November 21 ~ 22, 2026, London, United Kingdom
Bala Subramanyan, Verifoxx, United Kingdom
Resource bounds established for individual tools do not compose automatically across autonomous control. A tool-using agent may transform each returned observation into retry, fan-out, fallback, or delegated work;cumulative resource demand therefore depends on the controller–tool feedback closure rather than on anyinvocation in isolation. This paper introduces Execution-Demand Hypergraphs (EDHs), a compositionalabstraction in which nodes carry independently certified local bounds and observation-conditioned hyper-edges encode multisets of successor obligations. The induced monotone demand operator has a least fixedpoint equal to the exact worst-case local-bound demand represented by the abstraction, yielding a finitecertificate if and only if that demand is finite. A sound abstraction theorem transfers EDH certificates toconcrete controller executions, and an exact extractor is established for a core controller language withsequence, choice, retry, fan-out, delegation, and controlled recurrence. EDH-Check matched independentreference semantics on all 16 finite hand-designed models and 20,000 generated controllers; observationand recurrence stress tests further exercised the soundness boundary. EDH thus provides a reusable bridgefrom component-level resource certification to whole-agent resource containment.
Tool-using AI, formal verification, resource analysis, autonomous agents, WebAssembly.
Bala Subramanyan, Verifoxx, United Kingdom
Resource bounds established for individual tools do not compose automatically across autonomous control. A tool-using agent may transform each returned observation into retry, fan-out, fallback, or delegated work;cumulative resource demand therefore depends on the controller–tool feedback closure rather than on anyinvocation in isolation. This paper introduces Execution-Demand Hypergraphs (EDHs), a compositionalabstraction in which nodes carry independently certified local bounds and observation-conditioned hyper-edges encode multisets of successor obligations. The induced monotone demand operator has a least fixedpoint equal to the exact worst-case local-bound demand represented by the abstraction, yielding a finitecertificate if and only if that demand is finite. A sound abstraction theorem transfers EDH certificates toconcrete controller executions, and an exact extractor is established for a core controller language withsequence, choice, retry, fan-out, delegation, and controlled recurrence. EDH-Check matched independentreference semantics on all 16 finite hand-designed models and 20,000 generated controllers; observationand recurrence stress tests further exercised the soundness boundary. EDH thus provides a reusable bridgefrom component-level resource certification to whole-agent resource containment.
Tool-using AI, formal verification, resource analysis, autonomous agents, WebAssembly.
Dr. Alexandru I. Jittu, PE, AIJ R&D Engineering & Consulting Inc.Michigan, USA
This paper proposes AIJ-IERP , a distributed multimodal artificial intelligence and soft computing architecture for autonomous emergency response. The architecture connects satellite communications, regional and local AI stations, fire-station intelligence nodes, autonomous firefighting vehicles, aerial robotic platforms, humanoid emergency robots, multimodal sensors, and human operators. Thermal, visual, spatial, environmental, structural, and robotic information is fused into a continuously updated Emergency Digital Twin. A hybrid intelligence layer combines machine learning, fuzzy reasoning, probabilistic inference, anomaly detection, optimization, and confidence estimation to predict hazards, coordinate multiple agents, and select among available mitigation modalities. The proposed system operates through a closed-loop Sense Fuse Understand Predict Decide Act Verify Reassess cycle with supervised autonomy and human escalation for uncertain or safety-critical conditions. The work establishes a research framework and validation pathway rather than claiming completed operational deployment.
Artificial Intelligence, Autonomous Emergency Response, Multimodal Sensor Fusion, Soft Computing, Human AI Robot Collaboration.
Aniruddha Mukherjee, Vernon Rego1and Janche Sang2, 1Department of Computer Sciences, Purdue University, West Lafayette, IN, USA 2Department of Computer Science, Cleveland State University, Cleveland, OH,USA
We introduce the Hatter audit, a stochastic audit of causal exposure in parallel discrete-event simulation. A Hatter cell marks A → H → B, where a cross-process update must be incorporated before a later event commits. Using common-input ledgers, we compare unsafe, ordered-reference, conservative, and optimistic executions against the same sequential committed history. The experiments show that exposure probability is only the beginning: the same causal event may appear as wrong output, waiting, safety checks, rollback, replay, cancellation, stored history, or finite-account breach. Causal cost therefore cannot be inferred from exposure probability alone. It must be followed through the policy clocks and, ultimately, through the weighted global critical path.
Parallel discrete-event simulation, synchronization, causality, rollback, global critical path.
Siang-Ting Huang, Cheng-I Chen and Wei-Chung Lee, Singular Wings Medical Co. Ltd., Hsinchu, Taiwan (R.O.C.)
Continuous, non-invasive monitoring of blood glucose from physiological signals is an attractive alternative to invasive fingerstick testing or continuous glucose monitor sensors. This paper presents a deep learning architecture that classifies short, single-lead electrocardiogram (ECG) segments into blood glucose level categories, requiring no invasive measurement at inference time. Both a three-class (Low, Normal, High) and a two-class (Normal, High) formulation are supported. The proposed model combines a three-stage convolutional front end with a Transformer encoder: the convolutional stages compress the input waveform into a short sequence of feature vectors, and the encoder models temporal dependencies across that sequence before classification. Focal loss and balanced batch sampling address the severe class imbalance of the task. Across 49 subjects the model attains an overall sensitivity of 60.00% for three classes and 70.69% for two classes, against 58.77% and 66.38% for a CNN-LSTM baseline, using 44% fewer parameters.
Electrocardiogram, Transformer, Non-Invasive Blood Glucose Monitoring, Focal Loss .