Meta's AI agent plans reportedly include an OpenClaw competitor that can shop on Instagram Meta's AI agent plans reportedly include an OpenClaw competitor that can shop on Instagram Last week during Meta's earnings, Mark Zuckerberg said that the company is working on new AI agents for people and businesses on the company's platform. Now, we know a bit more about what those plans entail, thanks to a new report from . The publication reports that Meta is working on an OpenClaw-inspired agent currently dubbed Hatch. It sounds like the company intends for Hatch to work within its own apps, including agentic shopping on Instagram, as well as with outside services. The company has tested Hatch on simulated versions of third-party services like DoorDash, Reddit and Outlook, according to .

Anthropic announced "dreaming" for AI agents to sort through "memories" at its developer conference. Anthropic just announced a new feature called "dreaming" at the company's developer conference in San Francisco. It's part of Anthropic's recently launched AI agent infrastructure designed to help users manage and deploy tools that automate software processes. This "dreaming" aspect sorts through the transcript of what an agent recently completed and attempts to glean insights to improve the agent's performance. Folks using AI agents often send them on multistep journeys, like visiting a few websites or reading multiple files, to complete online tasks.

PCWorld explores OpenAI's new Codex AI pets, which provide visual status indicators for desktop AI agents through customizable on-screen companions. These pets address a key user experience issue by displaying red clocks when agent approval is needed and green checks upon task completion. The feature enhances multitasking efficiency by keeping users informed of AI agent activity without constant monitoring of the main interface. Whether I'm using Claude's desktop Cowork application or OpenAI's Codex coding app, I prefer that my AI agents check back with me before making high-stakes decisions. But while that makes for a safer setup, it also means my agents are often waiting around, twiddling their thumbs as they wait for me to approve their next steps. Now, if I'm sitting and watching the Cowork or Codex apps in action, I'll see right away when an agent is awaiting my approval. But if I'm working in another window or multitasking, I could easily miss the fact that an idled Cowork or Codex agent is sitting around, staring vacantly into space.

Independent sample generation is the prevailing paradigm in modern diffusion-based generative models of AI. We ask a different question: can samples coordinate through shared population statistics to transport probability mass more efficiently? We introduce Mean-Field Path-Integral Diffusion (MF-PID), a framework in which samples are promoted to interacting agents whose drift depends self-consistently on the evolving population density. We identify two analytically tractable regimes: a Linear-Quadratic-Gaussian (LQG) benchmark in which the infinite-dimensional mean-field system reduces to a finite set of Riccati and linear ODEs, and a Gaussian-mixture regime governed by a piecewise-constant protocol that preserves closed-form solvability. For a quadratic interaction potential with schedule βt and zero base drift we prove that the self-consistent MF guidance is the exact linear interpolant between initial and target global means -- a result that holds for arbitrary initial and target densities and any βt. Applied to demand-response control of energy systems, where agents aggregated into an ensemble are energy consumers (e.g. The energy saving is independent of the number of zones per building (d = 1-32 tested), confirming that the linear guidance formula broadcasts a single d-vector with O(d) communication and grows mildly in compute (sub-cubically for d 32, asymptotically O(d3) for d 1). Introduction Generative AI has been transformed by diffusion models, which frame sample generation as a stochastic process steered from noise to data [1-3]. A key structural feature of these models -- shared with other generative models, e.g. Similarly, stochastic optimal transport (SOT) and Schrödinger bridge formulations [6-8] cast distribution matching as an independent-particle path optimization, yielding tractable convolutions of Green functions but discarding inter-particle information; stochastic interpolants [9] construct flexible transport bridges between arbitrary densities via tunable continuous-time stochastic processes, recovering the Schrödinger bridge as a special limit -- again in an independent-particle framework.

Decentralized learning is a learning process in which data is distributed across computational agents or collected by individual agents, and model parameters are computed as the consensus of the agents. It has gained a lot of interest for applications where agents can collaboratively learn a predictive model without sharing their own data, but sharing only their local models with their immediate neighbors to generate a global model [He et al., 2018, Hendrikx et al., 2019, Arjevani et al., 2020]. We assume there are N agents who are connected over an undirected communication network G = (V,E) where V = {1,...,N} represents the agents and E V V denotes the set of edges; i.e., if agent i and j are connected then (i,j) E implies (j,i) E. Suppose we have a collection of n independent and identically distributed (i.i.d.) data pairs zi = (ai,yi), where ai Rp is the feature vector and yi the label or response of the i-th observation. Let Z = [z1,z2,,zn] Rnp be sampled from the distribution p(Z|x) where the parameter x Rd has a common prior. The goal is to sample from the posterior distribution p(x|Z) p(Z|x)p(x) by distributing Z among N agents such that Zi = {zi1,zi2,,zini} is the subset of data exclusive to agent i.

One of the main challenges in offline Reinforcement Learning (RL) is the distribution shift that arises from the learned policy deviating from the data collection policy. This is often addressed by avoiding out-of-distribution (OOD) actions during policy improvement as their presence can lead to substantial performance degradation. This challenge is amplified in the offline Multi-Agent RL (MARL) setting since the joint action space grows exponentially with the number of agents. To avoid this curse of dimensionality, existing MARL methods adopt either value decomposition methods or fully decentralized training of individual agents. However, even when combined with standard conservatism principles, these methods can still result in the selection of OOD joint actions in offline MARL. To this end, we introduce AlberDICE, an offline MARL algorithm that alternatively performs centralized training of individual agents based on stationary distribution optimization. AlberDICE circumvents the exponential complexity of MARL by computing the best response of one agent at a time while effectively avoiding OOD joint action selection. Theoretically, we show that the alternating optimization procedure converges to Nash policies. In the experiments, we demonstrate that AlberDICE significantly outperforms baseline algorithms on a standard suite of MARL benchmarks.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we meet PhD students and early-career researchers, find out how machine learning is used for particle physics discoveries, cast an eye over the latest AI Index Report, and watch a robot beating elite players at table tennis. In an article published in Nature this month, Sony AI introduced Ace, a table tennis robot that has beaten professional players in competitive matches. The system combines event-based vision sensors and a control system based on model-free reinforcement learning, as well as state-of-the-art high-speed robot hardware. The ninth edition of the Artificial Intelligence Index Report was published on 13 April 2026 .