Explainable Wastewater Digital Twins: Adaptive Context-Conditioned Structured Simulators with Self-Falsifying Decision Support

• URL: http://arxiv.org/abs/2605.19826v1
• Date: Tue, 19 May 2026 13:19:27 GMT
• Title: Explainable Wastewater Digital Twins: Adaptive Context-Conditioned Structured Simulators with Self-Falsifying Decision Support
• Abstract summary: CCSS-IX is a bank of interpretable locally linear state-space "experts" adaptively mixed by a context-aware gating network.<n>A runtime decision layer applies conformal risk control to abstain, reopen, or return a falsifying temporal witness for any operator-proposed action that cannot be statistically certified.
• Score: 2.3895981099137535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Operators of safety-critical industrial processes increasingly rely on digital twins to screen control interventions, but such simulators rarely carry certified safety guarantees. Wastewater treatment plants exemplify the gap: operators face a daily safety-efficiency trade-off where aerating too little risks effluent violations and nitrous-oxide (N2O) spikes, and aerating too much wastes energy. We develop an explainable digital twin for aeration and dosing setpoints. CCSS-IX, the simulator, is a bank of interpretable locally linear state-space "experts" adaptively mixed by a context-aware gating network, building on a continuous-time regime-switching scaffold. A runtime decision layer applies conformal risk control to abstain, reopen, or return a falsifying temporal witness for any operator-proposed action that cannot be statistically certified. The artificial-intelligence contribution is twofold: an identifiable, context-conditioned structured surrogate that retains operator-readable dynamics, and a self-falsifying decision rule with finite-sample coverage guarantees. The engineering contribution is a validated, end-to-end decision-support pipeline, tested on a 1000-step slice of the Avedøre full-scale plant (42.6% sensor missingness, 2-minute sampling), the Agtrup/BlueKolding full-scale plant in Denmark, and the Benchmark Simulation Model No. 2 (BSM2) international benchmark, under a matched ten-seed protocol. The static structured ensemble lies within 0.78% root-mean-square error of an unconstrained black-box reference, and the adaptive variant within 1.08%. The calibrated reopen rule cuts aggregate two-plant regret by 43.6% at an unsafe-action cost weight of 4 and eliminates unsafe chosen actions on the BSM2 main slice. Event-aligned temporal witnesses prevent 93 of 187 false-safe N2O approvals, about 4.65x the dyadic baseline (paired McNemar p < 1e-21).

Related papers

• Reversibility-Verified De-identification for Cloud-Local LLM Inference: A Locally Certified Dehydrate-Rehydrate Loop with Layered Assurance (DR-SL) [1.802678437602184]
  DR-SL (Dehydrate-Rehydrate with Self-Learning loop) formalizes de-identification completeness as two measurable conditions.<n>A fully local two-branch verifier iterates dehydration under a lexicographic gate with guaranteed termination.<n>We prove Fano-type lower bounds, a Pufferfish witness, and a rate-privacy feasibility criterion, and state their scope plainly.
  arXiv  Detail & Related papers  (2026-09-14T01:04:58Z)
• Safety-Aware Cascaded Inference for Crop Damage Assessment with Controlled Error Trade-offs [0.0]
  In picture-based agricultural insurance for smallholder farmers, missed damage detections carry substantially higher cost than false alarms.<n>We propose CascadeCropNet, a two-stage cascade architecture calibrated to satisfy a target recall constraint.<n>A lightweight Sentinel model performs binary health triage; samples exceeding a calibrated damage probability threshold tau are escalated to a specialist Expert model for fine-grained diagnosis.
  arXiv  Detail & Related papers  (2026-07-28T09:01:27Z)
• What Accuracy and Gradient Cosine Miss: Evaluating Feedback Alignment via Scale Stability, Reference Validity, and Depth Utility [48.10132234701036]
  We propose a diagnostic evaluation protocol based on three checks -- scale stability, reference validity, and depth utility -- together with per-layer rather than aggregate cosine reporting.<n>Across multiple architectures and methods, our protocol identifies all failures with wide calibration margins.
  arXiv  Detail & Related papers  (2026-06-19T06:04:17Z)
• Unpredictable Safety: Domain-Dependent Compliance and the Transparency Gap in Open-Weight LLMs [0.0]
  We study domain-dependent safety behavior in open-weight LLMs.<n>We find compliance rates vary from 14.7% (human trafficking) to 85.7% (surveillance design)<n>Results show current safety mechanisms lack the transparency and consistency required for trustworthy AI deployment.
  arXiv  Detail & Related papers  (2026-06-01T22:41:32Z)
• Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
  Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
  arXiv  Detail & Related papers  (2026-05-15T17:08:27Z)
• Action-Conditioned Risk Gating for Safety-Critical Control under Partial Observability [79.08785366532287]
  We propose a lightweight risk-gated reinforcement learning approximation for risk-sensitive control under partial observability.<n>We evaluate the approach in two safety-critical partially observable domains: automated glucose regulation and safety-constrained navigation.
  arXiv  Detail & Related papers  (2026-05-14T01:23:09Z)
• From Agent Failure Paths to Quantified Residual Risk: A Compositional Framework for Resilient Agentic AI [8.947753534117298]
  Agentic AI is crossing trust boundaries faster than current risk models can represent.<n>We propose a mechanism-to-magnitude pipeline for resilient agentic and embodied AI.<n>We show the framework on two contrasting scenarios a real-time warehouse robot and a governance-instrumented financial-services agent.
  arXiv  Detail & Related papers  (2026-04-28T23:40:24Z)
• Reliable Self-Harm Risk Screening via Adaptive Multi-Agent LLM Systems [6.14481021961242]
  We present a statistical framework for multi-agent pipelines structured as directed acyclic graphs (DAGs)<n>We model each agent as a categorical decision and introduce tighter agent-level performance confidence bounds.<n>We evaluate our system on two labeled datasets in behavioral health.
  arXiv  Detail & Related papers  (2026-04-24T01:52:54Z)
• Claw-Eval: Toward Trustworthy Evaluation of Autonomous Agents [66.97968363332465]
  We introduce Claw-Eval, an end-to-end evaluation suite addressing all three gaps in agent benchmarks.<n>Claw-Eval comprises 300 human-verified tasks spanning 9 categories across three groups.<n>Every agent action is recorded through three independent evidence channels.
  arXiv  Detail & Related papers  (2026-04-07T17:43:18Z)
• Partial Number Theoretic Transform Masking in Post Quantum Cryptography Hardware: A Security Margin Analysis [0.0]
  Adams Bridge, a hardware accelerator for ML-DSA and ML-KEM, masks 1 of its Inverse Number Theoretic Transform layers and relies on shuffling for the remainder.<n>We evaluate these claims against published side-channel literature across seven analysis tracks with confidence-rated evidence.
  arXiv  Detail & Related papers  (2026-04-04T17:38:34Z)
• Severe Domain Shift in Skeleton-Based Action Recognition:A Study of Uncertainty Failure in Real-World Gym Environments [0.0]
  We present a systematic study of a severe domain shift using a novel Gym2D dataset and the UCF101 dataset.<n>Our work challenges standard deployment assumptions, providing a principled safety analysis of both semantic and geometric skeleton recognition deployment.
  arXiv  Detail & Related papers  (2026-03-16T17:37:17Z)
• TRIAGE: Type-Routed Interventions via Aleatoric-Epistemic Gated Estimation in Robotic Manipulation and Adaptive Perception -- Don't Treat All Uncertainty the Same [2.755751829139168]
  Most uncertainty-aware robotic systems collapse prediction uncertainty into a single scalar score and use it to trigger uniform corrective responses.<n>This aggregation obscures whether uncertainty arises from corrupted observations or from mismatch between the learned model and the true system dynamics.<n>We introduce a lightweight post hoc framework that decomposes uncertainty into aleatoric and epistemic components and uses these signals to regulate system responses at inference time.
  arXiv  Detail & Related papers  (2026-03-09T09:07:43Z)
• Beyond Reward Suppression: Reshaping Steganographic Communication Protocols in MARL via Dynamic Representational Circuit Breaking [0.0]
  steganographic collusion, where agents develop private protocols to evade monitoring, presents a critical AI safety threat.<n>Existing defenses, limited to behavioral or reward layers, fail to detect coordination in latent communication channels.<n>We introduce the Dynamic Representational Circuit Breaker (DRCB), an architectural defense operating at the optimization substrate.
  arXiv  Detail & Related papers  (2026-03-07T04:14:38Z)
• Unsupervised Anomaly Detection in Multi-Agent Trajectory Prediction via Transformer-Based Models [45.08545174556591]
  We propose an unsupervised anomaly detection framework based on a multi-agent Transformer.<n>A dual evaluation scheme has been proposed to assess both detection stability and physical alignment.<n>Our framework identifies 388 unique anomalies missed by Time-to-Collision and statistical baselines.
  arXiv  Detail & Related papers  (2026-01-28T08:33:10Z)
• Reinforcement Learning from Probabilistic Forecasts for Safe Decision-Making via Conditional Value-at-Risk Planning [41.52380204321823]
  This paper presents the Uncertainty-Aware Markov Decision Process (UAMDP), a unified framework that couples Bayesian forecasting, posterior-sampling reinforcement learning, and planning.<n>We evaluate UAMDP in two domains-high-frequency equity trading and retail inventory control-both marked by structural uncertainty and economic volatility.
  arXiv  Detail & Related papers  (2025-10-09T13:46:32Z)
• Multimodal Safety Evaluation in Generative Agent Social Simulations [43.972551727499116]
  We introduce a reproducible simulation framework for evaluating agents along three dimensions.<n>We show that agents often fail to align local revisions with global safety, reaching only a 55 percent success rate in correcting unsafe plans.<n> Notably, 45 percent of unsafe actions were accepted when paired with misleading visuals, showing a strong tendency to overtrust images.
  arXiv  Detail & Related papers  (2025-10-09T02:42:57Z)
• SEA: Bridging the Gap Between One- and Two-stage Detector Distillation via SEmantic-aware Alignment [76.80165589520385]
  We name our method SEA (SEmantic-aware Alignment) distillation given the nature of abstracting dense fine-grained information. It achieves new state-of-the-art results on the challenging object detection task on both one- and two-stage detectors.
  arXiv  Detail & Related papers  (2022-03-02T04:24:05Z)

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.