Search: “agents”

arXiv:2605.08527v1 Announce Type: cross Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has significantly improved the reasoning capabilities of large language models (LLMs), particula

arXiv:2605.09990v1 Announce Type: new Abstract: Data-intensive applications, ranging from large-scale retrieval systems to advanced data pipelines, are increasingly bottlenecked by the processing of h

arXiv:2605.09708v1 Announce Type: cross Abstract: We present Metal-Sci, a 10-task benchmark of scientific Apple Silicon Metal compute kernels spanning six optimization regimes (stencils, all-pairs in

arXiv:2605.09364v1 Announce Type: new Abstract: This paper investigates robust representation learning in offline goal-conditioned reinforcement learning (GCRL). Particularly in sparse reward scenario

arXiv:2511.07833v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a standard recipe for post-training LLMs on reasoning tasks, with Group Relat

arXiv:2605.08762v1 Announce Type: cross Abstract: Current omni-modal benchmarks mainly evaluate models under settings where multiple modalities are provided simultaneously, while the ability to start

arXiv:2605.10484v1 Announce Type: new Abstract: Scene graph alignment establishes object correspondences between two 3D scene graphs constructed from partially overlapping observations. This enables e

arXiv:2605.10341v1 Announce Type: new Abstract: A LaTeX manuscript that compiles without error is not necessarily publication-ready. The resulting PDFs frequently suffer from misplaced floats, overflo

arXiv:2605.10936v1 Announce Type: new Abstract: As wearable devices like smart glasses integrate Large Multimodal Models (LMMs) into the continuous first-person visual streams of individual users, the

arXiv:2602.00593v2 Announce Type: replace Abstract: Despite progress on general tasks, vision-language models (VLMs) still struggle with challenges that demand both fine-grained visual grounding and e

arXiv:2605.10118v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated exceptional general reasoning capabilities. However, their performance in embodied navigation remains hi

arXiv:2605.10404v1 Announce Type: new Abstract: With the growing prevalence of always-on hardware such as smart glasses, body cameras, and home security systems, life-logging visual sensing is becomin

arXiv:2605.10310v1 Announce Type: new Abstract: Existing alignment research is dominated by concerns about safety and preventing harm: safeguards, controllability, and compliance. This paradigm of ali

arXiv:2605.08687v1 Announce Type: cross Abstract: Data preparation is a central and time-consuming stage in data analysis workflows. Traditionally, commercial tools have relied on graphical user inter

arXiv:2605.10529v1 Announce Type: new Abstract: Biomedical knowledge graphs underwrite drug repurposing and clinical decision support, yet the upstream ontologies they depend on update on independent

arXiv:2605.09893v1 Announce Type: cross Abstract: Large language models (LLMs) are often evaluated based on their stated values, yet these do not reliably translate into their actions, a discrepancy t

arXiv:2507.07969v4 Announce Type: replace-cross Abstract: We present Q-chunking, a simple yet effective recipe for improving reinforcement learning (RL) algorithms for long-horizon, sparse-reward task

arXiv:2605.10909v1 Announce Type: new Abstract: This work revisits standard policy gradient methods used on restricted policy classes, which are known to get stuck in suboptimal critical points. We id

arXiv:2509.02372v3 Announce Type: replace-cross Abstract: Large Language Models have become critical to modern software development, but their reliance on uncurated web-scale datasets for training int

arXiv:2605.10376v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have advanced rapidly in multimodal perception and language understanding, yet it remains unclear whether they can reliabl

Financial Times: Sources: some Amazon employees are using in-house OpenClaw-like tool MeshClaw for unnecessary tasks to inflate AI token use after Amazon set weekly AI targets — In-house MeshClaw tool

arXiv:2605.09929v1 Announce Type: new Abstract: Deploying large language models in telecommunications requires more than task accuracy. In realistic workflows, a model may inherit partially completed

There will be no AI jobpocalypse. The story that AI will lead to massive unemployment is stoking unnecessary fear. AI — like any other technology — does affect jobs, but telling overblown stories of l

tl;dr - let's not just remove negative behavior from models, but also add positive ones 👍 If anyone builds it, everyone thrives. Over the past decade, a lot of important work on AI alignment has focus

arXiv:2605.08129v1 Announce Type: new Abstract: Unified multimodal understanding and generation models enable richer human-AI interaction. Yet jointly customizing a character's persona, dialogue style

arXiv:2605.10843v1 Announce Type: cross Abstract: Large language models increasingly mediate decisions that turn on moral judgement, yet a growing body of evidence shows that their implicit preference

arXiv:2605.08766v1 Announce Type: cross Abstract: Personalized user understanding from large-scale digital traces remains a fundamental challenge. Traditional user profiling methods rely on discrimina

arXiv:2605.10172v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have achieved remarkable success in general perception, yet complex multi-step visual reasoning remains a per

We just crossed 1,000,000 public datasets on Hugging Face! That's petabytes of data available that millions of AI builders are downloading, analyzing, and training AI models on every day! What's inter

arXiv:2510.09096v3 Announce Type: replace-cross Abstract: Learning from demonstrations enables experts to teach robots complex tasks using interfaces such as kinesthetic teaching, joystick control, an

arXiv:2605.07066v1 Announce Type: new Abstract: Autonomous systems that build structures from natural-language instructions need reliable spatial reasoning, yet large language models (LLMs) make syste

arXiv:2502.07143v3 Announce Type: replace Abstract: The severe shortage of medical doctors limits access to timely and reliable healthcare, leaving millions underserved. Large language models (LLMs) o

arXiv:2605.07782v1 Announce Type: new Abstract: LLMs can generate hardware descriptions from natural language specifications, but the resulting Verilog often contains width mismatches, combinational l

At Google Cloud Next ’26 we announced Cloud Storage Rapid, a family of object storage capabilities for data-intensive workloads like AI and analytics. Out of the gate, Cloud Storage Rapid consists of

DeepSeek V4 Flash is ~90% cheaper than GPT 5.4 Mini and ~70% cheaper than Gemini 3.1 Flash Lite For devs pushing ~500M tok/month, this is the difference between: GPT 5.4 Mini: ~394/mo Gemini 3.1 Flash

arXiv:2605.07061v1 Announce Type: cross Abstract: Joint audio-video generation models are rapidly approaching professional production quality, raising a central question: do they understand audio-visu

arXiv:2605.07299v1 Announce Type: cross Abstract: Existing Multimodal Large Language Models (MLLMs) remain primarily reactive, failing to continuously perceive environments or proactively assist users

arXiv:2604.05777v2 Announce Type: replace Abstract: How do people acquire rich, flexible knowledge about their environment from others despite limited cognitive capacity? Humans are often thought to r

arXiv:2605.05340v2 Announce Type: replace-cross Abstract: As Vision-Language Models (VLMs) are increasingly deployed as autonomous cognitive cores for embodied assistants, evaluating their privacy awa

arXiv:2510.01569v3 Announce Type: replace Abstract: We present InvThink, a training and prompting framework that requires the model to enumerate, analyze, and constrain potential failures before gener

Managing a modern database fleet is both a scale and cognitive problem. As database estates grow, the effort required to monitor, troubleshoot, and optimize them often outpaces teams’ capacity, who fi

arXiv:2603.09652v3 Announce Type: replace Abstract: With the rapid advancement of Large Language Models (LLMs) in code generation, human-AI interaction is evolving from static text responses to dynami

arXiv:2605.07649v1 Announce Type: cross Abstract: Over the last few years, research on autonomous systems has matured to such a degree that the field is increasingly well-positioned to translate resea

arXiv:2605.07334v1 Announce Type: new Abstract: Video Reasoning Segmentation (VRS) aims to segment target objects in videos based on implicit instructions that convey human intent and temporal logic.

arXiv:2605.06764v1 Announce Type: cross Abstract: Learning from a sequence of interactions, as soon as observations are perceived and acted upon, without explicitly storing them, holds the promise of

arXiv:2605.07129v1 Announce Type: cross Abstract: Large Language Models (LLMs) have emerged as a promising paradigm for next-generation recommender systems, offering strong semantic understanding and