Gradientsys: A Multi-Agent LLM Scheduler with ReAct Orchestration

• URL: http://arxiv.org/abs/2507.06520v1
• Date: Wed, 09 Jul 2025 03:40:56 GMT
• Title: Gradientsys: A Multi-Agent LLM Scheduler with ReAct Orchestration
• Authors: Xinyuan Song, Zeyu Wang, Siyi Wu, Tianyu Shi, Lynn Ai,
• Abstract summary: We present Gradientsys, a next-generation multi-agent scheduling framework.<n>It coordinates diverse specialized AI agents using a typed Model-Context Protocol (MCP) and a ReAct-based dynamic planning loop.<n>Experiments on the GAIA general-assistant benchmark show that Gradientsys achieves higher task success rates with reduced latency and lower API costs.
• Score: 4.66888457790348
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Gradientsys, a next-generation multi-agent scheduling framework that coordinates diverse specialized AI agents using a typed Model-Context Protocol (MCP) and a ReAct-based dynamic planning loop. At its core, Gradientsys employs an LLM-powered scheduler for intelligent one-to-many task dispatch, enabling parallel execution of heterogeneous agents such as PDF parsers, web search modules, GUI controllers, and web builders. The framework supports hybrid synchronous/asynchronous execution, respects agent capacity constraints, and incorporates a robust retry-and-replan mechanism to handle failures gracefully. To promote transparency and trust, Gradientsys includes an observability layer streaming real-time agent activity and intermediate reasoning via Server-Sent Events (SSE). We offer an architectural overview and evaluate Gradientsys against existing frameworks in terms of extensibility, scheduling topology, tool reusability, parallelism, and observability. Experiments on the GAIA general-assistant benchmark show that Gradientsys achieves higher task success rates with reduced latency and lower API costs compared to a MinionS-style baseline, demonstrating the strength of its LLM-driven multi-agent orchestration.

Related papers

• Visual Document Understanding and Question Answering: A Multi-Agent Collaboration Framework with Test-Time Scaling [83.78874399606379]
  We propose MACT, a Multi-Agent Collaboration framework with Test-Time scaling.<n>It comprises four distinct small-scale agents, with clearly defined roles and effective collaboration.<n>It shows superior performance with a smaller parameter scale without sacrificing the ability of general and mathematical tasks.
  arXiv  Detail & Related papers  (2025-08-05T12:52:09Z)
• DynaSwarm: Dynamically Graph Structure Selection for LLM-based Multi-agent System [0.276240219662896]
  DynaSwarm is a dynamic framework that enhances multi-agent systems.<n>It uses an actor-critic reinforcement learning mechanism to optimize graph structures.<n>It also has a dynamic graph selector that adaptively chooses the optimal graph structure for each input sample.
  arXiv  Detail & Related papers  (2025-07-31T05:52:30Z)
• Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation [72.44384066166147]
  Multi-agent systems (MAS) based on large language models (LLMs) have emerged as a powerful solution for dealing with complex problems across diverse domains.<n>Existing approaches are fundamentally constrained by their reliance on a template graph modification paradigm with a predefined set of agents and hard-coded interaction structures.<n>We propose ARG-Designer, a novel autoregressive model that operationalizes this paradigm by constructing the collaboration graph from scratch.
  arXiv  Detail & Related papers  (2025-07-24T09:17:41Z)
• HAWK: A Hierarchical Workflow Framework for Multi-Agent Collaboration [3.2588674134593942]
  Multi-agent systems face persistent challenges in cross-platform interoperability, dynamic task scheduling, and efficient resource sharing.<n>We propose Hierarchical Agent (Hawk), a modular framework comprising five layers-User, Operator, Agent, Resource-and supported by sixteen standardized interfaces.<n>Hawk delivers an end-to-end pipeline covering task parsing, workflow orchestration, intelligent scheduling, resource invocation, and data synchronization.
  arXiv  Detail & Related papers  (2025-07-05T15:03:53Z)
• What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities [56.646832992178105]
  We introduce OmniBench, a cross-platform, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity.<n>We present OmniEval, a multidimensional evaluation framework that includes subtask-level evaluation, graph-based metrics, and comprehensive tests across 10 capabilities.<n>Our dataset contains 36k graph-structured tasks across 20 scenarios, achieving a 91% human acceptance rate.
  arXiv  Detail & Related papers  (2025-06-10T15:59:38Z)
• Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition [95.54406667705999]
  Pangu Embedded is an efficient Large Language Model (LLM) reasoner developed on Ascend Neural Processing Units (NPUs)<n>It addresses the significant computational costs and inference latency challenges prevalent in existing reasoning-optimized LLMs.<n>It delivers rapid responses and state-of-the-art reasoning quality within a single, unified model architecture.
  arXiv  Detail & Related papers  (2025-05-28T14:03:02Z)
• Understanding and Optimizing Multi-Stage AI Inference Pipelines [11.254219071373319]
  HERMES is a Heterogeneous Multi-stage LLM inference Execution Simulator.<n> HERMES supports heterogeneous clients executing multiple models concurrently unlike prior frameworks.<n>We explore the impact of reasoning stages on end-to-end latency, optimal strategies for hybrid pipelines, and the architectural implications of remote KV cache retrieval.
  arXiv  Detail & Related papers  (2025-04-14T00:29:49Z)
• Performant LLM Agentic Framework for Conversational AI [1.6114012813668932]
  We introduce the Performant Agentic Framework (PAF), a novel system that assists Large Language Models (LLMs) in selecting appropriate nodes and executing actions in order when traversing complex graphs.<n>PAF combines LLM-based reasoning with a mathematically grounded vector scoring mechanism, achieving both higher accuracy and reduced latency.<n>Experiments demonstrate that PAF significantly outperforms baseline methods, paving the way for scalable, real-time Conversational AI systems in complex business environments.
  arXiv  Detail & Related papers  (2025-03-09T02:58:34Z)
• AgentPS: Agentic Process Supervision for Content Moderation with Multimodal LLMs [9.35901507816989]
  We introduce AgentPS, a framework that integrates Agentic Process Supervision into large language models.<n>We show that AgentPS achieves substantial improvements over baseline MLLMs on public benchmarks and proprietary datasets.<n>These results establish AgentPS as a scalable and effective solution for complex multimodal classification in large-scale industrial applications.
  arXiv  Detail & Related papers  (2024-12-15T04:58:00Z)
• The BrowserGym Ecosystem for Web Agent Research [151.90034093362343]
  BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents.<n>We propose an extended BrowserGym-based ecosystem for web agent research, which unifies existing benchmarks from the literature.<n>We conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across 6 popular web agent benchmarks.
  arXiv  Detail & Related papers  (2024-12-06T23:43:59Z)
• Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks [39.084974125007165]
  We introduce Magentic-One, a high-performing open-source agentic system for solving complex tasks. Magentic-One uses a multi-agent architecture where a lead agent, the Orchestrator, tracks progress, and re-plans to recover from errors. We show that Magentic-One achieves statistically competitive performance to the state-of-the-art on three diverse and challenging agentic benchmarks.
  arXiv  Detail & Related papers  (2024-11-07T06:36:19Z)
• Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement [112.04307762405669]
  G"odel Agent is a self-evolving framework inspired by the G"odel machine.<n>G"odel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.
  arXiv  Detail & Related papers  (2024-10-06T10:49:40Z)
• Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
  Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents. We propose the Internet of Agents (IoA), a novel framework that addresses these limitations. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
  arXiv  Detail & Related papers  (2024-07-09T17:33:24Z)
• A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration [55.35849138235116]
  We propose automatically selecting a team of agents from candidates to collaborate in a dynamic communication structure toward different tasks and domains. Specifically, we build a framework named Dynamic LLM-Powered Agent Network ($textDyLAN$) for LLM-powered agent collaboration. We demonstrate that DyLAN outperforms strong baselines in code generation, decision-making, general reasoning, and arithmetic reasoning tasks with moderate computational cost.
  arXiv  Detail & Related papers  (2023-10-03T16:05:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.