Blog
276개 중 1-12번째 포스트
![[논문 리뷰] From AGI to ASI](/assets/images/blog/20260613-paper-2606-12683-from-agi-to-asi.jpg)
[논문 리뷰] From AGI to ASI
Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations. Achie...
![[논문 리뷰] End-to-End Context Compression at Scale](/assets/images/blog/20260613-paper-2606-09659-end-to-end-context-compression.jpg)
[논문 리뷰] End-to-End Context Compression at Scale
Long-context language model inference is bottlenecked by memory, as the KV cache grows with context length. Recent techniques to compress the KV cache fall short: they either degrade model quality sub...
![[논문 리뷰] LeanMarathon: Toward Reliable AI Co-Mathematicians through Long-Horizon Lean Autoformalization](/assets/images/blog/20260607-paper-2606-05400-leanmarathon-toward-reliable-a.jpg)
[논문 리뷰] LeanMarathon: Toward Reliable AI Co-Mathematicians through Long-Horizon Lean Autoformalization
Long-horizon autoformalization of research mathematics fails not only at hard lemmas, but at scale: statements drift, dependencies tangle, context decays, and local repairs corrupt distant work. We pr...
![[논문 리뷰] Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence](/assets/images/blog/20260607-paper-2606-01444-self-revising-discovery-system.jpg)
[논문 리뷰] Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence
Scientific discovery is not only answer generation but revision of the representational regime in which evidence, artifacts, operations, and verifiers are typed. We develop a category-theoretic accoun...
![[논문 리뷰] Memory Caching: RNNs with Growing Memory](/assets/images/blog/20260607-paper-2602-24281-memory-caching-rnns-with-growi.jpg)
[논문 리뷰] Memory Caching: RNNs with Growing Memory
Transformers have been established as the de-facto backbones for most recent advances in sequence modeling, mainly due to their growing memory capacity that scales with the context length. While plaus...
![[논문 리뷰] From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning](/assets/images/blog/20260607-paper-2505-17117-from-tokens-to-thoughts-how-ll.jpg)
[논문 리뷰] From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning
Humans organize knowledge into compact conceptual categories that balance compression with semantic richness. Large Language Models (LLMs) exhibit impressive linguistic abilities, but whether they nav...
![[논문 리뷰] LT2: Linear-Time Looped Transformers](/assets/images/blog/20260603-paper-2605-20670-lt2-linear-time-looped-transfo.jpg)
[논문 리뷰] LT2: Linear-Time Looped Transformers
Looped Transformers (LT) have emerged as a powerful architecture by iterating their layers multiple times before decoding the final token. However, pairing them with full attention retains quadratic c...
![[논문 리뷰] Hallucinations Undermine Trust; Metacognition is a Way Forward](/assets/images/blog/20260603-paper-2605-01428-hallucinations-undermine-trust.jpg)
[논문 리뷰] Hallucinations Undermine Trust; Metacognition is a Way Forward
Despite significant strides in factual reliability, errors -- often termed hallucinations -- remain a major concern for generative AI, especially as LLMs are increasingly expected to be helpful in mor...
![[논문 리뷰] AI Must Embrace Specialization via Superhuman Adaptable Intelligence](/assets/images/blog/20260603-paper-2602-23643-ai-must-embrace-specialization.jpg)
[논문 리뷰] AI Must Embrace Specialization via Superhuman Adaptable Intelligence
Everyone from AI executives and researchers to doomsayers, politicians, and activists is talking about Artificial General Intelligence (AGI). Yet, they often don't seem to agree on its exact definitio...
![[논문 리뷰] AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation](/assets/images/blog/20260602-paper-2605-28655-autoscientists-self-organizing.jpg)
[논문 리뷰] AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation
Scientific research proceeds through iterative cycles of hypothesis generation, experiment design, execution, and revision. AI agents can automate parts of this process, but existing approaches typica...
![[논문 리뷰] AI for Auto-Research: Roadmap & User Guide](/assets/images/blog/20260602-paper-2605-18661-ai-for-auto-research-roadmap-a.jpg)
[논문 리뷰] AI for Auto-Research: Roadmap & User Guide
AI-assisted research is crossing a threshold: fully automated systems can now generate research papers for as little as $15, while long-horizon agents can execute experiments, draft manuscripts, and s...
![[논문 리뷰] Neural Weight Norm = Kolmogorov Complexity](/assets/images/blog/20260602-paper-2605-10878-neural-weight-norm-kolmogorov-.jpg)
[논문 리뷰] Neural Weight Norm = Kolmogorov Complexity
Why does weight decay work? We prove that, in any fixed-precision regime, the smallest weight norm of a looped neural network outputting a binary string equals the Kolmogorov complexity of that string...