「RAG」の検索結果
32 件Embedding models underpin virtually every modern AI system, from search engines to recommendation systems. Des
Unlike autoregressive (AR) models, diffusion language models (DLMs) permit parallel token updates, flexible ge
Retrieval-augmented generation (RAG) lets a large language model (LLM) answer questions by retrieving relevant
Answer-level rewards cannot distinguish reasoning traces with equally correct outcomes. We study whether auxil
Linear attention scales linearly with sequence length, but its finite-state memory can limit long-context retr
Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation
Existing sketch generation methods often rely on image guidance and are largely limited to individual objects
Predicting future physical states stands as a central challenge in embodied intelligence. Existing video world
Recent advances in video world models have enabled the generation of increasingly realistic and interactive vi
Achieving the broad task generalization of embodied models in physical environments remains a central challeng
One-step and few-step text-to-image generation must accurately render the details specified in the prompt desp
Foundation models are typically trained in stages: pretraining, midtraining, supervised fine-tuning (SFT), and
While frontier vision-language models demonstrate strong capabilities in user interface (UI) navigation, their
Recursive self-improvement requires models not only to acquire useful training experience, but also to decide
Quarterly earnings forecasting requires an agent to connect financial evidence to the income-statement line it
Self-distillation offers a way to improve reasoning models when a suitable external teacher is unavailable. Ho
Unified conditional image generation requires a single model to preserve different aspects of a visual input w
Real-world forgery is inherently heterogeneous: it manifests not only as digital manipulation but also as phys
Metis is a bet that better data, better training methods and new architectures can buy what a bigger parameter
Diffusion vision-language models (DVLMs) process their entire input at every denoising step. Since visual toke
Web agents require diverse, executable, and resettable website environments for reproducible training and eval
Evaluating human-AI co-design requires testing whether coding agents can translate real users' multimodal requ
GUI grounding requires locating small controls in cluttered screenshots and identifying which control a natura
Low-rank SVD-based compression offers a powerful strategy to reduce the computational costs of LLMs. However,
Locating relevant evidence in long inputs is a core capability of Multimodal Large Language Models. Prior stud
Low-light image enhancement (LLIE) aims to recover well-exposed and perceptually faithful images from severely
Edge medical segmentation must reconcile small model size, high accuracy, and fast inference. TernScope addres
A benchmark is not merely a collection of test questions; it naturally induces an empirical measure over diffi
Multimodal large language models (MLLMs) show promising reasoning capabilities, but reliable physical interact
Recent advances in large (vision) language models have enabled computer-use agents that automate digital workf
Speculative decoding accelerates autoregressive (AR) language models by letting a small model draft tokens ser
Command-line interface (CLI) agents increasingly execute longer-horizon tasks, yet existing benchmarks either