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DynaFix: Iterative Automated Program Repair Driven by Execution-Level Dynamic Information

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์ •์ โ€‘์ค‘์‹ฌ APR์˜ ํ•œ๊ณ„ : ๊ธฐ์กด LLM ๊ธฐ๋ฐ˜ APR์€ ์†Œ์Šค ์ฝ”๋“œ, ์ฃผ์„, ํ…Œ์ŠคํŠธ ๊ฒฐ๊ณผ ๋“ฑ ์ •์  ์ •๋ณด๋ฅผ ์ฃผ๋กœ ์‚ฌ์šฉํ•œ๋‹ค. ์ด๋Š” ๋ฒ„๊ทธ ์›์ธ์ด ์‹คํ–‰ ์‹œ์ ์˜ ์ƒํƒœ์— ํฌ๊ฒŒ ์˜์กดํ•˜๋Š” ๊ฒฝ์šฐ(์˜ˆ: ๋ณ€์ˆ˜ ๊ฐ’, ์ œ์–ด ํ๋ฆ„) ํƒ์ƒ‰ ๊ณต๊ฐ„์„ ๊ธ‰๊ฒฉํžˆ ํ™•๋Œ€ํ•œ๋‹ค. ๋™์  ์ •๋ณด ํ™œ์šฉ์˜ ๋ถ€์žฌ : ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ๋™์  ์ •๋ณด๋ฅผ ํ›ˆ๋ จ ๋‹จ๊ณ„ ์—๋งŒ ์‚ฝ์ž…ํ•˜๊ฑฐ๋‚˜ ํ•œ ๋ฒˆ ํ”„๋กฌํ”„ํŠธ์— ํฌํ•จ์‹œ์ผœ, ๋ฐ˜๋ณต์ ์ธ ์ˆ˜๋ฆฌ ๊ณผ์ •์—์„œ ์žฌํ™œ์šฉํ•˜์ง€ ์•Š๋Š”๋‹ค. ์ด๋Š” ์ธ๊ฐ„ ๊ฐœ๋ฐœ์ž๊ฐ€ โ€œ์‹คํ–‰ โ†’ ๊ด€์ฐฐ โ†’ ์ˆ˜์ •โ€์„ ๋ฐ˜๋ณตํ•˜๋Š” ๋””๋ฒ„๊น… ํ”„๋กœ์„ธ์Šค์™€ ๊ดด๋ฆฌ๋œ๋‹ค. ํ”ผ๋“œ๋ฐฑ์˜ ๊ฑฐ์นœ ์ˆ˜์ค€ : ํ˜„์žฌ ๋ฐ˜๋ณตํ˜•

Software Engineering Computer Science
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Improving Multi-step RAG with Hypergraph-based Memory for Long-Context Complex Relational Modeling

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ๋‹ค๋‹จ๊ณ„ RAG์˜ ํ•„์š”์„ฑ : ๋‹จ์ผ ๋‹จ๊ณ„ ๊ฒ€์ƒ‰โ€‘์ƒ์„ฑ์€ ๊ธด ๋ฌธ์„œ๋‚˜ ๋ณตํ•ฉ ์งˆ์˜์— ๋Œ€ํ•ด ์ถฉ๋ถ„ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ์ ์ด ์ตœ๊ทผ ์—ฐ๊ตฌ(Trivedi 2023, Shao 2023 ๋“ฑ)์—์„œ ๊ฐ•์กฐ๋˜๊ณ  ์žˆ๋‹ค. ๊ธฐ์กด ์ž‘์—… ๋ฉ”๋ชจ๋ฆฌ์˜ ํ•œ๊ณ„ : ๋Œ€๋ถ€๋ถ„์˜ ๋ฉ”๋ชจ๋ฆฌ ์„ค๊ณ„๋Š” ํ…์ŠคํŠธ ์š”์•ฝ ํ˜น์€ ๊ด€๊ณ„ํ˜• ํ…Œ์ด๋ธ”ยท์ง€์‹ ๊ทธ๋ž˜ํ”„์™€ ๊ฐ™์€ ์ •ํ˜•ํ™”๋œ ๊ตฌ์กฐ์— ๋จธ๋ฌผ๋Ÿฌ, ์›์‹œ ์‚ฌ์‹ค ๊ฐ„์˜ ๊ณ ์ฐจ์›(nโ€‘ary) ๊ด€๊ณ„ ๋ฅผ ๋™์ ์œผ๋กœ ๋ชจ๋ธ๋งํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์ด๋Š” โ€œ์ „์—ญ ์˜๋ฏธ ํŒŒ์•…(global senseโ€‘making)โ€์ด ์š”๊ตฌ๋˜๋Š” ์žฅ๊ธฐ ์ปจํ…์ŠคํŠธ์—์„œ ๋‹จํŽธ์  ์ถ”๋ก (fra

Model Computer Science NLP
Hindsight is 20/20: Building Agent Memory that Retains, Recalls, and Reflects

Hindsight is 20/20: Building Agent Memory that Retains, Recalls, and Reflects

HINDSIGHT๋Š” ์—์ด์ „ํŠธ์˜ ๊ธฐ์–ต์„ ๊ตฌ์กฐํ™”๋œ ์ฒซ ๋ฒˆ์งธ ํด๋ž˜์Šค ๊ธฐ๋ฐ˜์œผ๋กœ ์ฒ˜๋ฆฌํ•˜์—ฌ ๋Œ€ํ™”ํ˜• ์ธ๊ณต์ง€๋Šฅ์˜ ์„ฑ๋Šฅ์„ ํฌ๊ฒŒ ๊ฐœ์„ ํ•˜๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์ž…๋‹ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์€ ํ˜„์žฌ์˜ ์—์ด์ „ํŠธ ๊ธฐ์–ต ์‹œ์Šคํ…œ์ด ์ฆ๊ฑฐ์™€ ์ถ”๋ก  ์‚ฌ์ด์˜ ๊ฒฝ๊ณ„๋ฅผ ํ๋ฆฌ๊ฒŒ ํ•˜๊ณ  ์žฅ๊ธฐ์ ์ธ ์ •๋ณด ์กฐ์ง์— ์–ด๋ ค์›€์„ ๊ฒช๋Š” ๋ฌธ์ œ์ ์„ ์ง€์ ํ•˜๊ณ , ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด HINDSIGHT๋ผ๋Š” ์ƒˆ๋กœ์šด ์•„ํ‚คํ…์ฒ˜๋ฅผ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. HINDSIGHT๋Š” ์„ธ๊ณ„ ์‚ฌ์‹ค, ์—์ด์ „ํŠธ ๊ฒฝํ—˜, ํ•ฉ์„ฑ๋œ ์—”ํ‹ฐํ‹ฐ ์š”์•ฝ ๋ฐ ์ง„ํ™”ํ•˜๋Š” ๋ฏฟ์Œ์„ ๊ตฌ๋ถ„ํ•˜์—ฌ ์ •๋ณด๋ฅผ 4๊ฐœ์˜ ๋…ผ๋ฆฌ ๋„คํŠธ์›Œํฌ๋กœ ์กฐ์งํ•จ์œผ๋กœ์จ ์ถ”๋ก ์„ ์œ„ํ•œ ๊ธฐ๋ฐ˜์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. HINDSIGHT

Prompt-to-Parts: Generative AI for Physical Assembly and Scalable Instructions

Prompt-to-Parts: Generative AI for Physical Assembly and Scalable Instructions

๋ณธ ๋…ผ๋ฌธ์€ ์ž์—ฐ์–ด๋กœ๋ถ€ํ„ฐ ๋ฌผ๋ฆฌ์ ์œผ๋กœ ์‹คํ˜„ ๊ฐ€๋Šฅํ•œ ์กฐ๋ฆฝ ์ง€์นจ์„ ์ƒ์„ฑํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด ์„ค๋ช…ํ•˜๊ณ  ์žˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๊ธฐ์กด์˜ ํ…์ŠคํŠธ 3D ๋ณ€ํ™˜ ์ ‘๊ทผ ๋ฐฉ์‹๊ณผ ๋‹ฌ๋ฆฌ, ์ด์‚ฐ ๋ถ€ํ’ˆ ์–ดํœ˜ ๋‚ด์—์„œ ์ž‘๋™ํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๊ธฐํ•˜ํ•™์  ์œ ํšจ์„ฑ, ์—ฐ๊ฒฐ ์ œ์•ฝ ๋ฐ ์กฐ๋ฆฝ ๊ฐ€๋Šฅ ์ˆœ์„œ๋ฅผ ๊ฐ•์ œํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์€ LDraw๋ผ๋Š” ์ค‘๊ฐ„ ํ‘œํ˜„์„ ์‚ฌ์šฉํ•˜์—ฌ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ์ด ์ž์—ฐ์–ด๋กœ๋ถ€ํ„ฐ ์œ ํšจํ•œ ์กฐ๋ฆฝ ์ง€์นจ์„ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ ํ•ต์‹ฌ ๊ธฐ์—ฌ๋Š” '๋ฒฝ๋Œ ๊ฐ€๋ฐฉ' ๋ฐฉ๋ฒ•์œผ๋กœ, ์ด๋Š” ์ •ํ™•ํ•˜๊ฒŒ ๋ฐฉํ–ฅ์ด ์ง€์ •๋œ ๋ฒฝ๋Œ ์œ„์น˜์™€ '๋‹จ์–ด์˜ ๊ฐ€๋ฐฉ'์„ ์—ฐ๊ฒฐํ•˜๋Š” ์ œ์•ฝ ์–ดํœ˜๋กœ์„œ ์ž‘๋™ํ•œ๋‹ค. ์ด๋ฅผ ํ†ต

Structured Event Representation and Stock Return Predictability

Structured Event Representation and Stock Return Predictability

์ด ๋…ผ๋ฌธ์€ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM)์„ ํ™œ์šฉํ•˜์—ฌ ๋‰ด์Šค ๊ธฐ์‚ฌ๋กœ๋ถ€ํ„ฐ ์ด๋ฒคํŠธ ํŠน์„ฑ์„ ์ถ”์ถœํ•˜๋Š” ๋ฐฉ๋ฒ•๊ณผ ์ด๋ฅผ ์ด์šฉํ•œ ์ฃผ์‹ ์ˆ˜์ต๋ฅ  ์˜ˆ์ธก์— ๋Œ€ํ•ด ๋‹ค๋ฃน๋‹ˆ๋‹ค. ํŠนํžˆ, ๊ตฌ์กฐํ™”๋œ ์ด๋ฒคํŠธ ํ‘œํ˜„(SER)๊ณผ ์–ดํ…์…˜ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์‚ฌ์šฉํ•œ ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ์ด ๊ธฐ์กด์˜ ํ…์ŠคํŠธ ์ฃผ๋„ํ˜• ๋ชจ๋ธ๋ณด๋‹ค ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ๋Š” ๊ฒƒ์„ ๋ฐœ๊ฒฌํ•˜์˜€์Šต๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” LLM์ด ๋‰ด์Šค ๊ธฐ์‚ฌ์—์„œ ์ค‘์š”ํ•œ ์ •๋ณด๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ์ถ”์ถœํ•˜๊ณ  ์ด๋ฅผ ํ†ตํ•ด ์ฃผ์‹ ์‹œ์žฅ ๋™ํ–ฅ์„ ์˜ˆ์ธกํ•˜๋Š” ๋ฐ ์œ ์šฉํ•˜๋‹ค๋Š” ์ ์„ ์ž…์ฆํ•ฉ๋‹ˆ๋‹ค. ๋˜ํ•œ, ์ด ๋ชจ๋ธ์€ ํ•ด์„ ๊ฐ€๋Šฅํ•œ ํŠน์„ฑ ๊ตฌ์กฐ๋ฅผ ์ œ๊ณตํ•˜์—ฌ ์ฃผ์‹ ์ˆ˜์ต๋ฅ  ์˜ˆ์ธก์˜ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์ดํ•ดํ•˜๋Š”๋ฐ ๋„์›€์ด

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Generalization of RLVR Using Causal Reasoning as a Testbed

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ RLVR์˜ ๋ถ€์ƒ : ์ˆ˜ํ•™ ๋ฌธ์ œ ํ’€์ด, ์ •๋ฆฌ ์ฆ๋ช…, ์ฝ”๋“œ ์ƒ์„ฑ ๋“ฑ ๋‹ค์–‘ํ•œ ๋„๋ฉ”์ธ์—์„œ ๊ฒ€์ฆ ๊ฐ€๋Šฅํ•œ ๋ณด์ƒ์„ ํ™œ์šฉํ•œ ๊ฐ•ํ™”ํ•™์Šต์ด ํฐ ์„ฑ๊ณผ๋ฅผ ๋ณด์ด๊ณ  ์žˆ๋‹ค. ์ผ๋ฐ˜ํ™” ๋ฌธ์ œ : ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” RL๊ณผ SFT ๊ฐ„์˜ ์ผ๋ฐ˜ํ™” ์ฐจ์ด๋ฅผ ์ผ๋ถ€ ๊ณผ์ œ(ํ…์ŠคํŠธยท์‹œ๊ฐ ์ถ”๋ก )์—์„œ๋งŒ ์‚ดํŽด๋ณด์•˜์œผ๋ฉฐ, ์ธ๊ณผ ์ถ”๋ก ์ฒ˜๋Ÿผ ๊ตฌ์กฐ์ ยท๋…ผ๋ฆฌ์  ๋ณต์žก์„ฑ์ด ๋†’์€ ์˜์—ญ์— ๋Œ€ํ•œ ์ฒด๊ณ„์  ๋ถ„์„์€ ๋ถ€์กฑํ–ˆ๋‹ค. 2. ์‹คํ—˜ ์„ค๊ณ„ | ์š”์†Œ | ๋‚ด์šฉ | | | | | ํ…Œ์ŠคํŠธ๋ฒ ๋“œ | ํ™•๋ฅ ์  ์ธ๊ณผ ๊ทธ๋ž˜ํ”„(์ด์ง„ ๋ณ€์ˆ˜, DAG, ๋…๋ฆฝ ๋…ธ์ด์ฆˆ) ์œ„์—์„œ ์—ฐ๊ด€ยท๊ฐœ์ž…ยท๋ฐ˜์‚ฌ์‹ค ์งˆ์˜๋ฅผ ์ˆ˜ํ–‰ | | ๋‚œ์ด๋„

A superpersuasive autonomous policy debating system

A superpersuasive autonomous policy debating system

DeepDebater ๋…ผ๋ฌธ์€ ํ˜„์žฌ ์ธ๊ณต์ง€๋Šฅ ๊ธฐ๋ฐ˜ ์„ค๋“ ์‹œ์Šคํ…œ์ด ์ง๋ฉดํ•œ ๊ฐ€์žฅ ๊ทผ๋ณธ์ ์ธ ํ•œ๊ณ„๋ฅผ ๋›ฐ์–ด๋„˜๋Š” ํ˜์‹ ์ ์ธ ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ๊ธฐ์กด IBM Project Debater์™€ ๊ฐ™์€ ์‹œ์Šคํ…œ์€ ์ œํ•œ๋œ ํ† ๋ก  ํ˜•์‹, ์งง์€ ๋ฐœ์–ธ ์‹œ๊ฐ„, ๊ทธ๋ฆฌ๊ณ  ์ฃผ๋กœ ๋น„์ „๋ฌธ๊ฐ€ ์ฒญ์ค‘์„ ๋Œ€์ƒ์œผ๋กœ ์„ค๊ณ„๋˜์—ˆ๋‹ค๋Š” ์ ์—์„œ ์‹ค์ œ ์ •์ฑ… ํ† ๋ก  ํ˜„์žฅ์˜ ๋ณต์žก์„ฑ์„ ์ถฉ๋ถ„ํžˆ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ–ˆ๋‹ค. ๋ฐ˜๋ฉด DeepDebater๋Š” โ€˜์ „์ฒด ์ •์ฑ… ํ† ๋ก (full, unmodified, twoโ€‘team competitive policy debate)โ€™์ด๋ผ๋Š” ๊ฐ€์žฅ ๊นŒ๋‹ค๋กœ์šด ํ™˜๊ฒฝ์„ ๊ทธ๋Œ€๋กœ ์žฌํ˜„ํ•จ์œผ๋กœ์จ, ๋…ผ์ฆ

System
Gate-level boolean evolutionary geometric attention neural networks

Gate-level boolean evolutionary geometric attention neural networks

์ด ๋…ผ๋ฌธ์ด ์ œ์‹œํ•˜๋Š” ์•„์ด๋””์–ด๋Š” โ€œ์ด๋ฏธ์ง€๋ฅผ ๋ถˆ๋ฆฌ์–ธ ํ•„๋“œ๋กœ ๋ณธ๋‹คโ€๋Š” ๊ทผ๋ณธ์ ์ธ ํŒจ๋Ÿฌ๋‹ค์ž„ ์ „ํ™˜์— ์žˆ๋‹ค. ๊ธฐ์กด์˜ ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ์€ ์‹ค์ˆ˜๊ฐ’ ํ…์„œ๋ฅผ ์—ฐ์‚ฐ์˜ ๊ธฐ๋ณธ ๋‹จ์œ„๋กœ ์‚ผ์•„ ๊ฐ€์ค‘์น˜์™€ ํ™œ์„ฑํ™” ํ•จ์ˆ˜๋ฅผ ํ†ตํ•ด ์—ฐ์†์ ์ธ ํŠน์ง•์„ ์ถ”์ถœํ•œ๋‹ค. ๋ฐ˜๋ฉด ์—ฌ๊ธฐ์„œ๋Š” ๊ฐ ํ”ฝ์…€์„ 0ยท1 ์ด์ง„ ๋ณ€์ˆ˜๋กœ ์ œํ•œํ•˜๊ณ , ์ด๋ฅผ 2์ฐจ์› ๊ฒฉ์ž ์œ„์— ๋ฐฐ์น˜ํ•จ์œผ๋กœ์จ ๋ฌผ๋ฆฌํ•™์˜ ๋ฐ˜์‘โ€‘ํ™•์‚ฐ ์‹œ์Šคํ…œ์„ ๋””์ง€ํ„ธ ๋…ผ๋ฆฌ ํšŒ๋กœ์™€ ๋™์ผ์‹œํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ์€ ๋‘ ๊ฐ€์ง€ ์ค‘์š”ํ•œ ์žฅ์ ์„ ์ œ๊ณตํ•œ๋‹ค. ์ฒซ์งธ, ๋ชจ๋“  ์—ฐ์‚ฐ์ด ๋…ผ๋ฆฌ ๊ฒŒ์ดํŠธ ์ˆ˜์ค€์—์„œ ์ด๋ฃจ์–ด์ง€๋ฏ€๋กœ ASIC์ด๋‚˜ FPGA์™€ ๊ฐ™์€ ๋””์ง€ํ„ธ ํšŒ๋กœ์— ์ง์ ‘ ๋งคํ•‘ํ•˜๊ธฐ๊ฐ€ ์šฉ์ดํ•ด์ง„๋‹ค.

Network
No Image

Sparsity-Controllable Dynamic Top-p MoE for Large Foundation Model Pre-training

์ด ๋…ผ๋ฌธ์€ ํฌ์†Œ Mixture of Experts (MoE) ์•„ํ‚คํ…์ฒ˜์˜ ํšจ์œจ์ ์ธ ํ™œ์šฉ์„ ์œ„ํ•ด DTop p ๋ผ์šฐํŒ… ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๊ธฐ์กด์˜ Top k ๋ฐ ๊ณ ์ • ์ž„๊ณ—๊ฐ’ Top p ๋ผ์šฐํŒ… ์ „๋žต๋“ค์€ ๋ชจ๋ธ์˜ ๊ณ„์‚ฐ ๋น„์šฉ๊ณผ ์„ฑ๋Šฅ์— ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๋ฌธ์ œ์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ํŠนํžˆ, Top k ๋ผ์šฐํŒ…์€ ๋ชจ๋“  ํ† ํฐ์— ๋Œ€ํ•ด ๋™์ผํ•œ ์ˆ˜์˜ ์ „๋ฌธ๊ฐ€๋ฅผ ํ™œ์„ฑํ™”ํ•˜๋ฏ€๋กœ, ๊ฐ ํ† ํฐ์˜ ๋ณต์žก๋„์— ๋”ฐ๋ฅธ ์ ์ ˆํ•œ ์ž์› ํ• ๋‹น์ด ์ด๋ฃจ์–ด์ง€์ง€ ์•Š๋Š”๋‹ค. ๊ณ ์ • ์ž„๊ณ—๊ฐ’ Top p ๋ผ์šฐํŒ…์€ ์œ ์—ฐ์„ฑ์„ ์ œ๊ณตํ•˜์ง€๋งŒ, ๊ณ„์‚ฐ ๋น„์šฉ์„ ์ œ์–ดํ•˜๊ธฐ ์–ด๋ ต๊ณ  ํ•˜์ดํผํŒŒ๋ผ๋ฏธํ„ฐ ์„ ํƒ์— ๋ฏผ๊ฐํ•˜๋‹ค๋Š” ๋ฌธ์ œ์ ์ด

Model
AI- and Ontology-Based Enhancements to FMEA for Advanced Systems Engineering: Current Developments and Future Directions

AI- and Ontology-Based Enhancements to FMEA for Advanced Systems Engineering: Current Developments and Future Directions

์ด ๋ฆฌ๋ทฐ๋Š” ์ „ํ†ต์ ์ธ FMEA๊ฐ€ ์ง๋ฉดํ•œ ๊ตฌ์กฐ์  ํ•œ๊ณ„๋ฅผ ๋ช…ํ™•ํžˆ ์ง„๋‹จํ•˜๊ณ , ์ด๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•œ ๋‘ ์ถ•์˜ ๊ธฐ์ˆ โ€”์ธ๊ณต์ง€๋Šฅ(AI)๊ณผ ์˜จํ†จ๋กœ์ง€(ontology)โ€”์„ ์ฒด๊ณ„์ ์œผ๋กœ ์—ฐ๊ฒฐํ•œ๋‹ค๋Š” ์ ์—์„œ ํ•™์ˆ ์ ยท์‹ค๋ฌด์  ์˜์˜๊ฐ€ ํฌ๋‹ค. ์ฒซ์งธ, AI ๊ธฐ๋ฐ˜ ์ ‘๊ทผ๋ฒ•์€ ํฌ๊ฒŒ ๋‘ ๊ฐ€์ง€ ํ๋ฆ„์œผ๋กœ ๊ตฌ๋ถ„๋œ๋‹ค. ๋จธ์‹ ๋Ÿฌ๋‹(ML)์€ ๋Œ€๊ทœ๋ชจ ์šด์˜ยท์‹œํ—˜ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•ด ๊ณ ์žฅ ๋ฐœ์ƒ ํ™•๋ฅ ์„ ์ •๋Ÿ‰์ ์œผ๋กœ ์˜ˆ์ธกํ•˜๊ณ , ์œ„ํ—˜ ์šฐ์„ ์ˆœ์œ„(RPN)๋ฅผ ๋™์ ์œผ๋กœ ์žฌ๊ณ„์‚ฐํ•œ๋‹ค. ํŠนํžˆ ๋น„์ง€๋„ ํ•™์Šต๊ณผ ์ด์ƒ ํƒ์ง€ ๊ธฐ๋ฒ•์€ ์‚ฌ์ „ ๋ผ๋ฒจ๋ง์ด ์–ด๋ ค์šด ๋ณตํ•ฉ ์‹œ์Šคํ…œ์—์„œ ์ˆจ์€ ๊ณ ์žฅ ๋ชจ๋“œ๋ฅผ ์ž๋™ ๋ฐœ๊ตดํ•˜๋Š” ๋ฐ ์œ ์šฉํ•˜๋‹ค. ๋‘˜์งธ,

System
Bidirectional RAG: Safe Self-Improving Retrieval-Augmented Generation Through Multi-Stage Validation

Bidirectional RAG: Safe Self-Improving Retrieval-Augmented Generation Through Multi-Stage Validation

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ LLM์˜ ์ง€์‹ ์ •์ฒด์„ฑ ๋ฌธ์ œ : ํ›ˆ๋ จ ์‹œ์  ์ดํ›„ ์ง€์‹์ด ๊ณ ์ •๋˜๊ณ , ๋„๋ฉ”์ธโ€‘ํŠนํ™” ์ •๋ณด๊ฐ€ ๋ถ€์กฑํ•˜๋ฉฐ, ํ™˜๊ฐ(halucination)์œผ๋กœ ์ธํ•œ ์˜ค๋ฅ˜๊ฐ€ ๋นˆ๋ฒˆํžˆ ๋ฐœ์ƒํ•œ๋‹ค. ์ „ํ†ต์  RAG์˜ ๊ตฌ์กฐ์  ๋น„๋Œ€์นญ : โ€œ์ฝ๊ธฐ ์ „์šฉโ€ ์ฝ”ํผ์Šค๋Š” ๋ฐฐํฌ ํ›„ ์ƒ์„ฑ๋œ ์œ ์šฉํ•œ ์ง€์‹(์š”์•ฝ, ์ •๋ฆฌ, ์ƒˆ๋กœ์šด ์‚ฌ์‹ค)์„ ์ €์žฅํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์ด๋Š” ์žฅ๊ธฐ์ ์ธ ์„œ๋น„์Šค ์œ ์ง€๋ณด์ˆ˜์™€ ๋น„์šฉ ํšจ์œจ์„ฑ ์ธก๋ฉด์—์„œ ํฐ ์†์‹ค์ด๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ Bidirectional RAG | ์š”์†Œ | ์„ค๋ช… | ๊ธฐ์กด ๋Œ€๋น„ ์ฐจ๋ณ„์  | | | | | | Forward Path | ๊ธฐ์กด

Geometric Data Science

Geometric Data Science

: ์ด ์ฑ…์€ '๋ฐ์ดํ„ฐ๊ฐ€ ์žˆ๋Š” ๊ณณ์— ๊ธฐํ•˜ํ•™์ด ์žˆ๋‹ค'๋ผ๋Š” ์ฃผ์ œ๋กœ, ์‹ค์ œ ๋ฐ์ดํ„ฐ์™€ ๊ทธ ํ‘œํ˜„์„ ๊ธฐํ•˜ํ•™์ ์œผ๋กœ ๋ถ„์„ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ด๋Š” ๋‹จ์ˆœํžˆ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆซ์ž๋‚˜ ์ด๋ฏธ์ง€๋กœ๋งŒ ๋ณด๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ, ๋ฐ์ดํ„ฐ์˜ ๊ธฐํ•˜ํ•™์  ๊ตฌ์กฐ๋ฅผ ์ดํ•ดํ•˜๊ณ  ์ด๋ฅผ ํ†ตํ•ด ๋” ๊นŠ์€ ํ†ต์ฐฐ๋ ฅ์„ ์–ป์œผ๋ ค๋Š” ์‹œ๋„์ด๋‹ค. 1. ๋ฐ์ดํ„ฐ์™€ ๊ทธ ํ‘œํ˜„ ๋ฐ์ดํ„ฐ์˜ ๋ชจํ˜ธ์„ฑ : ์‹ค์ œ ๊ฐ์ฒด์™€ ๋””์ง€ํ„ธ ํ‘œํ˜„ ๊ฐ„์—๋Š” ํฐ ์ฐจ์ด๊ฐ€ ์žˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ์ž๋™์ฐจ๋Š” ํ”ฝ์…€ ๊ธฐ๋ฐ˜ ์ด๋ฏธ์ง€๋กœ ํ‘œํ˜„๋˜์ง€๋งŒ, ์ด๋Š” ๋ฌผ๋ฆฌ์  ๊ฐ์ฒด์™€๋Š” ๋งค์šฐ ๋‹ค๋ฅด๋‹ค. ๋˜ํ•œ, ๊ฐ™์€ ๊ฐ์ฒด๋ผ๋„ ๋‹ค์–‘ํ•œ ํ‘œํ˜„์„ ๊ฐ€์งˆ ์ˆ˜ ์žˆ์–ด ๋ชจํ˜ธ์„ฑ์ด ๋ฐœ์ƒํ•œ๋‹ค. ๊ธฐํ•˜ํ•™์  ์ ‘๊ทผ :

Data
InstructAudio: Unified speech and music generation with natural language instruction

InstructAudio: Unified speech and music generation with natural language instruction

InstructAudio๋Š” ์Œ์„ฑ ํ•ฉ์„ฑ(TTS)๊ณผ ์Œ์•… ์ƒ์„ฑ(TTM)์ด๋ผ๋Š” ๋‘ ๊ฐœ์˜ ์ „ํ†ต์ ์œผ๋กœ ๋ถ„๋ฆฌ๋œ ์—ฐ๊ตฌ ๋ถ„์•ผ๋ฅผ ์ž์—ฐ์–ด ๋ช…๋ น์–ด๋ผ๋Š” ๊ณตํ†ต ์ธํ„ฐํŽ˜์ด์Šค๋ฅผ ํ†ตํ•ด ํ•˜๋‚˜์˜ ๋ชจ๋ธ๋กœ ํ†ตํ•ฉํ•˜๋ ค๋Š” ์‹œ๋„์ด๋‹ค. ๊ธฐ์กด TTS ์‹œ์Šคํ…œ์€ ๋ณดํ†ต ์Šคํ”ผ์ปค ์ž„๋ฒ ๋”ฉ์ด๋‚˜ ๋ ˆํผ๋Ÿฐ์Šค ์˜ค๋””์˜ค๋ฅผ ์‚ฌ์šฉํ•ด ์Œ์ƒ‰์„ ๊ณ ์ •์‹œํ‚ค๊ณ , ์–ต์–‘ยท๊ฐ์ •ยท์†๋„์™€ ๊ฐ™์€ ์„ธ๋ถ€ ์ œ์–ด๋Š” ์ œํ•œ๋œ ํŒŒ๋ผ๋ฏธํ„ฐ ์ง‘ํ•ฉ์— ์˜์กดํ•œ๋‹ค. ๋ฐ˜๋ฉด TTM ์‹œ์Šคํ…œ์€ ์žฅ๋ฅดยท์•…๊ธฐยทํ…œํฌ์™€ ๊ฐ™์€ ๊ณ ์ˆ˜์ค€ ๋ฉ”ํƒ€๋ฐ์ดํ„ฐ๋ฅผ ์ž…๋ ฅ์œผ๋กœ ์š”๊ตฌํ•˜์ง€๋งŒ, ์ด๋Ÿฌํ•œ ๋ฉ”ํƒ€๋ฐ์ดํ„ฐ๋Š” ์ „๋ฌธ๊ฐ€๊ฐ€ ์ง์ ‘ ๋ผ๋ฒจ๋งํ•ด์•ผ ํ•˜๋ฏ€๋กœ ๋ฐ์ดํ„ฐ ๊ตฌ์ถ• ๋น„์šฉ์ด ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•œ๋‹ค. ๋‘ ์‹œ์Šคํ…œ ๋ชจ๋‘

No Free Lunch in Language Model Bias Mitigation? Targeted Bias Reduction Can Exacerbate Unmitigated LLM Biases

No Free Lunch in Language Model Bias Mitigation? Targeted Bias Reduction Can Exacerbate Unmitigated LLM Biases

๋ณธ ๋…ผ๋ฌธ์€ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM)์˜ ํŽธํ–ฅ ์™„ํ™”๊ฐ€ ๋‹จ์ผ ์ฐจ์›์—์„œ์˜ ์„ฑ๊ณต์— ๋จธ๋ฌด๋ฅด์ง€ ์•Š๊ณ , ๋‹ค๋ฅธ ์ฐจ์›์—์„œ ์ƒˆ๋กœ์šด ํŽธํ–ฅ์„ ์œ ๋ฐœํ•˜๊ฑฐ๋‚˜ ๊ธฐ์กด ํŽธํ–ฅ์„ ์‹ฌํ™”์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค๋Š” ์ค‘์š”ํ•œ ๊ต์ฐจ ํšจ๊ณผ๋ฅผ ์ฒด๊ณ„์ ์œผ๋กœ ํƒ๊ตฌํ•œ๋‹ค. ์—ฐ๊ตฌ์ง„์€ ๋จผ์ € 7๊ฐœ์˜ ์„œ๋กœ ๋‹ค๋ฅธ ๋ชจ๋ธ ํŒจ๋ฐ€๋ฆฌ(์˜ˆ: GPT, BERT, T5 ๋“ฑ)์—์„œ ํŒŒ์ƒ๋œ 10๊ฐœ์˜ ๋ชจ๋ธ์„ ์„ ์ •ํ•˜๊ณ , ๊ฐ๊ฐ์— ๋Œ€ํ•ด ๋„ค ๊ฐ€์ง€ ๋Œ€ํ‘œ์ ์ธ ํŽธํ–ฅ ์™„ํ™” ๊ธฐ๋ฒ•(๋ฐ์ดํ„ฐ ์žฌ์ƒ˜ํ”Œ๋ง, ์†์‹ค ๊ฐ€์ค‘์น˜ ์กฐ์ •, ์‚ฌํ›„ ํ•„ํ„ฐ๋ง, ํ”„๋กฌํ”„ํŠธ ์—”์ง€๋‹ˆ์–ด๋ง)์„ ์ ์šฉํ•˜์˜€๋‹ค. ์ด๋•Œ ์ธ์ข…, ์ข…๊ต, ์ง์—…ยท์„ฑ๋ณ„์ด๋ผ๋Š” ์„ธ ๊ฐ€์ง€ ์ฃผ์š” ํŽธํ–ฅ ์ถ•์„ ์„ค์ •ํ•˜๊ณ , ๊ฐ ์ถ•์— ๋Œ€

Model
NystagmusNet: Explainable Deep Learning for Photosensitivity Risk Prediction

NystagmusNet: Explainable Deep Learning for Photosensitivity Risk Prediction

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ˆˆ์ง„ํƒ• + ๊ด‘๋ฏผ๊ฐ์„ฑ ์€ ๋ฐ๊ธฐ ๋ณ€ํ™”์— ๋”ฐ๋ผ ๋ˆˆ์˜ ์ง„๋™์ด ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•ด ์‹œ์•ผ ์œ ์ง€ยท๊ท ํ˜• ์žก๊ธฐ์— ์–ด๋ ค์›€์„ ์ดˆ๋ž˜ํ•œ๋‹ค. ํ˜„์žฌ ์น˜๋ฃŒ๋ฒ•(์ƒ‰์•ˆ๊ฒฝ, ์•ฝ๋ฌผ)์€ ๋ฐ˜์‘ํ˜• ์ด๋ฉฐ, ์‚ฌ์ „ ์œ„ํ—˜ ์˜ˆ์ธกยท๊ฐœ์ธํ™”๊ฐ€ ๋ถ€์กฑํ•˜๋‹ค. ๋”ฅ๋Ÿฌ๋‹์ด ์•ˆ๊ตฌ ์›€์ง์ž„ ์ถ”์ ยท์‹œ๊ฐ ํ™˜๊ฒฝ ๋ถ„์„์— ์„ฑ๊ณตํ•œ ์‚ฌ๋ก€๊ฐ€ ๋Š˜์–ด๋‚˜๋ฉด์„œ, ์˜ˆ์ธก ๊ธฐ๋ฐ˜ ๋ณด์กฐ๊ธฐ์ˆ  ์— ๋Œ€ํ•œ ๊ธฐ๋Œ€๊ฐ€ ๋†’์•„์กŒ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ (Contributions) | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | | | | | 1 | ์ด์ค‘โ€‘branch CNN ์„ค๊ณ„ โ€“ ๋ฐ๊ธฐ ์ด๋ฏธ์ง€์™€ ๋ˆˆ ์›€์ง์ž„ ๋ณ€๋™์„ฑ์„ ๋ณ„๋„ ์ฒ˜๋ฆฌ ํ›„ ์œตํ•ฉ | ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ ํŠน

Learning
ShareChat: A Dataset of Chatbot Conversations in the Wild

ShareChat: A Dataset of Chatbot Conversations in the Wild

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ƒ์šฉ ์ฑ—๋ด‡์˜ ๋‹ค์–‘์„ฑ : ChatGPT, Claude, Gemini ๋“ฑ์€ ๋™์ผํ•œ LLM ๊ธฐ๋ฐ˜์ด์ง€๋งŒ UIยท์ •์ฑ…ยท๋ณด์กฐ ๊ธฐ๋Šฅ(์˜ˆ: ์‹ค์‹œ๊ฐ„ ์ธ์šฉ, ์ฝ”๋“œ ์‹คํ–‰)์—์„œ ์ฐจ๋ณ„ํ™”๋œ๋‹ค. ์ด๋Ÿฌํ•œ ์ฐจ์ด๋Š” ์‚ฌ์šฉ์ž ํ”„๋กฌํ”„ํŠธ์™€ ๋ชจ๋ธ ์‘๋‹ต์— ์ง์ ‘์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๊ธฐ์กด ๋ฐ์ดํ„ฐ์…‹์˜ ํ•œ๊ณ„ : WildChat, LMSYSโ€‘Chatโ€‘1M, OpenAssistant ๋“ฑ์€ ๋‹จ์ผ ์ธํ„ฐํŽ˜์ด์Šค ํ˜น์€ ํ…์ŠคํŠธโ€‘์ „์šฉ ๋กœ๊ทธ์— ์˜์กดํ•ด ํ”Œ๋žซํผ ๊ณ ์œ  ๋ฉ”ํƒ€ ์ •๋ณด๋ฅผ ์†Œ์‹คํ•œ๋‹ค. ๋˜ํ•œ, ๋Œ€๋ถ€๋ถ„์ด ์‹คํ—˜์‹ค/์—ฐ๊ตฌ์ž ์ฃผ๋„ ์ˆ˜์ง‘์ด๋ผ ๊ด€์ฐฐ์ž ํšจ๊ณผ(Hawthorne effect)๋ฅผ

Data
Thucy: An LLM-based Multi-Agent System for Claim Verification across Relational Databases

Thucy: An LLM-based Multi-Agent System for Claim Verification across Relational Databases

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ฃผ์žฅ ๊ฒ€์ฆ์˜ ๋ฐ์ดํ„ฐ ๊ฒฉ์ฐจ : ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ˆ˜๋ฐฑ ํ–‰ ์ˆ˜์ค€์˜ ๋‹จ์ผ ํ…Œ์ด๋ธ”์— ๊ตญํ•œ๋ผ, ์‹ค์ œ ๊ณต๊ณต ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค(์ˆ˜์‹ญยท์ˆ˜๋ฐฑ ํ…Œ์ด๋ธ”, ์ˆ˜๋ฐฑ๋งŒ ํ–‰)์™€์˜ ์—ฐ๋™์ด ์–ด๋ ค์› ๋‹ค. ํˆฌ๋ช…์„ฑ ์š”๊ตฌ : ๊ฒ€์ฆ ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ โ€œ๋ธ”๋ž™๋ฐ•์Šคโ€์‹ ์„ค๋ช…์ด ์•„๋‹ˆ๋ผ, ์ „๋ฌธ๊ฐ€๊ฐ€ ์ง์ ‘ ๊ฒ€์ฆ ๊ณผ์ •์„ ์žฌํ˜„ยท์ˆ˜์ •ํ•  ์ˆ˜ ์žˆ๋Š” SQL ์ฆ๊ฑฐ ์ œ๊ณต์ด ํ•„์ˆ˜์ ์ด๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๊ตฌ๋ถ„ | ๊ธฐ์กด ์‹œ์Šคํ…œ | Thucy์˜ ์ฐจ๋ณ„์  | | | | | | ๋ฐ์ดํ„ฐ ๋ฒ”์œ„ | ๋‹จ์ผ ํ…Œ์ด๋ธ”, ์ž‘์€ ๊ทœ๋ชจ | ๋‹ค์ค‘ DBยท๋‹ค์ค‘ ํ…Œ์ด๋ธ” (ํฌ๋กœ์Šคโ€‘DB/ํฌ๋กœ์Šคโ€‘Table) | | ์Šคํ‚ค๋งˆ ์˜์กด์„ฑ

Data System
Visual Sync: Multi-Camera Synchronization via Cross-View Object Motion

Visual Sync: Multi-Camera Synchronization via Cross-View Object Motion

VisualSync๋Š” ๋ฉ€ํ‹ฐ๋ทฐ ๋น„๋””์˜ค ๋ถ„์„ ๋ถ„์•ผ์—์„œ ๊ฐ€์žฅ ๊ทผ๋ณธ์ ์ธ ๋ฌธ์ œ ์ค‘ ํ•˜๋‚˜์ธ ์‹œ๊ฐ„ ์ •๋ ฌ ๋ฌธ์ œ๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ํ•ด๊ฒฐํ•œ๋‹ค๋Š” ์ ์—์„œ ํ•™์ˆ ์ ยท์‚ฐ์—…์  ํŒŒ๊ธ‰๋ ฅ์ด ํฌ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ฃผ๋กœ ์˜ค๋””์˜ค ์‹ ํ˜ธ, ์ดฌ์˜ ์žฅ๋น„์˜ ํƒ€์ž„์ฝ”๋“œ, ํ˜น์€ ์™ธ๋ถ€ ์„ผ์„œ(์˜ˆ: GPS, IMU)๋ฅผ ํ™œ์šฉํ•ด ๋™๊ธฐํ™”๋ฅผ ์‹œ๋„ํ–ˆ์ง€๋งŒ, ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•์€ ์ถ”๊ฐ€ ํ•˜๋“œ์›จ์–ด ๋น„์šฉ์ด๋‚˜ ์‚ฌ์ „ ์„ค์ •์ด ํ•„์š”ํ•˜๊ณ , ์‹ค์ œ ํ˜„์žฅ ์ดฌ์˜์—์„œ๋Š” ์ ์šฉ์ด ์–ด๋ ค์šด ๊ฒฝ์šฐ๊ฐ€ ๋งŽ๋‹ค. VisualSync๋Š” ์ˆœ์ˆ˜ํžˆ ์˜์ƒ ์ž์ฒด๋งŒ์„ ์ด์šฉํ•ด ์‹œ๊ฐ„ ์˜คํ”„์…‹์„ ์ถ”์ •ํ•œ๋‹ค๋Š” ์ ์—์„œ ์ œํ•œ์กฐ๊ฑด์ด ์ตœ์†Œํ™”๋œ๋‹ค. ํ•ต์‹ฌ ์•„์ด๋””์–ด๋Š” โ€œ๋™์  ์žฅ๋ฉด์˜ ๊ณตํ†ต์ 

Wearable-informed generative digital avatars predict task-conditioned post-stroke locomotion

Wearable-informed generative digital avatars predict task-conditioned post-stroke locomotion

์ด ๋…ผ๋ฌธ์€ ๋‡Œ์กธ์ค‘ ํ™˜์ž์˜ ๋ณดํ–‰ ๋Šฅ๋ ฅ์„ ์˜ˆ์ธกํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๋” ํšจ๊ณผ์ ์ธ ์žฌํ™œ ์น˜๋ฃŒ๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค. ๊ธฐ์กด์˜ ํ‰๊ฐ€๋Š” ๋Œ€๋ถ€๋ถ„ ์ •์ ์ด๊ณ  ์ผ๋ฐ˜ํ™”๋œ ๊ฒฐ๊ณผ๋งŒ ์ œ๊ณตํ•˜๋Š” ๋ฐ˜๋ฉด, ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์›จ์–ด๋Ÿฌ๋ธ” ์„ผ์„œ์™€ ๋ฌผ๋ฆฌํ•™ ๋ชจ๋ธ์„ ๊ฒฐํ•ฉํ•œ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ํ†ตํ•ด ํ™˜์ž์˜ ๊ฐœ๋ณ„์ ์ธ ๋ณดํ–‰ ๋Šฅ๋ ฅ์„ ๋™์ ์œผ๋กœ ์˜ˆ์ธกํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, ๋‹จ์ผ ์‹œํ—˜์—์„œ ์–ป์€ ๋ฐ์ดํ„ฐ๋งŒ์œผ๋กœ๋„ ๊ฒฝ์‚ฌ๋ฉด ๊ฑท๊ธฐ๋‚˜ ๊ณ„๋‹จ ์˜ค๋ฅด๋‚ด๋ฆผ๊ณผ ๊ฐ™์€ ํŠน์ • ์ž‘์—… ์ˆ˜ํ–‰์„ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์ด ์ฃผ๋ชฉ๋ฉ๋‹ˆ๋‹ค. ์ด ๋ฐฉ๋ฒ•์˜ ํšจ๊ณผ๋Š” ๋‹ค์ค‘ ์„ผํ„ฐ ์ž„์ƒ ๋ฌด์ž‘์œ„ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ž…์ฆ๋˜์—ˆ์œผ๋ฉฐ, ํŠนํžˆ Fugl

When AI Bends Metal: AI-Assisted Optimization of Design Parameters in Sheet Metal Forming

When AI Bends Metal: AI-Assisted Optimization of Design Parameters in Sheet Metal Forming

: ๋ณธ ๋…ผ๋ฌธ์€ AI์™€ ๊ณ ์„ฑ๋Šฅ ์ปดํ“จํŒ…(HPC)์„ ํ™œ์šฉํ•˜์—ฌ ์‹œํŠธ ๊ธˆ์† ํ˜•์„ฑ ๊ณผ์ •์—์„œ ์„ค๊ณ„ ๋งค๊ฐœ๋ณ€์ˆ˜ ์ตœ์ ํ™”๋ฅผ ์ž๋™ํ™”ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Š” ์ „ํ†ต์ ์ธ ์ˆ˜์ž‘์—… ๋ฐฉ์‹์— ๋น„ํ•ด ์‹œ๊ฐ„๊ณผ ๋น„์šฉ์„ ํฌ๊ฒŒ ์ ˆ๊ฐํ•  ์ˆ˜ ์žˆ๋Š” ํ˜์‹ ์ ์ธ ์ ‘๊ทผ๋ฒ•์ด๋‹ค. 1. AI ์ง€์› ์›Œํฌํ”Œ๋กœ์šฐ์˜ ๊ตฌ์„ฑ ์š”์†Œ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆ๋œ AI ์ง€์› ์›Œํฌํ”Œ๋กœ์šฐ๋Š” ๋‘ ๊ฐ€์ง€ ์ฃผ์š” ๊ตฌ์„ฑ ์š”์†Œ๋ฅผ ํฌํ•จํ•œ๋‹ค: ์ „๋ฌธ๊ฐ€ ์ฐธ์—ฌ ๊ฐ์†Œ : ๋ฒ ์ด์ฆˆ ์ตœ์ ํ™”๋ฅผ ํ†ตํ•ด ์ „๋ฌธ๊ฐ€์˜ ๊ฐœ์ž…์„ ์ค„์ด๊ณ , ์ดˆ๊ธฐ ์„ค๊ณ„ ์ถ”์ •๊ณผ ์ตœ์ ํ™” ๋ฃจํ”„์—์„œ ์ž๋™ํ™”๋ฅผ ๋„์ž…ํ•œ๋‹ค. ํšจ์œจ์ ์ธ ์„ค๊ณ„ ๋งค๊ฐœ๋ณ€์ˆ˜ ํƒ์ƒ‰ : ๊ฐ€์šฐ์Šค ํ”„๋กœ์„ธ์Šค ์ž ์žฌ ๋ณ€์ˆ˜ ๋ชจ๋ธ(GPL

Domain-Specific Foundation Model Improves AI-Based Analysis of Neuropathology

Domain-Specific Foundation Model Improves AI-Based Analysis of Neuropathology

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋„๋ฉ”์ธ ๋ถˆ์ผ์น˜ : ๊ธฐ์กด ๋Œ€ํ˜• ํŒŒ์šด๋ฐ์ด์…˜ ๋ชจ๋ธ์€ ์ฃผ๋กœ ๋น„์‹ ๊ฒฝ ์กฐ์ง(๊ฐ„, ํ, ์œ ๋ฐฉ ๋“ฑ)๊ณผ ์ข…์–‘ยท์—ผ์ฆยท๋Œ€์‚ฌ ์งˆํ™˜์— ์ดˆ์ ์„ ๋งž์ถ˜ ๋ฐ์ดํ„ฐ์…‹์œผ๋กœ ํ•™์Šต๋ผ, ์‹ ๊ฒฝ์„ธํฌยท๊ต์„ธํฌยทํŠน์ˆ˜ํ•œ ๋‡Œ ๊ตฌ์กฐ(์‹œ๋ƒ…์Šค, ์ธต๊ตฌ์กฐ ๋“ฑ)๋ฅผ ์ถฉ๋ถ„ํžˆ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์‹ ๊ฒฝ๋ณ‘๋ฆฌํ•™์˜ ํŠน์ˆ˜์„ฑ : ์•Œ์ธ ํ•˜์ด๋จธ์˜ ์‹ ๊ฒฝ์„ฌ์œ  ์—‰ํ‚ด, ์•„๋ฐ€๋กœ์ด๋“œ ํ”Œ๋ผํฌ, ํŒŒํ‚จ์Šจ์˜ ๋ฃจ์ด์†Œ์ฒด, ์†Œ๋‡Œ ์œ„์ถ•์˜ ํŠน์ด์  ๋ณ‘๋ณ€ ๋“ฑ์€ ๋ฏธ์„ธํ•œ ํ˜•ํƒœํ•™์  ์ฐจ์ด๋ฅผ ๊ตฌ๋ถ„ํ•ด์•ผ ํ•˜๋Š” ๊ณ ๋‚œ์ด๋„ ๊ณผ์ œ๋‹ค. ์ผ๋ฐ˜ ๋ชจ๋ธ์€ ์ด๋Ÿฌํ•œ ๋ฏธ์„ธ ํŒจํ„ด์„ ๋†“์น  ์œ„ํ—˜์ด ์žˆ๋‹ค(๋ถ„ํฌ ์ด๋™, ํŠน์ง• ์†์‹ค). 2. ๋ฐ์ดํ„ฐ์…‹ ๊ตฌ์ถ• | ๊ตฌ๋ถ„ |

Analysis Model
NVIDIA Nemotron 3: Efficient and Open Intelligence

NVIDIA Nemotron 3: Efficient and Open Intelligence

1. ํ•ต์‹ฌ ๊ธฐ์ˆ  ์š”์•ฝ | ๊ธฐ์ˆ  | ์„ค๋ช… | ๊ธฐ๋Œ€ ํšจ๊ณผ | | | | | | Hybrid MoE + Mambaโ€‘Transformer | MoE ๋ ˆ์ด์–ด์™€ ๋น„์šฉ์ด ๋‚ฎ์€ Mambaโ€‘2 ๋ ˆ์ด์–ด๋ฅผ ๊ต์ฐจ ๋ฐฐ์น˜ํ•˜๊ณ , ํ•„์ˆ˜์ ์ธ Selfโ€‘Attention ๋ ˆ์ด์–ด๋Š” ์ตœ์†Œํ™” | ํ† ํฐ๋‹น ์—ฐ์‚ฐ๋Ÿ‰ยท๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ ๊ฐ์†Œ โ†’ 8k/16k ์ž…๋ ฅโ€‘์ถœ๋ ฅ ์‹œ 3.3๋ฐฐ ๋†’์€ ์ฒ˜๋ฆฌ๋Ÿ‰ | | LatentMoE | ํ† ํฐ์„ ์ˆจ๊น€ ์ฐจ์› d โ†’ ์ž‘์€ ์ž ์žฌ ์ฐจ์› โ„“ ๋กœ ํˆฌ์‚ฌ ํ›„ ์ „๋ฌธ๊ฐ€ ๋ผ์šฐํŒ…, ๋ผ์šฐํŒ… ์ฐจ์› ์ถ•์†Œ๋กœ ํ†ต์‹ ยท๋ฉ”๋ชจ๋ฆฌ ๋น„์šฉ ์ ˆ๊ฐ | ๋™์ผ ์—ฐ์‚ฐ ๋น„์šฉ์—์„œ ์ „๋ฌธ๊ฐ€ ์ˆ˜์™€ ํ™œ์„ฑ K๋ฅผ ํ™•๋Œ€

Connectivity-Preserving Cortical Surface Tetrahedralization

Connectivity-Preserving Cortical Surface Tetrahedralization

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํ”ผ์งˆ ํ‘œ๋ฉด์˜ ๋ณต์žก์„ฑ : ์ธ๊ฐ„ ๋‡Œ๋Š” ์ˆ˜๋งŽ์€ ๊ณจ์ง€(sulci)์™€ ์œต์„ (gyri)์œผ๋กœ ์ด๋ฃจ์–ด์ง„ ๊ณ ๋„๋กœ ์ ‘ํžŒ ๊ตฌ์กฐ์ด๋ฉฐ, ํ‘œ๋ฉด์„ ์™ธ๋ถ€๋กœ โ€œ๋ฐ€์–ด๋‚ด๋Š”โ€ ๊ณผ์ •์—์„œ ์ž‘์€ ๊ฐ„๊ฒฉ์— ์ž๊ธฐ๊ต์ฐจ๊ฐ€ ๋นˆ๋ฒˆํžˆ ๋ฐœ์ƒํ•œ๋‹ค. ๊ธฐ์กด ๋ฉ”์‰ฌ ๋ณต๊ตฌ ๋„๊ตฌ์˜ ํ•œ๊ณ„ : Attene(2014), Campen & Kobbelt(2010) ๋“ฑ์€ ๊ฒฐํ•จ์„ ์ œ๊ฑฐํ•˜๋ ค ํ•˜์ง€๋งŒ, ํฐ ์˜์—ญ์„ ์‚ญ์ œํ•˜๊ฑฐ๋‚˜ ์„œ๋กœ ๋‹ค๋ฅธ ํ•ด๋ถ€ํ•™์  ์˜์—ญ์„ ๋ณ‘ํ•ฉํ•ด ๋ฒ„๋ฆฌ๋Š” ๋ถ€์ž‘์šฉ์ด ์žˆ๋‹ค. ์ œ์•ฝ vs ๋น„์ œ์•ฝ ํ…ŒํŠธ๋ผํ—ค๋“œ๋ก ํ™” : TetGen, Gmsh ๋“ฑ์€ PLC(์กฐ๊ฐ์„ ํ˜• ๋ณตํ•ฉ์ฒด) ์ž…๋ ฅ์„ ์ „์ œ๋กœ ํ•˜์—ฌ ๊ฒฐํ•จ

Convergence of Outputs When Two Large Language Models Interact in a Multi-Agentic Setup

Convergence of Outputs When Two Large Language Models Interact in a Multi-Agentic Setup

: ๋ณธ ์—ฐ๊ตฌ๋Š” ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(Large Language Model, LLM)์ด ์„œ๋กœ ์ƒํ˜ธ์ž‘์šฉํ•˜๋Š” ๋‹ค์ค‘ ์—์ด์ „ํŠธ ์„ค์ •์—์„œ์˜ ์ˆ˜๋ ด ํ˜„์ƒ์„ ํƒ๊ตฌํ•˜์˜€์Šต๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ๋ฏธ์ŠคํŠธ๋ž„ ๋„ค๋ชจ ๋ฒ ์ด์Šค 2407๊ณผ ๋ผ๋งˆ 2 13B HF ๋‘ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜์˜€์œผ๋ฉฐ, ๊ฐ ๋ชจ๋ธ์€ ๋…๋ฆฝ์ ์œผ๋กœ ํ›ˆ๋ จ๋œ ๊ณ ์œ ํ•œ ๊ฐ€์ค‘์น˜์™€ ํ† ํฐ๋ผ์ด์ €๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์‹คํ—˜์—์„œ๋Š” ์ดˆ๊ธฐ ์งง์€ ๋ฌธ์žฅ์œผ๋กœ ์‹œ์ž‘ํ•˜๋Š” ๋Œ€ํ™”์—์„œ ๋‘ ๋ชจ๋ธ์ด ์ƒ๋Œ€๋ฐฉ์˜ ์ถœ๋ ฅ์— ์‘๋‹ตํ•˜๋ฉฐ 25ํšŒ ๋ฐ˜๋ณต๋˜๋Š” ๊ณผ์ •์„ ๊ด€์ฐฐํ•˜์˜€์Šต๋‹ˆ๋‹ค. ์ˆ˜๋ ด ํ˜„์ƒ์˜ ํŠน์ง• ์ˆ˜๋ ด ํ˜„์ƒ์€ ๋Œ€ํ™”๊ฐ€ ์ง„ํ–‰๋จ์— ๋”ฐ๋ผ ์ผ๊ด€์„ฑ ์žˆ๋Š” ํŒจํ„ด์ด ๋‚˜ํƒ€๋‚˜

Model
Planning as Descent: Goal-Conditioned Latent Trajectory Synthesis in Learned Energy Landscapes

Planning as Descent: Goal-Conditioned Latent Trajectory Synthesis in Learned Energy Landscapes

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์˜คํ”„๋ผ์ธ ๋ชฉํ‘œโ€‘์กฐ๊ฑด๋ถ€ RL(GCRL) ์€ ๋ณด์ƒ ์—†์ด ์ˆ˜์ง‘๋œ ์ •์  ๋ฐ์ดํ„ฐ๋งŒ์œผ๋กœ ์ž„์˜์˜ ๋ชฉํ‘œ ์ƒํƒœ์— ๋„๋‹ฌํ•˜๋Š” ์ •์ฑ…์„ ํ•™์Šตํ•ด์•ผ ํ•˜๋Š” ์–ด๋ ค์šด ๋ฌธ์ œ๋‹ค. ๊ธฐ์กด ์ ‘๊ทผ๋ฒ•์€ ํฌ๊ฒŒ (i) ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ํ”Œ๋ž˜๋‹ (ํ•™์Šต๋œ ๋™์—ญํ•™ + MPC)์™€ (ii) ์‹œํ€€์Šค ๊ธฐ๋ฐ˜ ์ƒ์„ฑ ๋ชจ๋ธ (Decision Transformers, Diffusion ๋“ฑ)์œผ๋กœ ๋‚˜๋‰œ๋‹ค. ๋‘ ๋ฐฉ๋ฒ• ๋ชจ๋‘ ํ•™์Šตโ€‘์ถ”๋ก  ๋ถˆ์ผ์น˜ (modelโ€‘exploitation, ์ƒ˜ํ”Œ๋ง ํŽธํ–ฅ)์™€ ๋…ธ์ด์ฆˆยท๋น„์ตœ์  ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ์ทจ์•ฝ์„ฑ ์„ ์•ˆ๊ณ  ์žˆ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ โ€œPlanning

World Models for Autonomous Navigation of Terrestrial Robots from LIDAR Observations

World Models for Autonomous Navigation of Terrestrial Robots from LIDAR Observations

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๊ณ ์ฐจ์› LIDAR ์ž…๋ ฅ ๋ฌธ์ œ : 360๊ฐœ์˜ ๊ฑฐ๋ฆฌ๊ฐ’์„ ๊ทธ๋Œ€๋กœ ์ •์ฑ…๋ง์— ๋„ฃ์œผ๋ฉด ํ‘œํ˜„ ๋ณต์žก๋„๊ฐ€ ๊ธ‰์ฆํ•˜๊ณ , ๋ณด์ƒ์ด ํฌ์†Œํ•ด์ ธ ํ•™์Šต์ด ๋ถˆ์•ˆ์ •ํ•ด์ง„๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” 20~30๊ฐœ์˜ ์ƒ˜ํ”Œ๋งŒ ์‚ฌ์šฉํ•ด ์ฐจ์›์„ ์ธ์œ„์ ์œผ๋กœ ์ถ•์†Œํ–ˆ์ง€๋งŒ, ์ด๋Š” ํ™˜๊ฒฝ ์ธ์‹ ๋Šฅ๋ ฅ์„ ํฌ๊ฒŒ ์ €ํ•˜์‹œํ‚จ๋‹ค. ๋ชจ๋ธโ€‘ํ”„๋ฆฌ RL์˜ ํ•œ๊ณ„ : SACยทDDPGยทTD3 ๋“ฑ์€ ์ง์ ‘ ํ™˜๊ฒฝ๊ณผ ์ƒํ˜ธ์ž‘์šฉํ•˜๋ฉด์„œ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜๋ฏ€๋กœ ์ƒ˜ํ”Œ ํšจ์œจ์„ฑ์ด ๋‚ฎ๊ณ , ํ›ˆ๋ จ ์‹œ๊ฐ„์ด ๊ธธ๋‹ค. ํŠนํžˆ ์—ฐ์† ์ œ์–ด ๋กœ๋ด‡์—์„  ์•ˆ์ „ํ•œ ํƒ์ƒ‰์ด ์–ด๋ ค์›Œ ์‹ค์ œ ์ ์šฉ์— ์ œ์•ฝ์ด ์žˆ๋‹ค. ๋ชจ๋ธโ€‘๋ฒ ์ด์Šค RL์˜ ๋ถ€์ƒ : DreamerV3

Model
Deep Research: A Systematic Survey

Deep Research: A Systematic Survey

1. ๋…ผ๋ฌธ์˜ ๊ตฌ์กฐ์™€ ํ•ต์‹ฌ ๋‚ด์šฉ | ์„น์…˜ | ํ•ต์‹ฌ ํฌ์ธํŠธ | ์˜์˜ | | | | | | 1. Introduction | LLM์˜ ํ•œ๊ณ„(๋‹จ์ผ ํŒŒ๋ผ๋ฏธํ„ฐ ์ง€์‹)์™€ DR ํ•„์š”์„ฑ ๊ฐ•์กฐ | ์—ฐ๊ตฌ ๋™๊ธฐ์™€ ๋ชฉํ‘œ๋ฅผ ๋ช…ํ™•ํžˆ ์ œ์‹œ | | 2. Preliminary Concept | DR ์ •์˜, 3โ€‘๋‹จ๊ณ„ ๋กœ๋“œ๋งต(Agentic Search โ†’ Evidence Synthesis โ†’ Insight Generation) | DR์„ ๋‹จ๊ณ„๋ณ„๋กœ ๊ตฌ๋ถ„ํ•ด ๊ธฐ์ˆ ยท์—ฐ๊ตฌ ๋กœ๋“œ๋งต์„ ์ œ๊ณต | | 3. Key Components | โ‘  Query Planning (Parallel,

System
Proportional integral derivative booster for neural networks-based time-series prediction: Case of water demand prediction

Proportional integral derivative booster for neural networks-based time-series prediction: Case of water demand prediction

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋‹ค๋‹จ๊ณ„ ์˜ˆ์ธก์˜ ํ•ต์‹ฌ ๊ณผ์ œ : ๋ฐ˜๋ณต(iterative) ์ „๋žต์„ ์‚ฌ์šฉํ•  ๊ฒฝ์šฐ, ์ด์ „ ๋‹จ๊ณ„์˜ ์˜ˆ์ธก๊ฐ’์ด ๋‹ค์Œ ๋‹จ๊ณ„ ์ž…๋ ฅ์— ์‚ฌ์šฉ๋ผ ์˜ค์ฐจ ๋ˆ„์  ๋ฌธ์ œ๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค. ๊ธฐ์กด ํ•ด๊ฒฐ์ฑ…์€ ๋ชจ๋ธ์„ ๋‹ค์ค‘์œผ๋กœ ๊ตฌ์„ฑํ•˜๊ฑฐ๋‚˜ ๋ณต์žกํ•œ ์‚ฌ์ „โ€‘ํ›„์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•์„ ๋„์ž…ํ•ด ๋ณต์žก๋„๊ฐ€ ๊ธ‰์ฆํ•œ๋‹ค. PID ์ œ์–ด์˜ ์žฅ์  : ํŒŒ๋ผ๋ฏธํ„ฐ๊ฐ€ 3๊ฐœ(Kp, Ki, Kd)๋ฟ์ด๋ฉฐ, ์‹ค์‹œ๊ฐ„ ์ œ์–ด์— ๊ฒ€์ฆ๋œ ์•ˆ์ •์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ด๋ฅผ ์˜ˆ์ธก ์˜ค๋ฅ˜ ๋ณด์ •์— ์ ์šฉํ•˜๋ฉด ๋ณต์žก๋„ ์ฆ๊ฐ€ ์—†์ด ์„ฑ๋Šฅ์„ ๋Œ์–ด์˜ฌ๋ฆด ์ˆ˜ ์žˆ๋‹ค. 2. ์ œ์•ˆ ๋ฐฉ๋ฒ•(PID ๋ถ€์Šคํ„ฐ)์˜ ํ•ต์‹ฌ ์•„์ด๋””์–ด | ๋‹จ๊ณ„ | ๋‚ด์šฉ | | |

Network
Satisfiability Modulo Theory Meets Inductive Logic Programming

Satisfiability Modulo Theory Meets Inductive Logic Programming

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ILP์˜ ๊ฐ•์  : ๊ด€๊ณ„ํ˜• ๋ฐ์ดํ„ฐ์—์„œ 1์ฐจ ๋…ผ๋ฆฌ ๊ทœ์น™์„ ํ•™์Šตํ•จ์œผ๋กœ์จ ๋†’์€ ํ•ด์„ ๊ฐ€๋Šฅ์„ฑ๊ณผ ๊ฐ•๋ ฅํ•œ ์ผ๋ฐ˜ํ™” ๋Šฅ๋ ฅ์„ ์ œ๊ณตํ•œ๋‹ค. ILP์˜ ํ•œ๊ณ„ : ์ˆ˜์น˜ ์ œ์•ฝ(์ž„๊ณ„๊ฐ’, ์„ ํ˜•/๋น„์„ ํ˜• ๊ด€๊ณ„ ๋“ฑ)์„ ์ง์ ‘ ์œ ๋„ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ๊ธฐ์กด ๋ฐฉ๋ฒ•์€ 1) ์ด์‚ฐํ™” (threshold discretisation) โ†’ ์ •๋ณด ์†์‹ค, 2) ์ˆ˜๋™ ์„ค๊ณ„ ์ˆ ์–ด โ†’ ๋„๋ฉ”์ธ ์˜์กด์„ฑ, 3) bottomโ€‘clause ๊ธฐ๋ฐ˜ โ†’ ๋‹จ์ผ ์˜ˆ์ œ์—๋งŒ ์˜์กด, ๋‹ค์ค‘ ์˜ˆ์ œ ๊ฐ„์˜ ์ „์—ญ ์ˆ˜์น˜ ํŒจํ„ด์„ ํฌ์ฐฉ ๋ชปํ•จ. 2. ์ œ์•ˆ ๋ฐฉ๋ฒ•๋ก  | ๋‹จ๊ณ„ | ๋‹ด๋‹น ์‹œ์Šคํ…œ | ์ฃผ์š” ์—ญํ•  | | | |

Catching UX Flaws in Code: Leveraging LLMs to Identify Usability Flaws at the Development Stage

Catching UX Flaws in Code: Leveraging LLMs to Identify Usability Flaws at the Development Stage

: ์ด ๋…ผ๋ฌธ์€ ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ(LLM)์ธ GPT 4o๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์›น ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์˜ ์‚ฌ์šฉ์„ฑ ๊ฒฐํ•จ์„ ์ดˆ๊ธฐ ๊ฐœ๋ฐœ ๋‹จ๊ณ„์—์„œ ์ž๋™์œผ๋กœ ์‹๋ณ„ํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ๋‹ค. ์ „ํ†ต์ ์ธ ์‚ฌ์šฉ์„ฑ ํ‰๊ฐ€๋Š” ์‹œ๊ฐ„๊ณผ ์ž์› ์†Œ๋ชจ๊ฐ€ ํฌ๊ณ  ์ฃผ๊ด€์ ์ผ ์ˆ˜ ์žˆ์–ด, ํŠนํžˆ ์†Œ๊ทœ๋ชจ ํŒ€์ด๋‚˜ ์ดˆ๊ธฐ ํ”„๋กœ์ ํŠธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์ด ํ•œ๊ณ„๋ฅผ ๊ฐ€์งˆ ์ˆ˜ ์žˆ๋‹ค. ์ด์— ๋”ฐ๋ผ LLM์˜ ๋ฐœ์ „์€ ์‚ฌ์šฉ์„ฑ ๋ฌธ์ œ๋ฅผ ์ž๋™ํ™”๋œ ๋ฐฉ์‹์œผ๋กœ ์‹๋ณ„ํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ๊ฐ€๋Šฅ์„ฑ์„ ์ œ์‹œํ•œ๋‹ค. 1. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๋ก  ๋…ผ๋ฌธ์€ Jakob Nielsen์˜ 10๊ฐ€์ง€ ์‚ฌ์šฉ์„ฑ ์ง๊ด€ ์ง€์นจ์„ ๊ธฐ๋ฐ˜์œผ๋กœ GPT 4o ๋ชจ๋ธ์ด ์›น์‚ฌ์ดํŠธ ์†Œ์Šค ์ฝ”๋“œ๋ฅผ

Efficient Kernel Mapping and Comprehensive System Evaluation of LLM Acceleration on a CGLA

Efficient Kernel Mapping and Comprehensive System Evaluation of LLM Acceleration on a CGLA

๋ณธ ๋…ผ๋ฌธ์€ ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ(LLM)์˜ ์ถ”๋ก  ๊ฐ€์†ํ™”์— ๋Œ€ํ•œ ์ข…ํ•ฉ์ ์ธ ํ‰๊ฐ€๋ฅผ ์ œ์‹œํ•˜๋ฉฐ, ํŠนํžˆ ์žฌ๊ตฌ์„ฑ ๊ฐ€๋Šฅํ•œ ์„ ํ˜• ๋ฐฐ์—ด(CGLA) ๊ธฐ๋ฐ˜ ์•„ํ‚คํ…์ฒ˜์ธ IMAX๋ฅผ ์‚ฌ์šฉํ•œ ์„ฑ๋Šฅ ๋ถ„์„์„ ์ค‘์ ์œผ๋กœ ๋‹ค๋ฃน๋‹ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” LLM์ด ์š”๊ตฌํ•˜๋Š” ๋†’์€ ๊ณ„์‚ฐ ์ž์›๊ณผ ์—๋„ˆ์ง€ ์†Œ๋น„ ๋ฌธ์ œ์— ๋Œ€์‘ํ•˜๊ธฐ ์œ„ํ•ด, CGLA๊ฐ€ ์ œ๊ณตํ•˜๋Š” ํšจ์œจ์„ฑ๊ณผ ์œ ์—ฐ์„ฑ์„ ๊ฐ•์กฐํ•ฉ๋‹ˆ๋‹ค. 1. LLM ์ถ”๋ก  ๊ฐ€์†ํ™”์˜ ํ•„์š”์„ฑ LLM์€ ํ…์ŠคํŠธ ์ฒ˜๋ฆฌ, ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ, ์ฝ”๋“œ ์ƒ์„ฑ ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ํ™œ์šฉ๋˜๋ฉฐ, ์ด์— ๋”ฐ๋ฅธ ๋†’์€ ๊ณ„์‚ฐ ์ž์› ์š”๊ตฌ์™€ ์—๋„ˆ์ง€ ์†Œ๋น„๋Š” ๋ฐ์ดํ„ฐ ์„ผํ„ฐ์˜ ์šด์˜ ๋น„์šฉ๊ณผ ํ™˜๊ฒฝ ๋ฌธ์ œ๋ฅผ ์•ผ๊ธฐํ•ฉ๋‹ˆ๋‹ค.

System
CRAFT-E: A Neuro-Symbolic Framework for Embodied Affordance Grounding

CRAFT-E: A Neuro-Symbolic Framework for Embodied Affordance Grounding

: ๋ณธ ๋…ผ๋ฌธ์˜ ํ•ต์‹ฌ ๋ชฉํ‘œ๋Š” ๋ณด์กฐ ๋กœ๋ด‡์ด ์ธ๊ฐ„ ํ™˜๊ฒฝ์—์„œ ์ž‘๋™ํ•  ๋•Œ, ์‚ฌ์šฉ์ž์˜ ์š”์ฒญ์— ๋”ฐ๋ผ ์ ์ ˆํ•œ ๋ฌผ์ฒด๋ฅผ ์„ ํƒํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ด ๊ณผ์ œ๋Š” ๋‹จ์ˆœํžˆ ๋ฌผ์ฒด๋ฅผ ์ธ์‹ํ•˜๋Š” ๊ฒƒ์„ ๋„˜์–ด, ๋ฌผ์ฒด๊ฐ€ ์ˆ˜ํ–‰ ๊ฐ€๋Šฅํ•œ ํ–‰๋™์„ ์ถ”๋ก ํ•ด์•ผ ํ•˜๋Š” ๋ณต์žกํ•œ ๊ณผ์ •์„ ํ•„์š”๋กœ ํ•ฉ๋‹ˆ๋‹ค. 1. ๊ธฐ์กด ์ ‘๊ทผ ๋ฐฉ์‹์˜ ํ•œ๊ณ„: ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ(LLM)๊ณผ ๋น„์ „ ์–ธ์–ด ๋ชจ๋ธ(VLM) : ์ด๋Ÿฌํ•œ ๋ชจ๋ธ๋“ค์€ ๊ฐœ๋ฐฉ ์–ดํœ˜ ์ถ”๋ก ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜์ง€๋งŒ, ๋‚ด๋ถ€ ์˜์‚ฌ๊ฒฐ์ • ๊ณผ์ •์ด ๋ถˆํˆฌ๋ช…ํ•˜๊ณ  ์˜ˆ์ธก์„ฑ์ด ๋‚ฎ๋‹ค๋Š” ๋ฌธ์ œ์ ์ด ์žˆ์Šต๋‹ˆ๋‹ค. ๋˜ํ•œ, ์‚ฌ์šฉ์ž๊ฐ€ ๋กœ๋ด‡์˜ ๊ฒฐ์ • ์ด์œ ๋ฅผ ์ดํ•ดํ•˜๊ฑฐ๋‚˜ ์ˆ˜์ •ํ•  ์ˆ˜ ์—†๊ธฐ ๋•Œ๋ฌธ์— ๋ณด์กฐ

Framework
ELANA: A Simple Energy and Latency Analyzer for LLMs

ELANA: A Simple Energy and Latency Analyzer for LLMs

ELANA๋Š” ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLMs)์˜ ์„ฑ๋Šฅ ๋ถ„์„์„ ์œ„ํ•œ ๋„๊ตฌ๋กœ, ํŠนํžˆ ์ง€์—ฐ ์‹œ๊ฐ„๊ณผ ์—๋„ˆ์ง€ ์†Œ๋น„๋ฅผ ์ค‘์ ์ ์œผ๋กœ ๋‹ค๋ฃน๋‹ˆ๋‹ค. ์ด ๋„๊ตฌ๋Š” ๋‹ค์–‘ํ•œ ํ•˜๋“œ์›จ์–ด ํ”Œ๋žซํผ์—์„œ LLMs์˜ ์‹ค์ œ ์„ฑ๋Šฅ์„ ์ธก์ •ํ•˜๊ณ  ์ตœ์ ํ™”ํ•˜๋Š” ๋ฐ ํ•„์š”ํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ELANA์˜ ํ•ต์‹ฌ ๊ธฐ๋Šฅ์€ ๋ชจ๋ธ ํฌ๊ธฐ, ํ‚ค ๊ฐ’(KV) ์บ์‹œ ํฌ๊ธฐ, ์‚ฌ์ „ ์ฑ„์šฐ๊ธฐ ์ง€์—ฐ ์‹œ๊ฐ„(TTFT), ์ƒ์„ฑ ์ง€์—ฐ ์‹œ๊ฐ„(TPOT), ๊ทธ๋ฆฌ๊ณ  ์—”๋“œํˆฌ์—”๋“œ ์ง€์—ฐ ์‹œ๊ฐ„(TTLT)์„ ๋ถ„์„ํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์„ฑ๋Šฅ ์ง€ํ‘œ๋Š” ๋ชจ๋ธ์˜ ์‹ค์ œ ์‚ฌ์šฉ์„ฑ๊ณผ ํšจ์œจ์„ฑ์„ ํ‰๊ฐ€ํ•˜๋Š”๋ฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•ฉ๋‹ˆ๋‹ค. ELANA๋Š” Hugging F

Evolutionary Architecture Search through Grammar-Based Sequence Alignment

Evolutionary Architecture Search through Grammar-Based Sequence Alignment

: ๋ณธ ๋…ผ๋ฌธ์€ ์‹ ๊ฒฝ๋ง ์•„ํ‚คํ…์ฒ˜ ๊ฒ€์ƒ‰(NAS) ๋ถ„์•ผ์—์„œ ์ค‘์š”ํ•œ ๋„์ „ ๊ณผ์ œ์ธ ํšจ๊ณผ์ ์ธ ๊ต์ฐจ ๊ต๋ฐฐ ์—ฐ์‚ฐ์ž์˜ ๊ฐœ๋ฐœ์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. NAS๋Š” ์ธ๊ณต์ง€๋Šฅ ๋ชจ๋ธ์˜ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ์ตœ์ ์˜ ๋„คํŠธ์›Œํฌ ๊ตฌ์กฐ๋ฅผ ์ž๋™์œผ๋กœ ์ฐพ๋Š” ๊ณผ์ •์ž…๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ณต์žกํ•˜๊ณ  ํ‘œํ˜„๋ ฅ์ด ๋†’์€ ๊ฒ€์ƒ‰ ๊ณต๊ฐ„์—์„œ ํšจ๊ณผ์ ์ธ ํƒ์ƒ‰์„ ์ˆ˜ํ–‰ํ•˜๋Š” ๊ฒƒ์€ ์—ฌ์ „ํžˆ ํฐ ๋„์ „๊ณผ์ œ์ž…๋‹ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ํšจ์œจ์ ์ธ ๋ฌธ๋ฒ• ๊ธฐ๋ฐ˜ ๊ต์ฐจ ๊ต๋ฐฐ ์—ฐ์‚ฐ์ž ๋ฅผ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ์‚ฐ์ž๋Š” ์ง€์—ญ ์„œ์—ด ์ •๋ ฌ ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ํ‘œํ˜„๋ ฅ ๋†’์€ NAS ๊ณต๊ฐ„์—์„œ ๋น ๋ฅด๊ณ  ํšจ๊ณผ์ ์œผ๋กœ ์ตœ์ ์˜ ์•„ํ‚คํ…์ฒ˜

FIN-bench-v2: A Unified and Robust Benchmark Suite for Evaluating Finnish Large Language Models

FIN-bench-v2: A Unified and Robust Benchmark Suite for Evaluating Finnish Large Language Models

: 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ณธ ๋…ผ๋ฌธ์€ ํ•€๋ž€๋“œ์–ด ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM)์˜ ํ‰๊ฐ€๋ฅผ ์œ„ํ•œ ์ƒˆ๋กœ์šด ๋ฒค์น˜๋งˆํฌ ์Šค์œ„ํŠธ, FIN bench v2๋ฅผ ์†Œ๊ฐœํ•ฉ๋‹ˆ๋‹ค. LLM์€ ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ(NLP) ๋ถ„์•ผ์—์„œ ํš๊ธฐ์ ์ธ ๋ฐœ์ „์„ ์ด๋Œ์—ˆ์œผ๋ฉฐ, ํŠนํžˆ ์ €์ž์› ์–ธ์–ด์ธ ํ•€๋ž€๋“œ์–ด์—์„œ ๋ชจ๋ธ ๊ฐœ๋ฐœ์— ํ•„์š”ํ•œ ํ‰๊ฐ€ ์ž์›์ด ๋ถ€์กฑํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋…ธ๋ ฅํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. 2. ๊ธฐ์กด ๋ฆฌ์†Œ์Šค์˜ ๋ฌธ์ œ์  ๋ฐ์ดํ„ฐ ํ’ˆ์งˆ : ๊ธฐ๊ณ„ ๋ฒˆ์—ญ์œผ๋กœ ์ƒ์„ฑ๋œ ๋ฐ์ดํ„ฐ์…‹์€ ์ธ๊ฐ„ ๊ฒ€ํ†  ์—†์ด ์ œ๊ณต๋˜์–ด, ๋ชจ๋ธ ์„ฑ๋Šฅ์„ ์ œํ•œํ•  ์ˆ˜ ์žˆ๋Š” ์š”์ธ์ด ๋ฉ๋‹ˆ๋‹ค. ์ž‘์—… ๊ตฌ์„ฑ : ์ž‘์—… ๊ตฌ์„ฑ์ด ๋‹จ์ˆœํ•˜๋ฉฐ ํ”„๋กฌํ”„ํŠธ ๊ฐ์ˆ˜์„ฑ์„ ๊ณ ๋ ค

Model
No Image

From Theory of Mind to Theory of Environment: Counterfactual Simulation of Latent Environmental Dynamics

์ด ๋…ผ๋ฌธ์€ ๊ธฐ์กด์˜ ์šด๋™ ์ œ์–ด ์ด๋ก ๊ณผ ์‚ฌํšŒ์ธ์ง€ ์ด๋ก ์„ ์œตํ•ฉํ•˜์—ฌ โ€˜ํ™˜๊ฒฝ ์ด๋ก (Theory of Environment)โ€™์ด๋ผ๋Š” ์ƒˆ๋กœ์šด ๊ฐœ๋…์„ ์ œ์‹œํ•œ๋‹ค. ์ „ํ†ต์ ์œผ๋กœ ์ฒ™์ถ”๋™๋ฌผ์˜ ์šด๋™ ์‹œ์Šคํ…œ์€ ๊ณ ์ฐจ์› ์ž์œ ๋„๋ฅผ ์ €์ฐจ์› ์ œ์–ด ๋ณ€์ˆ˜๋กœ ์••์ถ•ํ•จ์œผ๋กœ์จ ์‹ ๊ฒฝ ์ž์›์˜ ํšจ์œจ์„ฑ์„ ํ™•๋ณดํ•œ๋‹ค๋Š” ์ฐจ์› ์ถ•์†Œ(dimโ€‹ensionality reduction) ์›๋ฆฌ๋ฅผ ๋”ฐ๋ฅธ๋‹ค. ์ด๋Š” ๋‡Œ๊ฐ€ ๊ทผ์œก ํ™œ๋™์„ ์ง์ ‘ ์ œ์–ดํ•˜๊ธฐ๋ณด๋‹ค โ€˜์šด๋™ ํ”„๋ฆฌ๋ฏธํ‹ฐ๋ธŒโ€™๋ฅผ ์กฐํ•ฉํ•ด ๋ณต์žกํ•œ ํ–‰๋™์„ ์ƒ์„ฑํ•œ๋‹ค๋Š” ๋ชจ๋ธ๊ณผ ์ผ๋งฅ์ƒํ†ตํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ํšจ์œจ์„ฑ์€ ํ™˜๊ฒฝ์ด ๋น„๊ต์  ์•ˆ์ •์ ์ด๊ณ , ํ–‰๋™โ€‘๊ฒฐ๊ณผ ๊ด€๊ณ„๊ฐ€ ๋ช…์‹œ์ ์ธ ๊ฒฝ์šฐ์—

Quantitative Biology
Improving VQA Reliability: A Dual-Assessment Approach with Self-Reflection and Cross-Model Verification

Improving VQA Reliability: A Dual-Assessment Approach with Self-Reflection and Cross-Model Verification

๋ณธ ๋…ผ๋ฌธ์€ ์‹œ๊ฐโ€‘์–ธ์–ด ๋ชจ๋ธ(VLM)์ด VQA ์ž‘์—…์—์„œ ๋ณด์—ฌ์ฃผ๋Š” ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๋ชจ๋ธ์ด ์ƒ์„ฑํ•˜๋Š” ๋‹ต๋ณ€์ด ์ข…์ข… ์‹ค์ œ ์ด๋ฏธ์ง€ ๋‚ด์šฉ๊ณผ ๋ถˆ์ผ์น˜ํ•˜๋Š” โ€˜ํ™˜๊ฐ(hallucination)โ€™ ๋ฌธ์ œ์— ์ง๋ฉดํ•˜๊ณ  ์žˆ์Œ์„ ์ง€์ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ํ™˜๊ฐ์€ ํŠนํžˆ ๋ชจ๋ธ์ด ๋†’์€ ํ™•์‹ (confidence)์„ ๋ณด์ผ ๋•Œ ์‚ฌ์šฉ์ž๊ฐ€ ๊ฒฐ๊ณผ๋ฅผ ์‹ ๋ขฐํ•˜๊ฒŒ ๋งŒ๋“ค๋ฉฐ, ์‹ค์šฉ์ ์ธ ์‹œ์Šคํ…œ์—์„œ ์น˜๋ช…์ ์ธ ์˜ค๋ฅ˜๋กœ ์ด์–ด์งˆ ์œ„ํ—˜์ด ์žˆ๋‹ค. ์ €์ž๋“ค์€ ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋‘ ๊ฐ€์ง€ ์ƒ๋ณด์ ์ธ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๊ฒฐํ•ฉํ•œ Dualโ€‘Assessment ํ”„๋ ˆ์ž„์›Œํฌ, ์ฆ‰ DAVR์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ ๋ฉ”์ปค๋‹ˆ์ฆ˜์ธ

Model
Suzume-chan: Your Personal Navigator as an Embodied Information Hub

Suzume-chan: Your Personal Navigator as an Embodied Information Hub

๋ณธ ๋…ผ๋ฌธ์€ ๊ธฐ๊ณ„์™€ ์ธ๊ฐ„ ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ์„ ๊ฐ•ํ™”ํ•˜๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, 'Social Presence Theory'์— ๊ทผ๊ฑฐํ•˜์—ฌ ์‹ค์ œ ๋Œ€๋ฉด ์˜์‚ฌ์†Œํ†ต์—์„œ ๋А๋ผ๋Š” ์—ฐ๊ฒฐ๊ฐ์„ ๋””์ง€ํ„ธ ๋„๊ตฌ๋ฅผ ํ†ตํ•ด ์žฌํ˜„ํ•˜๋ ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ์Šค์ฆˆ๋ฉ”์งฑ์ด๋ผ๋Š” ์†Œํ”„ํŠธ AI ๋™๋ฐ˜์ž๋ฅผ ๊ฐœ๋ฐœํ•˜๊ณ  ์ด๋ฅผ ํ™œ์šฉํ•œ 'Embodied Information Hub' ๊ฐœ๋…์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ์ด ํ”„๋กœํ† ํƒ€์ž…์€ ์–ธ์–ด ๋ชจ๋ธ๊ณผ RAG ๊ธฐ์ˆ ์„ ํ†ตํ•ฉํ•˜์—ฌ ์‚ฌ์šฉ์ž์˜ ๊ตฌ์–ด ์„ค๋ช…์„ ํ•™์Šตํ•˜๊ณ  ๋Œ€ํ™”๋ฅผ ํ†ตํ•ด ์‘๋‹ตํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ์ž‘๋™ํ•ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ๋ฒ•์€ ๋‹จ์ˆœํžˆ ์ •๋ณด ์ œ๊ณต์— ๊ทธ์น˜๋Š” ๊ฒƒ์ด ์•„

The body is not there to compute: Comment on 'Informational embodiment: Computational role of information structure in codes and robots' by Pitti et al

The body is not there to compute: Comment on 'Informational embodiment: Computational role of information structure in codes and robots' by Pitti et al

๋ณธ ๋…ผํ‰์€ Pitti ๋“ฑ(2023)์ด ์ œ์‹œํ•œ โ€œ์ •๋ณด์  ๊ตฌํ˜„โ€ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ๋น„ํŒ์ ์œผ๋กœ ์žฌ๊ฒ€ํ† ํ•œ๋‹ค. ์ €์ž๋“ค์€ ๋ชธ์„ โ€˜์ •๋ณด ์ฒ˜๋ฆฌ ์žฅ์น˜โ€™ ํ˜น์€ โ€˜๊ณ„์‚ฐ๊ธฐโ€™๋กœ ๋ณด๋Š” ๊ด€์ ์„ ๋„˜์–ด์„œ, ๋ชธ ์ž์ฒด๊ฐ€ ํ™˜๊ฒฝ๊ณผ์˜ ์ง€์†์ ์ด๊ณ  ๋™์ ์ธ ์ƒํ˜ธ์ž‘์šฉ์„ ํ†ตํ•ด ๊ธฐ๋Šฅ์„ ๊ตฌํ˜„ํ•œ๋‹ค๋Š” ์ƒ๋ฌผํ•™์ ยท๊ณตํ•™์  ๊ทผ๊ฑฐ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ์ฒซ์งธ, ์ง„ํ™”์ƒ๋ฌผํ•™์  ์ฆ๊ฑฐ๋Š” ์‹ ๊ฒฝ๊ณ„์™€ ๊ทผ์œก, ๊ณจ๊ฒฉ ๋“ฑ ๋ฌผ๋ฆฌ์  ๊ตฌ์กฐ๊ฐ€ ๋‹จ์ˆœํžˆ ์‹ ํ˜ธ๋ฅผ ์ „์†กํ•˜๊ณ  ์—ฐ์‚ฐ์„ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด ์กด์žฌํ•œ๋‹ค๊ธฐ๋ณด๋‹ค, ์—๋„ˆ์ง€ ํšจ์œจ์„ฑ, ๊ตฌ์กฐ์  ์•ˆ์ •์„ฑ, ๊ทธ๋ฆฌ๊ณ  ํ™˜๊ฒฝ ์ ์‘์„ฑ์„ ๊ทน๋Œ€ํ™”ํ•˜๊ธฐ ์œ„ํ•ด ํ˜•์„ฑ๋˜์—ˆ๋‹ค๋Š” ์ ์„ ๊ฐ•์กฐํ•œ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ๊ณค์ถฉ์˜ ๋ณตํ•ฉ ๋ˆˆ์€ ๊ณ ํ•ด์ƒ

The Erosion of LLM Signatures: Can We Still Distinguish Human and LLM-Generated Scientific Ideas After Iterative Paraphrasing?

The Erosion of LLM Signatures: Can We Still Distinguish Human and LLM-Generated Scientific Ideas After Iterative Paraphrasing?

: ๋ณธ ๋…ผ๋ฌธ์€ ์ธ๊ณต์ง€๋Šฅ ์–ธ์–ด ๋ชจ๋ธ(LLM)์ด ์ƒ์„ฑํ•œ ๊ณผํ•™ ์•„์ด๋””์–ด์™€ ์ธ๊ฐ„์ด ์ž‘์„ฑํ•œ ์•„์ด๋””์–ด๋ฅผ ๊ตฌ๋ณ„ํ•˜๋Š” ๋Šฅ๋ ฅ์„ ์ฒด๊ณ„์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜๊ณ , ์ด๋ฅผ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ํŒจ๋Ÿฌํ”„๋ž˜์ง• ๋‹จ๊ณ„๋ฅผ ๊ฑฐ์นœ ํ›„์˜ ์„ฑ๋Šฅ์„ ๋ถ„์„ํ•ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” LLM๊ณผ ์ธ๊ฐ„์˜ ์•„์ด๋””์–ด๋ฅผ ๊ตฌ๋ถ„ํ•˜๋Š” ๋ฐ ์žˆ์–ด ์ค‘์š”ํ•œ ๋ฌธ์ œ๋ฅผ ์ œ๊ธฐํ•˜๋ฉฐ, ํŠนํžˆ ๋ฐ˜๋ณต์ ์ธ ํŒจ๋Ÿฌํ”„๋ ˆ์ด์ง•์ด ์–ด๋–ป๊ฒŒ LLM ์ƒ์„ฑ ์•„์ด๋””์–ด์˜ ๋ณธ์งˆ์  ํŠน์„ฑ์„ ๋ณ€ํ™”์‹œํ‚ค๊ณ  ํƒ์ง€ ๋Šฅ๋ ฅ์„ ์ €ํ•˜์‹œํ‚ค๋Š”์ง€๋ฅผ ์‚ดํŽด๋ด…๋‹ˆ๋‹ค. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์ค‘์š”์„ฑ LLM ๊ธฐ์ˆ ์˜ ๋ฐœ์ „์€ ๋ณต์žกํ•œ ์ธ์ง€ ํ™œ๋™ ์ˆ˜ํ–‰ ๋Šฅ๋ ฅ๊นŒ์ง€ ๋ณด์—ฌ์ฃผ๋ฉฐ, ์ด๋กœ ์ธํ•ด LLM์ด ๊ณผํ•™์  ์•„์ด๋””์–ด ์ƒ์„ฑ์— ์ฐธ์—ฌํ• 

The stationary focus of the Kiepert parabola over a special Poncelet triangle family

The stationary focus of the Kiepert parabola over a special Poncelet triangle family

ํ‚ค์—ํผํŠธ ๋‚ดํฌ๋ฌผ(Kiepert inโ€‘parabola)์€ ์‚ผ๊ฐํ˜•์˜ ๊ฐ ๊ผญ์ง“์ ์— ์ผ์ •ํ•œ ๊ฐ์„ ์ด๋ฃจ๋Š” ์ง์„ ์„ ๊ทธ๋ ค ๋งŒ๋“  Kiepert ์‚ผ๊ฐํ˜•๋“ค์˜ ์™ธ์ ‘์›์— ๋Œ€ํ•œ ํŠน์ˆ˜ํ•œ ํฌ๋ฌผ์„ ์ด๋‹ค. ์ด ํฌ๋ฌผ์„ ์˜ ์ดˆ์ ์€ ์›๋ž˜ ์‚ผ๊ฐํ˜•์˜ ๋ชจ์–‘์— ๋”ฐ๋ผ ์›€์ง์ด์ง€๋งŒ, ํŠน์ •ํ•œ ๋ณ€ํ™˜๊ตฐ ์•„๋ž˜์—์„œ๋Š” ๋ถˆ๋ณ€์„ฑ์„ ๋ณด์ผ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ํŠนํžˆ โ€œ์›์— ๋‚ด์ ‘ํ•˜๋Š” ํฌ๋„ค๋ ˆ ์‚ผ๊ฐํ˜•(Poncelet triangles)โ€์ด๋ผ๋Š” ๊ณ ์ „์ ์ธ ๊ธฐํ•˜ํ•™์  ๊ตฌ์„ฑ์— ์ฃผ๋ชฉํ•œ๋‹ค. ํฌ๋„ค๋ ˆ ์‚ผ๊ฐํ˜•์€ ๋‘ ์›(ํ•˜๋‚˜๋Š” ๋‚ด๋ถ€, ํ•˜๋‚˜๋Š” ์™ธ๋ถ€) ์‚ฌ์ด์—์„œ ํ•œ ์ ์„ ์‹œ์ž‘์œผ๋กœ ์ ‘์„ ๊ณผ ํ˜ธ๋ฅผ ๊ต๋Œ€๋กœ ๊ทธ๋ฆฌ๋ฉฐ ์ƒ์„ฑ๋˜๋Š” ์‚ผ๊ฐํ˜•๋“ค์˜ ๋ฌดํ•œ

A Linear Expectation Constraint for Selective Prediction and Routing with False-Discovery Control

A Linear Expectation Constraint for Selective Prediction and Routing with False-Discovery Control

: ๋ณธ ๋…ผ๋ฌธ์€ ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ(LLM)๊ณผ ๋น„์ „ ์–ธ์–ด ๋ชจ๋ธ(LVLM)์˜ ์‹ ๋ขฐ์„ฑ ์žˆ๋Š” ์˜ˆ์ธก์„ ์œ„ํ•œ ์„ ํ˜• ๊ธฐ๋Œ€๊ฐ’ ์ œ์•ฝ(LEC) ๋ฐฉ๋ฒ•์„ ์†Œ๊ฐœํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด FDR(False Discovery Rate)์„ ์—„๊ฒฉํ•˜๊ฒŒ ์ œ์–ดํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” LEC๊ฐ€ ๋‹ค์–‘ํ•œ ํ‰๊ฐ€ ์„ค์ •์—์„œ ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด์ด๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜๋ฉฐ, ํŠนํžˆ ์‹ ๋ขฐ ๊ตฌ๊ฐ„ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•๋ณด๋‹ค ๋” ๋งŽ์€ ํ—ˆ์šฉ ๊ฐ€๋Šฅํ•œ ์ƒ˜ํ”Œ์„ ์œ ์ง€ํ•˜๋ฉด์„œ๋„ FDR ์ œ์–ด๋ฅผ ์—„๊ฒฉํ•˜๊ฒŒ ํ•  ์ˆ˜ ์žˆ์Œ์„ ์ž…์ฆํ•œ๋‹ค. 1. LEC์˜ ๊ฐœ๋…๊ณผ ํ•„์š”์„ฑ LEC๋Š” ์„ ํƒ์  ์˜ˆ์ธก ๋ฌธ์ œ๋ฅผ ํ†ต๊ณ„์  ์ œ์•ฝ์œผ๋กœ ์žฌ๊ตฌ์„ฑํ•˜๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์ด๋‹ค.

An AI Monkey Gets Grapes for Sure -- Sphere Neural Networks for Reliable Decision-Making

An AI Monkey Gets Grapes for Sure -- Sphere Neural Networks for Reliable Decision-Making

: ๋ณธ ๋…ผ๋ฌธ์€ ๊ณ ์œ„ํ—˜ ์‘์šฉ ๋ถ„์•ผ์—์„œ ํ•„์ˆ˜์ ์ธ ์‹ ๋ขฐ์„ฑ ์žˆ๋Š” ์˜์‚ฌ๊ฒฐ์ • ๋ฐฉ๋ฒ•๋ก ์„ ๊ฐœ๋ฐœํ•˜๊ธฐ ์œ„ํ•ด, ์–ธ์–ด ๋ชจ๋ธ(LLM)์˜ ํ•œ๊ณ„๋ฅผ ํƒ์ƒ‰ํ•˜๊ณ  ์ด๋ฅผ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋Š” ๋Œ€์•ˆ์„ ์ œ์‹œํ•œ๋‹ค. LLM์€ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ๋›ฐ์–ด๋‚œ ์„ฑ๊ณผ๋ฅผ ๋ณด์—ฌ์ฃผ์ง€๋งŒ, ๋‹จ์ˆœ ์ถ”๋ก ์—์„œ๋Š” ์—ฌ์ „ํžˆ ์˜ค๋ฅ˜๋ฅผ ๋ฒ”ํ•˜๋ฉฐ, ์˜ฌ๋ฐ”๋ฅธ ์˜ˆ์ธก๊ณผ ์ž˜๋ชป๋œ ์„ค๋ช… ์‚ฌ์ด์˜ ๋ถˆ์ผ์น˜๊ฐ€ ๋ฐœ์ƒํ•˜๋Š” ๋ฌธ์ œ์ ์ด ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์ ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ตฌํ˜• ์‹ ๊ฒฝ๋ง(Sphere Neural Network)์„ ํ™œ์šฉํ•˜์—ฌ ์—ฐ์—ญ์  ์ถ”๋ก  ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ๊ตฌํ˜• ์‹ ๊ฒฝ๋ง์€ n์ฐจ์› ๊ตฌ๋ฉด์—์„œ ์›์„ ๊ตฌ์„ฑํ•˜์—ฌ ๋…ผ๋ฆฌ ์ถ”

Computer Science Network Artificial Intelligence
AncientBench: Towards Comprehensive Evaluation on Excavated and Transmitted Chinese Corpora

AncientBench: Towards Comprehensive Evaluation on Excavated and Transmitted Chinese Corpora

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ณธ ๋…ผ๋ฌธ์€ ๊ณ ๋Œ€ ์ค‘๊ตญ์–ด ๋ฌธ์„œ๋ฅผ ์ดํ•ดํ•˜๋Š” ๋ฐ ์žˆ์–ด ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ(LLM)์˜ ์—ญํ• ๊ณผ ์ค‘์š”์„ฑ์„ ๊ฐ•์กฐํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, ๋ฐœ๊ตด๋œ ๊ณ ๋Œ€ ๋ฌธ์„œ๋Š” ์›์‹œ์ ์ด๊ณ  ์ง„์‹คํ•œ ์–ธ์–ด ์–‘์‹์„ ๋ณด์œ ํ•˜๊ณ  ์žˆ์–ด, ์ด๋“ค ๋ฌธ์„œ์— ๋Œ€ํ•œ ์ฒด๊ณ„์ ์ธ ํ‰๊ฐ€ ๋ฐฉ๋ฒ•๋ก ์ด ํ•„์š”ํ•˜๋‹ค๋Š” ์ ์„ ์ง€์ ํ•ฉ๋‹ˆ๋‹ค. ๊ธฐ์กด ๋ฒค์น˜๋งˆํฌ๋Š” ์ฃผ๋กœ ํ˜„๋Œ€ ์ค‘๊ตญ์–ด์™€ ์ „์Šน๋œ ๊ณ ๋Œ€ ๋ฌธ์„œ๋ฅผ ๋‹ค๋ฃจ๊ณ  ์žˆ์ง€๋งŒ, ๋ฐœ๊ตด๋œ ๋ฌธ์„œ์˜ ํŠน์„ฑ๊ณผ ๋ณต์žก์„ฑ์„ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•˜๊ณ  ์žˆ์–ด, ์ด์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ํ‰๊ฐ€ ์ฒด๊ณ„์ธ AncientBench ๋ฅผ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. 2. AncientBench ๊ตฌ์„ฑ ๋ฐ ํŠน์ง• AncientBench๋Š”

Arc Spline Approximation of Envelopes of Evolving Planar Domains

Arc Spline Approximation of Envelopes of Evolving Planar Domains

: ๋ณธ ๋…ผ๋ฌธ์€ ํ‰๋ฉด ๋„๋ฉ”์ธ์ด ๋ฏธ๋ฆฌ ์ •์˜๋œ ๊ถค์ ์„ ๋”ฐ๋ผ ์Šค์œ•ํ•˜๋Š” ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋ณต์žกํ•œ ๋„๋ฉ”์ธ ์ƒ์„ฑ ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃน๋‹ˆ๋‹ค. ํŠนํžˆ, ์ง„ํ™” ๋„๋ฉ”์ธ์˜ ๋ด‰์šฐ๋ฆฌ๋ฅผ ์•„ํฌ ์Šคํ”Œ๋ผ์ธ์œผ๋กœ ๊ทผ์‚ฌํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๋ณต์žกํ•œ ํ˜•ํƒœ ์„ค๊ณ„์— ์œ ์šฉํ•œ ๋„๊ตฌ๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ์ง„ํ™” ๋„๋ฉ”์ธ์€ ํฌ๊ธฐ์™€ ๋ชจ์–‘์ด ์‹œ๊ฐ„์— ๋”ฐ๋ผ ๋ณ€ํ™”ํ•˜๋Š” ํ‰๋ฉด ๋„๋ฉ”์ธ์ž…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋„๋ฉ”์ธ์˜ ๋ด‰์šฐ๋ฆฌ๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ๊ณ„์‚ฐํ•˜๋Š” ๊ฒƒ์€ ๋ณต์žกํ•œ ํ˜•ํƒœ ์„ค๊ณ„์—์„œ ์ค‘์š”ํ•œ ๋ฌธ์ œ๋กœ, ์ด๋Š” ์Šค์œ• ๋ณผ๋ฅจ๊ณผ ๊ด€๋ จ๋œ ์—ฐ๊ตฌ์—์„œ ์ฃผ์š” ๊ด€์‹ฌ์‚ฌ์˜€์Šต๋‹ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋ฉ”๋””์–ผ ์ถ• ๋ณ€ํ™˜(MAT)์„ ํ™œ์šฉํ•˜์—ฌ ์ง„ํ™”

Beyond Additivity: Sparse Isotonic Shapley Regression toward Nonlinear Explainability

Beyond Additivity: Sparse Isotonic Shapley Regression toward Nonlinear Explainability

๋ณธ ๋…ผ๋ฌธ์€ ๊ธฐ์กด์˜ ์ƒคํ”Œ๋ฆฌ ๊ฐ€์น˜ ๋ถ„์„์„ ๋„˜์–ด, ๋น„์„ ํ˜•์ ์ธ ํŠน์ง•๊ณผ ํฌ์†Œ์„ฑ์„ ๊ณ ๋ คํ•˜๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์ธ SISR(Sparse Isotonic Shapley Regression)์„ ์ œ์•ˆํ•œ๋‹ค. ์ด๋Š” ์„ค๋ช… ๊ฐ€๋Šฅํ•œ ์ธ๊ณต์ง€๋Šฅ(XAI) ๋ถ„์•ผ์—์„œ ์ค‘์š”ํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ๋…ธ๋ ฅ์˜ ์ผํ™˜์ด๋‹ค. 1. ์ƒคํ”Œ๋ฆฌ ๊ฐ€์น˜์˜ ํ•œ๊ณ„์™€ ํ•„์š”์„ฑ ์ƒคํ”Œ๋ฆฌ ๊ฐ€์น˜๋Š” ๊ฒฝ์ œํ•™๊ณผ ํ˜‘๋ ฅ ๊ฒŒ์ž„ ์ด๋ก ์—์„œ ์ค‘์š”ํ•œ ๊ฐœ๋…์œผ๋กœ, ๋ชจ๋ธ์˜ ํŠน์ง• ์ค‘์š”๋„๋ฅผ ์ •๋Ÿ‰ํ™”ํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด ์ ‘๊ทผ๋ฒ•์€ ์„ ํ˜•์ ์ธ ๊ฐ€์ •์„ ๋ฐ”ํƒ•์œผ๋กœ ํ•˜์—ฌ ๋ณต์žกํ•œ ๋น„๊ฐ€์šฐ์‹œ์•ˆ ๋ฐ์ดํ„ฐ์™€ ๋‹ค์–‘ํ•œ ๋ณด์ƒ ๊ตฌ์กฐ์— ๋Œ€ํ•œ ์„ค๋ช…๋ ฅ์„ ์ œ์•ฝํ•œ๋‹ค.

Dynamically Scaled Activation Steering

Dynamically Scaled Activation Steering

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋ณธ ๋…ผ๋ฌธ์€ ์ƒ์„ฑ ๋ชจ๋ธ๋ง์˜ ํ•ต์‹ฌ ๊ณผ์ œ์ธ ์ธ๊ฐ„ ๊ธฐ๋Œ€์— ๋ถ€ํ•ฉํ•˜๋Š” ๋ชจ๋ธ ํ–‰๋™๊ณผ ํ•ด๋กญ๊ฑฐ๋‚˜ ํŽธํ–ฅ๋œ ์ถœ๋ ฅ์„ ์–ต์ œํ•˜๋ฉด์„œ ์ผ๋ฐ˜ ๋Šฅ๋ ฅ์„ ์œ ์ง€ํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ๋‹ค. ํŠนํžˆ, ํ™œ์„ฑํ™” ์œ ๋„๋Š” ์ตœ๊ทผ ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋Š” ๊ธฐ์ˆ ๋กœ, ๋‚ด๋ถ€ ํ‘œํ˜„์„ ์›ํ•˜๋Š” ํ–‰๋™์— ์ง์ ‘ ์กฐ์ž‘ํ•˜์—ฌ ์„ธ๋ถ„ํ™”๋œ ์ œ์–ด ๋ฐ ํ•ด์„์„ฑ์„ ์ œ๊ณตํ•˜๋ฉฐ ๋ชจ๋ธ ๊ฐ€์ค‘์น˜๋ฅผ ์ˆ˜์ •ํ•˜์ง€ ์•Š๋Š”๋‹ค๋Š” ์žฅ์ ์ด ์žˆ๋‹ค. 2. DSAS์˜ ํ•ต์‹ฌ ์•„์ด๋””์–ด DSAS๋Š” ๊ฐœ์ž… ๊ฐ•๋„ ๋งค๊ฐœ๋ณ€์ˆ˜ ฮป๋ฅผ ๊ฐ ํ† ํฐ ๋˜๋Š” ๊ณต๊ฐ„ ํŠน์ง•๋ณ„๋กœ ์ ์‘์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์„ ํ˜• ํšŒ๊ท€๊ธฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์›์น˜ ์•Š๋Š” ์ž„๋ฒ ๋”ฉ ํ™•๋ฅ ์„ ๊ณ„์‚ฐํ•˜๊ณ ,

E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Driving

E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Driving

๋ณธ ๋…ผ๋ฌธ์€ ์ž์œจ์ฃผํ–‰ ์ฐจ๋Ÿ‰(AV)์—์„œ ์ธ๊ฐ„ ์ค‘์‹ฌ์ ์ธ ์ ‘๊ทผ ๋ฐฉ์‹์„ ๊ฐ•์กฐํ•˜๊ณ , ์ด๋ฅผ ์œ„ํ•ด ๊ฐ์ • ์ธ์‹๊ณผ ๊ณต๊ฐ„ ์ถ”๋ก ์„ ํ†ตํ•ฉํ•œ End to End Vision Language Action (E3AD) ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๊ธฐ์กด์˜ E2E ์ž์œจ์ฃผํ–‰ ์‹œ์Šคํ…œ์ด ์Šน๊ฐ์˜ ์‹ ๋ขฐ์™€ ์ˆ˜์šฉ์„ฑ์„ ์ €ํ•ดํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ฐ์ • ์ธ์‹๊ณผ ๊ณต๊ฐ„ ์ถ”๋ก ์„ ํ†ตํ•ฉํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์‹œํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. 1. ์ธ๊ฐ„ ์ค‘์‹ฌ์  ์ž์œจ์ฃผํ–‰์˜ ํ•„์š”์„ฑ ์ž์œจ์ฃผํ–‰ ๊ธฐ์ˆ ์€ ๋ชจ๋“ˆํ˜• ํŒŒ์ดํ”„๋ผ์ธ์—์„œ ๋น„์ „ ์–ธ์–ด ํ–‰๋™(VLA) ์ „์ฒด ์‹œ์Šคํ…œ์œผ๋กœ ์ง„ํ™”ํ•ด์™”์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ๊ธฐ์ˆ ์  ๋ฐœ

Model
ElecTwit: A Framework for Studying Persuasion in Multi-Agent Social Systems

ElecTwit: A Framework for Studying Persuasion in Multi-Agent Social Systems

: ๋ณธ ๋…ผ๋ฌธ์€ ElecTwit ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ํ†ตํ•ด LLM์ด ๋‹ค์ค‘ ์—์ด์ „ํŠธ ์‚ฌํšŒ ์‹œ์Šคํ…œ์—์„œ ์–ด๋–ป๊ฒŒ ์„ค๋“๋ ฅ์„ ๋ฐœํœ˜ํ•˜๋Š”์ง€ ๋ถ„์„ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๊ธฐ์กด์˜ ๋‹จ์ˆœํ™”๋œ ๊ฒŒ์ž„ ๊ธฐ๋ฐ˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ๋„˜์–ด, ์‹ค์ œ ์‚ฌํšŒ ์ƒํ˜ธ์ž‘์šฉ์— ๋” ๊ฐ€๊นŒ์šด ํ™˜๊ฒฝ์„ ๊ตฌ์ถ•ํ•˜์—ฌ LLM์˜ ํ–‰๋™ ํŒจํ„ด๊ณผ ์„ค๋“ ์ „๋žต์„ ํƒ๊ตฌํ•ฉ๋‹ˆ๋‹ค. 1. ElecTwit ํ”Œ๋žซํผ ๋ฐ ์‹คํ—˜ ์„ค๊ณ„ ElecTwit๋Š” ํŠธ์œ„ํ„ฐ(X)๋ฅผ ๋ชจ๋ฐฉํ•œ ์†Œ์…œ ๋ฏธ๋””์–ด ํ”Œ๋žซํผ์œผ๋กœ, ํˆฌํ‘œ์ž์™€ ํ›„๋ณด์ž๊ฐ€ ์ƒํ˜ธ์ž‘์šฉํ•˜๋Š” ์ •์น˜ ์„ ๊ฑฐ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•ฉ๋‹ˆ๋‹ค. ๊ฐ ์—์ด์ „ํŠธ๋Š” 280์ž ์ œํ•œ ๋‚ด์—์„œ ๊ฒŒ์‹œ๋ฌผ๊ณผ ๋Œ“๊ธ€์„ ์ž‘์„ฑํ•˜๊ณ , ์ข‹์•„์š”๋ฅผ ๋ˆ„

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