OrgForge: A Multi-Agent Simulation Framework for Verifiable Synthetic Corporate Corpora

• URL: http://arxiv.org/abs/2603.14997v1
• Date: Mon, 16 Mar 2026 09:02:24 GMT
• Title: OrgForge: A Multi-Agent Simulation Framework for Verifiable Synthetic Corporate Corpora
• Abstract summary: We present OrgForge, an open-source multi-agent simulation framework that enforces a strict physics-cognition boundary.<n>An actor-local clock enforces causal timestamp correctness across all artifact types.<n>Running a N-day simulation, OrgForge produces interleaved Slack threads, tickets, Confluence pages, Git pull requests, and emails, all traceable to a shared, immutable event log.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluating retrieval-augmented generation (RAG) pipelines requires corpora where ground truth is knowable, temporally structured, and cross-artifact properties that real-world datasets rarely provide cleanly. Existing resources such as the Enron corpus carry legal ambiguity, demographic skew, and no structured ground truth. Purely LLM-generated synthetic data solves the legal problem but introduces a subtler one: the generating model cannot be prevented from hallucinating facts that contradict themselves across documents.We present OrgForge, an open-source multi-agent simulation framework that enforces a strict physics-cognition boundary: a deterministic Python engine maintains a SimEvent ground truth bus; large language models generate only surface prose, constrained by validated proposals. An actor-local clock enforces causal timestamp correctness across all artifact types, eliminating the class of timeline inconsistencies that arise when timestamps are sampled independently per document. We formalize three graph-dynamic subsystems stress propagation via betweenness centrality, temporal edge-weight decay, and Dijkstra escalation routing that govern organizational behavior independently of any LLM. Running a configurable N-day simulation, OrgForge produces interleaved Slack threads, JIRA tickets, Confluence pages, Git pull requests, and emails, all traceable to a shared, immutable event log. We additionally describe a causal chain tracking subsystem that accumulates cross-artifact evidence graphs per incident, a hybrid reciprocal-rank-fusion recurrence detector for identifying repeated failure classes, and an inbound/outbound email engine that routes vendor alerts, customer complaints, and HR correspondence through gated causal chains with probabilistic drop simulation. OrgForge is available under the MIT license.

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)
• TELLER: Non-intrusive Cross-Layer Root-Cause Analysis for LLM Inference [47.62941623025392]
  We present TELLER, a non-intrusive and Log-aware LLM inference Root-cause analysis framework.<n>TELLER collects NVTX/CUPTI traces and service logs without modifying model binaries.<n>It reconstructs per-request call-chain trees and aligns log lines with corresponding execution steps.<n>On top of these representations, TELLER combines numeric candidate localization with a multimodal root-cause model.
  arXiv  Detail & Related papers  (2026-08-03T09:39:11Z)
• From Resource Flow to Executable Tests: Petri-Net-Guided LLM Test Generation for Concurrent Stateful Rust APIs [5.064868362565062]
  Concurrent stateful library APIs expose behavior through evolving resource ownership, lifecycle states, and competing interleavings.<n>Large language models can synthesize executable Rust tests, but their outputs often violate API preconditions, remain shallow, or reduce to accidental traces.<n>This paper addresses the gap between formal scenario design and low-cost test concretization.
  arXiv  Detail & Related papers  (2026-07-23T17:15:53Z)
• PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution [49.776611937968]
  We present PhyAgentOS, a foundation delivering scheduling, verification, memory, benchmarking, and safety as system-level services.<n>Its Session-Centered treats a session, not an action, as the minimum unit of scheduling, compatibility, supervised execution, evidence collection, and acceptance.<n>SessionVerifier distinguishes execution termination from semantic task completion via evidence-grounded verdicts of success, failure, or replan.<n> Benchmarking reuses the deployment session and verification path, so results trace to real execution.
  arXiv  Detail & Related papers  (2026-07-18T04:46:53Z)
• Quantum-Inspired Contextual Learning for Sparse-Ring Fraud Detection in Dynamic Transaction Graphs [0.0]
  Coordinated fraud may not be visible from individual transactions alone, but may emerge as a multi-period relational pattern.<n>We focus on sparse-ring fraud, a stylized pattern in which a completed directed cycle is distributed across several days.<n>We study this problem using a synthetic transaction simulator with completed sparse-ring injections and broken-ring decoys.
  arXiv  Detail & Related papers  (2026-06-22T05:38:02Z)
• TimelineReasoner: Advancing Timeline Summarization with Large Reasoning Models [54.737142557469916]
  We propose TimelineReasoner, a framework that shifts Timeline Summarization from static generation to an active, reasoning-driven process.<n>We show that TimelineReasoner significantly outperforms existing LLM-based TLS methods in terms of timeline accuracy, coverage, and coherence.<n>This work not only pushes the boundaries of TLS but also highlights the broader potential of LRM-based reasoning frameworks for timeline summarization.
  arXiv  Detail & Related papers  (2026-04-03T12:30:15Z)
• RAGTrack: Language-aware RGBT Tracking with Retrieval-Augmented Generation [71.2136732268131]
  RGB-Thermal (RGBT) tracking aims to achieve robust object localization across diverse environmental conditions.<n>Existing RGBT trackers rely solely on initial-frame visual information for target modeling.<n>We propose RAGTrack, a novel Retrieval-Augmented Generation framework for robust RGBT tracking.
  arXiv  Detail & Related papers  (2026-03-04T01:02:04Z)
• LOGIGEN: Logic-Driven Generation of Verifiable Agentic Tasks [4.6880826836662814]
  We introduce textbfLOGIGEN, a logic-driven framework that synthesizes verifiable training data.<n>On $2$-Bench, LOGIGEN-32B(RL) achieves a textbf79.5% success rate, substantially outperforming the base model.
  arXiv  Detail & Related papers  (2026-02-28T08:35:30Z)
• AgentSkiller: Scaling Generalist Agent Intelligence through Semantically Integrated Cross-Domain Data Synthesis [30.512393568258105]
  Large Language Model agents demonstrate potential in solving real-world problems via tools, yet generalist intelligence is bottlenecked by scarce high-quality, long-horizon data.<n>We propose AgentSkiller, a fully automated framework synthesizing multi-turn interaction data across realistic, semantically linked domains.
  arXiv  Detail & Related papers  (2026-02-10T03:21:42Z)
• DiffuGR: Generative Document Retrieval with Diffusion Language Models [80.78126312115087]
  We propose generative document retrieval with diffusion language models, dubbed DiffuGR.<n>For inference, DiffuGR attempts to generate DocID tokens in parallel and refine them through a controllable number of denoising steps.<n>In contrast to conventional left-to-right auto-regressive decoding, DiffuGR provides a novel mechanism to first generate more confident DocID tokens.
  arXiv  Detail & Related papers  (2025-11-11T12:00:09Z)
• FABRIC: Framework for Agent-Based Realistic Intelligence Creation [3.940391073007047]
  Large language models (LLMs) are increasingly deployed as agents, expected to decompose goals, invoke tools, and verify results in dynamic environments.<n>We present a unified framework for synthesizing agentic data using only LLMs, without any human-in-the-loop supervision.
  arXiv  Detail & Related papers  (2025-10-20T18:20:22Z)
• Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Models [99.85131798240808]
  We introduce a novel generative framework called textitGuided Topology Diffusion (GTD)<n>Inspired by conditional discrete graph diffusion models, GTD formulates topology synthesis as an iterative construction process.<n>At each step, the generation is steered by a lightweight proxy model that predicts multi-objective rewards.<n>Experiments show that GTD can generate highly task-adaptive, sparse, and efficient communication topologies.
  arXiv  Detail & Related papers  (2025-10-09T05:28:28Z)
• RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation [21.764973680014368]
  RetroLLM is a unified framework that integrates retrieval and generation into a single, cohesive process.<n>To mitigate false pruning in the process of constrained evidence generation, we introduce hierarchical FM-Index constraints.<n>Experiments on five open-domain QA datasets demonstrate RetroLLM's superior performance across both in-domain and out-of-domain tasks.
  arXiv  Detail & Related papers  (2024-12-16T16:03:25Z)
• Large Language Models as Realistic Microservice Trace Generators [48.730974361862366]
  This paper proposes a first-of-a-kind approach that relies on training a large language model (LLM) to generate synthetic workload traces.<n>We show that TraceLLM produces diverse, realistic traces under varied conditions, outperforming existing approaches in both accuracy and validity.<n>TraceLLM adapts to downstream trace-related tasks, such as predicting key trace features and infilling missing data.
  arXiv  Detail & Related papers  (2024-12-16T12:48:04Z)
• GenDFIR: Advancing Cyber Incident Timeline Analysis Through Retrieval Augmented Generation and Large Language Models [0.08192907805418582]
  Cyber timeline analysis is crucial in Digital Forensics and Incident Response (DFIR)<n>Traditional methods rely on structured artefacts, such as logs and metadata, for evidence identification and feature extraction.<n>This paper introduces GenDFIR, a framework leveraging large language models (LLMs), specifically Llama 3.1 8B in zero shot mode, integrated with a Retrieval-Augmented Generation (RAG) agent.
  arXiv  Detail & Related papers  (2024-09-04T09:46:33Z)

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.