Blog
241개 중 1-12번째 포스트
![[논문 리뷰] LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling](/assets/images/blog/20260514-paper-2605-08083-llms-improving-llms-agentic-di.jpg)
[논문 리뷰] LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling
Test-time scaling (TTS) has become an effective approach for improving large language model performance by allocating additional computation during inference. However, existing TTS strategies are larg...
![[논문 리뷰] Complex-Valued Phase-Coherent Transformer](/assets/images/blog/20260513-paper-2605-10123-complex-valued-phase-coherent-.jpg)
[논문 리뷰] Complex-Valued Phase-Coherent Transformer
Complex-valued Transformers have largely inherited softmax attention from real-valued architectures. However, row-normalised token competition is not necessarily aligned with phase-preserving computat...
![[논문 리뷰] Fast Byte Latent Transformer](/assets/images/blog/20260513-paper-2605-08044-fast-byte-latent-transformer.jpg)
[논문 리뷰] Fast Byte Latent Transformer
Recent byte-level language models (LMs) match the performance of token-level models without relying on subword vocabularies, yet their utility is limited by slow, byte-by-byte autoregressive generatio...
![[논문 리뷰] Temporal Straightening for Latent Planning](/assets/images/blog/20260511-paper-2603-12231-temporal-straightening-for-lat.jpg)
[논문 리뷰] Temporal Straightening for Latent Planning
Learning good representations is essential for latent planning with world models. While pretrained visual encoders produce strong semantic visual features, they are not tailored to planning and contai...
자연어 처리를 위한 새로운 혁명: 대규모 언어 모델의 등장
최근 몇 년간 인공지능(AI) 분야에서 가장 주목할 만한 발전은 단연 대규모 언어 모델(Large Language Model, LLM)의 부상입니다. OpenAI의 GPT-4, Google의 Gemini, Meta의 Llama 3와 같은 모델들은 자연어 처리(Natural Language Processing, NLP) 분야에 혁신을 가져왔습니다. 이러한 모...
![[논문 리뷰] A Theory of Generalization in Deep Learning](/assets/images/blog/20260509-paper-2605-01172-a-theory-of-generalization-in-.jpg)
[논문 리뷰] A Theory of Generalization in Deep Learning
We present a non-asymptotic theory of generalization in deep learning where the empirical neural tangent kernel partitions the output space. In directions corresponding to signal, error dissipates rap...
![[논문 리뷰] Temporal Straightening for Latent Planning](/assets/images/blog/20260509-paper-2603-12231-temporal-straightening-for-lat.jpg)
[논문 리뷰] Temporal Straightening for Latent Planning
Learning good representations is essential for latent planning with world models. While pretrained visual encoders produce strong semantic visual features, they are not tailored to planning and contai...
![[논문 리뷰] From Context to Skills: Can Language Models Learn from Context Skillfully?](/assets/images/blog/20260507-paper-2604-27660-from-context-to-skills-can-lan.jpg)
[논문 리뷰] From Context to Skills: Can Language Models Learn from Context Skillfully?
Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learning, where LMs directly learn relevant knowledge...
![[논문 리뷰] Information Gravity: A Field-Theoretic Model for Token Selection in Large Language Models](/assets/images/blog/20260506-paper-2504-20951-information-gravity-a-field-th.jpg)
[논문 리뷰] Information Gravity: A Field-Theoretic Model for Token Selection in Large Language Models
We propose a theoretical model called "information gravity" to describe the text generation process in large language models (LLMs). The model uses physical apparatus from field theory and spacetime g...
자연어 처리의 미래를 여는 열쇠: 휴먼-인-더-루프(Human-in-the-Loop)
자연언어처리(Natural Language Processing, NLP)는 인공지능(AI) 분야에서 가장 빠르게 발전하는 영역 중 하나입니다. 특히 거대 언어 모델(Large Language Models, LLM)의 등장은 인간의 언어를 이해하고 생성하는 AI의 능력을 전례 없는 수준으로 끌어올렸습니다. 하지만 이러한 눈부신 발전에도 불구하고, AI는 여전...
![[논문 리뷰] The Last Human-Written Paper: Agent-Native Research Artifacts](/assets/images/blog/20260504-paper-2604-24658-the-last-human-written-paper-a.jpg)
[논문 리뷰] The Last Human-Written Paper: Agent-Native Research Artifacts
Scientific publication compresses a branching, iterative research process into a linear narrative, discarding the majority of what was discovered along the way. This compilation imposes two structural...
![[논문 리뷰] The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook](/assets/images/blog/20260504-paper-2604-02029-the-latent-space-foundation-ev.jpg)
[논문 리뷰] The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook
Latent space is rapidly emerging as a native substrate for language-based models. While modern systems are still commonly understood through explicit token-level generation, an increasing body of work...