ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models
- URL: http://arxiv.org/abs/2605.22222v3
- Date: Mon, 25 May 2026 07:06:51 GMT
- Title: ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models
- Abstract summary: ARC-STAR organizes correction into three stages: a global corrector removes broad solver bias, a blockwise local refiner cleans the post-global residual, and, at deployment, a label-free score routes refinement to high-risk blocks under a compute budget.<n>ARC-STAR is the only method that cuts velocity rollout error by at least 36x over raw Poseidon on every cell.
- Score: 47.9430487327673
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Partial differential equation (PDE) foundation models are pretrained networks that forecast how physical fields like velocity and pressure evolve from a single reusable solver. On unfamiliar flows their predictions drift step by step, errors concentrate in a few regions, yet retraining destabilizes the network and uniform post-hoc correction overlooks this spatial concentration. To address this, we propose a frozen-solver post-hoc correction framework, Adaptive Risk-Calibrated Spatial Triage for Auditable Refinement (ARC-STAR). ARC-STAR organizes correction into three stages: a global corrector removes broad solver bias, a blockwise local refiner cleans the post-global residual, and, at deployment, a label-free score routes refinement to high-risk blocks under a compute budget. The framework is designed to be (i) frozen-host, preserving the pretrained solver without fine-tuning; (ii) auditable, with global and local stages trained and evaluated separately for measurable contributions; and (iii) budget-aware, using a blockwise interface that either refines the full field or routes limited compute to high-risk regions. Across five flow benchmarks spanning ten regime cells, ARC-STAR is the only method that cuts velocity rollout error by at least 36x over raw Poseidon on every cell. The global stage reduces raw host error by 91-99%, and the local stage further reduces the remaining post-global residual by up to 94.4%.
Related papers
- LoRA-RC: Reservoir Computing with Low-Rank Adaptation [0.0]
LoRA-RC adapts the recurrent matrix through a low-rank correction driven by streaming prediction errors.<n>On a Lorenz system with an abrupt parameter drift, LoRA-RC cuts post-drift prediction error by 56% versus a fixed RC and 51% versus readout-only adaptation.
arXiv Detail & Related papers (2026-09-11T01:19:48Z) - Local Path Planning and Obstacle Avoidance for an Omnicopter Platform [5.329751638083761]
This paper presents a real-time local planning and obstacle avoidance module for an omnidirectional multirotor (omnicopter)<n>Our method achieves real-time feasibility through (i) local-map voxelisation, (ii) a compact sphere-based approximation of the vehicle geometry, and (iii) adaptive velocity sampling in the 6D search space.<n>We evaluate our approach in simulation across computational stress tests, dense-waypoint path tracking, and static/unknown obstacle scenarios.
arXiv Detail & Related papers (2026-09-03T10:25:14Z) - SHIFT-LLM: Distribution Shift Correction in Depth-Pruned LLMs [18.494055578192015]
We introduce SHIFT-LLM, a training-free post-pruning correction framework that inserts a Linear Residual Adapter at each pruning site.<n>Each LRA preserves the identity pathway of the original residual block and adds a lightweight affine residual correction.<n>Experiments on five model families, six layer-selection criteria, and seven zero-shot benchmarks show that SHIFT-LLM consistently recovers accuracy lost to depth pruning.
arXiv Detail & Related papers (2026-08-25T19:01:58Z) - Error-Conditioned Neural Solvers [36.51479208528461]
Neural surrogate models offer fast approximate mappings from PDE parameters to solutions, but they typically treat solving as a purely statistical task.<n>Recent hybrid methods promote physical correctness by targeting the PDE residual via gradient descent or Gauss-Newton steps, but inherit the compute cost and instability of the underlying classical gradients.<n>We show, theoretically and empirically, that numerically minimizing the PDE residual can be an unreliable proxy for reconstruction accuracy in ill-conditioned systems.
arXiv Detail & Related papers (2026-06-25T17:56:27Z) - FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models [50.86041384789834]
We propose a two-stage PTQ framework for dLLMs that prioritizes the protection of fragile frontier states without requiring end-to-end diffusion rollouts.<n>We show that FAIR-Calib consistently outperforms state-of-the-art baselines on LLaDA and Dream (W4A4), reducing frontier decision flips and suppressing post-commit mismatches across diverse benchmarks.
arXiv Detail & Related papers (2026-06-04T08:00:51Z) - Amplifying, Not Learning: Fine-Tuned AI Text Detectors Amplify a Pretrained Direction [51.56484100374058]
Text detectors amplify a pretrained typicality axis; they do not construct an AI-vs-human boundary.<n>On raw encoders before any task supervision, projecting onto centroid(AI)-centroid(HC3) achieves NYT-vs-HC3 AUROC 0.806/0.944/0.834 across three architectures.<n>On RoBERTa-base, raw projection exceeds fine-tuning; on RoBERTa-base, full fine-tuning reduces discrimination below raw on both fluent-formal populations tested.
arXiv Detail & Related papers (2026-05-20T19:08:38Z) - Hybrid Iterative Neural Low-Regularity Integrator for Nonlinear Dispersive Equations [0.0]
HIN-LRI is a hybrid framework that augments a classical numerical solver with a neural operator trained to correct the solver's structured truncation error.<n>A lightweight neural network, operating on a low-dimensional latent manifold, learns the residual defect that analytical methods cannot close.<n>Experiments show that HIN-LRI improves accuracy over analytical, splitting methods, and neural PDE surrogates, with stable spatial refinement, effective out-of-distribution transfer, and modest online overhead.
arXiv Detail & Related papers (2026-05-06T12:50:36Z) - SOAR: Self-Correction for Optimal Alignment and Refinement in Diffusion Models [48.335262141752715]
Post-training pipeline for diffusion models has two stages: supervised fine-tuning (SFT) on curated data and reinforcement learning (RL) with reward models.<n>We propose SOAR (Self-Correction for Optimal Alignment and Refinement), a bias-correction post-training method that fills this gap.<n>It is on-policy, reward-free, and provides dense per-time supervision with no credit-assignment problem.
arXiv Detail & Related papers (2026-04-14T11:45:15Z) - K-GMRF: Kinetic Gauss-Markov Random Field for First-Principles Covariance Tracking on Lie Groups [8.489406212619164]
We propose K-GMRF, an online, training-free framework for covariance tracking.<n>Our method interprets observations as torques driving a latent angular velocity, propagated via a structure-preserving symplectic integrator.<n>We theoretically prove that this second-order dynamics achieves zero steady-state error under constant rotation.
arXiv Detail & Related papers (2026-03-20T03:16:36Z) - DARC: Disagreement-Aware Alignment via Risk-Constrained Decoding [59.16244104797919]
We propose **Disagreement-Aware Alignment via Risk-Constrained Decoding (DARC)**.<n>DARC frames response selection as distributionally robust, risk-sensitive decision making.<n>Experiments on alignment benchmarks show that DARC reduces disagreement and tail risk while maintaining competitive average quality.
arXiv Detail & Related papers (2026-03-09T09:21:29Z) - Diffusion Controller: Framework, Algorithms and Parameterization [54.82539154511621]
We present a unified control-theoretic view that casts reverse diffusion sampling as state-only control within (generalized) linearly-solvable Markov Decision Processes.<n>Under this framework, control acts by reweighting the pretrained reverse-time transition kernels, balancing terminal objectives against an $f$-divergence cost.<n>Experiments on Stable Diffusion v1.4 show consistent gains in preference-alignment win rates and improved quality-efficiency trade-offs.
arXiv Detail & Related papers (2026-03-07T01:49:59Z) - ODAR: Principled Adaptive Routing for LLM Reasoning via Active Inference [60.958331943869126]
ODAR-Expert is an adaptive routing framework that optimize the accuracy-efficiency trade-off via principled resource allocation.<n>We show strong and consistent gains, including 98.2% accuracy on MATH and 54.8% on Humanity's Last Exam.
arXiv Detail & Related papers (2026-02-27T05:22:01Z) - Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning [17.384089089363382]
We identify a root cause that existing methods overlook: the uniform penalization of errors.<n>Current approaches treat all incorrect rollouts within a group identically.<n>We propose the Asymmetric Confidence-aware Error Penalty (ACE)
arXiv Detail & Related papers (2026-02-24T22:46:43Z) - INC: An Indirect Neural Corrector for Auto-Regressive Hybrid PDE Solvers [61.84396402100827]
We propose the Indirect Neural Corrector ($mathrmINC$), which integrates learned corrections into the governing equations.<n>$mathrmINC$ reduces the error amplification on the order of $t-1 + L$, where $t$ is the timestep and $L$ the Lipschitz constant.<n>We test $mathrmINC$ in extensive benchmarks, covering numerous differentiable solvers, neural backbones, and test cases ranging from a 1D chaotic system to 3D turbulence.
arXiv Detail & Related papers (2025-11-16T20:14:28Z) - A Momentum Accelerated Adaptive Cubic Regularization Method for
Nonconvex Optimization [18.81683506516341]
The cubic regularization method (CR) and its adaptive version (ARC) are popular Newton-type methods in solving unconstrained non-constrained problems.
The main aim of this paper is to develop a momentum-accelerated adaptive regularization method (ARCm) to improve the convergent performance.
arXiv Detail & Related papers (2022-10-12T07:53:39Z) - $\texttt{FedBC}$: Calibrating Global and Local Models via Federated
Learning Beyond Consensus [66.62731854746856]
In federated learning (FL), the objective of collaboratively learning a global model through aggregation of model updates across devices tends to oppose the goal of personalization via local information.
In this work, we calibrate this tradeoff in a quantitative manner through a multi-criterion-based optimization.
We demonstrate that $texttFedBC$ balances the global and local model test accuracy metrics across a suite datasets.
arXiv Detail & Related papers (2022-06-22T02:42:04Z)
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.