Search: “llm”

arXiv:2608.21420v1 Announce Type: new Abstract: Thematic analysis (TA) has long been regarded as an inherently human, reflexive, and interpretive process. However, the extent to which LLM-generated TA

arXiv:2608.21365v1 Announce Type: cross Abstract: As a low-resource language, Khmer presents several retrieval challenges, including limited annotated data, ambiguous word boundaries, weak support in

arXiv:2608.23179v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly attractive for automating network configuration, yet their reliability and failure patterns are poo

arXiv:2608.22118v1 Announce Type: new Abstract: LLM responses are based on the internet (via training or RAG), and AI is now used to generate a significant amount of content online (Paredes et al., 20

arXiv:2411.08881v3 Announce Type: replace-cross Abstract: AI-based systems, including Large Language Models (LLMs), impact millions by supporting diverse tasks but face issues like misinformation, bia

An AQuA research loop let one LLM write an intraday trading feature and a second LLM review it for causality. The feature divided the volume seen so far by the day's final total volume. At prediction

arXiv:2608.20801v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have significantly advanced reranking in recommendation, effectively leveraging item-side information remains chall

arXiv:2607.27984v2 Announce Type: replace Abstract: Agentic systems generate outputs faster than human review. We contrast two LLM evaluator specialization strategies: specialized judge weights, or ru

arXiv:2608.20631v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated the ability to solve multi-step tasks requiring planning, tool use, and external information access,

Hi everyone, I'm currently working on optimizing an LLM setup at my company and I'm trying to identify where the actual performance bottleneck is. Our current setup is roughly: LLM: NVIDIA Nemotron mo

We’re Hiring: Member of Technical Staff (LLM Development) 🐟 https://sakana.ai/careers/member-of-technical-staff-llm-development/#English The role: ・Develop LLMs that form the foundation of Sakana AI’s

arXiv:2608.19760v1 Announce Type: cross Abstract: Audited against causal ground truth from executed replay in a single-agent tool environment (ALFWorld), none of the step-level credit signals used to

arXiv:2608.19861v1 Announce Type: new Abstract: Customer-service LLM agents must follow organizational policy when acting on a user's behalf. Compliance failures arise from either forbidden actions, s

arXiv:2608.19689v1 Announce Type: new Abstract: LLM-based social simulation is a promising complement to traditional methods such as surveys and behavioral experiments. A core question is how to evalu

arXiv:2608.17310v1 Announce Type: new Abstract: Reinforcement Learning (RL) has been promising in single-turn LLM fine-tuning. However, long-horizon agentic reasoning introduces increasingly branching

arXiv:2608.16461v1 Announce Type: cross Abstract: Central Bank Digital Currency (CBDC)-based welfare schemes may be potentially privacy invasive as they process significant volumes of beneficiary pers

arXiv:2608.14635v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly trained with reinforcement learning in long-horizon, sandboxed environments. Unlike conventional RL

arXiv:2608.14668v1 Announce Type: cross Abstract: LLM-based multi-agent systems (LLM-MAS) solve complex tasks through specialized collaboration, but inter-agent dependencies can propagate hallucinated

arXiv:2608.14950v1 Announce Type: new Abstract: Generative AI systems are increasingly producing real-world artifacts, however their efficacy and validity are often evaluated via context-free LLM-scor

arXiv:2608.14613v1 Announce Type: new Abstract: Modern LLM-agent frameworks increasingly interoperate through standards such as Anthropic's Model Context Protocol (MCP) for agent-to-tool access and Go

arXiv:2608.16438v1 Announce Type: new Abstract: In a world where valuable artifacts are increasingly created, completed, or processed by LLMs, the central economic question is not only what the LLM ca

arXiv:2608.14573v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly capable of tool use, code execution, artifact inspection, and iterative revision, creating new oppo

arXiv:2608.13581v1 Announce Type: cross Abstract: Personalized glucose regulation remains a central yet unresolved challenge in precision nutrition, as postprandial glucose response varies substantial

arXiv:2608.14425v1 Announce Type: new Abstract: LLM evaluations often use fixed sampling budgets, testing every item the same number of times even after estimates are precise. We introduce optstop, a

arXiv:2608.12921v1 Announce Type: cross Abstract: The performance of large language model (LLM)-based multi-agent systems (MAS) largely depends on effective communication topologies. Existing topology

arXiv:2411.16818v2 Announce Type: replace-cross Abstract: To evaluate a multi-representational framework in which large language model (LLM)-generated expert summaries of intensive care unit (ICU) not

Sakana Chat just got a big upgrade. No login required, free to use: https://chat.sakana.ai/ Powered by Fugu and Namazu, our Japanese LLM. With newly added code execution, you can vibe-code interactive

arXiv:2608.10885v1 Announce Type: new Abstract: Pulmonary nodule malignancy prediction typically depends on image-trained specialist deep learning (DL) models that require substantial annotated imagin

arXiv:2608.09946v1 Announce Type: cross Abstract: Social service navigation requires connecting help-seeking individuals to resources that satisfy their needs and specific constraints. Although LLM ag

arXiv:2608.10492v1 Announce Type: new Abstract: Large Language Model (LLM)-based simulators often reproduce observable actions but fail to capture the underlying reasoning behind them. In education, w

arXiv:2608.09253v1 Announce Type: new Abstract: LLM agents are increasingly equipped with skills to perform complex tasks through multi-step reasoning and tool use. Although skills provide reusable pr

arXiv:2608.06485v1 Announce Type: cross Abstract: Personality-conditioned LLM agents (PC-Agents) are increasingly used in emotional support, social simulation, and role-playing, motivating the develop

arXiv:2608.06949v1 Announce Type: new Abstract: Prior benchmarking work has shown that a single large language model (LLM), forced to make life-or-death resource-allocation decisions, exhibits measura

arXiv:2608.07437v1 Announce Type: new Abstract: Reliable hypothesis testing is the foundation of many empirical scientific claims. Large language model (LLM) agents are increasingly used to automate t

arXiv:2411.00028v3 Announce Type: replace-cross Abstract: Socioeconomic prediction aims to leverage various urban data to predict the socioeconomic indicators of regions such as population and commerc

arXiv:2608.04095v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used as personalized assistants in high-stakes domains such as financial advising, yet it remains unc

arXiv:2608.02966v1 Announce Type: new Abstract: Most multiple-choice question (MCQ) benchmarks evaluate Large Language Models (LLMs) only by whether they select the correct answers. This binary scorin

arXiv:2608.03636v1 Announce Type: cross Abstract: Large language model-based automated heuristic design (LLM-AHD) has shown strong potential in discovering effective heuristics for combinatorial optim

arXiv:2608.01050v1 Announce Type: cross Abstract: Production LLM agents that select from large skill libraries face a limitation that semantic relevance alone cannot resolve: a skill may match a user'

arXiv:2608.00902v1 Announce Type: new Abstract: LLM agents accumulate long trajectories of reasoning steps, tool calls, and environment feedback, making the KV cache a major inference bottleneck. KV c

arXiv:2608.01652v1 Announce Type: new Abstract: LLM-based multi-agent coordination faces a fundamental trade-off between efficiency and adaptivity in dynamic environments. Existing approaches typicall

arXiv:2607.18529v2 Announce Type: replace-cross Abstract: Teaching videos are becoming a major medium for education, creating a growing need for scalable evaluation of their pedagogical quality. Exist

arXiv:2607.28889v1 Announce Type: cross Abstract: Qualitative researchers increasingly encounter interaction corpora whose scale exceeds what manual coding alone can address, and large language models

arXiv:2607.29433v1 Announce Type: new Abstract: As large language model (LLM) agents evolve into personalized companions, memory has emerged as a core capability. However, LLMs face a knowledge utiliz

arXiv:2607.29378v1 Announce Type: new Abstract: Large language models (LLMs) generate text by auto-regressively sampling the next token. This inherently leads to a many-to-many mapping between prompts

arXiv:2607.28862v1 Announce Type: cross Abstract: The rapid development of Large Language Models (LLMs) has led to significant advances across a wide range of language tasks, while simultaneously rais