Search: “agents”

Azure Content Understanding (CU) in Foundry Tools is Microsoft’s comprehensive content AI service. It ingests diverse data types — documents, audio, images, and video — and extracts the most critical

arXiv:2606.03951v1 Announce Type: new Abstract: Human experience in digital environments offers a vast, underexplored resource of authentic, untrimmed interactions that contain rich procedural knowled

arXiv:2606.03874v1 Announce Type: new Abstract: We present DyaPlex, a streaming, full-duplex speech-and-motion model designed for dyadic interaction. To capture the continuous and reciprocal nature of

arXiv:2606.03804v1 Announce Type: new Abstract: Safe exploration is a key challenge in Reinforcement Learning (RL) that aims to prevent agents from making harmful decisions while exploring their envir

arXiv:2606.03739v1 Announce Type: new Abstract: LLM pipelines waste substantial token budgets on low-information content: repeated context, verbose responses, and redundant boilerplate. We introduce E

arXiv:2606.03876v1 Announce Type: cross Abstract: With the growing prevalence of modern ubiquitous computing technologies, multi-modal tracking systems hold promise for providing timely awareness and

langsmith! ✅ Sandbox: https://docs.langchain.com/langsmith/sandboxes ✅ Gateway: https://docs.langchain.com/langsmith/llm-gateway ✅ Observability: https://docs.langchain.com/langsmith/observability eve

arXiv:2606.03521v1 Announce Type: cross Abstract: To improve the real-world applicability of reinforcement learning (RL), the field of adversarially robust RL studies how to train agents under adversa

arXiv:2602.14279v2 Announce Type: replace-cross Abstract: Eliciting information to reduce uncertainty about latent group-level properties from surveys and other collective assessments requires allocat

With LangSmith Engine, systemic issues get surfaced automatically instead of getting buried in traces. @ollieelmgren from @ListenLabs on how LangSmith Engine changed the way his team evaluates their a

arXiv:2606.00416v1 Announce Type: new Abstract: Cooperative perception is important for autonomous driving but remains fragile when cameras and LiDAR degrade in adverse weather. We address this challe

arXiv:2507.08038v3 Announce Type: replace-cross Abstract: Language model agents are increasingly used to automate scientific research, yet evaluating their scientific contributions remains a challenge

arXiv:2606.01015v1 Announce Type: cross Abstract: The convergence of Artificial Intelligence, the Internet of Things, and Robotics is no longer a futuristic vision; it is rapidly becoming the foundati

arXiv:2606.01363v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) infers information about the environment from a learned dynamics model and bears the potential to address open

arXiv:2606.01229v1 Announce Type: new Abstract: During green building design, computer-aided energy assessment is widely used to improve efficiency and achieve overall optimization. This paper present

arXiv:2606.00780v1 Announce Type: cross Abstract: Offline meta-reinforcement learning leverages static datasets to enable agents to generalize to unseen environments by combining offline efficiency wi

Come check out our booth at Snowflake Summit 2026 ❄️ We Parse PDFs ❄️ Come meet the LlamaIndex Team at Snowflake Summit 2026. It might be chilly in Snow Park ☃️ but the AI infrastructure market is red

arXiv:2606.00047v1 Announce Type: cross Abstract: Frontier AI governance often centres on the model-level governance paradigm, which assumes that a model's capability profile is primarily a function o

arXiv:2512.23351v2 Announce Type: replace Abstract: The flexibility and accuracy of methods for automatically counting objects in images and videos are limited by the way the object can be specified.

arXiv:2606.01557v1 Announce Type: new Abstract: Everywhere learning is a new paradigm whereby Artificial Intelligence (AI) systems are trained to satisfy loss constraints with probability one over the

arXiv:2606.01008v1 Announce Type: cross Abstract: We present a benchmark for evaluating AI models and agents on real-world formal software verification tasks. We first scrape 11,039 property-based tes

arXiv:2606.00136v1 Announce Type: cross Abstract: The proliferation of adversarial synthetic content, accelerated by Generative AI (GenAI) is rendering traditional reactive detection methods ineffecti

arXiv:2606.01461v1 Announce Type: new Abstract: Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across can

arXiv:2606.01621v1 Announce Type: new Abstract: Vision-language models (VLMs) have become a common foundation for vision-and-language navigation in continuous environments (VLN-CE). Yet most VLM-based

arXiv:2606.00050v1 Announce Type: new Abstract: We present Grokers, an architecture for building persistent, structured comprehension of typed knowledge graphs through bottom-up inductive traversal of

This post documents a developer successfully implementing Git functionality (cloning repositories, creating branches, and committing pull requests) in what appears to be an AI system or autonomous age

arXiv:2602.13937v2 Announce Type: replace Abstract: Automated Machine Learning (AutoML) has improved access to machine learning, yet existing techniques often remain limited in flexibility, transparen

arXiv:2311.00260v2 Announce Type: replace-cross Abstract: In collaborative active learning, where multiple agents try to learn labels from a common hypothesis, we introduce an innovative framework for

arXiv:2510.12624v2 Announce Type: replace-cross Abstract: Active feature acquisition (AFA) is a sequential decision-making problem where the goal is to improve model performance for test instances by

Microsoft’s annual developer conference is kicking off on June 2nd in San Francisco with the keynote presentation streaming live at 12:30PM ET / 9:30AM PT, and we will be following along here with eve

arXiv:2606.02433v1 Announce Type: cross Abstract: The rapid development of LLMs has significantly advanced tabular question answering, but most systems cannot perform future-oriented numerical predict

Shirin Ghaffary / Bloomberg: OpenAI unveils new Codex plugins for tasks related to public equity investment, banking and sales, and other roles, and plans to integrate Codex into ChatGPT — OpenAI is e

arXiv:2606.01722v1 Announce Type: cross Abstract: For decades, distributed systems have typically assumed that correct participants execute protocol-specified behavior with stable, externally defined,

arXiv:2606.00015v1 Announce Type: cross Abstract: Operating Systems (OS) courses are among the most challenging in computer science education due to the complexity of internal structures and the diver

arXiv:2512.04069v2 Announce Type: replace Abstract: Vision Language Models (VLMs) demonstrate strong qualitative visual understanding, but struggle with metrically precise spatial reasoning required f

arXiv:2606.00376v1 Announce Type: new Abstract: Extended chain-of-thought reasoning can degrade performance on deterministic state-tracking tasks, not due to preference biases, but limits rooted in th

The future of design doesn’t interrupt your process. @NousResearch Hermes keeps designers in creative flow - Rhino → ComfyUI → @Blender in one seamless agent workflow. No context switching. Just visio

arXiv:2606.02580v1 Announce Type: new Abstract: Inverse graphics is a longstanding and highly underconstrained problem that seeks to reconstruct images as editable 3D scenes which can be rendered, rel

arXiv:2602.07883v3 Announce Type: replace Abstract: LLM-powered agentic systems excel at complex long-horizon tasks, but remain constrained by static configurations fixed before execution. Such rigidi

arXiv:2602.12724v3 Announce Type: replace Abstract: This study introduces TRANS: Terrain-aware Reinforcement learning for Agile Navigation under Social interactions, a deep reinforcement learning (DRL

arXiv:2512.10958v2 Announce Type: replace Abstract: Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fa

arXiv:2605.30899v1 Announce Type: cross Abstract: Speech foundation models and Speech LLMs have advanced speech understanding, yet deployment-oriented model selection is hindered by non-comparable eva

❄️ Come meet the LlamaIndex Team at Snowflake Summit 2026. It might be chilly in Snow Park ☃️ but the AI infrastructure market is red hot 🔥. Come visit our team at the expo floor and explore how you c

arXiv:2605.31064v1 Announce Type: cross Abstract: Large Language Models (LLMs) have significantly advanced online data services, particularly in the domain of financial question answering (FinQA). How

longmemeval experiment arch: 1) deterministic ingestion/extraction (85.6% accuracy, 86.2% retrieval) 2) semantic ingestion/extraction (84.8% accuracy, 94.9% retention) 3) semantic ingestion/determinis

arXiv:2509.16187v3 Announce Type: replace-cross Abstract: Code translation transforms source code from one programming language (PL) to another. Validating the functional equivalence of translation an