Physics-Informed Causal MDPs for Sequential Constraint Repair in Engineering Simulation Pipelines

• URL: http://arxiv.org/abs/2604.17910v1
• Date: Mon, 20 Apr 2026 07:40:15 GMT
• Title: Physics-Informed Causal MDPs for Sequential Constraint Repair in Engineering Simulation Pipelines
• Abstract summary: We introduce PI-CMDP, a framework for CMDPs whose constraint dependencies form a layered DAG.<n>We instantiate PI-CMDP on constraint repair in engineering simulation pipelines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Off-policy learning in constrained MDPs with large binary state spaces faces a fundamental tension: causal identification of transition dynamics requires structural assumptions, while sample-efficient policy learning requires state-space compression. We introduce PI-CMDP, a framework for CMDPs whose constraint dependencies form a layered DAG under a Lifecycle Ordering Assumption (LOA). We propose an Identify-Compress-Estimate pipeline: (i) Identify: LOA enables backdoor identification of causal edge weights for cross-layer pairs, with formal partial-identification bounds when LOA is violated; (ii) Compress: a Markov abstraction compresses state cardinality from 2^(WL) to (W+1)^L under layer-priority regularity and exchangeability; and (iii) Estimate: a physics-guided doubly-robust estimator remains unbiased and reduces the variance constant when the physics prior outperforms a learned model. We instantiate PI-CMDP on constraint repair in engineering simulation pipelines. On the TPS benchmark (4,206 episodes), PI-CMDP achieves 76.2% repair success rate with only 300 training episodes versus 70.8% for the strongest baseline (+5.4 pp), narrowing to +2.8 pp (83.4% vs. 80.6%) in the full-data regime, while substantially reducing cascade failure rates. All improvements are consistent across 5 independent seeds (paired t-test p < 0.02).

Related papers

• WDL-OPD: Weak-Driven On-Policy Distillation via Mixture-Constrained Co-Training [48.290951447286744]
  WDL-OPD is a mixture-constrained co-training method with two trainable policies.<n>An anchor policy generates every rollout, an auxiliary policy evaluates the same visited states, and a geometric mixture of their token distributions is matched to a frozen teacher by reverse KL.<n>We show that freezing the auxiliary recovers an anchor-plus-contrast proxy target closely related to OPD$2$ and W2S-OPD, whereas joint training creates branch-level degrees of freedom that a static delta cannot express.
  arXiv  Detail & Related papers  (2026-08-10T11:22:53Z)
• Loss-Conditional PINNs for Parametric PDE Families [45.945826972787415]
  Physics-informed neural networks (PINNs) approximate solutions of ODEs and PDEs by minimising a weighted combination of residual, boundary, initial, and data losses.<n>We introduce LC-PINN, which adapts the lossconditional training of Dosovitskiy and Djolonga ( 2020) to the PDE-residual setting.<n>It stays fully physics-informed but amortises training over a parametric family.
  arXiv  Detail & Related papers  (2026-06-03T04:02:10Z)
• SkillDAG: Self-Evolving Typed Skill Graphs for LLM Skill Selection at Scale [54.70985426016736]
  We present SkillDAG, which models inter-skill relationships as a typed directed graph.<n>Each search returns vector matches, typed-edge neighbors, and conflict signals, and a propose-then-commit protocol lets the agent register execution-backed edges.<n>On ALFWorld and SkillsBench with MiniMax-M2.7, SkillDAG reaches 67.1% success and 27.3% reward.
  arXiv  Detail & Related papers  (2026-06-02T02:45:21Z)
• DynaGraph: Lightweight Multi-Model Interaction Framework via Dynamic Topological Reconfiguration [7.0972889296550825]
  DynaGraph is a lightweight multi-model framework driven by dynamic topological reconfiguration.<n>It multiplexes time-division PEFT adapters over a shared base model, enabling both full system training and deployment.<n>It reduces latency by up to 68.1% and token consumption by 68.6% compared to unconstrained dynamic architectures.
  arXiv  Detail & Related papers  (2026-05-28T07:33:29Z)
• Learned Lyapunov Shielding for Adaptive Control [1.2891210250935148]
  We augment the Slotine--Li adaptive controller for Euler--Lagrange systems with three learned components.<n>A closed-form safety filter projects every policy output onto the safe set without requiring an online QP solver.<n>A 7-DOF scalability study on a Franka Emika Panda confirms clean convergence of the full pipeline at industrial scale.
  arXiv  Detail & Related papers  (2026-05-07T20:49:22Z)
• Beyond the Attention Stability Boundary: Agentic Self-Synthesizing Reasoning Protocols [6.357772907811544]
  Self- Synthesizing Reasoning Protocols (SSRP) is a metacognitive framework that implements a discrete separation between architectural planning and procedural execution.<n>We present 3 experimental tiers: a shallow recency-based retrieval pilot, a high-entropy SOP, and a semantic hijacked 3-hop Multi-Fact Synthesis task.<n>Our results empirically locate the Attention Stability Boundary, where stateless Vanilla ReAct baselines for GPT 5.4 collapse to 0.1% success while SSRP achieves a 715X Resilience Lift.
  arXiv  Detail & Related papers  (2026-04-27T14:13:30Z)
• RPMS: Enhancing LLM-Based Embodied Planning through Rule-Augmented Memory Synergy [34.723296971298424]
  LLM agents often fail in closed-world embodied environments because actions must satisfy strict preconditions.<n>We identify two structurally coupled failure modes: (P1) invalid action generation and (P2) state drift.<n>We present RPMS, a conflict-managed architecture that enforces action feasibility via structured rule retrieval.
  arXiv  Detail & Related papers  (2026-03-18T15:26:00Z)
• Improving Search Agent with One Line of Code [68.58667107354253]
  Tool-based Agentic Reinforcement Learning (TARL) has emerged as a promising paradigm for training search agents to interact with external tools.<n>We propose textbfSearch textbfAgent textbfPolicy textbfOptimization (textbfSAPO), which stabilizes training via a conditional token-level KL constraint.
  arXiv  Detail & Related papers  (2026-03-10T04:07:39Z)
• Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments [31.754045125599305]
  Plateaus in certain regimes arise because sample-based estimates of the loss become poor proxies for the true objective over the course of training.<n>We show that there are two ways to address this type of learning stagnation: either reduce the step size or increase the number of samples collected between updates.<n>We vastly outperform prior baselines in a complex openended domain by scaling PPO to more than 1M parallel environments.
  arXiv  Detail & Related papers  (2026-03-06T08:07:08Z)
• SENTINEL: Stagewise Integrity Verification for Pipeline Parallel Decentralized Training [54.8494905524997]
  Decentralized training introduces critical security risks when executed across untrusted, geographically distributed nodes.<n>We propose SENTINEL, a verification mechanism for pipeline parallelism (PP) training without duplication.<n>Experiments demonstrate successful training of up to 4B- parameter LLMs across untrusted distributed environments with up to 176 workers while maintaining model convergence and performance.
  arXiv  Detail & Related papers  (2026-03-03T23:51:10Z)
• BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models [56.504879072674015]
  We propose Bit-Plane Decomposition Quantization (BPDQ), which constructs a variable quantization grid via bit-planes and scalar coefficients.<n>BPDQ enables serving Qwen2.5-72B on a single GTX 3090 with 83.85% GSM8K accuracy (vs. 90.83% at 16-bit)
  arXiv  Detail & Related papers  (2026-02-04T02:54:37Z)
• Environment-Aware Indoor LoRaWAN Path Loss: Parametric Regression Comparisons, Shadow Fading, and Calibrated Fade Margins [3.776919981139063]
  Indoor LoRaWAN propagation is shaped by structural and time-varying context factors.<n>We present an environment-aware, statistically disciplined path loss framework evaluated using leakage-safe cross-validation.
  arXiv  Detail & Related papers  (2025-10-05T20:14:48Z)
• Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression [1.85373927927491]
  Modern models are often compressed via a combination of structured pruning and re-training to meet the strict compute, memory, and connectivity constraints of edge deployments.<n>Our focus is not on achieving maximal compression, but on isolating the impact of the re-training loss function.<n>We evaluate both pipelines on the OLMo2- 7B-SFT model for CommonsenseQA suitable for intermittent or denied connectivity scenarios typical of edge networks.
  arXiv  Detail & Related papers  (2025-05-13T19:06:32Z)
• Semi-Supervised Coupled Thin-Plate Spline Model for Rotation Correction and Beyond [84.56978780892783]
  We propose CoupledTPS, which iteratively couples multiple TPS with limited control points into a more flexible and powerful transformation. In light of the laborious annotation cost, we develop a semi-supervised learning scheme to improve warping quality by exploiting unlabeled data. Experiments demonstrate the superiority and universality of CoupledTPS over the existing state-of-the-art solutions for rotation correction.
  arXiv  Detail & Related papers  (2024-01-24T13:03:28Z)
• Quantized Distributed Training of Large Models with Convergence Guarantees [34.054462975511996]
  We present QSDP, a variant of FSDP which supports both quant and weight gradientization with theoretical guarantees. We show that QSDP preserves accuracy, while completely removing the communication of FSDP, providing the speed-to-endups of up to 2.2x.
  arXiv  Detail & Related papers  (2023-02-05T14:20:55Z)
• On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times [51.61278695776151]
  Federated Learning (FL) is well known for its privacy protection when training machine learning models among distributed clients collaboratively. Recent studies have pointed out that the naive FL is susceptible to gradient leakage attacks. Differential Privacy (DP) emerges as a promising countermeasure to defend against gradient leakage attacks.
  arXiv  Detail & Related papers  (2021-01-11T19:43:12Z)
• Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels [69.18343801164741]
  partitioned edge learning (PARTEL) implements parameter-server training, a well known distributed learning method, in wireless network. We consider the case of deep neural network (DNN) models which can be trained using PARTEL by introducing some auxiliary variables.
  arXiv  Detail & Related papers  (2020-10-08T15:27:50Z)

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.