agents

arXiv:2512.18662v2 Announce Type: replace-cross Abstract: End-to-end (E2E) autonomous driving models that take only camera images as input and directly predict a future trajectory are appealing for th

arXiv:2512.14735v2 Announce Type: replace-cross Abstract: This paper proposes PyFi, a novel framework for pyramid-like financial image understanding that enables vision language models (VLMs) to reaso

arXiv:2604.07939v1 Announce Type: new Abstract: In this work, we present RAGE-XY, an extended version of RAGE, a real-time estimation framework that simultaneously infers vehicle velocity, tire slip a

arXiv:2604.06268v1 Announce Type: new Abstract: RL training of multi-turn LLM agents is inherently unstable, and reasoning quality directly determines task performance. Entropy is widely used to track

Ramp has launched **AI Spend Intelligence**, a new product that gives finance teams token-level visibility into AI costs across providers like OpenAI and Anthropic. The New York-based company pull...

arXiv:2604.07595v1 Announce Type: cross Abstract: Language model agents reason from scratch on every query: each time an agent retrieves evidence and deliberates, the chain of thought is discarded and

arXiv:2604.07036v1 Announce Type: cross Abstract: Recently, LLM-based agents have become increasingly popular across many applications, including complex sequential decision-making problems. However,

arXiv:2604.07765v1 Announce Type: new Abstract: Earth Observation (EO) systems are essentially designed to support domain experts who often express their requirements through vague natural language ra

arXiv:2604.07575v1 Announce Type: new Abstract: Autonomous multi-agent target tracking in GPS-denied and communication-restricted environments (e.g., underwater exploration, subterranean search and re

arXiv:2505.17732v2 Announce Type: replace Abstract: Accurate, fast, and reliable 3D perception is essential for autonomous driving. Recently, bird's-eye view (BEV)-based perception approaches have eme

arXiv:2604.08366v1 Announce Type: cross Abstract: Large-scale deep learning models for physical AI applications depend on diverse training data collection efforts. These models and correspondingly, th

arXiv:2604.07126v1 Announce Type: cross Abstract: Predicting vehicle trajectories plays an important role in autonomous driving and ITS applications. Although multiple deep learning algorithms are dev

arXiv:2604.06204v1 Announce Type: cross Abstract: Personalization is essential for Large Language Model (LLM)-based agents to adapt to users' preferences and improve response quality and task performa

arXiv:2604.08377v1 Announce Type: cross Abstract: Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after depl

Speed of open source! Anthropic adds advisor strategy yesterday -> Emanuele adds an implementation as middleware less than 24 hours later! Just shipped advisor-middleware: an open-source implementatio

arXiv:2502.19559v3 Announce Type: replace Abstract: Multi-agent debate - multiple instances of large language models discussing problems in turn-based interaction - has shown promise for solving knowl

Sub-agent Model Selection — Different Tasks, Different Models Your main agent runs Qwen3.6-Plus for quality. But not every subtask needs a flagship model. Now sub-agents can use a different model. Cre

arXiv:2604.07415v1 Announce Type: cross Abstract: Large language models (LLMs) are probabilistic in nature and perform more reliably when augmented with external information. As complex queries often

arXiv:2604.06332v1 Announce Type: cross Abstract: Autonomous highway driving, especially for long-haul heavy trucks, requires detecting objects at long ranges beyond 500 meters to satisfy braking dist

The ‘agent harness’ is essentially the new runtime. It’s like a lightweight OS around an LLM—handling memory, tool calls, retries, and control flow. @hwchase17 im excited about agent harnesses because

arXiv:2604.06240v1 Announce Type: cross Abstract: Verifying the success of computer use agent (CUA) trajectories is a critical challenge: without reliable verification, neither evaluation nor training

The challenge with agent harnesses is not only building them but distribution. If we want agent products to scale like saas, the interfaces and ecosystem still need a fundamental breakthrough. im exci

arXiv:2604.06543v1 Announce Type: cross Abstract: In this work, we demonstrate that reliable stochastic sampling is a fundamental yet unfulfilled requirement for Large Language Models (LLMs) operating

The keynote lineup for AI Engineer this morning is so good🔥 Running late so watching the livestream this morning! @dsp_ on the stage right now! He is saying the number 1 thing we need to focus on for

arXiv:2604.04956v2 Announce Type: replace-cross Abstract: The recent, super-exponential scaling of autonomous Large Language Model (LLM) agents signals a broader, fundamental paradigm shift from machi

This Week in Replit. New Skills: SEO Auditor, BDR, Stock Analyst, Interviewer. SEO Auditor is blowing up. Amazing work, @samuel_spitz ! Accenture partnership is live. Consultants shipping working prot

tldr > evals are the new training data. instead of updating weights, you're updating the agent harness > problem is agents are famous cheaters. they will reward-hack your evals and overfit just to mak

The AI Agent Mastermind is a four-week, live cohort program created by Allie K. Miller — a TIME 100 Most Influential People in AI honoree and the most followed voice in AI business, with nearly 2 ...

arXiv:2604.07960v1 Announce Type: cross Abstract: Computer-Aided Design (CAD) is an expert-level task that relies on long-horizon reasoning and coherent modeling actions. Large Language Models (LLMs)

arXiv:2604.06411v1 Announce Type: cross Abstract: CubeSats have revolutionized access to space by providing affordable and accessible platforms for research and education. However, their reliance on C

arXiv:2604.06747v2 Announce Type: new Abstract: The aerodynamic design of turbomachinery is a complex and tightly coupled multi-stage process involving geometry generation, performance prediction, opt

arXiv:2507.22025v4 Announce Type: replace Abstract: The emergence of Multimodal Large Language Models (MLLMs) has driven significant advances in Graphical User Interface (GUI) agent capabilities. Neve

arXiv:2604.06387v1 Announce Type: cross Abstract: Accurate reconstruction of environmental scalar fields from sparse onboard observations is essential for autonomous vehicles engaged in aquatic monito

arXiv:2604.03486v2 Announce Type: replace-cross Abstract: We present VisionClaw, an always-on wearable AI agent that integrates live egocentric perception with agentic task execution. Running on Meta

Nick Taylor (@nickytonline) shared a post highlighting a talk at AI Engineer (@aiDotEngineer) featuring Liad Yosef (@liadyosef) and Ido Salomon (@idosal1) on the topic of MCP apps and their importa...

Sam Morrow, a Senior Software Engineer at GitHub on the Copilot Agent Services team, spoke at the ai.engineer conference about the challenges of building and scaling the GitHub MCP Server — an open...

Week 3 winner of the Agent 4 Content Challenge: Magomed Kurbaitaev 🎉 Built: https://help-dagestan.replit.app/ A real-time disaster coordination platform that connects flood victims with verified local

Welcome to the singularity 😎 hermes agent from @NousResearch is the fastest growing agent of all time. @OpenClaw went from 0 → 40K stars in 61 days. hermes did it in 45 days. in the past 7 days alone,

What does it actually take to make agents better over time? A system that starts with a trace. You capture traces of agent behavior, enrich them with evaluations and human feedback, identify what’s fa

why not? what in the current harnesses are not bitter lesson-pilled? off top of head: will go away: - planning tools - compaction (or at least will matter less) not going away: - sub agents - skills -

arXiv:2604.08032v1 Announce Type: cross Abstract: Automated maritime collision avoidance will rely on human supervision for the foreseeable future. This necessitates transparency into how the system p

arXiv:2603.28906v2 Announce Type: replace Abstract: AGI has become the Holly Grail of AI with the promise of level intelligence and the major Tech companies around the world are investing unprecedente

@3blue1brown https://x.com/verncrawford/status/2042344703077859402?s=46 @NousResearch 2 prompts guys. 2 prompts!!! The second prompt was “go from 6 camera moves to 3.” (Hermes Agent, Codex5.4, Manim V

a useful mental model on how teams can think about good data design to improve their models/agents: Evals ~= Training Data ~= Environments - in Classical Deep Learning, we learn from each training exa

Agents like @openclaw are incredibly powerful, as long as the information they receive is clean and structured🦞 When it comes to PDFs and other unstructured documents, most agents struggle. The tools

Supabase has launched **Agent Skills**, an open-source set of instructions designed to teach AI coding agents how to build on Supabase correctly, addressing the problem that while AI agents have ge...