283개 중 1-12번째 포스트
![[논문 리뷰] TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models](/assets/images/blog/20261005-paper-2609-37989-tabfm-auto-self-evolving-pipel.jpg)
[논문 리뷰] TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models
Tabular foundation models achieve strong zero-shot accuracy on structured data by pretraining on synthetic tables, but they ignore the column names, task descriptions, and auxiliary files that carry d...
![[논문 리뷰] The Teacher Is a Direction, Not a Destination: Extrapolating RL-Induced Representation Residuals in On-Policy Distillation](/assets/images/blog/20261003-paper-2609-36484-the-teacher-is-a-direction-not.jpg)
[논문 리뷰] The Teacher Is a Direction, Not a Destination: Extrapolating RL-Induced Representation Residuals in On-Policy Distillation
On-policy distillation (OPD) trains a student to match the teacher's next-token distributions on the student's own trajectories and has yielded substantial empirical gains. Generalized variants allow ...
![[논문 리뷰] RePo: Language Models with Context Re-Positioning](/assets/images/blog/20260626-paper-2512-14391-repo-language-models-with-cont.jpg)
[논문 리뷰] RePo: Language Models with Context Re-Positioning
In-context learning is fundamental to modern Large Language Models (LLMs); however, prevailing architectures impose a rigid and fixed contextual structure by assigning linear or constant positional in...
![[논문 리뷰] Self-Harness: Harnesses That Improve Themselves](/assets/images/blog/20260624-paper-2606-09498-self-harness-harnesses-that-im.jpg)
[논문 리뷰] Self-Harness: Harnesses That Improve Themselves
The performance of LLM-based agents is jointly shaped by their base models and the harnesses that mediate their interaction with the environment. Because different models exhibit distinct behaviors, e...
![[논문 리뷰] When Does LeJEPA Learn a World Model?](/assets/images/blog/20260624-paper-2605-26379-when-does-lejepa-learn-a-world.jpg)
[논문 리뷰] When Does LeJEPA Learn a World Model?
A representation that scrambles the true degrees of freedom of the world cannot support reliable planning or compositional generalization. We prove that LeJEPA (alignment plus Gaussian regularization)...
![[논문 리뷰] SkillOpt: Executive Strategy for Self-Evolving Agent Skills](/assets/images/blog/20260624-paper-2605-23904-skillopt-executive-strategy-fo.jpg)
[논문 리뷰] SkillOpt: Executive Strategy for Self-Evolving Agent Skills
Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, and none of which reli...
![[논문 리뷰] Memory Caching: RNNs with Growing Memory](/assets/images/blog/20260622-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 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...