「qa」の検索結果
55 件Answering geographic queries over Earth observation (EO) archives requires solving a joint retrieval-and-deriv
Recent multimodal agents have shown promising progress in embodied planning and web interaction. However, exis
Large Language Models (LLMs) often generate fluent yet incorrect responses, making un- certainty estimation im
Large foundation models often answer even when the available visual or textual evidence is insufficient to sup
Multimodal language models continue to improve on broad music understanding benchmarks, but which musical feat
Generative AI is billed per token, yet tokens are not equally expensive. Each token enters the KV-cache and ra
Scientific discovery builds on accurate observations interpreted through domain knowledge and reasoning. For s
As large language models are fine-tuned for increasingly specialized tasks, their performance depends more on
Small language models (<=9B parameters) can support local medical question answering (QA) while reducing priva
Chain-of-Thought (CoT) generation has substantially improved the performance of Large Language Models (LLMs) o
As pretraining scales, medical language models have advanced rapidly in clinical question answering. Their abi
A memory-augmented agent must decide which memories to keep, merge and retrieve, and its only evidence of thei
With the popularization of livestreaming applications, automatic camera tuning has become indispensable for or
Large Language Models (LLMs) have shown promise for empowering search agents to tackle complex information-see
Large Language Models (LLMs) are increasingly used in medical question answering (QA) systems. While recent wo
Speculative decoding (SD) accelerates large language model inference by reducing the sequential cost of token-
Law changes over time. A court decides a case under the law in force on that day, and legal QA benchmarks grad
Scaling depth is a key route to stronger large language models, but Pre-LN architectures often suffer from dep
Prompt compression is aimed at reducing input prompt lengths to enable cheaper, faster, and potentially higher
Large language models (LLMs) have achieved strong performance across diverse tasks, from question answering to
Automated liver ultrasound report generation improves the diagnosis of liver diseases by producing clinically
Scientific discovery from the literature requires assessing connections among findings obtained under differen
Language models with similar benchmark accuracy can succeed on different items. Overall scores do not show whe
Rewards for diversity, coverage and distribution matching make the reward of each sampled response depend on t
Retrieval-augmented generation (RAG) extends large language models with external evidence, yet relevant eviden
Looped language models can stop iterating early, and recent work shows that post-hoc readouts of the loop traj
Text-to-SQL systems answer natural language questions over structured data, but can fail before SQL generation
Multimodal large language models (MLLMs) can generate detailed video captions, yet they may overemphasize mino
Multimodal large language models (MLLMs) have shown strong potential in robotic surgery for question answering
Misleading charts can lead vision-language models to conclusions that conflict with underlying data. Existing
Understanding how objects and their spatial relationships evolve in 3D space over time remains challenging for
Robust multimodal perception for soft robotic grasping is challenged by modality-dependent noise, semantic amb
Reasoning models answer better when they think longer, but a thinking trace keeps going well after its answer
Key-value (KV) cache eviction methods rank tokens by attention scores, and each layer scores its own cache. To
On-policy distillation (OPD) can improve answer accuracy while weakening responsiveness to supporting evidence
LLM-based agentic systems are increasingly combined with CoT reasoning to solve tasks that require accurate re
When user queries are incomplete, large language models should ask targeted questions before committing to an
Temporal Knowledge Graph Question Answering (TKGQA) requires language model agents to retrieve evidence, selec
Representation steering methods like difference-in-means (DIM) offer a lightweight approach to controlling LLM
Humans continuously acquire knowledge from carefully crafted materials such as books and tutorial videos, carr
Vision-language models for remote sensing answer questions about satellite images by generating text: every an
The spatial intelligence of large vision-language models (LVLMs) requires the ability to combine local visual
Embodied question answering asks an agent to navigate, observe, and then answer questions about what it saw, y
User simulators generate responses in place of human participants, allowing researchers to estimate the result
Faced with an unanswerable question, small language models can either refuse or confabulate: confidently produ
Retrieval-augmented generation (RAG) systems have seen growing adoption in embodied settings as language-query
Embodied agents are usually organized around individual tasks, yet the physical world keeps changing between t
Retrieval-augmented generation (RAG) lets a large language model (LLM) answer questions by retrieving relevant
An effective agent memory system should adapt to a user's history and task requirements. Human-annotated super
Retrieval-augmented generation commonly serves separate models for reranking and answering, duplicating model
In recent years, agentic orchestration has been widely applied to solve complex human tasks. However, existing
Agents that run for a long time have a memory problem, and it is not the one most systems optimize for. Storin
General-sum games are more challenging than fully cooperative or adversarial settings because agents may decei
Face image quality assessment (FIQA) estimates the utility of a face image for automated face recognition and
Variational quantum algorithms turn choices of Hamiltonian, ansatz, and parameterization into a classical nonc