Search: “inference”

arXiv:2606.07577v1 Announce Type: new Abstract: Audio-visual large language models (LLMs) hold strong promise for long-form video understanding, yet their long-video inference is fundamentally limited

arXiv:2606.09189v1 Announce Type: cross Abstract: EEG foundation-model releases are usually audited one endpoint at a time: raw-reconstruction, membership inference, identity linkage, or DP-SGD on the

arXiv:2606.08804v1 Announce Type: new Abstract: Linear attention reformulates sequence modeling as recurrent state evolution, enabling efficient linear-time inference. Under the key-value associative

arXiv:2606.08779v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a pivotal post-training paradigm, yet it frequently suffers from unpredictable sub-optimum performance or eve

arXiv:2606.09367v1 Announce Type: new Abstract: In real-world deployment under strict real-time constraints, weather and imaging variations induce significant distribution shifts, severely degrading d

arXiv:2606.08402v1 Announce Type: cross Abstract: Generating complete 3D scenes from a single image requires inferring globally consistent geometry, object relationships, and environmental context fro

arXiv:2606.07684v1 Announce Type: cross Abstract: Disaggregated serving alleviates memory bottlenecks in Large Language Model (LLM) inference but creates a severe communication bottleneck: transmittin

arXiv:2507.09751v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but exhibit problems with l

arXiv:2606.09135v1 Announce Type: cross Abstract: We demonstrate that widely deployed Large Language Model (LLM) inference stacks harbor a steganographic channel that requires no modification to model

arXiv:2510.16028v4 Announce Type: replace-cross Abstract: Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces). Yet ML-as-a-Service reveals litt

arXiv:2606.07895v1 Announce Type: new Abstract: Discrete Vision-Language-Action (VLA) models typically formulate action generation as next-token prediction over discretized action spaces, conditioning

arXiv:2606.08122v1 Announce Type: new Abstract: Predicting a user's next Point-of-Interest (POI) based on their historical check-in records is a fundamental task in location-based services. While rece

arXiv:2606.08408v1 Announce Type: cross Abstract: We extend activation steering to diffusion language models (DLMs) and study a novel problem that arose due to the inference mechanism of DLMs: Modifyi

arXiv:2606.09474v1 Announce Type: new Abstract: Generalized Few-Shot Semantic Segmentation (GFSS) has traditionally been approached as a representation-learning problem, requiring task-specific adapta

arXiv:2601.22736v2 Announce Type: replace-cross Abstract: Causal inference from observational data can provide strong evidence for finding the best action in a decision-making scenario without having

arXiv:2606.08202v1 Announce Type: cross Abstract: Most statistical and machine learning methods for directed interactions focus on pairwise effects among variables. Even existing cyclic models represe

arXiv:2606.07410v1 Announce Type: cross Abstract: The emergence of 'Aha moments' in large language models, particularly DeepSeek-R1-0120, has raised the question of whether these systems genuinely rea

arXiv:2606.06946v1 Announce Type: cross Abstract: We present LoRA-MINT, a new methodology for Membership Inference Test (MINT) applied to recent Large Language Models (LLMs) fine-tuned for specific Na

llama.cpp is an LLM inference tool in C/C++ , and b9555 represents a specific build or version release from the ggml-org/llama.cpp GitHub repository. The project does not follow traditional release pr

B9561 is an intermediate build release of llama.cpp, the C/C++ implementation of large language model inference. Llama.cpp releases use build identifiers (b-numbers) to track development versions betw

b9563 is a release build of llama.cpp, an open-source software library for large language model inference that is co-developed alongside the GGML tensor library. This intermediate build includes vario

arXiv:2606.06712v1 Announce Type: cross Abstract: We study the transformation of autoregressive models (ARLMs) into diffusion language models (DLMs). Rather than pretraining from scratch, prior work r

deepagent for competitive analysis Introducing 'Signals of AI' - DeepAgent powered by - @nebiustf for Inference - @NVIDIAAI Nemotron 3 Ultra - @tavilyai for Web Search - @LangChain for the core deepag

DeepSeek is increasing its presence in the AI inference market by competing for higher token volumes, while Anthropic maintains its leadership position in terms of overall spending. This analysis like

arXiv:2606.06570v1 Announce Type: cross Abstract: Malware detection remains largely reactive: machine learning models trained on known samples degrade as threats evolve. Understanding evolutionary rel

arXiv:2606.07116v1 Announce Type: cross Abstract: Low-bit quantization has been widely adopted to accelerate the inference of large language models (LLMs) by significantly reducing computational cost

arXiv:2403.10318v3 Announce Type: replace Abstract: Recent advances have shifted the paradigm of tabular learning toward tabular foundation models, yet their accuracy relies on a heavy inference cost

arXiv:2606.06870v1 Announce Type: new Abstract: Assistive robots operating under shared autonomy must balance user control with autonomous assistance. Because robot actions depend on internal intent i

arXiv:2606.06745v1 Announce Type: new Abstract: Reasoning Large Language Models can improve problem-solving performance through deliberative inference, but invoking slow reasoning for every input is c

llama.cpp is a project for LLM inference in C/C++ . Release b9551 is a build version from the llama.cpp project's GitHub releases, following the project's rapid development cycle where multiple releas

arXiv:2602.21889v2 Announce Type: replace Abstract: Predictions from ML models support human decision making in several fields, including high-stakes ones such as healthcare and the judiciary. Yet, we

b9538 is a release version of llama.cpp, an open-source C/C++ project for LLM inference. As a commit hash-based release in the llama.cpp repository, it represents a specific point in the project's dev

arXiv:2606.05682v1 Announce Type: new Abstract: Demand for low-precision inference, including NVFP4-based approaches, has grown as large language models are increasingly deployed in latency and cost c

The Mac mini M4 uses unified memory architecture where CPU and GPU share a single 24GB memory pool, while the RTX 5060 has dedicated VRAM. Mac mini M4 is preferred for large model inference (70B param

arXiv:2606.05864v1 Announce Type: new Abstract: We investigate the extent to which the language processing of LLMs resembles human cognitive processes, focusing on a human cognitive bias called the ex

b9521 is an intermediate build release of llama.cpp, an open-source C/C++ library for large language model inference. The release is part of the continuous development cycle following other recent bui

b9523 is a release build of llama.cpp, an open-source tool for LLM inference in C/C++ that enables language model execution with minimal setup on a wide range of hardware locally and in the cloud. The

B9524 is a release of llama.cpp, an LLM inference project in C/C++ . This specific build number represents a snapshot from the llama.cpp GitHub releases, which are regularly published to track increme

Based on the available search results, I cannot locate specific details about the b9533 release. However, b9533 is a release from the llama.cpp project, which provides LLM inference in C/C++ . The lla

arXiv:2606.06219v1 Announce Type: new Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints. While diffusion mod

arXiv:2511.13183v2 Announce Type: replace Abstract: Tractography is the process of inferring the trajectories of white-matter pathways in the brain from diffusion magnetic resonance imaging (dMRI). Lo

arXiv:2606.05769v1 Announce Type: new Abstract: Video event prediction (VEP) requires models to infer unobserved future states from partial video evidence. Existing video MLLMs usually verbalize inter

arXiv:2606.06076v1 Announce Type: cross Abstract: While vision-language models excel at general multimodal understanding, they still struggle with visual spatial planning. We attribute this to a perce

arXiv:2606.06079v1 Announce Type: new Abstract: Agent skills, which consist of reusable strategies that guide agent reasoning and action, have shown strong potential for improving model capability at

arXiv:2606.06416v1 Announce Type: cross Abstract: Inference-time skill augmentation provides a lightweight way to improve data-analytic agents by injecting reusable procedural knowledge without updati

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