Analysis of Asset Administration Shell-based Negotiation Processes for Scaling Applications

• URL: http://arxiv.org/abs/2602.13029v1
• Date: Fri, 13 Feb 2026 15:35:01 GMT
• Title: Analysis of Asset Administration Shell-based Negotiation Processes for Scaling Applications
• Abstract summary: The proactive Asset Administration Shell (AAS) enables bidirectional communication between assets.<n>This paper investigates the efficiency of the negotiation, based on criteria, such as message load, for applications with a scaling number of assets.
• Score: 44.149698389822355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proactive Asset Administration Shell (AAS) enables bidirectional communication between assets. It uses the Language for I4.0 Components in VDI/VDE 2193 to facilitate negotiations, such as allocating products to available production resources. This paper investigates the efficiency of the negotiation, based on criteria, such as message load, for applications with a scaling number of assets. Currently, the focus of AAS standardization is on submodels and their security to enable interoperable data access. Their proactive behavior remains conceptual and is still a subject of scientific research. Existing studies examine proactive AAS architecture examples with a limited number of assets, raising questions about their scalability in industrial environments. To analyze proactive AAS for scaling applications, a scenario and evaluation criteria are introduced. A scalable implementation is developed using current architectures for proactive AAS, upon which experiments are conducted with a varying number of assets. The results reveal the performance limitations, communication overhead, and adaptability of the AAS-based negotiation mechanism scaling. This information can improve the further development and standardization of the AAS.

Related papers

• From Profiling to Synthesis: Benchmarking Implicit Behavioral Alignment in Personalized LLM Agents [56.187590188698955]
  Large Language Models have enabled increasingly capable autonomous agents, yet personalization remains critical for making such agents practically useful.<n>Recent benchmarks have begun evaluating personalization in agents, but they largely rely on static preference snapshots, fixed interaction logs, or question answering over predefined user profiles.<n>We introduce IBA-Bench, a benchmark for implicit behavioral alignment constructed from longitudinal interaction histories.<n>We propose IBA-Agent, an agent framework that reconciles conflicting priorities through broad retrieval and trajectory-level alignment.
  arXiv  Detail & Related papers  (2026-08-03T12:52:04Z)
• ExToken: Structured Exploration for Efficient Vision-Language-Action Reinforcement Fine-tuning [53.17987412112712]
  Reinforcement Learning (RL) has demonstrated significant potential for improving Vision-Language-Action (VLA) models on complex manipulation tasks.<n>In this work, we first investigate the exploration stagnation bottleneck in current VLA-RL frameworks.<n>We introduce RL Exploration Token (ExToken), a simple yet general framework that condition VLA policies on discrete behavioral priors.
  arXiv  Detail & Related papers  (2026-07-14T16:04:41Z)
• Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models [80.45129499188461]
  We introduce Qwen-Scope, an open-source suite of sparse autoencoders (SAEs) built on the Qwen model family.<n>We show that SAEs can go beyond post-hoc analysis to serve as practical interfaces for model development along four directions.
  arXiv  Detail & Related papers  (2026-05-12T10:01:06Z)
• SAGE: A Service Agent Graph-guided Evaluation Benchmark [27.342044311161654]
  We propose SAGE (Service Agent Graph-guided Evaluation), a universal benchmark for automated, dual-axis assessment.<n>SAGE formalizes unstructured SOPs into Dynamic Dialogue Graphs, enabling precise verification of logical compliance.<n>We also observe Empathy Resilience'', a phenomenon where models maintain polite conversational facades despite underlying logical failures.
  arXiv  Detail & Related papers  (2026-04-10T12:55:23Z)
• OdysseyArena: Benchmarking Large Language Models For Long-Horizon, Active and Inductive Interactions [66.84396313837765]
  We introduce OdysseyArena, which re-centers agent evaluation on long-horizon, active, and inductive interactions.<n>We provide a set of 120 tasks to measure an agent's inductive efficiency and long-horizon discovery.<n>We also introduce OdysseyArena-Challenge to stress-test agent stability across extreme interaction horizons.
  arXiv  Detail & Related papers  (2026-02-05T16:31:43Z)
• ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development [72.4729759618632]
  We introduce ABC-Bench, a benchmark to evaluate agentic backend coding within a realistic, executable workflow.<n>We curated 224 practical tasks spanning 8 languages and 19 frameworks from open-source repositories.<n>Our evaluation reveals that even state-of-the-art models struggle to deliver reliable performance on these holistic tasks.
  arXiv  Detail & Related papers  (2026-01-16T08:23:52Z)
• Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem [90.17610617854247]
  We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimize the production pipeline for agentic model.<n>ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering.<n>We release ROME, an open-source agent grounded by ALE and trained on over one million trajectories.
  arXiv  Detail & Related papers  (2025-12-31T14:03:39Z)
• A Container-based Approach For Proactive Asset Administration Shell Digital Twins [1.5549424176583662]
  In manufacturing, digital twins, realized as Asset Administration Shells (AAS), have emerged as a prevalent practice.<n>We propose a submodel-based architecture that introduces a structured service notion to the AAS, enabling services to dynamically interact with and adapt AAS instances at runtime.
  arXiv  Detail & Related papers  (2025-12-17T13:50:14Z)
• Where and What Matters: Sensitivity-Aware Task Vectors for Many-Shot Multimodal In-Context Learning [57.082554323521464]
  We propose a Sensitivity-aware Task Vector insertion framework (STV) to figure out where and what to insert.<n>Our key insight is that activation deltas across query-context pairs exhibit consistent structural patterns, providing a reliable cue for insertion.<n>Based on the identified sensitive-aware locations, we construct a pre-clustered activation bank for each location by clustering the activation values, and then apply reinforcement learning to choose the most suitable one to insert.
  arXiv  Detail & Related papers  (2025-11-11T13:42:13Z)
• FLAS: a combination of proactive and reactive auto-scaling architecture for distributed services [0.0]
  We present FLAS (Forecasted Load Auto-Scaling), an auto-scaler for distributed services.<n>It combines the advantages of proactive and reactive approaches according to the situation to decide the optimal scaling actions.<n>We provide a FLAS implementation for the use case of a content-based publish-subscribe distributed systems.
  arXiv  Detail & Related papers  (2025-10-23T09:38:07Z)
• ContextNav: Towards Agentic Multimodal In-Context Learning [85.05420047017513]
  ContextNav is an agentic framework that integrates the scalability of automated retrieval with the quality and adaptiveness of human-like curation.<n>It builds a resource-aware multimodal embedding pipeline, maintains a retrievable vector database, and applies agentic retrieval and structural alignment to construct noise-resilient contexts.<n> Experimental results demonstrate that ContextNav achieves state-of-the-art performance across various datasets.
  arXiv  Detail & Related papers  (2025-10-06T07:49:52Z)
• On the Role of Feedback in Test-Time Scaling of Agentic AI Workflows [71.92083784393418]
  Agentic AI (systems that autonomously plan and act) are becoming widespread, yet their task success rate on complex tasks remains low.<n>Inference-time alignment relies on three components: sampling, evaluation, and feedback.<n>We introduce Iterative Agent Decoding (IAD), a procedure that repeatedly inserts feedback extracted from different forms of critiques.
  arXiv  Detail & Related papers  (2025-04-02T17:40:47Z)
• Dynamic benchmarking framework for LLM-based conversational data capture [0.0]
  This paper introduces a benchmarking framework to assess large language models (LLMs)<n>It integrates generative agent simulation to evaluate performance on key dimensions: information extraction, context awareness, and adaptive engagement.<n>Results show that adaptive strategies improve data extraction accuracy, especially when handling ambiguous responses.
  arXiv  Detail & Related papers  (2025-02-04T15:47:47Z)
• SWE-Arena: An Interactive Platform for Evaluating Foundation Models in Software Engineering [0.38677478583601776]
  SWE-Arena is an interactive platform designed to evaluate foundation models (FMs) in software engineering tasks.<n>SWE-Arena provides a transparent, open-source leaderboard, supports multi-round conversational conversations, and enables end-to-end model comparisons.
  arXiv  Detail & Related papers  (2025-02-03T22:19:28Z)
• TestAgent: Automatic Benchmarking and Exploratory Interaction for Evaluating LLMs in Vertical Domains [19.492393243160244]
  Large Language Models (LLMs) are increasingly deployed in highly specialized vertical domains.<n>Existing evaluations for vertical domains typically rely on the labor-intensive construction of static single-turn datasets.<n>We propose TestAgent, a framework for automatic benchmarking and exploratory dynamic evaluation in vertical domains.
  arXiv  Detail & Related papers  (2024-10-15T11:20:42Z)
• Active Feature Acquisition with Generative Surrogate Models [11.655069211977464]
  In this work, we consider models that perform active feature acquisition (AFA) and query the environment for unobserved features. Our work reformulates the Markov decision process (MDP) that underlies the AFA problem as a generative modeling task. We propose learning a generative surrogate model ( GSM) that captures the dependencies among input features to assess potential information gain from acquisitions.
  arXiv  Detail & Related papers  (2020-10-06T02:10:06Z)

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.