Search: “context”

arXiv:2608.10795v1 Announce Type: new Abstract: Successful mutation strategies in evolutionary code search may contain reusable knowledge that is useful beyond a single run, and in some cases may tran

arXiv:2608.10668v1 Announce Type: new Abstract: Temporal knowledge graphs are central to many uses of the Semantic Web, but existing completion methods assume the entities, relation names, and timesta

arXiv:2608.10182v1 Announce Type: cross Abstract: Large-scale targeting and recommendation systems are typically built around predictive scores fed into heuristic or local allocation. When the busines

arXiv:2608.10444v1 Announce Type: cross Abstract: Large language models (LLMs) have made substantial progress on reasoning tasks that require increasingly long and complex inferential chains. This pro

arXiv:2608.10494v1 Announce Type: new Abstract: Earth observation (EO) agents construct scientifically valid tool workflows and ground their conclusions in current geospatial evidence. This is challen

arXiv:2608.10426v1 Announce Type: new Abstract: Open-vocabulary remote sensing segmentation has recently emerged as a promising paradigm that enables pixel-level recognition of arbitrary categories sp

arXiv:2208.11582v2 Announce Type: replace-cross Abstract: The wide spread of false information online, including misinformation and disinformation, has become a major problem for our highly digitised

arXiv:2608.11016v1 Announce Type: cross Abstract: Clustering is a fundamental class of data analysis techniques with the most important representatives being centroid-based methods like k-means. Such

arXiv:2608.10545v1 Announce Type: cross Abstract: Edge LLMs must preserve inference continuity when a user hands over between edge nodes, requiring key-value (KV) cache transfer to the target node. Ho

arXiv:2601.10129v2 Announce Type: replace-cross Abstract: Current multimodal latent reasoning often relies on external supervision (e.g., auxiliary images), ignoring intrinsic visual attention dynamic

Live now: our Memory & Continual Learning Track from AI Engineer World's Fair 2026. Thesis: we scaled intelligence and got the world's smartest novice. https://www.youtube.com/watch?v=iqloyWCGYQQ&list

arXiv:2608.10002v1 Announce Type: cross Abstract: Hierarchical Phase-Contrast Tomography (HiP-CT) is a synchrotron based X-ray imaging technique that enables non-destructive, volumetric imaging of int

arXiv:2608.10162v1 Announce Type: new Abstract: Methods for text-based generation of hand-object interaction (HOI) sequences primarily focus on producing smooth, physically plausible trajectories. A t

arXiv:2608.10537v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) have helped uncover mechanistic explanations for LLM behaviours such as reasoning, jailbreaking etc., via understanding the c

arXiv:2608.10362v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive large language model (LLM) inference by using a lightweight draft model to speculate multiple tokens,

arXiv:2608.10218v1 Announce Type: new Abstract: AI agents are becoming more autonomous and increasingly interconnected, exposing them to new emergent risks arising from agent-to-agent interaction. One

Muse Glimmer is live on Fireworks. The new open-weight model from Meta Superintelligence Labs is a 30B dense model built for always-on agents that reason across many sequential tool calls and can reco

arXiv:2608.09944v1 Announce Type: cross Abstract: Modern web interfaces are increasingly difficult to use with screen readers, particularly when pages update dynamically or hide important structure be

arXiv:2608.11002v1 Announce Type: cross Abstract: Text-to-image (T2I) generation has achieved remarkable progress in recent years. However, existing research has largely focused on English-only settin

arXiv:2608.10729v1 Announce Type: cross Abstract: Foundation models can improve their outputs through a self-refinement process driven by external feedback. In this process, the model is embedded in a

arXiv:2608.10450v1 Announce Type: cross Abstract: Complex software systems develop over timescales that exceed the lifespan of any individual coding agent. Most agentic software systems preserve conti

arXiv:2608.10047v1 Announce Type: cross Abstract: In modern industry, keeping complex systems reliable, safe, and efficient hinges on Prognostics and Health Management (PHM). Machine Learning (ML) has

i was hoping they would release both today but the big one just dropped now , and it says its been listed since 5 hours ago on huggingface. I guess the real date for 27b is https://modelscope.cn/model

arXiv:2608.11017v1 Announce Type: cross Abstract: Long-horizon egocentric video is a rich substrate for wearable AI assistants, but object-centric questions such as where an item was moved, when it la

arXiv:2608.10669v1 Announce Type: new Abstract: Large language model (LLM) agents combine language-based reasoning with external tools to perform complex tasks. Adversarial inputs can exploit interact

I also have 64 gb ddr4 ryzen 5600 Using llama.cpp Ubuntu distro Settings are as follows --n-gpu-layers 999 --n-cpu-moe 36 --no-mmap -ctk q8_0 -ctv q8_0 -fa 1 -c 9000 So rebuilt my llama.cpp build to r

arXiv:2508.07617v2 Announce Type: replace-cross Abstract: AI has the potential to augment human decision making. However, even high-performing models can produce inaccurate predictions when deployed.

arXiv:2608.10016v1 Announce Type: cross Abstract: Heterogeneous federated systems require agents to learn and exchange informative representations despite differences in data distributions, sensing mo

arXiv:2608.10775v1 Announce Type: new Abstract: Computer-using agents can perceive rich software interfaces, yet their decisions often lack visual procedural memory: they may recognize individual cont

arXiv:2608.10692v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as mobile assistants, where a key challenge is leveraging personal information scattered across

arXiv:2608.10718v1 Announce Type: new Abstract: This technical report describes the RMC2 Team's champion solution for the WBCD 2026 Track 4: Deformable Manipulation Challenge. The task requires a robo

arXiv:2603.20907v5 Announce Type: replace Abstract: As users increasingly turn to LLMs for practical and personal advice, they become vulnerable to subtle steering toward hidden incentives misaligned

arXiv:2608.11146v1 Announce Type: new Abstract: Safety alignment in large language models (LLMs) is largely developed in English, assuming these safeguards generalize across multilingual settings. How

arXiv:2608.10402v1 Announce Type: new Abstract: Reinforcement learning (RL) for large language models is moving toward multi-turn agentic workloads, where rollout tasks repeatedly pause for external e

Running large language model inference at scale forces a KV cache trade-off: oversized GPU instances or slow time-to-first-token. This post builds a tiered KV cache on Amazon SageMaker HyperPod that e

arXiv:2608.10983v1 Announce Type: cross Abstract: Multi-modal recommenders fuse collaborative signals with item modalities such as text, images, and audio, but the usefulness of each drifts over time

arXiv:2608.10740v1 Announce Type: new Abstract: Effective research ideation requires moving beyond a static understanding of prior work to trace how research problems and solutions evolve across the l

arXiv:2608.11047v1 Announce Type: new Abstract: While existing benchmarks have made substantial progress in evaluating LLMs across STEM domains, financial reasoning over structured data remains compar

Very interesting new work from Anthropic. (bookmark it) They evolve mind viruses, ideas that spread through a multi-agent system by getting each host to pass them on, then measure what governs the spr

arXiv:2608.11201v1 Announce Type: new Abstract: Recent advances in video generation models have significantly improved the realism of synthetic videos, blurring the boundary between generated and auth

arXiv:2608.09942v1 Announce Type: cross Abstract: It is widely assumed that chain-of-thought (CoT) prompting universally improves LLM reasoning. We investigate this through the conceptual framework of

arXiv:2608.11022v1 Announce Type: cross Abstract: Model Cards and Data Cards have demonstrated the value of structured, human-readable documentation for machine learning artifacts, capturing their con

arXiv:2608.10703v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly act in interactive settings where their behavioral styles affect user experience, safety, and downstream dec

arXiv:2608.08663v1 Announce Type: cross Abstract: Humans converge on shared names for novel, hard-to-describe objects through repeated interaction, a process psycholinguists call lexical entrainment.

arXiv:2608.08163v1 Announce Type: new Abstract: The convergence of the Metaverse and Large Language Model (LLM)-based AI agent is catalyzing a shift toward autonomous, immersive, and personalized peda

arXiv:2608.08709v1 Announce Type: new Abstract: The reliability of AI generative models is typically measured by output correctness, yet in practice it depends on the effort required to verify those o