Search: “llm”

arXiv:2507.00460v3 Announce Type: replace Abstract: Open Large Language Model (LLM) benchmarks, such as HELM and BIG-Bench, provide standardized and transparent evaluation protocols that support compa

arXiv:2606.05395v1 Announce Type: new Abstract: Reusable robot skills are becoming the basic units through which embodied agents turn open-ended instructions into long-horizon physical behavior. We ar

arXiv:2606.05134v1 Announce Type: new Abstract: Deep active learning has previously been explored for LLM in-context sample selection, but not with methods that utilise recent advances in understandin

llama.cpp is a C/C++ implementation designed to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware locally and in the cloud. Build b9515 is an interme

llama.cpp is a C/C++ implementation that enables LLM inference with minimal setup and high performance across diverse hardware . Build b9518 is a release version from the llama.cpp project, which serv

arXiv:2510.13272v3 Announce Type: replace Abstract: Inspired by the success of reinforcement learning (RL) in Large Language Model (LLM) training for domains like math and code, recent work has begun

arXiv:2606.04362v1 Announce Type: cross Abstract: Large language model (LLM) 'answer engines' such as ChatGPT now send measurable referral traffic to the open web, and a practice analogous to search e

arXiv:2606.04145v1 Announce Type: cross Abstract: Cloud LLM fine-tuning platforms increasingly serve RLHF workloads, where a learned reward model is optimized as a proxy for human quality. As Gao et a

arXiv:2606.04315v1 Announce Type: new Abstract: LLM agents accumulate histories that outgrow their context windows, motivating a growing literature on memory systems. Yet most existing designs are tun

arXiv:2606.04516v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) significantly advances LLM reasoning, yet it faces a dilemma: standard supervised scaling is thr

arXiv:2606.04846v1 Announce Type: new Abstract: As Large Language Models (LLMs) become increasingly popular in educational settings, they raise important questions about the ethical implications of th

arXiv:2606.04815v1 Announce Type: cross Abstract: Lifelong learning is essential for Large Language Model (LLM) agents operating in dynamic, interactive environments. However, existing lifelong learni

Nemotron 3 Ultra, NVIDIA's advanced language model, is now accessible through Vercel's AI Gateway, enabling developers to integrate this model into their applications alongside other LLM options. The

nice post from @Harvey and @LangChain Labs, worth a read. improve agent feedback loop without setting $$ on fire Can we design legal agent verifiers that are up to 1,000x cheaper? Verifiers are LLM ju

This post likely discusses cost optimization strategies for managing API token usage and expenses, particularly relevant to users of language models or LLM-based services. The author emphasizes the pr

arXiv:2606.04602v1 Announce Type: new Abstract: As agents grow more capable, legal-domain LLM agents promise to turn document-heavy matters into reviewable work products -- yet reliable deployment fac

arXiv:2606.04628v1 Announce Type: new Abstract: RAMPART is a compile-time memory model and pure in-RAM block registry for LLM-based agents. Context assembly is a programmable runtime operation where c

arXiv:2606.04238v1 Announce Type: cross Abstract: Aggressive weight quantization to 2-bit precision offers substantial throughput and memory gains for large language model (LLM) inference, but typical

arXiv:2606.04923v1 Announce Type: cross Abstract: Rubric-based reinforcement learning (RL) uses an LLM-as-a-Judge (LaaJ) to score model outputs according to rubrics as rewards. However, policy models

arXiv:2606.04536v1 Announce Type: new Abstract: Existing memory-augmented LLM agents store past experience exclusively in prompt space, as textual summaries or retrieved passages, while keeping model

arXiv:2606.05004v1 Announce Type: cross Abstract: With the widespread deployment of public large language models (LLMs) such as ChatGPT, protecting user prompt privacy has become an increasingly criti

arXiv:2606.02871v1 Announce Type: cross Abstract: Large reasoning models improve performance by generating extended chain-of-thought (CoT) reasoning, but this behavior becomes inefficient when applied

Release b9488 of llama.cpp was published on June 3, 2026 , and includes support for Qwen3 SSM architectures with additions like LLM_KV_ATTENTION_RECURRENT_LAYERS . The release also fixes a bug in comm

B9493 is a release of llama.cpp, an LLM inference framework in C/C++ . This release includes updates to model support, such as centralized hidden activation mappings and additions for granite embeddin

The search didn't return specific information about release b9495. Based on the available information about llama.cpp releases, here's a summary: llama.cpp enables LLM inference with minimal setup and

The search results do not contain specific information about release b9496. Based on the available context, b9496 is a build/release version of llama.cpp, a C/C++ implementation for efficient LLM infe

arXiv:2606.03034v1 Announce Type: cross Abstract: Large language model (LLM) agents have begun to delegate work to one another. Protocols such as the Model Context Protocol (MCP) and the Agent2Agent p

arXiv:2606.03157v1 Announce Type: new Abstract: Large language models (LLMs) have been widely adopted in healthcare, yet they still encounter significant challenges in complex clinical decision-making

arXiv:2606.03323v1 Announce Type: cross Abstract: The rise of LLM-as-a-Service and other confidential cloud workloads demands cryptographic proof that user data is processed in a trusted, untampered e

arXiv:2606.03841v1 Announce Type: new Abstract: Recent progress in Large Language Model (LLM) agents has enabled promising advances in automated data science. However, existing approaches remain funda

Learn how new Document Translation capabilities in Azure Translator, available in Foundry Tools, help developers translate images, PDFs, Office files, DITA, XLIFF, and future LLM-powered document work

arXiv:2606.03143v1 Announce Type: cross Abstract: Modern LLM agents increasingly rely on skill libraries to handle complex tasks, making skill evolution a primary driver of self-improvement. However,

arXiv:2512.11213v2 Announce Type: replace Abstract: Scaling test-time computation has been shown to significantly improve large language model (LLM) performance without additional training. However, e

arXiv:2606.03131v1 Announce Type: new Abstract: Reward models are central to large language model (LLM) alignment, but they remain vulnerable to reward hacking. To evaluate reward-model robustness, we

arXiv:2606.03768v1 Announce Type: new Abstract: Extended chain-of-thought (CoT) traces improve LLM reasoning but incur substantial computational and memory costs. While existing CoT compression method

arXiv:2602.20217v2 Announce Type: replace-cross Abstract: Self-speculative decoding (SSD) accelerates LLM inference by skipping layers to create an efficient draft model, yet existing methods often re

arXiv:2507.16003v4 Announce Type: replace Abstract: One of the most striking features of Large Language Models (LLMs) is their ability to learn in-context. Namely at inference time an LLM is able to l

arXiv:2606.02907v1 Announce Type: cross Abstract: Linear probing of large language model (LLM) hidden states is widely used to claim that models learn distinct representations for different reasoning

arXiv:2606.03099v1 Announce Type: cross Abstract: Deep Image Search requires multi-step reasoning over rich contextual cues, such as time, location, and event relations. However, most existing LLM-bas

arXiv:2606.03620v1 Announce Type: cross Abstract: Self-distilled policy optimization (SDPO) has become a popular paradigm for LLM post-training, where a model learns from its own predictions condition

arXiv:2510.09711v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have recently emerged as a powerful paradigm for Knowledge Graph Completion (KGC), offering strong reasoning and

arXiv:2510.16392v3 Announce Type: replace Abstract: Personalized and continuous interactions are critical for LLM-based conversational agents, yet finite context windows and static parametric memory h

arXiv:2606.03980v1 Announce Type: cross Abstract: Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) p

arXiv:2606.03137v1 Announce Type: new Abstract: LLM-based multi-agent simulation offers a promising way to study social interaction, deliberation, and collective opinion dynamics. However, many existi

arXiv:2606.01770v1 Announce Type: cross Abstract: Auto-harness systems such as A-Evolve, GEPA, and Meta-Harness improve LLM agents by optimizing prompts, skills, tools, memories, and supporting infras

arXiv:2606.00370v1 Announce Type: cross Abstract: Diverse genomics data, scientific questions, and analysis tasks typically demand highly specialized visualizations. Therefore, users often must custom