Human Intelligence (HI) excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AIAgents, enabling them to harness expert models for complex task-solving towards Artificial General Intelligence (AGI). Large Language Models (LLMs) show promising learning and reasoning abilities, and can effectively use external models, tools, plugins, or APIs to tackle complex problems. In this work, we introduce OpenAGI, an open-source AGI research and development platform designed for solving multi-step, real-world tasks. Specifically, OpenAGI uses a dual strategy, integrating standard benchmark tasks for benchmarking and evaluation, and open-ended tasks including more expandable models, tools, plugins, or APIs for creative problem-solving. Tasks are presented as natural language queries to the LLM, which then selects and executes appropriate models. We also propose a Reinforcement Learning from Task Feedback (RLTF) mechanism that uses task results to improve the LLM's task-solving ability, which creates a self-improving AI feedback loop. While we acknowledge that AGI is a broad and multifaceted research challenge with no singularly defined solution path, the integration of LLMs with domain-specific expert models, inspired by mirroring the blend of general and specialized intelligence in humans, offers a promising approach towards AGI.

For our local hypergraph clustering experiments, we inserted SPARSECARD as a subroutine into the method HYPERLOCAL, which finds a cluster S in a hypergraph H = (V,E) that is localized around an input set Z V. It does so by minimizing the following ratio cut objective: φ(S) = cutH(S) vol(Z S) βvol( Z S), subject to vol( Z S) 0. (35) Here, Z = V\Z denotes the complement set of Z. For a node set T V, vol(T) denotes volume of T, i.e., the sum of node degrees. The term vol(Z S) in the denominator rewards a high overlap between the output cluster S and the input set Z. The second term βvol( Z S) is a penalty for including too many nodes outside the input set Z. This is tuned by a locality parameter β > 0. For smaller values of β, the algorithm will explore a larger region in the hypergraph in search for good clusters.

Multi-agent collaborative perception could significantly upgrade the perception performance by enabling agents to share complementary information with each other through communication. It inevitably results in a fundamental trade-off between perception performance and communication bandwidth. To tackle this bottleneck issue, we propose a spatial confidence map, which reflects the spatial heterogeneity of perceptual information. It empowers agents to only share spatially sparse, yet perceptually critical information, contributing to where to communicate. Based on this novel spatial confidence map, we propose Where2comm, a communication-efficient collaborative perception framework. Where2comm has two distinct advantages: i) it considers pragmatic compression and uses less communication to achieve higher perception performance by focusing on perceptually critical areas; and ii) it can handle varying communication bandwidth by dynamically adjusting spatial areas involved in communication. To evaluate Where2comm, we consider 3D object detection in both real-world and simulation scenarios with two modalities (camera/LiDAR) and two agent types (cars/drones) on four datasets: OPV2V, V2X-Sim, DAIR-V2X, and our original CoPerception-UAVs. Where2comm consistently outperforms previous methods; for example, it achieves more than 100,000 lower communication volume and still outperforms DiscoNet and V2X-ViT on OPV2V.

We propose a new approach to automated theorem proving where an AlphaZerostyle agent is self-training to refine a generic high-level expert strategy expressed as a nondeterministic program. An analogous teacher agent is self-training to generate tasks of suitable relevance and difficulty for the learner. This allows leveraging minimal amounts of domain knowledge to tackle problems for which training data is unavailable or hard to synthesize. As a specific illustration, we consider loop invariant synthesis for imperative programs and use neural networks to refine both the teacher and solver strategies.

We call c(m) the m-step concentrability of a future-state distribution and call Cρ,µ the discountedaverage concentrability coefficient of the future-state distributions. The class of MDPs that satisfies this concentrability assumption is quite large, which is further discussed in Munos and Szepesvári [18]. If Xi, i = 1,...,N is an i.i.d. And when q = 1, N is used instead of N1. From the definition, one can esasily see that Nq,FX1:N N. Lemma A.2. (Single Iteration Error Bound) Let Vk and Vk+1 be the value functions of iteration kand k+1, and Vmax = rmax/(1 γ).