SOCIA-Nabla: Textual Gradient Meets Multi-Agent Orchestration for Automated Simulator Generation

• URL: http://arxiv.org/abs/2510.18551v2
• Date: Mon, 10 Nov 2025 14:12:42 GMT
• Title: SOCIA-Nabla: Textual Gradient Meets Multi-Agent Orchestration for Automated Simulator Generation
• Abstract summary: SOCIA-Nabla is an end-to-end, agentic framework that treats simulator construction asinstance optimization over code within a textual graph.<n>Specialized computation-driven agents are embedded as graph nodes, and a workflow manager executes a loss-driven loop: code synthesis -> execution -> evaluation -> code repair.
• Score: 16.707429579441435
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we present SOCIA-Nabla, an end-to-end, agentic framework that treats simulator construction asinstance optimization over code within a textual computation graph. Specialized LLM-driven agents are embedded as graph nodes, and a workflow manager executes a loss-driven loop: code synthesis -> execution -> evaluation -> code repair. The optimizer performs Textual-Gradient Descent (TGD), while human-in-the-loop interaction is reserved for task-spec confirmation, minimizing expert effort and keeping the code itself as the trainable object. Across three CPS tasks, i.e., User Modeling, Mask Adoption, and Personal Mobility, SOCIA-Nabla attains state-of-the-art overall accuracy. By unifying multi-agent orchestration with a loss-aligned optimization view, SOCIA-Nabla converts brittle prompt pipelines into reproducible, constraint-aware simulator code generation that scales across domains and simulation granularities. This work is under review, and we will release the code soon.

Related papers

• Inference-Time Graph Engineering for Multi-Agent LLM Workflows [51.56038180639833]
  We introduce ReActNet, a training-free framework that compiles a query and a set of role-specialized agents into a sequence of directed communication graphs.<n>We show that ReActNet consistently improves over fixed-topology and learned-topology baselines while maintaining competitive inference cost.
  arXiv  Detail & Related papers  (2026-09-04T23:32:11Z)
• GASE: Gaussian Splatting-Based Automated System for Reconstructing Embodied-Simulation Environments [63.222920701596564]
  Training embodied agents in the real world requires skilled operators and expensive hardware.<n> Simulation environments offer a compelling alternative by enabling large-scale, cost-effective data augmentation.<n>Fastly constructing high-fidelity simulation scenes with a minimal sim-to-real gap has become a critical objective in robot learning.
  arXiv  Detail & Related papers  (2026-06-16T05:00:42Z)
• Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction [0.02446672595462589]
  We introduce Collider-Bench, a benchmark for evaluating whether language-model agents can reproduce experimental analyses from the Large Hadron Collider (LHC) using only public papers and open scientific software.<n>Agents must therefore rely on physical reasoning, domain knowledge, and trial-and-error to fill these gaps.<n>Our results show that on average no agent reliably beats the physicist-in-the-loop solution.
  arXiv  Detail & Related papers  (2026-05-13T18:00:00Z)
• MatClaw: An Autonomous Code-First LLM Agent for End-to-End Materials Exploration [0.0]
  We present MatClaw, a code-first agent that writes and executes Python directly.<n>We show that MatClaw handles code generation reliably but struggles with tacit domain knowledge.<n>Our results demonstrate that the gap between guided and fully autonomous computational materials research is narrower than ever before.
  arXiv  Detail & Related papers  (2026-04-03T03:32:15Z)
• Understanding by Reconstruction: Reversing the Software Development Process for LLM Pretraining [66.89012795621349]
  Large Language Models (LLMs) often struggle with the deep, long-horizon reasoning required for complex software engineering.<n>We propose a novel paradigm: understanding via reconstruction.<n>We introduce a framework that synthesizes latent agentic trajectories using a multi-agent simulation.
  arXiv  Detail & Related papers  (2026-03-11T09:23:20Z)
• KAPSO: A Knowledge-grounded framework for Autonomous Program Synthesis and Optimization [3.0268242725574215]
  KAPSO is a modular framework for autonomous program synthesis and optimization.<n>It iteratively performs ideation, code synthesis and editing, execution, evaluation, and learning to improve a runnable artifact.
  arXiv  Detail & Related papers  (2026-01-29T10:40:54Z)
• ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks [62.031889234230725]
  6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
  arXiv  Detail & Related papers  (2026-01-27T13:43:59Z)
• From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence [150.3696990310269]
  Large language models (LLMs) have transformed automated software development by enabling direct translation of natural language descriptions into functional code.<n>We provide a comprehensive synthesis and practical guide (a series of analytic and probing experiments) about code LLMs.<n>We analyze the code capability of the general LLMs (GPT-4, Claude, LLaMA) and code-specialized LLMs (StarCoder, Code LLaMA, DeepSeek-Coder, and QwenCoder)
  arXiv  Detail & Related papers  (2025-11-23T17:09:34Z)
• LLMs as Scalable, General-Purpose Simulators For Evolving Digital Agent Training [55.72784274656801]
  We introduce a scalable paradigm that generates structured UI states and transitions to synthesize training trajectories at scale.<n>Our paradigm integrates a digital world simulator for diverse UI states, a guided rollout process for coherent exploration, and a trajectory wrapper.<n>Experiments on WebArena and AndroidWorld show that UI-Simulator rivals or surpasses open-source agents trained on real UIs with significantly better robustness.
  arXiv  Detail & Related papers  (2025-10-16T17:59:38Z)
• AutoMLGen: Navigating Fine-Grained Optimization for Coding Agents [27.864519204078004]
  Large language models (LLMs) have shown impressive performance in general programming tasks.<n>We introduce AutoMLGen, an LLM-based coding agent that integrates a domain knowledge base for high-quality prior guidance.<n>We show that AutoMLGen achieves state-of-the-art performance in numerous dimensions, such as the average medal rate and the valid submission rate.
  arXiv  Detail & Related papers  (2025-10-09T17:45:05Z)
• SynthCoder: A Synthetical Strategy to Tune LLMs for Code Completion [7.668823606571788]
  Code completion is a prominent application of Large Language Models (LLMs) in software engineering.<n>This paper proposes SynthCoder, a model that integrates leading industry practices to achieve state-of-the-art on the Fill-in-the-Middle (FIM) code completion task.
  arXiv  Detail & Related papers  (2025-08-21T12:23:49Z)
• ComfyGPT: A Self-Optimizing Multi-Agent System for Comprehensive ComfyUI Workflow Generation [96.44354750396019]
  ComfyGPT is a self-optimizing multi-agent system designed to generate ComfyUI based on task descriptions automatically.<n> FlowDataset is a large-scale dataset containing 13,571 workflow-description pairs.<n>FlowBench is a benchmark for evaluating workflow generation systems.
  arXiv  Detail & Related papers  (2025-03-22T06:48:50Z)
• GNNs as Predictors of Agentic Workflow Performances [48.34485750450876]
  Agentic invoked by Large Language Models (LLMs) have achieved remarkable success in handling complex tasks.<n>This paper formulates agentic as computational graphs and advocates Graph Neural Networks (GNNs) as efficient predictors of agentic performances.<n>We construct FLORA-Bench, a unified platform for benchmarking GNNs for predicting agentic workflow performances.
  arXiv  Detail & Related papers  (2025-03-14T11:11:00Z)
• 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)
• Benchmarking Agentic Workflow Generation [80.74757493266057]
  We introduce WorfBench, a unified workflow generation benchmark with multi-faceted scenarios and intricate graph workflow structures.<n>We also present WorfEval, a systemic evaluation protocol utilizing subsequence and subgraph matching algorithms.<n>We observe that the generated can enhance downstream tasks, enabling them to achieve superior performance with less time during inference.
  arXiv  Detail & Related papers  (2024-10-10T12:41:19Z)
• StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback [58.20547418182074]
  We introduce StepCoder, a novel framework for code generation, consisting of two main components. CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks. FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization. Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
  arXiv  Detail & Related papers  (2024-02-02T13:14:31Z)

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.