Combining diverse foundation models is promising, but weight-merging is limited by mismatched architectures and closed APIs. The coordinator, comprising a compact language model ( 0.6B parameters) and a lightweight head ( 10K parameters), is optimized with an evolutionary strategy for efficient and adaptive delegation. Theoretical and empirical analyses highlight two key factors driving this success: (1) the coordinator's hidden-state representations provide rich contextualization of inputs, and (2) under high dimensionality and strict budget constraints, the separable Covariance Matrix Adaptation Evolution Strategy algorithm provides substantial advantages over RL, imitation learning, and random search, leveraging potential block-ε-separability. A prominent line of work involving large language models (LLMs) aspires to scale in line with empirical scaling laws, targeting gains by enlarging model size, training tokens, and compute (Kaplan et al., 2020; Hoffmann et al., 2022). Y et the extent to which such scaling remains efficient and yields sustained returns is uncertain and often resource intensive. An alternative at the micro level is model merging (Akiba et al., 2025; Wortsman et al., 2022; Y ang et al., 2024; Kuroki et al., 2024), which seeks parameter-level integration. However, this approach is frequently impractical due to architectural incompatibilities and the closed-source nature of many high-performing models. In light of these limitations, we adopt a macro-level approach: test-time model composition via coordination, which fuses the complementary strengths of multiple state-of-the-art models from diverse providers without modifying their weights. Leveraging prior data and training investments, this coordination can deliver performance improvements without retraining individual models. The central challenge for such a coordinator is to acquire a rich contextual understanding of a given query to make an effective decision. We posit that this signal can be efficiently extracted from the internal representation of a compact language model, specifically, its hidden states (Allen-Zhu & Li, 2023). In a self-attention-based transformer model, hidden states encode contextual representations of the input (and, after generation, the output) sequence.

The rapid development of large vision-language model (VLM) has greatly promoted the research of GUI agent. However, GUI agents still face significant challenges in handling long-horizon tasks. First, single-agent models struggle to balance high-level capabilities and low-level execution capability, facing prevalent issues of responsibility coupling and capability conflicts. Second, agents lack awareness of the task state, leading to progress loss in long-horizon tasks. To address these challenges, we propose a staged execution-feedback reinforcement learning algorithm. Unlike training a unified policy model, we focus on training high-level scheduling models. Specifically, we propose and train two agents: a Coordinator, responsible for the strategic planning and task decomposition; and a State Tracker, responsible for context compression and information management to maintain the task's state and coherence. Based on this, we built the Coordinator-Executor-State Tracker (CES) multi-agent framework, which can be integrated with any low-level Executor model, assisting the Executor in solving long-horizon tasks through task scheduling and state management. Experiments on long-horizon task benchmarks demonstrate that CES significantly enhances the system's planning and state management capabilities. Furthermore, analysis confirms that our trained high-level scheduling module is a generalizable, plug-and-play module that significantly enhances the long-horizon capabilities of various Executors. Code can be available at https://github.com/hehehahi4/CES.

Satellite constellations in low-Earth orbit are now widespread, enabling positioning, Earth imaging, and communications. In this paper we address the solution of learning problems using these satellite constellations. In particular, we focus on a federated approach, where satellites collect and locally process data, with the ground station aggregating local models. We focus on designing a novel, communication-efficient algorithm that still yields accurate trained models. To this end, we employ several mechanisms to reduce the number of communications with the ground station (local training) and their size (compression). We then propose an error feedback mechanism that enhances accuracy, which yields, as a byproduct, an algorithm-agnostic error feedback scheme that can be more broadly applied. We analyze the convergence of the resulting algorithm, and compare it with the state of the art through simulations in a realistic space scenario, showcasing superior performance.

For this scenario, one is asked to partition the vertices into clusters so that the "highly connected" vertices belong to the same cluster.

The rise of big data has brought the design of distributed learning algorithm to the forefront.

Neural Information Processing Systems http://nips.cc/

The rise of big data has brought the design of distributed learning algorithm to the forefront.

AI Scientific Assistant Core (AISAC) is an integrated multi-agent system developed at Argonne National Laboratory for scientific and engineering workflows. AISAC builds on established technologies - LangGraph for orchestration, FAISS for vector search, and SQLite for persistence - and integrates them into a unified system prototype focused on transparency, provenance tracking, and scientific adaptability. The system implements a Router-Planner-Coordinator workflow and an optional Evaluator role, using prompt-engineered agents coordinated via LangGraph's StateGraph and supported by helper agents such as a Researcher. Each role is defined through custom system prompts that enforce structured JSON outputs. A hybrid memory approach (FAISS + SQLite) enables both semantic retrieval and structured conversation history. An incremental indexing strategy based on file hashing minimizes redundant re-embedding when scientific corpora evolve. A configuration-driven project bootstrap layer allows research teams to customize tools, prompts, and data sources without modifying core code. All agent decisions, tool invocations, and retrievals are logged and visualized through a custom Gradio interface, providing step-by-step transparency for each reasoning episode. The authors have applied AISAC to multiple research areas at Argonne, including specialized deployments for waste-to-products research and energy process safety, as well as general-purpose scientific assistance, demonstrating its cross-domain applicability.

For a large number of experts and days, it may not be feasible to run classical low-regret algorithms.

Clustering is a fundamental problem in unsupervised learning and data analytics.