「LLM」の検索結果
25 件Hybrid LLMs pair softmax attention with linear-attention layers such as Gated DeltaNet (GDN), whose recurrent
Scaling interactive and verifiable environments is critical for training terminal agents. As frontier models b
Multi-agent LLM systems commonly use an orchestrator to decompose a task for a team of workers and then improv
Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for large language mo
Traditional speaker-attributed ASR systems treated ASR and speaker diarization as two separate tasks. Recently
LLMs are increasingly deployed as orchestrators that coordinate specialized subagents to solve complex tasks t
Model compression techniques such as pruning and quantization facilitate the efficient deployment and accelera
Ensuring factuality remains a critical challenge for deploying LLMs in high-stakes settings. Existing hallucin
As agents move from research prototypes to deployed tools, their capability increasingly depends on model-exte
Retrieval is the first stage of modern search and advertising systems, selecting a candidate set from a large
Text-rich visual inputs require models that can read, retrieve, and compress language directly in pixel space,
The attention prefilling phase of long-context LLM inference scales quadratically, making self-attention a sev
Large language models (LLMs) trained only on text and code can sometimes generate programs that draw recogniza
Modern Text-to-SQL systems often follow generate-execute-select pipelines, generating multiple candidate queri
Command-line coding agents (e.g., Claude Code, Gemini CLI) can already read and write files and sustain long s
Group relative policy optimization for reinforcement learning with verifiable rewards (RLVR) typically uses a
Coding agents are now commonly evaluated on the SWE-bench family of benchmarks, whose tasks are built from cur
Understanding agent behavior requires methods that scale to thousands of trajectories and surface new patterns
Many important forms of human learning begin with a vague goal, such as "become a better physicist" or "improv
Large language models (LLMs) increasingly interact with external environments and accumulate substantial behav
Professional agent tasks often depend on conventions that are absent from public corpora, yet benchmarks rarel
Rubric-based reinforcement learning extends RL beyond tasks with exact answers or rule-based verifiers by scor
An avatar that holds a conversation should decide what to say and to move while saying it, yet these abilities
Large language models offer broad capabilities, but adapting them to evolving domains, tools, and requirements
Knowledge-Based Visual Question Answering (KB-VQA) relies on retrieving external information to answer queries