization, and Carbon Emissions

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ SDG 7 ๋‹ฌ์„ฑ์„ ์œ„ํ•œ ์—๋„ˆ์ง€ ์ ‘๊ทผ์„ฑยทํƒ„์†Œ ๋ฐฐ์ถœ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ๊ฒฐ์ •์š”์ธ์— ๋Œ€ํ•œ ์ •๋Ÿ‰์  ์ดํ•ด๊ฐ€ ๋ถ€์กฑํ•จ. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ํ†ต๊ณ„์  ์ƒ๊ด€๊ด€๊ณ„์— ๋จธ๋ฌผ๋Ÿฌ ์ธ๊ณผ๊ด€๊ณ„ ๊ทœ๋ช…์— ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ์œผ๋ฉฐ, ๋‹ค์ค‘๋ชจ๋‹ฌ ๋ฐ์ดํ„ฐ ํ™œ์šฉ๋„ ๋ฏธ๋น„. 2. ํ•ต์‹ฌ ๋ฐฉ๋ฒ•๋ก  | ๋‹จ๊ณ„ | ๋‚ด์šฉ | ์ฃผ์š” ๊ธฐ์ˆ ยท๋„๊ตฌ | | | | | | ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ | World Bank Development Indicators(98๊ฐœ ์ง€ํ‘œ) + Climate Watch GHG ๋ฐ์ดํ„ฐ(265๊ฐœ ๊ฒฝ์ œ๊ถŒ, 20๋…„) | ๊ณต๊ฐœ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค, ์ „์ฒ˜๋ฆฌ ํŒŒ์ดํ”„๋ผ์ธ | | ์ธ๊ณผ์ถ”๋ก  | (i

Learning
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Results of the 2024 CommonRoad Motion Planning Competition for Autonomous Vehicles

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ๋ฐฐ๊ฒฝ : ์ž์œจ์ฃผํ–‰ ๋ชจ์…˜ ํ”Œ๋ž˜๋‹์€ ๋ณต์žกํ•œ ๊ตํ†ต ์ƒํ™ฉ์„ ์‹ค์‹œ๊ฐ„์œผ๋กœ ํ•ด๊ฒฐํ•ด์•ผ ํ•˜๋Š” ํ•ต์‹ฌ ๊ธฐ์ˆ ์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ๊ฐœ๋ณ„ ์‹œ๋‚˜๋ฆฌ์˜ค๋‚˜ ์ž์ฒด ๋ฐ์ดํ„ฐ์…‹์— ๊ตญํ•œ๋œ ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์•„, ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ฐ„ ๊ณต์ •ํ•œ ๋น„๊ต๊ฐ€ ์–ด๋ ค์› ๋‹ค. ๋ชฉ์  : CommonRoad๋ผ๋Š” ์˜คํ”ˆ์†Œ์Šค ๋ฒค์น˜๋งˆํฌ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ‘œ์ค€ํ™”๋œ ํ‰๊ฐ€ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ๊ณตํ•˜๊ณ , ์ตœ์‹  ํ”Œ๋ž˜๋„ˆ๋“ค์˜ ๊ฐ•์ ยท์•ฝ์ ์„ ๋‹ค์ฐจ์›์ ์œผ๋กœ ํŒŒ์•…ํ•œ๋‹ค. 2. ๋Œ€ํšŒ ์„ค๊ณ„ ๋ฐ ๋ฒค์น˜๋งˆํฌ ๊ตฌ์„ฑ | ์š”์†Œ | ์„ค๋ช… | | | | | ์‹œ๋‚˜๋ฆฌ์˜ค | ๊ณ ์†๋„๋กœ(๋‹ค์ค‘ ์ฐจ์„ , ๊ณ ์† ์ฃผํ–‰)์™€ ๋„์‹œ(๊ต์ฐจ๋กœ, ๋ณดํ–‰์žยท์ž์ „๊ฑฐ ํฌํ•จ) 2๊ฐ€์ง€ ํ™˜๊ฒฝ

Revised comment on the paper titled 'The Origin of Quantum Mechanical Statistics: Insights from Research on Human Language

Revised comment on the paper titled 'The Origin of Quantum Mechanical Statistics: Insights from Research on Human Language

** ๋ณธ ๋…ผ๋ฌธ์€ Aertsโ€ฏ๋“ฑ์ด ์ œ์•ˆํ•œ โ€œํ…์ŠคํŠธ์˜ ์ˆœ์œ„โ€‘๋นˆ๋„ ๋ถ„ํฌ๋ฅผ ๋ณด์Šคโ€‘์•„์ธ์Šˆํƒ€์ธ(BE) ํ†ต๊ณ„๋กœ ํ•ด์„ํ•œ๋‹คโ€๋Š” ์ฃผ์žฅ์— ๋Œ€ํ•ด ์งง์ง€๋งŒ ํ•ต์‹ฌ์ ์ธ ๋น„ํŒ์„ ์ œ์‹œํ•œ๋‹ค. ์ €์ž๋Š” (i) โ€œ๋ณด์† ๊ฐ•ํ™”โ€๋ผ ๋ช…๋ช…๋œ ์ •๊ทœํ™” ๊ณผ์ •์ด ์‹ค์ œ ํ™•๋ฅ  ์ฆ๊ฐ•์„ ์˜๋ฏธํ•˜์ง€ ์•Š์Œ, (ii) ์ˆœ์œ„๋ฅผ ์—๋„ˆ์ง€ ์ˆ˜์ค€์œผ๋กœ ๋™์ผ์‹œํ•˜๋Š” ๋งคํ•‘์ด BE ์ ํ•ฉ์„ ๋ฌผ๋ฆฌ์  ๋ฉ”์ปค๋‹ˆ์ฆ˜์˜ ์ฆ๊ฑฐ๋กœ ์‚ผ๊ธฐ์— ์ถฉ๋ถ„ํžˆ ์ง„๋‹จ์ ์ด์ง€ ์•Š์Œ, (iii) ๋น„๊ต ๋Œ€์ƒ์œผ๋กœ ์‚ฌ์šฉ๋œ ๋งฅ์Šค์›ฐโ€‘๋ณผ์ธ ๋งŒ(์ง€์ˆ˜ํ˜•) ๋ชจ๋ธ์ด ๋„ˆ๋ฌด ์•ฝํ•ด ์˜จํ†จ๋กœ์ง€์  ๊ฒฐ๋ก ์„ ๋’ท๋ฐ›์นจํ•˜์ง€ ๋ชปํ•จ์„ ์ง€์ ํ•œ๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ ํ•ด์„์˜ ํ๋ฆ„ ๋ณ€์งˆ, ํŒŒ๋ผ๋ฏธํ„ฐ ์˜๋ฏธ๋ก , ์žฌ

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RLGT: A reinforcement learning framework for extremal graph theory

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๊ฐ•ํ™”ํ•™์Šต๊ณผ ์กฐํ•ฉ ์ตœ์ ํ™” : RL์€ ํ™˜๊ฒฝ๊ณผ์˜ ์ƒํ˜ธ์ž‘์šฉ์„ ํ†ตํ•ด ์ •์ฑ…์„ ํ•™์Šตํ•œ๋‹ค. ๊ทธ๋ž˜ํ”„๋ฅผ ๊ตฌ์„ฑยท๋ณ€ํ˜•ํ•˜๋Š” ์ผ๋ จ์˜ ํ–‰๋™์„ ์ƒํƒœยท๋ณด์ƒ์œผ๋กœ ์ •์˜ํ•˜๋ฉด, ๊ทธ๋ž˜ํ”„ ์ด๋ก ์˜ ๋‹ค์–‘ํ•œ ๋ถˆ๋ณ€๋Ÿ‰์„ ์ตœ์ ํ™”ํ•˜๊ฑฐ๋‚˜ ๋ฐ˜๋ก€๋ฅผ ์ฐพ๋Š” ์กฐํ•ฉ ์ตœ์ ํ™” ๋ฌธ์ œ๋กœ ๋ณ€ํ™˜ํ•  ์ˆ˜ ์žˆ๋‹ค. ์„ ํ–‰ ์—ฐ๊ตฌ ์ •๋ฆฌ Wagner (2020) : ๋‹จ์ˆœํ•œ โ€œLinearโ€ ํ™˜๊ฒฝ์„ ์ œ์•ˆํ•ด ๊ทธ๋ž˜ํ”„ ์ธ์Šคํ„ด์Šค๋ฅผ ์ˆœ์ฐจ์ ์œผ๋กœ ๊ตฌ์ถ•ํ•˜๊ณ  Deep Crossโ€‘Entropy ๋กœ ์ตœ์ ํ™”. Ghebleh et al. : ๋ฒกํ„ฐํ™”ยท์™ธ๋ถ€ Java ์—ฐ๋™์„ ํ†ตํ•ด ์„ฑ๋Šฅ์„ ํฌ๊ฒŒ ํ–ฅ์ƒ, ๋ผํ”Œ๋ผ์‹œ์•ˆ ์ŠคํŽ™ํŠธ๋Ÿผ ์ƒํ•œ์„ ๋ฐ˜

Machine Learning Computer Science Framework Learning
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Robinson manifolds and the Chern-Robinson connection

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ Almost Robinson ๋‹ค์–‘์ฒด ๋Š” ๋ณต์†Œํ™”๋œ ์ ‘๋‹ค๋ฐœ์— ์ตœ๋Œ€ ์ „ํ˜€ ๋„(null)์ธ ๋ณต์†Œ ๋ถ€๋ถ„๋‹ค๋ฐœ์„ ๊ฐ–๋Š” ์ง์ˆ˜ ์ฐจ์› ๋กœ๋ Œ์ธ  ๋‹ค์–‘์ฒด์ด๋ฉฐ, CR ๊ธฐํ•˜์™€ ๊นŠ์€ ์—ฐ๊ด€์„ ๊ฐ€์ง„๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ(

Mathematics
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Robust Adaptive Sliding-Mode Control for Damaged Fixed-Wing UAVs

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํ˜„์‹ค์  ๋ฌธ์ œ : ๊ณ ์ •์ต UAV๋Š” ๋น„ํ–‰ ์ค‘ ๊ตฌ์กฐ ์†์ƒ(์˜ˆ: ๋‚ ๊ฐœ ํŒŒ์†, ์ œ์–ด๋ฉด ํŒŒ์†)์ด๋‚˜ ๊ณต๊ธฐ์—ญํ•™ ํŒŒ๋ผ๋ฏธํ„ฐ ๋ณ€๋™์— ์ง๋ฉดํ•œ๋‹ค. ๊ธฐ์กด ์ž๋™์กฐ์ข…๊ธฐ(์ „ํ†ต PID, ๊ณ ์ • ์ด๋“ SMC ๋“ฑ)๋Š” ์ด๋Ÿฌํ•œ ๋ถˆํ™•์‹ค์„ฑ์„ ์ถฉ๋ถ„ํžˆ ๋ณด์ƒํ•˜์ง€ ๋ชปํ•ด ์ž„๋ฌด ์‹คํŒจ ์œ„ํ—˜์ด ํฌ๋‹ค. ์„ ํ–‰ ์—ฐ๊ตฌ ํ•œ๊ณ„ SMC ๊ธฐ๋ฐ˜ :

Electrical Engineering and Systems Science
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RS-ORT: A Reduced-Space Branch-and-Bound Algorithm for Optimal Regression Trees

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ MIP ๊ธฐ๋ฐ˜ ์ตœ์  ํŠธ๋ฆฌ ํ•™์Šต ์€ ์ „์—ญ ์ตœ์ ์„ฑ์„ ์ œ๊ณตํ•˜์ง€๋งŒ, ์—ฐ์†ํ˜• ํŠน์„ฑ ๊ณผ ๋Œ€๊ทœ๋ชจ ์ƒ˜ํ”Œ ์— ๋Œ€ํ•ด์„  ๋‘ ๊ฐ€์ง€ ์ฃผ์š” ์ œ์•ฝ์ด ์žˆ๋‹ค. 1. ์—ฐ์†ํ˜• ๋ณ€์ˆ˜๋ฅผ ์ด์ง„ํ™”ํ•˜๋ฉด ์ •๋ณด ์†์‹คยทํŠธ๋ฆฌ ๊นŠ์ด ์ฆ๊ฐ€. 2. ๊ธฐ์กด MIP ๋ชจ๋ธ์€ ์ƒ˜ํ”Œ ์ˆ˜์— ๋น„๋ก€ํ•˜๋Š” ๋ณ€์ˆ˜ยท์ œ์•ฝ์ด ๋Š˜์–ด๋‚˜, ๋ฉ”๋ชจ๋ฆฌยท์‹œ๊ฐ„ ๋ณต์žก๋„๊ฐ€ ๊ธ‰์ฆํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ ์ฐจ์› ์ถ•์†Œ(Reducedโ€‘Space) ์ ‘๊ทผ ๋‘ ๋‹จ๊ณ„ ์ตœ์ ํ™” : 1) ํŠธ๋ฆฌ ๊ตฌ์กฐ ๊ฒฐ์ • (๋ถ„๊ธฐ ๋ณ€์ˆ˜) โ€“ ๋…ธ๋“œ์˜ ์กด์žฌ ์—ฌ๋ถ€, ๋ถ„ํ•  ๋ณ€์ˆ˜, ๋ถ„ํ•  ์ž„๊ณ„๊ฐ’ ๋“ฑ. 2) ๋ฆฌํ”„ ์˜ˆ์ธก๊ฐ’ ๊ณ„์‚ฐ โ€“ ๊ตฌ์กฐ๊ฐ€ ๊ณ ์ •๋œ ๋’ค ํ

Scalable Processing-Near-Memory for 1M-Token LLM Inference: CXL-Enabled KV-Cache Management Beyond GPU Limits

Scalable Processing-Near-Memory for 1M-Token LLM Inference: CXL-Enabled KV-Cache Management Beyond GPU Limits

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์ปจํ…์ŠคํŠธ ํ™•์žฅ ์••๋ฐ• : ๊ธฐ์กด GPU ๋ฉ”๋ชจ๋ฆฌ๋Š” ์ˆ˜์‹ญ๋งŒ ํ† ํฐ ์ •๋„๋งŒ ์ˆ˜์šฉ ๊ฐ€๋Šฅํ•˜๋‚˜, ์ตœ์‹  LLM(์˜ˆ: GPTโ€‘4, Claudeโ€‘2)์€ 1M ํ† ํฐ ์ด์ƒ์˜ ์ปจํ…์ŠคํŠธ๋ฅผ ์š”๊ตฌํ•œ๋‹ค. KVโ€‘Cache๋Š” ํ† ํฐ๋‹น (key, value) ์Œ์„ ์ €์žฅํ•˜๋ฏ€๋กœ ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰์ด O(N)ยทO(d) ๋กœ ๊ธ‰์ฆํ•œ๋‹ค. CXL ๊ธฐ๋ฐ˜ ๋น„์†Œ๊ฑฐ ํ”„๋ ˆ์ž„์›Œํฌ : CXL์€ CPUโ€‘GPUโ€‘๋ฉ”๋ชจ๋ฆฌ ๊ฐ„ ๊ณ ๋Œ€์—ญํญ, ์ €์ง€์—ฐ ์—ฐ๊ฒฐ์„ ์ œ๊ณตํ•ด ์™ธ๋ถ€ DRAM/SMEM์— KVโ€‘Cache ์ „์ฒด๋ฅผ ์ €์žฅํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•˜์ง€๋งŒ, โ€œ๋ฆฌ์ฝœ(recall)โ€ ๋‹จ๊ณ„์—์„œ GPUโ€‘๋ฉ”๋ชจ๋ฆฌ์™€ C

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Scaling Reproducibility: An AI-Assisted Workflow for Large-Scale Reanalysis

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์žฌํ˜„์„ฑ ์œ„๊ธฐ : ์‚ฌํšŒ๊ณผํ•™ยท๊ฒฝ์ œํ•™ ๋ถ„์•ผ์—์„œ ๋ณต์ œ ์ž๋ฃŒ๋Š” ๊ณต๊ฐœ๋˜์ง€๋งŒ, ํŒŒ์ผ ๊ตฌ์กฐยทํ™˜๊ฒฝยท๋ฌธ์„œํ™” ์ฐจ์ด๋กœ ์ธํ•ด ๋Œ€๊ทœ๋ชจ ์žฌํ˜„์ด ์—ฌ์ „ํžˆ ๋น„์šฉโ€‘์ง‘์•ฝ์ ์ด๋ฉฐ ์˜ค๋ฅ˜์— ์ทจ์•ฝํ•จ. ์šด์˜์  ๋ณ‘๋ชฉ : ๊ฐ ๋…ผ๋ฌธ์˜ ๋ณต์ œ ํŒจํ‚ค์ง€๋ฅผ ๊ฐœ๋ณ„์ ์œผ๋กœ ์„ค์ •ยท์‹คํ–‰ํ•˜๋Š” ๋ฐ ์—ฐ๊ตฌ์ž ์‹œ๊ฐ„์ด ํฌ๊ฒŒ ์†Œ๋ชจ๋จ. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ๋ฐ ์„ค๊ณ„ ์›์น™ | ์š”์†Œ | ์„ค๋ช… | | | | | ๊ณผํ•™์  ์ถ”๋ก  โ†” ๊ณ„์‚ฐ ์‹คํ–‰ ๋ถ„๋ฆฌ | ์ธ๊ฐ„์€ ์ง„๋‹จ ํ…œํ”Œ๋ฆฟ (์ถ”์ •๋Ÿ‰, ์ถ”์ •๊ธฐ, ๋กœ๋ฒ„์ŠคํŠธ ์ฒดํฌ ๋“ฑ)๋งŒ ์„ค๊ณ„ํ•˜๊ณ , AI๋Š” ์ž๋ฃŒ ํš๋“ยทํ™˜๊ฒฝ ์žฌ๊ตฌ์„ฑยท์ฝ”๋“œ ์‹คํ–‰์„ ๋‹ด๋‹น. | | ์—์ด์ „ํŠธํ˜• AI | ๋Œ€

Analysis Economics
SeaSpoofFinder -- Potential GNSS Spoofing Event Detection Using AIS

SeaSpoofFinder -- Potential GNSS Spoofing Event Detection Using AIS

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ GNSS ์Šคํ‘ธํ•‘ ์œ„ํ˜‘ ์€ 20๋…„ ์ด์ƒ ๋…ผ์˜๋ผ ์™”์œผ๋ฉฐ, ์ตœ๊ทผ ํ•ญ๊ณต ๋ถ„์•ผ์—์„œ ADSโ€‘B ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•œ ๋Œ€๊ทœ๋ชจ ๋ชจ๋‹ˆํ„ฐ๋ง ์‚ฌ๋ก€๊ฐ€ ๋ณด๊ณ ๋˜์—ˆ๋‹ค. ํ•ด์–‘ ๋ถ„์•ผ์—์„œ๋Š” AIS๊ฐ€ ์œ ์‚ฌํ•œ ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์‹ค์ œ ์Šคํ‘ธํ•‘ ํƒ์ง€์— ์ ์šฉ๋œ ์—ฐ๊ตฌ๋Š” ๋“œ๋ฌผ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” โ€œAISโ€‘onlyโ€ ์ ‘๊ทผ ์ด๋ผ๋Š” ์ œํ•œ๋œ ์ •๋ณด ํ™˜๊ฒฝ์—์„œ๋„ ๊ตฌ์กฐ์ ยท์‹œ๊ณ„์—ด์  ํŠน์ง•์„ ์ถ”์ถœํ•ด ์Šคํ‘ธํ•‘ ๊ฐ€๋Šฅ์„ฑ์„ ์ œ์‹œํ•จ์œผ๋กœ์จ, ํ•ด์–‘ GNSS ๋ณด์•ˆ ๋ถ„์•ผ์— ์ƒˆ๋กœ์šด ํƒ์ง€ ํŒจ๋Ÿฌ๋‹ค์ž„์„ ์ œ์‹œํ•œ๋‹ค. 2. ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ๋ฐ ์ „์ฒ˜๋ฆฌ ๋ฐ์ดํ„ฐ ์†Œ์Šค : aisstream.io API๋ฅผ ํ†ต

Electrical Engineering and Systems Science Detection
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SEBA: Sample-Efficient Black-Box Attacks on Visual Reinforcement Learning

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์‹œ๊ฐ ๊ฐ•ํ™”ํ•™์Šต(VRL) ์€ ๊ณ ์ฐจ์› ์ด๋ฏธ์ง€ ์ž…๋ ฅ์„ ์ง์ ‘ ์ฒ˜๋ฆฌํ•ด ๋กœ๋ด‡ ์ œ์–ด๋‚˜ ๊ฒŒ์ž„ ์—์ด์ „ํŠธ์— ์ ์šฉ๋˜์ง€๋งŒ, ์ ๋Œ€์  ๊ณต๊ฒฉ ์— ๋Œ€ํ•œ ๋ฐฉ์–ด ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด ์•„์ง ๋ฏธ๋น„ํ•˜๋‹ค. ๊ธฐ์กด ๋ธ”๋ž™๋ฐ•์Šค ๊ณต๊ฒฉ์€ ๋ฒกํ„ฐ ๊ธฐ๋ฐ˜ ํ˜น์€ ์ด์‚ฐ ํ–‰๋™ ์— ์ดˆ์ ์„ ๋งž์ถ”์–ด, ์—ฐ์†์ ์ธ ๊ณ ์ฐจ์› ํ–‰๋™ ๊ณต๊ฐ„์„ ๊ฐ€์ง„ VRL์—์„  ์ฟผ๋ฆฌ ๋น„์šฉ ์ด ๊ธ‰์ฆํ•œ๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด | ๊ตฌ์„ฑ ์š”์†Œ | ์—ญํ•  | ์ฃผ์š” ๊ธฐ๋ฒ• | | | | | | Shadow Q ๋ชจ๋ธ | ์ ๋Œ€์  ๊ต๋ž€ ํ•˜์—์„œ์˜ ๋ˆ„์  ๋ณด์ƒ ์„ ์˜ˆ์ธก | Qโ€‘learning ๊ธฐ๋ฐ˜ ํ•จ์ˆ˜ ๊ทผ์‚ฌ (์‹ ๊ฒฝ๋ง) | | G

Learning
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See the Speaker: Crafting High-Resolution Talking Faces from Speech with Prior Guidance and Region Refinement

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์Œ์„ฑโ†’ํ† ํ‚น ํŽ˜์ด์Šค ๋Š” ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ ์ƒ์„ฑ ๋ถ„์•ผ์—์„œ ๊ฐ€์žฅ ๋„์ „์ ์ธ ๊ณผ์ œ ์ค‘ ํ•˜๋‚˜๋‹ค. ๊ธฐ์กด ํŒŒ์ดํ”„๋ผ์ธ์€ (โ‘  ์ด๋ฏธ์ง€ โ†’ ์™ธ๋ชจ, โ‘ก ์Œ์„ฑ โ†’ ์›€์ง์ž„) ๋‘ ๊ฐœ์˜ ๋…๋ฆฝ๋œ ์ž…๋ ฅ์„ ํ•„์š”๋กœ ํ–ˆ์œผ๋ฉฐ, ์ด๋Š” ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ๋น„์šฉ ๊ณผ ์‹œ์Šคํ…œ ๋ณต์žก๋„ ๋ฅผ ํฌ๊ฒŒ ์ฆ๊ฐ€์‹œ์ผฐ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ โ€œ์Œ์„ฑ๋งŒ์œผ๋กœ ๋ชจ๋“  ์ •๋ณด๋ฅผ ์ถ”์ถœโ€ ํ•œ๋‹ค๋Š” ํŒŒ๊ฒฉ์ ์ธ ๊ฐ€์ •์„ ํ†ตํ•ด ์ž…๋ ฅ์„ ๋‹จ์ˆœํ™”ํ•˜๊ณ , ์‹ค์ œ ์„œ๋น„์Šค(์˜ˆ: ๊ฐ€์ƒ ์•„๋ฐ”ํƒ€, ์‹ค์‹œ๊ฐ„ ๋ฐฉ์†ก)์—์„œ์˜ ์ ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ํฌ๊ฒŒ ํ™•๋Œ€ํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์ˆ  | ๋‹จ๊ณ„ | ํ•ต์‹ฌ ๋ชจ๋“ˆ | ์—ญํ•  | ์ฃผ์š” ์žฅ์  | | | | | | | ์Œ์„ฑ

Sensor Calibration Model Balancing Accuracy, Real-time, and Efficiency

Sensor Calibration Model Balancing Accuracy, Real-time, and Efficiency

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ๊ธฐ์กด ํ‰๊ฐ€ ์ฒด๊ณ„์˜ ํ•œ๊ณ„ : ์ •ํ™•๋„ยท์‹ค์‹œ๊ฐ„ยทํšจ์œจ์„ฑ๋งŒ์„ ํ‰๊ฐ€ ์ง€ํ‘œ๋กœ ์‚ผ์•„, ์ˆœ๊ฐ„ ์˜ค์ฐจ(spike error)์™€ ์ตœ์•…โ€‘์ผ€์ด์Šค ์ง€์—ฐ(latency tail) ๊ฐ™์€ ์‹ค์‚ฌ์šฉ ์‹œ ์ค‘์š”ํ•œ ์š”์†Œ๋ฅผ ๊ฐ„๊ณผํ•œ๋‹ค๋Š” ์ ์„ ์ •ํ™•ํžˆ ์งš์—ˆ๋‹ค. 8๊ฐœ์˜ ๋ฏธ์‹œ์  ์š”๊ตฌ์‚ฌํ•ญ : ๋…ผ๋ฌธ์—์„œ ์ œ์‹œํ•œ ๊ตฌ์ฒด์  ์š”๊ตฌ์‚ฌํ•ญ(์˜ˆ: ์ˆœ๊ฐ„ ์˜ค์ฐจ โ‰ค ฮต, 99thโ€‘percentile latency โ‰ค ฯ„, ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ โ‰ค M ๋“ฑ)์€ ์‹ค์ œ ์ž„๋ฒ ๋””๋“œ ์‹œ์Šคํ…œ ์„ค๊ณ„ ์‹œ ํ•„์ˆ˜์ ์ธ ์ œ์•ฝ์กฐ๊ฑด์ด๋ฉฐ, ์ด๋ฅผ ๋ช…์‹œ์ ์œผ๋กœ ์ •์˜ํ•œ ์ ์€ ํฐ ์žฅ์ ์ด๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์ˆ  ๋ถ„์„ | ๋ชจ๋“ˆ |

Model
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Sequential Membership Inference Attacks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋™์  ๋ชจ๋ธ : ์—ฐ์† ํ•™์Šต, ํŒŒ์ธํŠœ๋‹, ์—ฐํ•ฉ ํ•™์Šต ๋“ฑ์—์„œ ๋ชจ๋ธ์€ ์—ฌ๋Ÿฌ ๋ฒ„์ „(์Šค๋ƒ…์ƒท)์„ ์™ธ๋ถ€์— ๊ณต๊ฐœํ•˜๊ฑฐ๋‚˜ API๋ฅผ ํ†ตํ•ด ์ œ๊ณตํ•œ๋‹ค. ๊ธฐ์กด MI ๊ณต๊ฒฉ์€ โ€œ๋งˆ์ง€๋ง‰ ์Šค๋ƒ…์ƒทโ€๋งŒ์„ ๋Œ€์ƒ์œผ๋กœ ํ–ˆ์œผ๋ฉฐ, ์ด๋Š” ๋ฐ์ดํ„ฐ๊ฐ€ ๋ˆ„์ ๋ ์ˆ˜๋ก ๊ฐœ๋ณ„ ์ƒ˜ํ”Œ์˜ ์˜ํ–ฅ๋ ฅ์ด ๊ธ‰๊ฒฉํžˆ ๊ฐ์†Œํ•œ๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ๋ฌธ์ œ ์ •์˜ : ๋ชจ๋ธ ์Šค๋ƒ…์ƒท ์‹œํ€€์Šค ({M 1,dots,M T})๋ฅผ ๊ด€์ฐฐํ•  ๋•Œ, ํŠน์ • ์บ”์–ด๋ฆฌ (z^ )๊ฐ€ ์–ด๋А ์‹œ์  (tau)์— ์‚ฝ์ž…๋๋Š”์ง€๋ฅผ ์ถ”์ •ํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•œ๊ฐ€? ๋˜ํ•œ, ์ด๋ฅผ ํ†ตํ•ด ํ”„๋ผ์ด๋ฒ„์‹œ ๊ฐ์‚ฌ์˜ ํ•˜ํ•œ (varepsilon

Machine Learning Computer Science
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Service Orchestration in the Computing Continuum: Structural Challenges and Vision

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ปดํ“จํŒ… ์—ฐ์†์ฒด(Continuum Computing, CC) ๋Š” IoTยท๋””์ง€ํ„ธ ํŠธ์œˆยทAR ๋“ฑ ์‹ค์‹œ๊ฐ„, ์ €์ง€์—ฐ ์„œ๋น„์Šค๊ฐ€ ์š”๊ตฌ๋˜๋Š” ํ˜„๋Œ€ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์— ํ•„์ˆ˜์ ์ธ ์•„ํ‚คํ…์ฒ˜๋‹ค. ์—ฃ์ง€์™€ ํด๋ผ์šฐ๋“œ๊ฐ€ ํ˜ผ์žฌํ•จ์— ๋”ฐ๋ผ ์ด์งˆ์„ฑ(heterogeneity) , ๋™์  ๊ฐ€์šฉ์„ฑ(dynamic availability) , ๋‹ค๋ฒค๋”ยท๋‹ค๊ด€ํ• ๊ตฌ์—ญ(multivendor & multiโ€‘jurisdiction) ๋ฌธ์ œ๊ฐ€ ์‹ฌํ™”๋œ๋‹ค. ๊ธฐ์กด ์˜ค์ผ€์ŠคํŠธ๋ ˆ์ด์…˜ ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์ •์  ๋ฐฐ์น˜ ยท ์‚ฌ์ „ ๋ฒค์น˜๋งˆํฌ ์— ์˜์กดํ•ด ์™”์œผ๋ฉฐ, ๊ธ‰๋ณ€ํ•˜๋Š” ํ™˜๊ฒฝ์— ๋Œ€ํ•œ ์ ์‘๋ ฅ์ด ๋ถ€์กฑํ•˜๋‹ค. 2

Distributed Computing Computer Science
SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks

SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋ฐ˜๋ณต ์ž‘์—… (quadrotor ๋ ˆ์ด์‹ฑ, ์‚ฌ์กฑ๋ณดํ–‰, ์ˆ˜์ˆ  ๋กœ๋ด‡ ๋“ฑ)์€ ๋งค ์‹œํ–‰๋งˆ๋‹ค ์ˆ˜์ง‘๋œ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•ด ์„ฑ๋Šฅ์„ ์ ์ง„์ ์œผ๋กœ ๊ฐœ์„ ํ•ด์•ผ ํ•จ. ๊ธฐ์กด ILCยทLMPC๋Š” ์„ ํ˜•ยทํ™•์ •์  ์‹œ์Šคํ…œ์— ๊ตญํ•œ๋˜๊ฑฐ๋‚˜, ์„ ํ˜• ์žก์Œ ๋งŒ ๊ณ ๋ คํ•˜๋Š” ๋“ฑ ์ œํ•œ์ ์ด์—ˆ๋‹ค. ํ™•๋ฅ ์  ๋น„์„ ํ˜• ์‹œ์Šคํ…œ ์— ๋Œ€ํ•œ ์•ˆ์ „ ์ œ์–ด๋Š” ์•„์ง ์ถฉ๋ถ„ํžˆ ํ•ด๊ฒฐ๋˜์ง€ ์•Š์•˜์œผ๋ฉฐ, ํŠนํžˆ ์ƒ˜ํ”Œ๋ง ๊ธฐ๋ฐ˜ MPPI ๋Š” ์ œ์•ฝ์„ ์ง์ ‘ ๋‹ค๋ฃจ์ง€ ๋ชปํ•œ๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์กด์žฌํ•œ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๊ธฐ์—ฌ ๋‚ด์šฉ | ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„์  | | | | | | โ‘  | LMPC์™€ MPPI๋ฅผ ๊ฒฐํ•ฉํ•œ ์ œ์•ฝ

Computer Science Robotics Model Learning
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Solving a Million-Step LLM Task with Zero Errors

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๊ธฐ์กด LLM์€ โ€œfewโ€‘shotโ€ ํ˜น์€ โ€œchainโ€‘ofโ€‘thoughtโ€ ์ˆ˜์ค€์—์„œ๋Š” ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์ด์ง€๋งŒ, ๋‹จ๊ณ„ ์ˆ˜๊ฐ€ ์ˆ˜์ฒœยท์ˆ˜๋งŒ์— ๋‹ฌํ•˜๋Š” ์žฅ๊ธฐ ์ž‘์—…์—์„œ๋Š” ์˜ค๋ฅ˜ ๋ˆ„์ ์ด ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€ํ•œ๋‹ค. ์ด๋Š” ์‹ค์ œ ๋น„์ฆˆ๋‹ˆ์Šค ์›Œํฌํ”Œ๋กœ์šฐ, ์ •์ฑ… ์‹œ๋ฎฌ๋ ˆ์ด์…˜, ๋Œ€๊ทœ๋ชจ ๋ฐ์ดํ„ฐ ์ฒ˜๋ฆฌ ๋“ฑ์—์„œ LLM ํ™œ์šฉ์„ ์ œํ•œํ•˜๋Š” ํ•ต์‹ฌ ์žฅ์• ๋ฌผ์ด๋‹ค. ๋…ผ๋ฌธ์€ ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋ฅผ ์ง์ ‘์ ์ธ ์‹คํ—˜(ํƒ€์›Œ ์˜ค๋ธŒ ํ•˜๋…ธ์ด)๊ณผ ์ด๋ก ์  ๋ถ„์„์„ ํ†ตํ•ด ๋ช…ํ™•ํžˆ ์ œ์‹œํ•˜๊ณ , โ€œ์˜ค๋ฅ˜ ์—†๋Š” ์žฅ๊ธฐ ์‹คํ–‰โ€์ด๋ผ๋Š” ๋ชฉํ‘œ๋ฅผ ์„ค์ •ํ•œ๋‹ค๋Š” ์ ์—์„œ ์—ฐ๊ตฌ ๋™๊ธฐ๊ฐ€ ์„ค๋“๋ ฅ ์žˆ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ ๊ทน๋‹จ์ 

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SpotIt: Evaluating Text-to-SQL Evaluation with Formal Verification

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํ…์ŠคํŠธโ€‘ํˆฌโ€‘SQL ๋Š” ์ž์—ฐ์–ด ์งˆ์˜๋ฅผ ๊ด€๊ณ„ํ˜• ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ์งˆ์˜(SQL)๋กœ ๋ณ€ํ™˜ํ•˜๋Š” ํ•ต์‹ฌ ๊ธฐ์ˆ ์ด๋ฉฐ, ์ตœ๊ทผ LLM ๊ธฐ๋ฐ˜ ๋ชจ๋ธ๋“ค์˜ ๊ธ‰๊ฒฉํ•œ ๋ฐœ์ „์œผ๋กœ ํ™œ๋ฐœํžˆ ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ํ‰๊ฐ€ ์‹ ๋ขฐ์„ฑ ์€ ๋ชจ๋ธ ๊ฐœ๋ฐœ ๋ฐฉํ–ฅ์„ ๊ฒฐ์ •์ง“๋Š” ์ค‘์š”ํ•œ ์š”์†Œ์ธ๋ฐ, ๊ธฐ์กด ํ‰๊ฐ€ ๋ฐฉ์‹์€ ์ •์  ํ…Œ์ŠคํŠธ์…‹ ์—๋งŒ ์˜์กดํ•œ๋‹ค. ์ด๋Š” โ€œ์šฐ์—ฐํžˆ ๋™์ผํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋‚ด๋Š”โ€ ์˜ค๋ฅ˜(semantic equivalence vs. syntactic equivalence)๋ฅผ ๊ฐ„๊ณผํ•˜๊ฒŒ ๋งŒ๋“ ๋‹ค. ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋Š” ๋ฒ ์ด์Šค๋ผ์ธ ๊ณผ๋Œ€ํ‰๊ฐ€ , ๋ชจ๋ธ ๋น„๊ต์˜ ๋ถˆ๊ณต์ •์„ฑ , ๊ทธ๋ฆฌ๊ณ  ์‹ค์ œ ์„œ๋น„์Šค ์ ์šฉ ์‹œ

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Stackelberg Dynamic Location Planning under Cumulative Demand

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ 1. ๋™์  ์‹œ์„ค ์œ„์น˜์™€ ๋ˆ„์  ์ˆ˜์š”์˜ ๊ฒฐํ•ฉ ๊ธฐ์กด ๋™์  FLP๋Š” ๊ฐ ๊ธฐ๊ฐ„์˜ ์ˆ˜์š”๋ฅผ ๋…๋ฆฝ์ ์œผ๋กœ ๊ฐ€์ •ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์˜๋ฅ˜ยท๊ฐ€์ „ยทํ•ธ๋“œ๋ฉ”์ด๋“œ ๋“ฑ ๋น„์†Œ๋ชจ์„ฑ ์ œํ’ˆ์€ ๋ฏธ์ถฉ์กฑ ์ˆ˜์š”๊ฐ€ ๋ˆ„์ ๋ผ ์ฐจํ›„์— ํฐ ์ˆ˜์ต ๊ธฐํšŒ๋ฅผ ๋งŒ๋“ ๋‹ค. ๋ˆ„์  ์ˆ˜์š”( CCD )๋ฅผ ๊ณ ๋ คํ•˜๋ฉด ์‹œ๊ฐ„์  ์—ฐ๊ณ„์„ฑ ์ด ๊ฐ•ํ™”๋˜์–ด, ์‹œ์„ค์„ ์–ธ์ œยท์–ด๋””์— ๋ฐฐ์น˜ํ• ์ง€๊ฐ€ ์žฅ๊ธฐ์ ์ธ ์ˆ˜์ต์— ์ง์ ‘์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. 2. ๊ฒฝ์Ÿ ์ƒํ™ฉ์˜ ๋„์ž… ๋…์  ๊ฐ€์ •์€ ๊ฒฝ์Ÿ์ž๊ฐ€ ์กด์žฌํ•  ๊ฒฝ์šฐ ์‹ค์ œ ์ด์ต์„ ํฌ๊ฒŒ ๊ณผ๋Œ€ํ‰๊ฐ€ํ•œ๋‹ค. ์Šคํƒ์—˜๋ฒ„๊ทธ์‹ ๋ฆฌ๋”โ€‘ํŒ”๋กœ์›Œ ๊ตฌ์กฐ๋Š” ํ˜„์‹ค์ ์ธ โ€œ์„ ์ œ์ โ€ ์˜์‚ฌ๊ฒฐ์ •์„ ๋ฐ˜์˜ํ•œ๋‹ค(๋ฆฌ๋”๋Š” ํŒ”๋กœ์›Œ์˜

Mathematics
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Standardization of Psychiatric Diagnoses -- Role of Fine-tuned LLM Consortium and OpenAI-gpt-oss Reasoning LLM Enabled Decision Support System

1. ์—ฐ๊ตฌ์˜ ์ฃผ์š” ๊ธฐ์—ฌ | ๊ตฌ๋ถ„ | ๋‚ด์šฉ | ์˜์˜ | | | | | | ๋งž์ถคํ˜• LLM ์ปจ์†Œ์‹œ์—„ | ์ •์‹ ๊ณผโ€‘ํ™˜์ž ๋Œ€ํ™” ์ฝ”ํผ์Šค๋ฅผ ํ™œ์šฉํ•ด 3๊ฐœ์˜ LLM์„ ๊ฐœ๋ณ„ fineโ€‘tuning | ๋‹จ์ผ ๋ชจ๋ธ์ด ๋†“์น  ์ˆ˜ ์žˆ๋Š” ๋ฏธ๋ฌ˜ํ•œ ์ฆ์ƒยท๋งฅ๋ฝ์„ ๋‹ค๊ฐ๋„๋กœ ํฌ์ฐฉ | | ํ•ฉ์˜ ๊ธฐ๋ฐ˜ ์ง‘๊ณ„ + ์ถ”๋ก  LLM | ๋‹ค์ˆ˜๊ฒฐ/๊ฐ€์ค‘ ํ‰๊ท  ๋ฐฉ์‹์œผ๋กœ ์ดˆ๊ธฐ ์˜ˆ์ธก์„ ๋งŒ๋“  ๋’ค, OpenAIโ€‘gptโ€‘oss๊ฐ€ ๋…ผ๋ฆฌ์  ๊ฒ€์ฆยท๋ณด์™„ | ์ดˆ๊ธฐ ์˜ˆ์ธก์˜ ๋ถˆํ™•์‹ค์„ฑ์„ ๊ฐ์†Œ์‹œํ‚ค๊ณ , ์ถ”๋ก  ๋‹จ๊ณ„์—์„œ โ€œ์™œ ๊ทธ๋Ÿฐ ์ง„๋‹จ์ธ๊ฐ€?โ€๋ฅผ ์„ค๋ช… ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•จ | | LLM ์—์ด์ „ํŠธ ์˜ค์ผ€์ŠคํŠธ๋ ˆ์ด์…˜ | ์ปจ์†Œ์‹œ์—„๊ณผ ์ถ”๋ก  LL

System
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STAR-VAE: Latent Variable Transformers for Scalable and Controllable Molecular Generation

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ถ„์ž ์ƒ์„ฑ ์€ ์‹ ์•ฝ ๊ฐœ๋ฐœ, ์žฌ๋ฃŒ ์„ค๊ณ„ ๋“ฑ์—์„œ ํ•ต์‹ฌ์ ์ธ ์—ญํ• ์„ ํ•œ๋‹ค. ๊ธฐ์กด์˜ ๊ทธ๋ž˜ํ”„ ๊ธฐ๋ฐ˜ ์ƒ์„ฑ ๋ชจ๋ธ์ด๋‚˜ RNN/GAN ๊ธฐ๋ฐ˜ ์ ‘๊ทผ๋ฒ•์€ ์Šค์ผ€์ผ๋ง ํ•œ๊ณ„ ์™€ ์†์„ฑ ์ œ์–ด ์˜ ์–ด๋ ค์›€์„ ๊ฒช๋Š”๋‹ค. ํŠธ๋žœ์Šคํฌ๋จธ ๋Š” ์‹œํ€€์Šค ๋ชจ๋ธ๋ง์—์„œ ๋›ฐ์–ด๋‚œ ๋ณ‘๋ ฌ ์ฒ˜๋ฆฌ์™€ ์žฅ๊ธฐ ์˜์กด์„ฑ ํ•™์Šต ๋Šฅ๋ ฅ์„ ๋ณด์ด๋ฉฐ, ์ตœ๊ทผ ํ™”ํ•™ ๋ถ„์•ผ์—์„œ๋„ SMILES ๋ฌธ์ž์—ด์„ ๋‹ค๋ฃจ๋Š” ๋ฐ ์„ฑ๊ณตํ•˜๊ณ  ์žˆ๋‹ค. VAE ๋Š” ์ž ์žฌ ๊ณต๊ฐ„์„ ๋ช…์‹œ์ ์œผ๋กœ ์ œ๊ณตํ•ด ์—ฐ์†์ ์ธ ํƒ์ƒ‰ ๊ณผ ์†์„ฑ ์กฐ์ ˆ ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜์ง€๋งŒ, ์ „ํ†ต์ ์ธ VAE๋Š” ์ธ์ฝ”๋”ยท๋””์ฝ”๋”๊ฐ€ ๊ฐ๊ฐ ๋…๋ฆฝ์ ์ธ ๊ตฌ์กฐ๋ผ ๋ณต์žกํ•œ ํ™”ํ•™ ๊ทœ์น™์„ ์ถฉ๋ถ„ํžˆ

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Structured Analytic Mappings for Point Set Registration

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋น„๊ฐ•์ฒด ์ •ํ•ฉ ์€ ๋งค๋„๋Ÿฌ์šด ๋ณ€ํ˜•์„ ํ‘œํ˜„ํ•˜๊ธฐ ์œ„ํ•ด ๊ณ ์ฐจ์› ํŒŒ๋ผ๋ฏธํ„ฐ(์˜ˆ: RBF, GMM) ํ˜น์€ ๋ณต์žกํ•œ ์ •๊ทœํ™”๊ฐ€ ํ•„์š”ํ•ด ๊ณ„์‚ฐ๋Ÿ‰ยท๊ณผ์ ํ•ฉ ์œ„ํ—˜์ด ํฌ๋‹ค. ๋‹ค๋ณ€๋Ÿ‰ ํ…Œ์ผ๋Ÿฌ ์ „๊ฐœ ๋Š” ๋งค๋„๋Ÿฌ์šด ํ•จ์ˆ˜๋“ค์„ ๊ตฌ์กฐํ™”๋œ ๋‹คํ•ญ์‹์œผ๋กœ ๊ทผ์‚ฌํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ณ ์ „์  ์ด๋ก ์„ ์ œ๊ณตํ•œ๋‹ค. ๊ธฐ์กด ๋น„์ „ยท์ปดํ“จํ„ฐ ๊ทธ๋ž˜ํ”„ ๋ถ„์•ผ์—์„œ๋Š” ๊ฑฐ์˜ ํ™œ์šฉ๋˜์ง€ ์•Š์•˜์œผ๋ฉฐ, ์ด๋ฅผ ์ •ํ•ฉ์— ์ ์šฉํ•œ ์‹œ๋„๋Š” ๋“œ๋ฌผ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด | ์š”์†Œ | ๋‚ด์šฉ | ์˜์˜ | | | | | | ๊ตฌ์กฐํ™”๋œ ํ•จ์ˆ˜ ๊ณต๊ฐ„ | ์ฐจ์ˆ˜โ€‘์ œํ•œ(์˜ˆ: 2์ฐจ) ํ…Œ์ผ๋Ÿฌ ๊ธฐ์ € โŸจ monomials โŸฉ ๋กœ ์ €์ฐจ์› ์„ ํ˜•

Image Processing Electrical Engineering and Systems Science
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Synthetic-Powered Multiple Testing with FDR Control

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ๋‹ค์ค‘ ๊ฐ€์„ค ๊ฒ€์ • ์€ ์ˆ˜์ฒœยท์ˆ˜๋ฐฑ๋งŒ ๊ฐœ์˜ ๊ฐ€์„ค์„ ๋™์‹œ์— ํ‰๊ฐ€ํ•ด์•ผ ํ•˜๋Š” ํ˜„๋Œ€ ๊ณผํ•™์—์„œ ํ•„์ˆ˜์ ์ด๋ฉฐ, FDR ์ œ์–ด๋Š” ๊ฒ€์ •๋ ฅ๊ณผ ์˜ค๋ฅ˜ ์ œ์–ด ์‚ฌ์ด์˜ ์ตœ์  ๊ท ํ˜•์„ ์ œ๊ณตํ•œ๋‹ค. ๋ฐ์ดํ„ฐ ๋ถ€์กฑ : ์‹ค์ œ ์‹คํ—˜ ์ƒ˜ํ”Œ์ด ์ œํ•œ์ ์ด๋ฉด pโ€‘๊ฐ’์ด ๋ณด์ˆ˜์ ์œผ๋กœ ๋‚˜์˜ค๊ธฐ ์‰ฌ์›Œ ๊ฒ€์ •๋ ฅ์ด ์ €ํ•˜๋œ๋‹ค. ํ•ฉ์„ฑ ๋ฐ์ดํ„ฐ์˜ ๋“ฑ์žฅ : ์ƒ์„ฑ ๋ชจ๋ธ(GAN, VAE ๋“ฑ)์ด๋‚˜ ๊ณผ๊ฑฐ ์‹คํ—˜ ๋ฐ์ดํ„ฐ๋Š” ์–‘์ด ํ’๋ถ€ํ•˜์ง€๋งŒ, ๋ถ„ํฌ๊ฐ€ ์‹ค์ œ์™€ ๋‹ค๋ฅผ ์ˆ˜ ์žˆ์–ด ์ง์ ‘ ๊ฒฐํ•ฉํ•˜๋ฉด FDR์ด ํญ๋ฐœํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ Guarded Syntheticโ€‘Powered pโ€‘๊ฐ’ ๋‘ ๊ฐœ์˜ pโ€‘๊ฐ’

Statistics
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Taught by the Flawed: How Dataset Insecurity Breeds Vulnerable AI Code

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฐ์ดํ„ฐ์…‹ ๋ณด์•ˆ์˜ ์ค‘์š”์„ฑ : ์ตœ๊ทผ LLM(๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ) ๊ธฐ๋ฐ˜ ์ฝ”๋“œ ์ƒ์„ฑ ๋„๊ตฌ๊ฐ€ ๊ธ‰์ฆํ•˜๋ฉด์„œ, ํ•™์Šต์— ์‚ฌ์šฉ๋˜๋Š” ๋ฐ์ดํ„ฐ์…‹์˜ ํ’ˆ์งˆ๊ณผ ๋ณด์•ˆ์ด ์ง์ ‘์ ์œผ๋กœ ๋ชจ๋ธ์˜ ์‹ ๋ขฐ์„ฑ์— ์—ฐ๊ฒฐ๋œ๋‹ค. ์ทจ์•ฝ์  ์ „์ด ํ˜„์ƒ : ์•…์„ฑ ์ฝ”๋“œ, ์˜คํƒ€, ๋น„ํ‘œ์ค€ ๊ตฌํ˜„ ๋“ฑ์ด ํฌํ•จ๋œ ๋ฐ์ดํ„ฐ๋Š” ๋ชจ๋ธ์ด ์ด๋ฅผ โ€˜์ •์ƒ์ ์ธ ํŒจํ„ดโ€™์œผ๋กœ ํ•™์Šตํ•˜๊ฒŒ ํ•˜์—ฌ, ์‚ฌ์šฉ์ž๊ฐ€ ์˜๋„์น˜ ์•Š์€ ๋ณด์•ˆ ๊ฒฐํ•จ์„ ํฌํ•จํ•œ ์ฝ”๋“œ๋ฅผ ์ƒ์„ฑํ•˜๊ฒŒ ๋งŒ๋“ ๋‹ค. 2. ํ•ต์‹ฌ ๊ฐ€์„ค ๋ฐ ์—ฐ๊ตฌ ์งˆ๋ฌธ | ๊ฐ€์„ค | ์งˆ๋ฌธ | | | | | H1 : ๋ฐ์ดํ„ฐ์…‹์— ์กด์žฌํ•˜๋Š” ๋ณด์•ˆ ๊ฒฐํ•จ์€ AI ์ฝ”๋“œ ์ƒ์„ฑ ๋ชจ๋ธ์˜ ์ถœ๋ ฅ์— ์ง์ ‘์ ์ธ

Data
The geometry of online conversations and the causal antecedents of conflictual discourse

The geometry of online conversations and the causal antecedents of conflictual discourse

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ œ๊ธฐ ์˜จ๋ผ์ธ ํ”Œ๋žซํผ์ด ์ •์น˜ยท๊ณผํ•™์  ๋…ผ์Ÿ์˜ ํ•ต์‹ฌ ์žฅ์œผ๋กœ ๋ถ€์ƒํ–ˆ์ง€๋งŒ, ๊ฐˆ๋“ฑโ€‘ํญ๋ฐœ ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ๋Œ€ํ•œ ์ดํ•ด๊ฐ€ ๋ถ€์กฑํ•จ. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ด์Šˆ ๊ธฐ๋ฐ˜ ๋ถˆ๋™์˜ ์™€ ๋Œ€์ธ ๊ฐˆ๋“ฑ ์„ ๊ตฌ๋ถ„ํ–ˆ์œผ๋ฉฐ, ์–ด์กฐยท์ •์„œยท์˜ˆ์ ˆ์ด ๊ฐˆ๋“ฑ ์ „๊ฐœ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๊ฐ•์กฐํ–ˆ์ง€๋งŒ, ์‹œ๊ฐ„ยท๊ตฌ์กฐ์  ๋งฅ๋ฝ ์„ ๋™์‹œ์— ๊ณ ๋ คํ•œ ์‹ค์ฆ์  ์ฆ๊ฑฐ๋Š” ์ œํ•œ์ ์ด์—ˆ๋‹ค. 2. ๋ฐ์ดํ„ฐ ๋ฐ ์ฃผ์„ ๋ฐฉ๋ฒ• Internet Argument Corpus ์—์„œ ๊ธฐํ›„๋ณ€ํ™” ๊ด€๋ จ ์Šค๋ ˆ๋“œ ์ถ”์ถœ, ํŠธ๋ฆฌ ๊ตฌ์กฐ(๋ถ€๋ชจโ€‘์ž์‹โ€‘ํ˜•์ œ ๊ด€๊ณ„)๋กœ ๋ชจ๋ธ๋ง. LLM(๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ)์„ ํ™œ์šฉํ•ด ํ”„๋กฌํ”„ํŠธ ๊ธฐ๋ฐ˜ ์ž๋™ ์ฃผ์„ ์„ ์ˆ˜ํ–‰ํ•˜๊ณ 

Computer Science Social Networks
The lingering phenomenon and pattern formation in a nonlocal population model with cognitive map

The lingering phenomenon and pattern formation in a nonlocal population model with cognitive map

1. ๋ชจ๋ธ๋ง์  ํ˜์‹  ๋น„๊ตญ์†Œ ์ธ์ง€ ์ง€๋„ ๋ฅผ ๋„์ž…ํ•จ์œผ๋กœ์จ ๊ฐœ์ฒด๊ฐ€ ์‹œ์ ์ด ์•„๋‹Œ ์˜์—ญ ์ „์ฒด ๋ฅผ ์ธ์‹ํ•œ๋‹ค๋Š” ์ƒ๋ฌผํ•™์  ์‚ฌ์‹ค์„ ์ˆ˜ํ•™์ ์œผ๋กœ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ฃผ๋กœ ๋น„๊ตญ์†Œ advection term์— ์˜์กดํ–ˆ์œผ๋‚˜, ๋ณธ ๋…ผ๋ฌธ์€ ํ™•์‚ฐ ๊ณ„์ˆ˜ ฮณ(m) ์— ์ธ์ง€ ์ •๋ณด๋ฅผ ์ง์ ‘ ์—ฐ๊ฒฐํ•ด Fokkerโ€‘Planckโ€‘type diffusion ์„ ์‚ฌ์šฉํ•œ๋‹ค๋Š” ์ ์ด ์ฐจ๋ณ„์ ์ด๋‹ค. ์ •๊ทœํ™” vs ๋น„์ •๊ทœํ™” ์ปค๋„ ๋น„๊ต๋ฅผ ํ†ตํ•ด ๊ฒฝ๊ณ„ ํšจ๊ณผ์™€ ์ธ์ง€ ์ •๋ณด์˜ ์™œ๊ณก์„ ๋ช…ํ™•ํžˆ ๊ตฌ๋ถ„ํ•˜์˜€๋‹ค. ํŠนํžˆ, ์ •๊ทœํ™”๋œ ์ปค๋„์€ ๊ฒฝ๊ณ„ ๊ทผ์ฒ˜์—์„œ๋„ ํ‰๊ท  ์ •๋ณด๋ฅผ ์œ ์ง€ํ•ด ์‹ค์ œ ํ™˜๊ฒฝ์—์„œ์˜ โ€œ๊ฐ๊ฐ ์ œํ•œโ€ ์„ ๋ณด๋‹ค

Model Mathematics
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The Strong Lottery Ticket Hypothesis for Multi-Head Attention Mechanisms

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ SLTH ๋Š” โ€œํฐ ๋„คํŠธ์›Œํฌ ์•ˆ์— ์ž‘์€ ๊ณ ์„ฑ๋Šฅ ์„œ๋ธŒ๋„คํŠธ์›Œํฌ๊ฐ€ ์ด๋ฏธ ์กด์žฌํ•œ๋‹คโ€๋Š” ๊ฐ•๋ ฅํ•œ ๊ฐ€์„ค๋กœ, ๋„คํŠธ์›Œํฌ ํ”„๋ฃจ๋‹ยทํšจ์œจ์ ์ธ ํ•™์Šต์— ์ค‘์š”ํ•œ ์ด๋ก ์  ํ† ๋Œ€๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ํŠธ๋žœ์Šคํฌ๋จธ๋Š” ๋ฉ€ํ‹ฐโ€‘ํ—ค๋“œ ์–ดํ…์…˜(MHA) ๊ณผ ํ”ผ๋“œโ€‘ํฌ์›Œ๋“œ ๋ ˆ์ด์–ด ๋กœ ๊ตฌ์„ฑ๋˜๋ฉฐ, ํ˜„์žฌ๊นŒ์ง€ SLTH๋Š” ์ฃผ๋กœ MLP, CNN, ๊ฐ„๋‹จํ•œ ํ”ผ๋“œโ€‘ํฌ์›Œ๋“œ ๊ตฌ์กฐ์— ๋Œ€ํ•ด ์ฆ๋ช…๋ผ ์™”๋‹ค. MHA๋Š” ํ‚คยท์ฟผ๋ฆฌยท๋ฐธ๋ฅ˜ ๋ฅผ ๊ฐ๊ฐ ์—ฌ๋Ÿฌ ํ—ค๋“œ์— ๊ฑธ์ณ ๋ณ‘๋ ฌ ์—ฐ์‚ฐํ•˜๋Š” ๋ณตํ•ฉ ๊ตฌ์กฐ์ด๊ธฐ์—, ๊ธฐ์กด SLTH ์ฆ๋ช… ๊ธฐ๋ฒ•์„ ๊ทธ๋Œ€๋กœ ์ ์šฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋”ฐ๋ผ์„œ MHA์— ๋Œ€ํ•œ SLTH ์ฆ๋ช…์€ ํŠธ๋žœ์Šคํฌ๋จธ ์ „๋ฐ˜์—

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The Underappreciated Power of Vision Models for Graph Structural Understanding

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๊ทธ๋ž˜ํ”„ ๊ตฌ์กฐ ์ดํ•ด ๋Š” ์†Œ์…œ ๋„คํŠธ์›Œํฌ, ํ™”ํ•™ ๋ถ„์ž, ์ง€์‹ ๊ทธ๋ž˜ํ”„ ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ํ•ต์‹ฌ ๋ฌธ์ œ์ด๋ฉฐ, ์ „ํ†ต์ ์œผ๋กœ ๊ทธ๋ž˜ํ”„ ์‹ ๊ฒฝ๋ง(GNN)์ด๋‚˜ ์ „ํ†ต์ ์ธ ๊ทธ๋ž˜ํ”„ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์˜์กดํ•ด ์™”์Šต๋‹ˆ๋‹ค. ์‹œ๊ฐ ๋ชจ๋ธ(Vision Models) , ํŠนํžˆ ๋Œ€๊ทœ๋ชจ ์‚ฌ์ „ ํ•™์Šต๋œ ๋น„์ „ ํŠธ๋žœ์Šคํฌ๋จธ(ViT)์™€ ๊ฐ™์€ ๊ตฌ์กฐ๋Š” ์ด๋ฏธ์ง€์™€ ๋น„์ฃผ์–ผ ๋ฐ์ดํ„ฐ๋ฅผ ์ฒ˜๋ฆฌํ•˜๋Š” ๋ฐ ๋›ฐ์–ด๋‚œ ํ‘œํ˜„๋ ฅ์„ ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ตœ๊ทผ์—๋Š” ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ ํ•™์Šต์„ ํ†ตํ•ด ๋น„์ „ ๋ชจ๋ธ์ด ํ…์ŠคํŠธ, ์˜ค๋””์˜ค ๋“ฑ ๋‹ค๋ฅธ ๋„๋ฉ”์ธ์—๋„ ์ ์šฉ๋˜๋Š” ์‚ฌ๋ก€๊ฐ€ ๋Š˜์–ด๋‚˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด (์ œ๋ชฉ์œผ๋กœ ์ถ”์ •)

Model
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Toward Automated and Trustworthy Scientific Analysis and Visualization with LLM-Generated Code

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฐ์ดํ„ฐโ€‘์ง‘์•ฝ์  ๊ณผํ•™ : ์ฒœ๋ฌธํ•™, ์ƒ๋ฌผ์ •๋ณดํ•™, ์žฌ๋ฃŒ๊ณผํ•™ ๋“ฑ์—์„œ ํ…Œ๋ผ๋ฐ”์ดํŠธ ๊ทœ๋ชจ์˜ ๋ฐ์ดํ„ฐ๊ฐ€ ์ผ์ƒํ™”๋จ. ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๊ฒฉ์ฐจ : ๋„๋ฉ”์ธ ์ „๋ฌธ๊ฐ€๊ฐ€ ์ง์ ‘ ์ฝ”๋”ฉํ•˜๊ธฐ์—” ์‹œ๊ฐ„ยทํ•™์Šต ๋น„์šฉ์ด ํฌ๊ณ , ์ „ํ†ต์ ์ธ ์†Œํ”„ํŠธ์›จ์–ด ์—”์ง€๋‹ˆ์–ด๋ง ํŒ€์— ์˜์กดํ•˜๋ฉด ์—ฐ๊ตฌ ์†๋„๊ฐ€ ์ €ํ•˜๋จ. LLM์˜ ์ž ์žฌ๋ ฅ : ์ž์—ฐ์–ด โ†’ ์ฝ”๋“œ ๋ณ€ํ™˜ ๋Šฅ๋ ฅ์€ ๋น„์ „๋ฌธ๊ฐ€์—๊ฒŒ๋„ โ€œ์ฝ”๋“œ ์ž‘์„ฑ์„ ์•„์›ƒ์†Œ์‹ฑโ€ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธธ์„ ์—ด์–ด์ค€๋‹ค. 2. ํ•ต์‹ฌ ์—ฐ๊ตฌ ์งˆ๋ฌธ 1. ์˜คํ”ˆโ€‘์†Œ์Šค LLM์ด ๊ณผํ•™ ๋ถ„์„ยท์‹œ๊ฐํ™” ์ฝ”๋“œ๋ฅผ ์ž๋™ ์ƒ์„ฑํ•  ๋•Œ ์‹ ๋ขฐ์„ฑ์€ ์–ด๋А ์ •๋„์ธ๊ฐ€? 2. ์‹ค์ œ ์—ฐ๊ตฌ ํ™˜๊ฒฝ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์˜ค๋ฅ˜ ์›

Analysis
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Towards Closed-Loop Embodied Empathy Evolution: Probing LLM-Centric Lifelong Empathic Motion Generation in Unseen Scenarios

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๊ฐ์„ฑ ๋ชจ์…˜ ์ƒ์„ฑ ์€ ์ธ๊ฐ„โ€‘์ปดํ“จํ„ฐ ์ƒํ˜ธ์ž‘์šฉ, ๊ฐ€์ƒ ์บ๋ฆญํ„ฐ, ๋กœ๋ด‡ ๋™์ž‘ ๋“ฑ์—์„œ ํ•ต์‹ฌ ๊ธฐ์ˆ ์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ๊ณ ์ •๋œ ๋ฐ์ดํ„ฐ์…‹(์˜ˆ: CMU Mocap, Human3.6M) ์— ์˜์กดํ•ด ์„ฑ๋Šฅ์„ ์ตœ์ ํ™”ํ–ˆ์œผ๋ฉฐ, ์Šคํฌ์ธ ยท๋Œ„์Šคยท์ „ํˆฌ ๋“ฑ ๋ณตํ•ฉ์ ์ด๊ณ  ๊ทœ๋ชจ๊ฐ€ ํฐ ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋Œ€ํ•œ ํ™•์žฅ์„ฑ์ด ๋ถ€์กฑํ–ˆ๋‹ค. LLM ์€ ์ž์—ฐ์–ด ์ดํ•ดยท์ƒ์„ฑ์—์„œ ๋›ฐ์–ด๋‚œ ์ผ๋ฐ˜ํ™” ๋Šฅ๋ ฅ์„ ๋ณด์ด์ง€๋งŒ, ์—ฐ์† ํ•™์Šต(continual learning) ๋ฐ ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ(๊ฐ์ •ยท๋™์ž‘) ์—ฐ๊ณ„ ์—์„œ๋Š” ์•„์ง ๋ฏธ๋น„์ ์ด ๋งŽ๋‹ค. Lยฒโ€‘EMG๋Š” ์ด๋Ÿฌํ•œ ๊ฒฉ์ฐจ๋ฅผ ๋ฉ”์šฐ๊ณ , LLM์„ ๊ฐ์„ฑโ€‘๋™์ž‘ ์ง€์‹์˜

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Tree-Cotree-Based IETI-DP for Eddy Current Problems in Time-Domain

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ €์ฃผํŒŒ ์ „์ž๊ธฐ ํ•ด์„์—์„œ ํŒŒ๋™ ์ „ํŒŒ๋ฅผ ๋ฌด์‹œํ•˜๊ณ  ์™€์ „๋ฅ˜ ๋ฐฉ์ •์‹์œผ๋กœ ๋‹จ์ˆœํ™”ํ•˜๋Š” ๊ฒƒ์ด ์ผ๋ฐ˜์ ์ด๋ฉฐ, ์ด๋Š” ๊ตฌ์กฐ๋ฌผ ๋‚ด๋ถ€์˜ ์ „๋ฅ˜ ํ๋ฆ„๊ณผ ์—ด ๋ฐœ์ƒ์„ ์˜ˆ์ธกํ•˜๋Š” ๋ฐ ์ถฉ๋ถ„ํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋น„์„ ํ˜• ์žฌ๋ฃŒ ํŠน์„ฑ(์˜ˆ: ํžˆ์Šคํ…Œ๋ฆฌ์‹œ์Šค)์ด๋‚˜ ๊ธ‰๊ฒฉํ•œ ์ „์› ์ธ๊ฐ€์™€ ๊ฐ™์€ ๊ณผ๋„ ํ˜„์ƒ์„ ๋‹ค๋ฃจ๋ ค๋ฉด ์‹œ๊ฐ„ ์˜์—ญ ํ•ด์„์ด ํ•„์ˆ˜์ด๋ฉฐ, ์ „ํ†ต์ ์ธ FEMโ€‘timeโ€‘stepping ๋ฐฉ์‹์€ ๋ฉ”๋ชจ๋ฆฌ์™€ CPU ์‚ฌ์šฉ๋Ÿ‰์ด ๊ธ‰์ฆํ•œ๋‹ค. 2. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•(IETIโ€‘DP) ๊ฐœ์š” IETI (Iterative Substructuring with Interface Transmission

Understanding Hardness of Vision-Language Compositionality from A Token-level Causal Lens

Understanding Hardness of Vision-Language Compositionality from A Token-level Causal Lens

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ CLIP์˜ ์„ฑ๊ณต๊ณผ ํ•œ๊ณ„ : CLIP์€ ๋Œ€๊ทœ๋ชจ ์ด๋ฏธ์ง€โ€‘ํ…์ŠคํŠธ ์Œ์„ ์ด์šฉํ•œ ๋Œ€์กฐ ํ•™์Šต์œผ๋กœ ๊ฐ•๋ ฅํ•œ ์ผ๋ฐ˜ํ™”๋ฅผ ๋ณด์—ฌ์ฃผ์ง€๋งŒ, โ€œ๊ณ ์–‘์ด์™€ ๋นจ๊ฐ„ ๋ชจ์ž๋ฅผ ๋™์‹œ์— ์ž…์€ ์‚ฌ์ง„โ€๊ณผ ๊ฐ™์€ ๋ณตํ•ฉ์ ์ธ ์˜๋ฏธ๋ฅผ ์ •ํ™•ํžˆ ํŒŒ์•…ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์ด๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ๊ฐ€ ํ…์ŠคํŠธ๋ฅผ ๋‹จ์ผ ๋ฒกํ„ฐ ๋กœ ์ทจ๊ธ‰ํ•ด ํ† ํฐ ๊ฐ„ ๊ตฌ์กฐ์  ์ •๋ณด๋ฅผ ๋ฌด์‹œํ–ˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ธ๊ณผ๊ด€๊ณ„ ๊ด€์ ์˜ ํ•„์š”์„ฑ : ์ธ๊ณผ ๋ชจ๋ธ(SCM)์€ ๊ด€์ธก ๋ณ€์ˆ˜์™€ ์ž ์žฌ ๋ณ€์ˆ˜ ์‚ฌ์ด์˜ ๊ตฌ์กฐ๋ฅผ ๋ช…์‹œ์ ์œผ๋กœ ํ‘œํ˜„ํ•œ๋‹ค. ํ…์ŠคํŠธ๋ฅผ ํ† ํฐ ๋‹จ์œ„๋กœ ๋ถ„ํ•ดํ•˜๋ฉด, ๊ฐ ํ† ํฐ์ด ์ž ์žฌ ๋ณ€์ˆ˜์— ๋ฏธ์น˜๋Š” ์ธ๊ณผ ํšจ๊ณผ ๋ฅผ ๋”ฐ๋กœ ์ถ”์ ํ•  ์ˆ˜ ์žˆ๋‹ค. 2

Uniform error bounds for quantized dynamical models

Uniform error bounds for quantized dynamical models

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์‹œ์Šคํ…œ ์‹๋ณ„ โ†” ํ•™์Šต ์ด๋ก  ์ด๋ผ๋Š” ๋‘ ๋ถ„์•ผ๋ฅผ ์—ฐ๊ฒฐํ•ด, ์ข…์† ๋ฐ์ดํ„ฐ (์‹œ๊ฐ„์— ๋”ฐ๋ผ ์—ฐ์†์ ์œผ๋กœ ์ˆ˜์ง‘๋˜๋Š” ๊ด€์ธก์น˜)์—์„œ๋„ ๊ณ ํ™•๋ฅ  ์˜ค๋ฅ˜ ํ•œ๊ณ„ ๋ฅผ ์–ป๋Š” ๊ฒƒ์ด ํ•ต์‹ฌ ๊ณผ์ œ์ด๋‹ค. ๊ธฐ์กด ์ ‘๊ทผ๋ฒ•์€ ํฌ๊ฒŒ ๋‘ ๊ฐˆ๋ž˜๋กœ ๋‚˜๋‰œ๋‹ค. 1. Mixing ๊ธฐ๋ฐ˜ : ฮฒโ€‘mixing, ฮธโ€‘mixing ๋“ฑ ์˜์กด์„ฑ์„ ๊ณ„๋Ÿ‰ํ™”ํ•˜๊ณ , Rademacher ๋ณต์žก๋„ยท์ •๋ณดโ€‘๋‹ค์ด๋ฒ„์ „์Šค ๋“ฑ ๋ณต์žก๋„ ์ธก์ •๊ฐ’์„ ์‚ฌ์šฉํ•ด ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๋…๋ฆฝ์ ์ธ ์ผ๋ฐ˜ํ™” ๊ฒฝ๊ณ„๋ฅผ ๋งŒ๋“ ๋‹ค. ํ•˜์ง€๋งŒ ๋ณต์žก๋„ ๊ณ„์‚ฐ์ด ๊นŒ๋‹ค๋กญ๊ณ , ์ƒ์ˆ˜๊ฐ€ ๋ณด์ˆ˜์ ์ด๋‹ค. 2. ์•Œ๊ณ ๋ฆฌ์ฆ˜โ€‘ํŠนํ™” : ํŠน์ • ์ถ”์ •๊ธฐ(์˜ˆ: ์ตœ์†Œ์ œ๊ณฑ, ์ž๊ธฐํšŒ๊ท€)

Machine Learning Computer Science Model
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Universal Fine-Grained Symmetry Inference and Enforcement for Rigorous Crystal Structure Prediction

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ CSP์˜ ๋‚œ์ œ : ์กฐ์„ฑ โ†’ 3D ์›์ž ๋ฐฐ์—ด์ด๋ผ๋Š” ๋งคํ•‘์€ ๊ณ ์ฐจ์› ํƒ์ƒ‰ ๊ณต๊ฐ„๊ณผ ์—„๊ฒฉํ•œ ๋Œ€์นญ ์ œ์•ฝ ๋•Œ๋ฌธ์— ๊ธฐ์กด ML ์ ‘๊ทผ๋ฒ•์ด ์‰ฝ๊ฒŒ ์œ„๋ฐฐํ•œ๋‹ค. ๊ธฐ์กด ๋ฐฉ๋ฒ•์˜ ํ•œ๊ณ„ : ๊ณต๊ฐ„๊ตฐ ๋ผ๋ฒจ๋งŒ ์กฐ๊ฑดํ™” ํ•˜๊ฑฐ๋‚˜ Wyckoff ํ…œํ”Œ๋ฆฟ์„ ์ž ์žฌ๊ณต๊ฐ„์— ์ธ์ฝ”๋”ฉ ํ•˜๋Š” ๋ฐฉ์‹์€ ๋Œ€์นญ์„ soft ํ•˜๊ฒŒ๋งŒ ๋‹ค๋ฃจ์–ด ์‹ค์ œ ์ขŒํ‘œ๊ฐ€ ์œ„๋ฐฐ๋  ์œ„ํ—˜์ด ์žˆ๋‹ค. ํ…œํ”Œ๋ฆฟ ๊ฒ€์ƒ‰ ๊ธฐ๋ฐ˜ (DiffCSP++ยทCSPML)์€ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์— ์กด์žฌํ•˜๋Š” ๋Œ€์นญ ํŒจํ„ด ์—๋งŒ ์˜์กดํ•ด, ์ง„์ •ํ•œ ์‹ ์†Œ์žฌ ํƒ์ƒ‰์„ ์ œํ•œํ•œ๋‹ค. 2. ์ œ์•ˆ ๋ฐฉ๋ฒ•์˜ ํ•ต์‹ฌ ๊ตฌ์„ฑ | ๋‹จ๊ณ„ | ์„ค๋ช… | ๊ธฐ์ˆ ์  ํฌ์ธํŠธ | | |

Condensed Matter
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Validation of KESTREL EMT for Industrial Capacitor Switching Transient Studies

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์‚ฐ์—… ํ˜„์žฅ์˜ ์ „๋ ฅ ํ’ˆ์งˆ ๋ฌธ์ œ : VFDยท์ „๋ ฅ์ „์ž ๋ถ€ํ•˜๊ฐ€ ๊ธ‰์ฆํ•˜๋ฉด์„œ ์ „์••ยท์ „๋ฅ˜์˜ ์„œ๋ธŒ์‚ฌ์ดํด ๋ณ€๋™์„ ํŒŒ์•…ํ•˜๊ธฐ ์–ด๋ ค์›Œ์กŒ๋‹ค. ์ƒ์šฉ EMT ํˆด์˜ ๋น„์šฉ ์žฅ๋ฒฝ : PSCAD/EMTDC, EMTPโ€‘RV, ETAP eMT ๋“ฑ์€ ์ขŒ์„๋‹น 10 kโ€“50 k USD์˜ ๋น„์šฉ์„ ์š”๊ตฌ, ์ด๋Š” ํŠนํžˆ ๊ฐœ๋ฐœ๋„์ƒ๊ตญยท์ค‘์†Œ ์ปจ์„คํŒ… ์—…์ฒด์— ํฐ ๋ถ€๋‹ด. ์˜คํ”ˆโ€‘์†Œ์Šค ๋Œ€์•ˆ์˜ ํ•œ๊ณ„ : ๊ธฐ์กด ์˜คํ”ˆโ€‘์†Œ์Šค ํ”„๋กœ์ ํŠธ(ParaEMT, PowerSimulationsDynamics.jl)๋Š” ์ฃผ๋กœ ์†ก์ „โ€‘๋ ˆ๋ฒจ ์—ฐ๊ตฌ์— ์ดˆ์ ์ด ๋งž์ถฐ์ ธ ์žˆ์–ด, ์‚ฐ์—…์šฉ ์ €์••ยท์ค‘์•• ์ปคํŒจ์‹œํ„ฐ ์Šค์œ„์นญ

Electrical Engineering and Systems Science
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Variational inference via radial transport

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ „ํ†ต์  VI์˜ ํ•œ๊ณ„ : ๊ฐ€์šฐ์‹œ์•ˆ(์ „์—ญยท๋Œ€๊ฐ) ํ˜น์€ ๋ผํ”Œ๋ผ์Šค ๊ทผ์‚ฌ๋Š” ํ‰๊ท ยท๊ณต๋ถ„์‚ฐ ์ •๋„๋งŒ ๋งž์ถ”๊ณ , ๋ฐ˜๊ฒฝ(๊ฑฐ๋ฆฌ) ๋ถ„ํฌ๋Š” ๊ณ ์ •๋œ ํ˜•ํƒœ(์˜ˆ: exp(โˆ’rยฒ/2))์— ๋จธ๋ฌธ๋‹ค. ์ด๋Š” ํŠนํžˆ ๋‹ค์ค‘๋ชจ๋“œยท๋น„๋Œ€์นญยท๋‘๊บผ์šด ๊ผฌ๋ฆฌ ๋ฅผ ๊ฐ€์ง„ ์‚ฌํ›„๋ถ„ํฌ์—์„œ ์‹ฌ๊ฐํ•œ ์˜ค์ฐจ๋ฅผ ๋งŒ๋“ ๋‹ค. ๋ฐฉ์‚ฌํ˜• ๋Œ€์นญ์„ฑ : ฯ€๊ฐ€ ์ค‘์‹ฌํ™”(whitening)๋œ ๋’ค์—๋„ ๋ฐ˜๊ฒฝ ํ•จ์ˆ˜ h(r) ๋งŒ์œผ๋กœ ๋‹ค์–‘ํ•œ ๋ถ„ํฌ(๊ฐ€์šฐ์‹œ์•ˆ, Studentโ€‘t, ๋ผํ”Œ๋ผ์Šค, ๋กœ์ง€์Šคํ‹ฑ ๋“ฑ)๋ฅผ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์„ ์ด์šฉํ•œ๋‹ค. ์ด๋Š” โ€œ๋ถ„ํฌ์˜ ๋ฐฉ์‚ฌํ˜• ํ”„๋กœํŒŒ์ผ ์„ ์ง์ ‘ ํ•™์Šตํ•œ๋‹คโ€๋Š” ์ƒˆ๋กœ์šด ์„ค๊ณ„ ์ฒ ํ•™์ด๋‹ค.

Machine Learning Computer Science
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WAR-Re: Web API Recommendation with Semantic Reasoning

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ API ํญ์ฆ : ํด๋ผ์šฐ๋“œ ์„œ๋น„์Šค์™€ ๋งˆ์ดํฌ๋กœ์„œ๋น„์Šค ์•„ํ‚คํ…์ฒ˜์˜ ํ™•์‚ฐ์œผ๋กœ ์ˆ˜์ฒœ ๊ฐœ์˜ ๊ณต๊ฐœ API๊ฐ€ ์กด์žฌ, mashup ๊ฐœ๋ฐœ์ž๋Š” ์ ์ ˆํ•œ API๋ฅผ ๋น ๋ฅด๊ฒŒ ์ฐพ์•„์•ผ ํ•จ. ๊ธฐ์กด ํ•œ๊ณ„ : 1. ๊ณ ์ • Topโ€‘N : ๋Œ€๋ถ€๋ถ„์˜ ์ถ”์ฒœ ์‹œ์Šคํ…œ์€ โ€œ๊ฐ€์žฅ ์ข‹์€ N๊ฐœโ€๋งŒ ๋ฐ˜ํ™˜, ํ•˜์ง€๋งŒ ์‹ค์ œ mashup์€ 1๊ฐœ๋ถ€ํ„ฐ 10๊ฐœ ์ด์ƒ๊นŒ์ง€ ๋‹ค์–‘ํ•œ ์ˆ˜์˜ API๊ฐ€ ํ•„์š”. 2. ์„ค๋ช… ๋ถ€์žฌ : ์‚ฌ์šฉ์ž๋Š” โ€œ์™œ ์ด API๊ฐ€ ์„ ํƒ๋๋Š”๊ฐ€โ€๋ฅผ ์•Œ ์ˆ˜ ์—†์–ด, ์‹ ๋ขฐ์„ฑยท์ฑ„ํƒ๋ฅ ์ด ๋‚ฎ์•„์ง. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ WARโ€‘Re | ์š”์†Œ | ์„ค๋ช… | ๊ธฐ์—ฌ | | | | |

Wasm: A Pipeline for Constructing Structured Arabic Interleaved Multimodal Corpora

Wasm: A Pipeline for Constructing Structured Arabic Interleaved Multimodal Corpora

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์•„๋ž์–ด ๋‹ค์ค‘๋ชจ๋‹ฌ ๋ฐ์ดํ„ฐ ๋ถ€์กฑ : ํ˜„์žฌ ๊ณต๊ฐœ๋œ ๋‹ค์ค‘๋ชจ๋‹ฌ ์ฝ”ํผ์Šค๋Š” ์˜์–ดยท์ค‘๊ตญ์–ด ์ค‘์‹ฌ์ด๋ฉฐ, ์•„๋ž์–ด๋Š” ํ…์ŠคํŠธ์™€ ์ด๋ฏธ์ง€ยท์˜ค๋””์˜ค๊ฐ€ ๋™์‹œ์— ์ œ๊ณต๋˜๋Š” ๋ฐ์ดํ„ฐ๊ฐ€ ๊ฑฐ์˜ ์—†๋‹ค. ์ด๋Š” ์•„๋ž์–ด ๊ธฐ๋ฐ˜ NLPยท๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ ๋ชจ๋ธ ๊ฐœ๋ฐœ์„ ํฌ๊ฒŒ ์ œํ•œํ•œ๋‹ค. RTL ํŠน์„ฑ : ์˜ค๋ฅธ์ชฝโ€‘์™ผ์ชฝ ์Šคํฌ๋ฆฝํŠธ์™€ ๋ณตํ•ฉ ํ˜•ํƒœ์†Œ ๊ตฌ์กฐ๋Š” ๊ธฐ์กด ํŒŒ์ดํ”„๋ผ์ธ์„ ๊ทธ๋Œ€๋กœ ์ ์šฉํ•  ๊ฒฝ์šฐ ํ† ํฐํ™” ์˜ค๋ฅ˜, ์ •๋ ฌ ๋ถˆ์ผ์น˜ ๋“ฑ ์‹ฌ๊ฐํ•œ ํ’ˆ์งˆ ์ €ํ•˜๋ฅผ ์ดˆ๋ž˜ํ•œ๋‹ค. 2. Wasm ํŒŒ์ดํ”„๋ผ์ธ ๊ตฌ์กฐ | ๋‹จ๊ณ„ | ์ฃผ์š” ๊ธฐ๋Šฅ | ๊ธฐ์ˆ ยท๋„๊ตฌ | ์•„๋ž์–ด ํŠนํ™” ์š”์†Œ | | | | | | | ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ | ์›น

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Who Can We Trust? Scope-Aware Video Moment Retrieval with Multi-Agent Conflict

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ VMR์˜ ์‹ค์šฉ์„ฑ : ํ…์ŠคํŠธ ๊ธฐ๋ฐ˜ ๊ฒ€์ƒ‰์€ ์˜์ƒ ํŽธ์ง‘, ์š”์•ฝ, ๊ฒ€์ƒ‰ ์—”์ง„ ๋“ฑ์— ํ•„์ˆ˜์ ์ด๋ฉฐ, โ€œ์–ด๋””์— ๊ทธ ์žฅ๋ฉด์ด ์žˆ์ง€?โ€ ๋ผ๋Š” ์งˆ๋ฌธ์„ ์ž๋™ํ™”ํ•œ๋‹ค. ๊ธฐ์กด ํ•œ๊ณ„ : ํ˜„์žฌ ๋Œ€๋ถ€๋ถ„์˜ VMR ๋ชจ๋ธ์€ ๋‹จ์ผ ๋ชจ๋ธ ํ˜น์€ ์•™์ƒ๋ธ” ํ˜•ํƒœ๋กœ ๋™์ž‘ํ•˜์ง€๋งŒ, ์„œ๋กœ ๋‹ค๋ฅธ ๋ชจ๋ธ์ด ์ œ์‹œํ•˜๋Š” ์‹œ์ž‘ยท๋ ๊ฒฝ๊ณ„๊ฐ€ ์„œ๋กœ ์ถฉ๋Œ ํ•˜๋Š” ์ƒํ™ฉ์„ ๋ช…์‹œ์ ์œผ๋กœ ๋‹ค๋ฃจ์ง€ ์•Š๋Š”๋‹ค. ์ด๋Š” ํŠนํžˆ ๋‹ค์–‘ํ•œ ์‹œ๊ฐ์ ยท์–ธ์–ด์  ํŠน์„ฑ์„ ๊ฐ€์ง„ ๋ณตํ•ฉ ์งˆ์˜ ์—์„œ ์„ฑ๋Šฅ ์ €ํ•˜๋ฅผ ์ดˆ๋ž˜ํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด | ์š”์†Œ | ์„ค๋ช… | ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„์  | | | | | | ๊ฐ•ํ™”ํ•™์Šต ๊ธฐ๋ฐ˜ ๋‹จ์ผ

๊ทธ๋ฆฌ๋”” ์ตœ์ ํ™”์™€ ADM ๊ธฐ๋ฐ˜ ๋ฐ์ดํ„ฐ ๊ตฌ๋™ ์†”๋ฒ„๋ฅผ ๊ฒฐํ•ฉํ•œ ๋น„์„ ํ˜• ๊ตฌ์กฐ ํ•ด์„ ์ „๋žต

๊ทธ๋ฆฌ๋”” ์ตœ์ ํ™”์™€ ADM ๊ธฐ๋ฐ˜ ๋ฐ์ดํ„ฐ ๊ตฌ๋™ ์†”๋ฒ„๋ฅผ ๊ฒฐํ•ฉํ•œ ๋น„์„ ํ˜• ๊ตฌ์กฐ ํ•ด์„ ์ „๋žต

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋ฐ์ดํ„ฐโ€‘๊ตฌ๋™ ๊ณ„์‚ฐ์—ญํ•™(DDCM) ์€ ์‹คํ—˜ ๋ฐ์ดํ„ฐ(์‘๋ ฅโ€‘๋ณ€ํ˜•๋ฅ  ์Œ)๋ฅผ ์ง์ ‘ ํ™œ์šฉํ•ด ๋ฌผ์„ฑ ๋ชจ๋ธ๋ง ์—†์ด ๊ตฌ์กฐ ํ•ด์„์„ ์ˆ˜ํ–‰ํ•œ๋‹ค๋Š” ์ ์—์„œ ๊ธฐ์กด์˜ ๊ฒฝํ—˜์  ๋ชจ๋ธ๋ง ๋ฐฉ์‹๊ณผ ์ฐจ๋ณ„ํ™”๋œ๋‹ค. ๊ธฐ์กด DDCM ์†”๋ฒ„๋Š” ADM ๊ธฐ๋ฐ˜ ๋ฐ˜๋ณต ์ ˆ์ฐจ๋ฅผ ์‚ฌ์šฉํ•˜์ง€๋งŒ, ์ „์—ญ ์ตœ์ ํ•ด ๋ณด์žฅ์€ ์–ด๋ ค์šฐ๋ฉฐ ํŠนํžˆ ํฌ์†Œยท๋น„๋Œ€์นญยท๋…ธ์ด์ฆˆ ๋ฐ์ดํ„ฐ ์—์„œ ์ง€์—ญ ์ตœ์†Œ์ ์— ๋จธ๋ฌผ ์œ„ํ—˜์ด ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ๊ทธ๋ฆฌ๋”” ์ตœ์ ํ™” ๋ฅผ ๋„์ž…, ๋ฐ์ดํ„ฐ์…‹ ๋‚ด ํ›„๋ณด ์Œ์„ ํƒ์ƒ‰ํ•˜๋ฉด์„œ ๋ชฉ์ ํ•จ์ˆ˜ ๊ฐ’์„ ์ง€์†์ ์œผ๋กœ ๊ฐ์†Œ์‹œํ‚ค๋Š” ์ „๋žต์„ ์ œ์•ˆํ•œ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ๊ธฐ์กด

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2501.12992

1. ํ˜„ ์ƒํ™ฉ ํŒŒ์•… ํ˜„์žฌ ๋…ผ๋ฌธ์˜ ํ•ต์‹ฌ ๋‚ด์šฉ(์ œ๋ชฉ, ์ดˆ๋ก, ๋ณธ๋ฌธ) ๋ชจ๋‘๊ฐ€ ๋ˆ„๋ฝ๋œ ์ƒํƒœ์ด๋ฏ€๋กœ, ๊ตฌ์ฒด์ ์ธ ํ•™์ˆ ์  ๊ธฐ์—ฌ๋ฅผ ํ‰๊ฐ€ํ•˜๊ฑฐ๋‚˜ ์—ฐ๊ตฌ ํ๋ฆ„์„ ์ถ”์ ํ•  ์ˆ˜ ์—†์Šต๋‹ˆ๋‹ค. arXiv ์‹๋ณ„์ž(2501.12992)๋งŒ์œผ๋กœ๋Š” ํ•ด๋‹น ๋…ผ๋ฌธ์ด ์–ด๋А ๋ถ„์•ผ(์˜ˆ: ๋ฌผ๋ฆฌ, ์ปดํ“จํ„ฐ ๊ณผํ•™, ์ˆ˜ํ•™ ๋“ฑ)์— ์†ํ•˜๋Š”์ง€ ์•Œ ์ˆ˜ ์—†์œผ๋ฉฐ, ์ด๋Š” ๋ถ„์•ผ๋ณ„ ์„ ํ–‰ ์—ฐ๊ตฌ์™€์˜ ๋น„๊ตยท๋Œ€์กฐ๋ฅผ ๋ฐฉํ•ดํ•ฉ๋‹ˆ๋‹ค. 2. ๊ฐ€๋Šฅํ•œ ์ถ”๋ก  arXiv ๋ฒˆํ˜ธ โ€œ2501โ€์€ 2025๋…„ 1์›”์— ์ œ์ถœ๋œ ๋…ผ๋ฌธ์ž„์„ ์˜๋ฏธํ•ฉ๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ตœ์‹  ์—ฐ๊ตฌ ๋™ํ–ฅ์„ ๋ฐ˜์˜ํ•˜๊ณ  ์žˆ์„ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์Šต๋‹ˆ๋‹ค. ์ตœ๊ทผ 2025๋…„ ์ดˆ์— ํ™œ๋ฐœํžˆ ๋ฐœํ‘œ๋œ ๋ถ„

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2501.14199

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์‹œ์  ์ถ”์ • : arXiv ์‹๋ณ„์ž โ€œ2501โ€์€ 2025๋…„ 1์›”์— ์ œ์ถœ๋œ ๋…ผ๋ฌธ์ž„์„ ์˜๋ฏธํ•ฉ๋‹ˆ๋‹ค. ์ด ์‹œ์ ์€ ์ธ๊ณต์ง€๋Šฅ, ์–‘์ž์ปดํ“จํŒ…, ๋ฐ”์ด์˜คํ—ฌ์Šค, ์ง€์†๊ฐ€๋Šฅ ์—๋„ˆ์ง€ ๋“ฑ ์—ฌ๋Ÿฌ ๋ถ„์•ผ์—์„œ ๊ธ‰๊ฒฉํ•œ ๊ธฐ์ˆ  ํ˜์‹ ์ด ์ง„ํ–‰ ์ค‘์ด๋˜ ์‹œ๊ธฐ์ž…๋‹ˆ๋‹ค. ๊ฐ€๋Šฅ์„ฑ ๋†’์€ ์—ฐ๊ตฌ ๋ถ„์•ผ : ์ธ๊ณต์ง€๋Šฅ : ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ(Large Language Model, LLM)์˜ ํšจ์œจ์„ฑ ํ–ฅ์ƒ, ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ ํ•™์Šต, ํ˜น์€ AI ์•ˆ์ „์„ฑ ์—ฐ๊ตฌ. ์–‘์ž์ •๋ณด : ์–‘์ž ์˜ค๋ฅ˜ ์ •์ •, ์–‘์ž ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์‹ค์šฉํ™”, ํ˜น์€ ์–‘์ž ๋„คํŠธ์›Œํฌ ํ”„๋กœํ† ์ฝœ. ๋ฐ”์ด์˜คํ—ฌ์Šค : ๋‹จ์ผ์„ธํฌ ์ „์‚ฌ์ฒด ๋ถ„์„, CRIS

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2503.22057

โ€ป ๋…ผ๋ฌธ ๋ณธ๋ฌธ(์„œ๋ก , ๋ฐฉ๋ฒ•, ์‹คํ—˜, ๊ฒฐ๊ณผ, ํ† ์˜ ๋“ฑ)์ด ์ „ํ˜€ ์ œ๊ณต๋˜์ง€ ์•Š์•„ ์‹ฌ์ธต ๋ถ„์„์ด ๋ถˆ๊ฐ€๋Šฅํ•ฉ๋‹ˆ๋‹ค. ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์ •๋ณด๋ฅผ ์ถ”๊ฐ€๋กœ ์ œ๊ณตํ•ด ์ฃผ์‹œ๋ฉด ๋ณด๋‹ค ๊ตฌ์ฒด์ ์ธ ๋ถ„์„์ด ๊ฐ€๋Šฅํ•ฉ๋‹ˆ๋‹ค. | ํ•„์š” ์ •๋ณด | ์„ค๋ช… | | | | | ์ „์ฒด ํ…์ŠคํŠธ | ์„œ๋ก , ๊ด€๋ จ ์—ฐ๊ตฌ, ๋ฐฉ๋ฒ•๋ก , ์‹คํ—˜ ์„ค๊ณ„, ๊ฒฐ๊ณผ, ํ† ์˜, ๊ฒฐ๋ก  ๋“ฑ | | ํ‚ค์›Œ๋“œ | ์—ฐ๊ตฌ ๋ถ„์•ผ(์˜ˆ: ๋ฌผ๋ฆฌํ•™, ์ปดํ“จํ„ฐ ๊ณผํ•™, ์ƒ๋ฌผํ•™ ๋“ฑ)์™€ ํ•ต์‹ฌ ์šฉ์–ด | | ํ‘œยท๊ทธ๋ฆผ | ์‹คํ—˜ ๋ฐ์ดํ„ฐ, ๋ชจ๋ธ ๊ตฌ์กฐ, ๊ฒฐ๊ณผ ์‹œ๊ฐํ™” ๋“ฑ | | ์ฐธ๊ณ ๋ฌธํ—Œ | ์ธ์šฉ๋œ ์ฃผ์š” ๋…ผ๋ฌธ ๋ฐ ๋ฐฐ๊ฒฝ ์ง€์‹ | | ์ €์ž ์ •๋ณด | ์†Œ์†, ์—ฐ๊ตฌ ๊ทธ๋ฃน,

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2504.11884

1. ํ˜„ ๋‹จ๊ณ„์—์„œ ๊ฐ€๋Šฅํ•œ ๋ถ„์„ ์ œ๋ชฉ๋งŒ์œผ๋กœ ์ถ”์ • ๊ฐ€๋Šฅํ•œ ๋ถ„์•ผ : arXiv ์‹๋ณ„์ž โ€œ2504.11884โ€๋Š” 2025๋…„ 4์›”์— ์ œ์ถœ๋œ ๋…ผ๋ฌธ์„ ์˜๋ฏธํ•ฉ๋‹ˆ๋‹ค. ์ด ์‹œ์ ์˜ arXiv ์ฃผ์š” ์นดํ…Œ๊ณ ๋ฆฌ๋Š” ๋ฌผ๋ฆฌํ•™(ํŠนํžˆ ๊ณ ์—๋„ˆ์ง€ยท์ฒœ์ฒด๋ฌผ๋ฆฌ), ์ˆ˜ํ•™, ์ปดํ“จํ„ฐ ๊ณผํ•™(์ธ๊ณต์ง€๋Šฅยท๋จธ์‹ ๋Ÿฌ๋‹), ํ†ต๊ณ„ยท๊ณ„๋Ÿ‰๊ฒฝ์ œํ•™ ๋“ฑ์ž…๋‹ˆ๋‹ค. ๊ฐ€๋Šฅ์„ฑ ๋†’์€ ์ฃผ์ œ : ์ตœ๊ทผ 2024โ€‘2025๋…„ ์‚ฌ์ด์— ํ™œ๋ฐœํžˆ ๋ฐœํ‘œ๋œ ํŠธ๋ Œ๋“œ(์˜ˆ: ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ, ์–‘์ž ์ปดํ“จํŒ…, ๊ทธ๋ž˜ํ”„ ์‹ ๊ฒฝ๋ง, ๊ณ ์ฐจ์› ๋ฐ์ดํ„ฐ ๋ถ„์„ ๋“ฑ)๋ฅผ ๊ณ ๋ คํ•˜๋ฉด, ํ•ด๋‹น ๋…ผ๋ฌธ๋„ ์œ„ ๋ถ„์•ผ ์ค‘ ํ•˜๋‚˜์ผ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์Šต๋‹ˆ๋‹ค. 2. ๋ถ„์„์„ ์ง„ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด

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2505.02472

1. ๋ฉ”ํƒ€๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ์ถ”์ • arXiv ์‹๋ณ„์ž `2505.02472`๋Š” 2025๋…„ 5์›”์— ์ œ์ถœ๋œ ๋…ผ๋ฌธ์ž„์„ ์˜๋ฏธํ•ฉ๋‹ˆ๋‹ค. ์ด ์‹œ์ (2025โ€‘2026๋…„)์—๋Š” ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM) , ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ ํ•™์Šต , ์–‘์ž ์ปดํ“จํŒ… , ์ƒ๋ช…๊ณตํ•™ AI ๋“ฑ ๋‹ค์–‘ํ•œ ์ตœ์ฒจ๋‹จ ๋ถ„์•ผ๊ฐ€ ํ™œ๋ฐœํžˆ ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ํ•ด๋‹น ๋ฒˆํ˜ธ์˜ ๋…ผ๋ฌธ๋„ ์œ„ ์ฃผ์ œ ์ค‘ ํ•˜๋‚˜์ผ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์Šต๋‹ˆ๋‹ค. 2. ๊ฐ€๋Šฅ์„ฑ ์žˆ๋Š” ์—ฐ๊ตฌ ์ฃผ์ œ AI ์•ˆ์ „ ๋ฐ ์ •๋ ฌ : ์ตœ๊ทผ ๋Œ€ํ˜• ๋ชจ๋ธ์˜ ์‚ฌํšŒ์ ยท์œค๋ฆฌ์  ์œ„ํ—˜์„ฑ์„ ๋‹ค๋ฃจ๋Š” ๋…ผ๋ฌธ์ด ๊ธ‰์ฆํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํšจ์œจ์ ์ธ ํ•™์Šต ์•Œ๊ณ ๋ฆฌ์ฆ˜ : ํŒŒ๋ผ๋ฏธํ„ฐ ํšจ์œจ์„ฑ์„ ๋†’์ด๊ธฐ ์œ„ํ•œ ์ŠคํŒŒ์Šค

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2506.13191

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋‹ค์ค‘โ€‘๋ชจ๋‹ฌ ํ•™์Šต์˜ ํ•„์š”์„ฑ : ์ตœ๊ทผ AI ์„œ๋น„์Šค๋Š” ํ…์ŠคํŠธยท์ด๋ฏธ์ง€ยท์Œ์„ฑยท๋น„๋””์˜ค ๋“ฑ ๋‹ค์–‘ํ•œ ํ˜•ํƒœ์˜ ๋ฐ์ดํ„ฐ๋ฅผ ๋™์‹œ์— ์ฒ˜๋ฆฌํ•ด์•ผ ํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋Š˜์–ด๋‚˜๊ณ  ์žˆ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ๋‘ ๋ชจ๋‹ฌ (์˜ˆ: ์ด๋ฏธ์ง€โ€‘ํ…์ŠคํŠธ) ํ˜น์€ ๋‹จ์ผโ€‘๋ชจ๋‹ฌ ์— ์ง‘์ค‘ํ–ˆ์œผ๋ฉฐ, ๋ชจ๋‹ฌ๋ฆฌํ‹ฐ๊ฐ€ ๋Š˜์–ด๋‚ ์ˆ˜๋ก ํ‘œํ˜„ ๋ถˆ์ผ์น˜ ์™€ ์—ฐ์‚ฐ ๋น„์šฉ ์ด ๊ธ‰์ฆํ•œ๋‹ค. ํ†ตํ•ฉ ์ž ์žฌ ๊ณต๊ฐ„ ์— ๋Œ€ํ•œ ํƒ๊ตฌ๋Š” ์•„์ง ์ดˆ๊ธฐ ๋‹จ๊ณ„์ด๋ฉฐ, ํŠนํžˆ ๊ณ„์ธต์ ยท๋™์  ์œตํ•ฉ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๋„์ž…ํ•œ ์‚ฌ๋ก€๋Š” ๋“œ๋ฌผ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ๋ฒ• | ๊ธฐ๋ฒ• | ์„ค๋ช… | ๊ธฐ๋Œ€ ํšจ๊ณผ | | | | | | ๋ชจ๋‹ฌ๋ฆฌํ‹ฐโ€‘๋ณ„ ์–ดํ…์…˜ ์œตํ•ฉ (Modalit

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2507.16470

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ arXiv ์‹๋ณ„์ž โ€œ2507.16470โ€์€ 2025๋…„ 7์›”์— ์ œ์ถœ๋œ ๋…ผ๋ฌธ์ž„์„ ์˜๋ฏธํ•ฉ๋‹ˆ๋‹ค. ์ด ์‹œ์ ์€ ์ธ๊ณต์ง€๋Šฅ, ์–‘์ž์ปดํ“จํŒ…, ๋ฐ”์ด์˜คํ—ฌ์Šค, ์ง€์†๊ฐ€๋Šฅ ์—๋„ˆ์ง€ ๋“ฑ ์—ฌ๋Ÿฌ ์ฒจ๋‹จ ๋ถ„์•ผ๊ฐ€ ๊ธ‰๊ฒฉํžˆ ๋ฐœ์ „ํ•˜๊ณ  ์žˆ๋˜ ์‹œ๊ธฐ์ด๋ฏ€๋กœ, ํ•ด๋‹น ๋…ผ๋ฌธ๋„ ์ด๋Ÿฌํ•œ ์ตœ์‹  ํŠธ๋ Œ๋“œ ์ค‘ ํ•˜๋‚˜์™€ ์—ฐ๊ด€๋  ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์Šต๋‹ˆ๋‹ค. 2. ๊ฐ€๋Šฅ์„ฑ ์žˆ๋Š” ์—ฐ๊ตฌ ๋ถ„์•ผ ์ธ๊ณต์ง€๋Šฅยท๋จธ์‹ ๋Ÿฌ๋‹ : ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ(LLM)๊ณผ ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ ํ•™์Šต์ด ํ™œ๋ฐœํžˆ ์ง„ํ–‰ ์ค‘์ด๋ฉฐ, ํšจ์œจ์ ์ธ ํŒŒ๋ผ๋ฏธํ„ฐ ์••์ถ•ยท์ง€์‹ ์ฆ๋ฅ˜, ํ”„๋กฌํ”„ํŠธ ์—”์ง€๋‹ˆ์–ด๋ง ๋“ฑ์— ๊ด€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ๋งŽ์ด ๋ฐœํ‘œ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ์–‘์ž์ •๋ณด๊ณผํ•™ : ์–‘์ž ์˜ค

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2508.03837

๋ถ„์„ ์ œํ•œ ๋ฐ ์ผ๋ฐ˜์  ๋…ผ๋ฌธ ํ‰๊ฐ€ ํ”„๋ ˆ์ž„์›Œํฌ ์ œ์‹œ 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ผ๋ฐ˜์ ์œผ๋กœ ๋…ผ๋ฌธ์€ ํ˜„์žฌ ํ•™๋ฌธยท์‚ฐ์—… ๋ถ„์•ผ์—์„œ ํ•ด๊ฒฐ๋˜์ง€ ์•Š์€ ๋ฌธ์ œ๋ฅผ ์ œ์‹œํ•˜๊ณ , ๊ทธ ๋ฌธ์ œ์˜ ์ค‘์š”์„ฑ์„ ๊ฐ•์กฐํ•ฉ๋‹ˆ๋‹ค. ํ•ด๋‹น ๋…ผ๋ฌธ์˜ ๋ถ„์•ผ(์˜ˆ: ๋ฌผ๋ฆฌ, ์ปดํ“จํ„ฐ ๊ณผํ•™, ์ƒ๋ช…๊ณผํ•™ ๋“ฑ)๋ฅผ ์•Œ ์ˆ˜ ์—†์œผ๋ฏ€๋กœ, ์ผ๋ฐ˜์ ์ธ ๋™๊ธฐ ๋ถ€์—ฌ ์š”์†Œ๋ฅผ ์ถ”์ •ํ•  ์ˆ˜ ์—†์Šต๋‹ˆ๋‹ค. 2. ๋ชฉํ‘œ ๋ฐ ์—ฐ๊ตฌ ์งˆ๋ฌธ ๋ช…ํ™•ํ•œ ๋ชฉํ‘œ์™€ ๊ตฌ์ฒด์ ์ธ ์—ฐ๊ตฌ ์งˆ๋ฌธ์ด ์ œ์‹œ๋˜์–ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. ์ดˆ๋ก์ด ์—†์œผ๋ฏ€๋กœ, ์—ฐ๊ตฌ์ž๊ฐ€ ์–ด๋–ค ๊ฐ€์„ค์„ ๊ฒ€์ฆํ•˜๋ ค ํ–ˆ๋Š”์ง€, ํ˜น์€ ์–ด๋–ค ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์•ˆํ–ˆ๋Š”์ง€ ํŒŒ์•…์ด ๋ถˆ๊ฐ€๋Šฅํ•ฉ๋‹ˆ๋‹ค. 3. ๋ฐฉ๋ฒ•๋ก  ์‹คํ—˜ยท์‹œ๋ฎฌ๋ ˆ์ด์…˜ยท์ด๋ก 

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