Search: “llm”

arXiv:2609.15013v1 Announce Type: new Abstract: Guardrail models are classifiers deployed to screen malicious prompts and responses in LLM-based services. To meet latency constraints, many lightweight

arXiv:2407.02395v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for program synthesis, yet they often generate code that is functionally plausible but inse

arXiv:2609.12537v1 Announce Type: new Abstract: AI-assisted writing can flatten meaning in human storytelling, enabling the production of homogeneous outputs without the intentional effort and sense-m

arXiv:2609.11135v1 Announce Type: new Abstract: Short-term photovoltaic (PV) power and global horizontal irradiance (GHI) forecasts are essential for effective dispatch, reserve scheduling, and grid o

So you want to use OpenRouter? One of OpenRouter's selling points is that it 'handles fallbacks automatically and picks the most cost-effective option for each request', so you can call a single API e

arXiv:2609.03247v1 Announce Type: cross Abstract: Large language model (LLM) security has largely focused on role-playing jailbreaks, with less attention to what happens when a user asks an LLM to ver

arXiv:2609.11772v1 Announce Type: cross Abstract: Cross-cultural understanding has become increasingly important in today's highly connected, cross-national world. The success of LLM-based technologie

arXiv:2609.07434v1 Announce Type: new Abstract: Natural-language Computer-Aided Design (CAD) code generation aims to turn design intent into executable and editable parametric programs. Large language

arXiv:2609.05801v1 Announce Type: new Abstract: A document modeled as a discrete sequence of tokens can be thought of as being generated from a composition of texts from different domains; a README fi

arXiv:2609.07095v1 Announce Type: new Abstract: LLM assistants often produce more answers than humans can review before users see them. Most evaluations ask whether an answer is wrong, unsupported, or

Goodfire used Ai2’s fully open post-training stack to predict LLM behavioral changes, trace unwanted model behavior back to individual training examples, and test targeted fixes without sacrificing br

Encode‑Prefill‑Decode (EPD) disaggregation is an inference optimization in NVIDIA Dynamo that separates vision encoding from LLM prefill–decode stages for multimodal requests. On image‑heavy prompts,

arXiv:2609.05133v1 Announce Type: cross Abstract: This paper addresses navigation by composite heterogeneous robots in a decentralized system when policy reasoning and local control operate at differe

arXiv:2609.03201v1 Announce Type: new Abstract: Long-term LLM agents must preserve information across interactions while distinguishing repeated evidence, historical states, updates, and unresolved co

arXiv:2609.03920v1 Announce Type: new Abstract: The emergence of agentic AI and LLM-based multi-agent systems (MAS) presents unprecedented opportunities for automating complex tasks, while simultaneou

arXiv:2602.12639v2 Announce Type: replace Abstract: Legal text generated by large language models (LLMs) can usually achieve reasonable factual accuracy, but it frequently fails to adhere to the speci

arXiv:2608.27984v2 Announce Type: replace Abstract: Multi-Agent Systems (MAS) have recently moved from static workflows toward dynamically generated collaboration topologies. However, existing topolog

arXiv:2412.12144v5 Announce Type: replace-cross Abstract: Personality assessment through situational judgment tests (SJTs) offers unique advantages over traditional Likert-type self-report scales, yet

arXiv:2609.00474v1 Announce Type: cross Abstract: LLMs are increasingly deployed as orchestrators that coordinate specialized subagents to solve complex tasks through natural language. However, in man

arXiv:2609.00334v1 Announce Type: new Abstract: Across law, education, policy analysis, and public moral argumentation, LLM outputs are being used often for work that requires interpretations to be ju

arXiv:2609.00304v1 Announce Type: new Abstract: Models express values and welfare-relevant self-reports, but it is unclear whether these outputs reflect stable preferences or a stable self. We thus in

arXiv:2609.00597v1 Announce Type: new Abstract: With the rapid rise of large language models (LLMs), controlling undesirable model behaviors has become increasingly important. Existing behavioral cont

arXiv:2609.00805v1 Announce Type: new Abstract: LLM-based software systems increasingly require effective 'evals' as quality gates in the development lifecycle. However, existing work typically addres

arXiv:2609.01428v1 Announce Type: new Abstract: Large Language Model (LLM) agents based on the ReAct paradigm have demonstrated remarkable capabilities in tool use and task execution. However, ReAct s

arXiv:2602.06449v2 Announce Type: replace Abstract: Privacy and computational constraints limit the use of large language models in psychiatry, while adapting small language models (SLMs) often requir

arXiv:2608.29128v1 Announce Type: new Abstract: Tool-using agents are commonly evaluated by a single bit: whether an end-to-end workflow completed. This metric fails to distinguish failures that matte

arXiv:2607.10286v2 Announce Type: replace Abstract: Large language model (LLM) agents are increasingly used in trading systems, where model reasoning, tool use, and continual decisions incur costs tha

arXiv:2608.30429v1 Announce Type: new Abstract: LLM-based agent systems increasingly adopt skill-based architectures to reduce repetitive reasoning costs and improve stable, efficient task execution.

arXiv:2608.30428v1 Announce Type: cross Abstract: Strategic deception by LLM and VLM agents has emerged as a central AI alignment and safety concern. Social-deduction games (where each player holds a

arXiv:2608.29228v1 Announce Type: new Abstract: Failures in agentic AI systems can arise from interactions among messages exchanged by multiple large language model (LLM) agents. Pointwise attribution

arXiv:2608.29596v1 Announce Type: new Abstract: Autonomous large language model (LLM) agents increasingly face reliability, context consumption, and execution stability bottlenecks when deployed on co

arXiv:2608.29903v1 Announce Type: cross Abstract: The rapid advancement of large language models (LLMs) has made AI-generated text detection increasingly critical. Existing zero-shot detectors assume

arXiv:2608.27855v1 Announce Type: new Abstract: Text generated by large language models (LLMs) has been shown to be stylometrically distinct from human-written text itep{andreDetectingAIAuthorship2023

arXiv:2608.27466v1 Announce Type: new Abstract: Web content extraction is essential for reliable LLM data pipelines, yet existing methods often struggle to jointly satisfy accuracy, scalability, and a

arXiv:2608.26133v1 Announce Type: new Abstract: Evaluating AI agents that use external tools requires realistic test scenarios that capture how practitioners compose tools and iterate across conversat

arXiv:2608.26710v1 Announce Type: new Abstract: AI text detectors are increasingly employed in academic settings, but it remains unclear whether their outputs reflect AI authorship itself or broader l

arXiv:2608.24954v1 Announce Type: new Abstract: Large language models (LLMs) hallucinate numerical values when generating high-stakes meteorological text, posing risks for weather communication. We pr

arXiv:2509.02855v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to produce open-ended, interpretive annotations, yet there is no validated, scalable measure of i

arXiv:2507.13579v4 Announce Type: replace Abstract: As everyday use cases of large language model (LLM) AI assistants have expanded, it is becoming increasingly important to personalize responses to a

arXiv:2608.24188v1 Announce Type: new Abstract: Coding agents re-send large file reads and tool outputs to a frontier LLM every turn, and this context dominates their token bill. General-purpose promp

OpenAI’s Jalapeño is a self‑designed ASIC for large‑language‑model inference that was taped‑out in about 16 months, developed with Broadcom and featuring HBM4 memory. Benchmarks on the InferenceX suit

arXiv:2608.22797v1 Announce Type: new Abstract: Background This study was designed to evaluate whether a domain-specific large language model (LLM) trained exclusively on patient education resources c

Today we’re launching Portable Computer on @NVIDIA DGX Spark. Portable Computer is a fully local version of Perplexity Computer, where the entire runtime: orchestrator LLM, subagent LLM, agent harness

arXiv:2608.20381v1 Announce Type: cross Abstract: Automating slide editing requires simultaneously satisfying modification accuracy, preservation fidelity, and robustness to deck length. Existing LLM-

arXiv:2608.20392v1 Announce Type: cross Abstract: LLM-powered meeting assistants are deployed at scale, yet systematic evaluation of their grounding fidelity remains limited to static benchmarks that

arXiv:2608.20099v1 Announce Type: cross Abstract: LLM-based Multi-Agent Systems (MAS) achieve strong performance on complex reasoning tasks by coordinating multiple agents, but at the cost of substant