Multi-Agent Regime-Conditioned Diffusion (MARCD) for CVaR-Constrained Portfolio Decisions

• URL: http://arxiv.org/abs/2510.10807v3
• Date: Mon, 03 Nov 2025 12:18:32 GMT
• Title: Multi-Agent Regime-Conditioned Diffusion (MARCD) for CVaR-Constrained Portfolio Decisions
• Abstract summary: We present MARCD, a generative-to-decision framework with: (i) a Gaussian HMM to infer latent regimes; (ii) a diffusion generator that produces regime-conditioned scenarios; (iii) signal extraction via blended, shrunk moments; and (iv) a governed CVaR epigraph program.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We examine whether regime-conditioned generative scenarios combined with a convex CVaR allocator improve portfolio decisions under regime shifts. We present MARCD, a generative-to-decision framework with: (i) a Gaussian HMM to infer latent regimes; (ii) a diffusion generator that produces regime-conditioned scenarios; (iii) signal extraction via blended, shrunk moments; and (iv) a governed CVaR epigraph quadratic program. Contributions: Within the Scenario stage we introduce a tail-weighted diffusion objective that up-weights low-quantile outcomes relevant for drawdowns and a regime-expert (MoE) denoiser whose gate increases with crisis posteriors; both are evaluated end-to-end through the allocator. Under strict walk-forward on liquid multi-asset ETFs (2005-2025), MARCD exhibits stronger scenario calibration and materially smaller drawdowns: MaxDD 9.3% versus 14.1% for BL (a 34% reduction) over 2020-2025 out-of-sample. The framework provides an auditable pipeline with explicit budget, box, and turnover constraints, demonstrating the value of decision-aware generative modeling in finance.

Related papers

• MorphoQuant: Modality-Aware Quantization for Omni-modal Large Language Models [57.87791117741788]
  We propose a modality-aware PTQ framework to preserve cross-modal morphology and mitigate outlier loss.<n>Distribution-Aware Bias Compensation (DABC) selectively absorbs long-tailed outliers into channel-wise biases.<n>We also propose Morphology-Directed Quantization Function Optimization (MDQFO) to co-optimize the quantization grid with the bias mask.
  arXiv  Detail & Related papers  (2026-06-03T02:05:10Z)
• D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalities [54.87947751720332]
  We propose a novel segmentation model D3Seg to maintain stable performance under missing-modality settings.<n>The proposed model achieves approximately 1.5-2.0% Dice improvement on enhancing tumor (ET) and around 1.0% on tumor core (TC) across multiple missing modality configurations.
  arXiv  Detail & Related papers  (2026-05-21T09:55:42Z)
• Optimal sequential decision-making for error propagation mitigation in digital twins [0.0]
  We develop a Markov Decision Process (MDP) in which the inferred regimes serve as states, corrective interventions serve as actions, and a scalar reward takes into consideration the cost-benefit tradeoff between system fidelity and maintenance expense.<n>We then extend the formulation to a Partially Observable MDP (POMDP) that accounts for the imperfect nature of regime classification by maintaining a belief updated via Bayesian filtering.<n>Both formulations are solved via dynamic programming and simulation. We then benchmark two model-free reinforcement learning algorithms, Q-learning and REINFORCE, to assess whether effective policies can be learned without explicit model knowledge.
  arXiv  Detail & Related papers  (2026-04-24T02:36:23Z)
• Variational Routing: A Scalable Bayesian Framework for Calibrated Mixture-of-Experts Transformers [5.534626267734823]
  We introduce Variational Mixture-of-Experts Routing (VMoER), a structured Bayesian approach for modelling uncertainty in MoE layers.<n>VMoER confines inference to the expert-selection stage which is typically done by a deterministic routing network.<n>Across tested foundation models, VMoER improves routing under noise by 38%, and increases out-of-distribution AUROC by 12%.
  arXiv  Detail & Related papers  (2026-03-10T10:07:53Z)
• Diffusion-MPC in Discrete Domains: Feasibility Constraints, Horizon Effects, and Critic Alignment: Case study with Tetris [0.0]
  We study diffusion-based model predictive control (Diffusion-MPC) in discrete domains using Tetris as a case study.<n>Our planner samples candidate placement sequences with a MaskGIT-style discrete denoiser and selects actions via reranking.<n>We analyze three key factors: (1) feasibility-constrained sampling via logit masking over valid placements, (2) reranking strategies using a score, a pretrained DQN critic, and a hybrid combination, and (3) compute scaling in candidate count and planning horizon.
  arXiv  Detail & Related papers  (2026-03-02T19:35:38Z)
• LEC: Linear Expectation Constraints for False-Discovery Control in Selective Prediction and Routing Systems [95.35293543918762]
  Large language models (LLMs) often generate unreliable answers, while uncertainty methods fail to fully distinguish correct from incorrect predictions.<n>We address this issue through the lens of false discovery rate (FDR) control, ensuring that among all accepted predictions, the proportion of errors does not exceed a target risk level.<n>We propose LEC, which reinterprets selective prediction as a constrained decision problem by enforcing a Linear Expectation Constraint.
  arXiv  Detail & Related papers  (2025-12-01T11:27:09Z)
• Prune-Then-Plan: Step-Level Calibration for Stable Frontier Exploration in Embodied Question Answering [52.69447404069251]
  Large vision-language models (VLMs) have improved embodied question answering (EQA) agents by providing strong semantic priors for open-vocabulary reasoning.<n>We propose Prune-Then-Plan, a framework that stabilizes exploration through step-level calibration.
  arXiv  Detail & Related papers  (2025-11-24T22:50:50Z)
• Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning [70.56067503630486]
  We argue that sixth-generation (6G) intelligence is not fluent token prediction but calibrated the capacity to imagine and choose.<n>We show that WM-MS3M cuts mean absolute error (MAE) by 1.69% versus MS3M with 32% fewer parameters and similar latency, and achieves 35-80% lower root mean squared error (RMSE) than attention/hybrid baselines with 2.3-4.1x faster inference.
  arXiv  Detail & Related papers  (2025-11-04T17:22:22Z)
• 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)
• Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization [53.82400605816587]
  Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation.<n>A major challenge lies in the non-stationary nature of quality distributions in real-world scenarios.<n>We introduce Continual AQA (CAQA), which equips with Continual Learning capabilities to handle evolving distributions.
  arXiv  Detail & Related papers  (2025-10-08T10:09:47Z)
• HydroFusion-LMF: Semi-Supervised Multi-Network Fusion with Large-Model Adaptation for Long-Term Daily Runoff Forecasting [3.3915788299794767]
  HydroFusion-LMF performs trend-residual decomposition to reduce non-stationarity.<n>It fuses expert outputs via a hydrologic context-aware gate conditioned on day-of-year phase.<n>It attains MSE 1.0128 / MAE 0.5818 on a 10-year daily dataset.
  arXiv  Detail & Related papers  (2025-10-04T09:09:06Z)
• Learning Robust Diffusion Models from Imprecise Supervision [75.53546939251146]
  DMIS is a unified framework for training robust Conditional Diffusion Models from Imprecise Supervision.<n>Our framework is derived from likelihood and decomposes the objective into generative and classification components.<n>Experiments on diverse forms of imprecise supervision, covering tasks covering image generation, weakly supervised learning, and dataset condensation demonstrate that DMIS consistently produces high-quality and class-discriminative samples.
  arXiv  Detail & Related papers  (2025-10-03T14:00:32Z)
• MMR1: Enhancing Multimodal Reasoning with Variance-Aware Sampling and Open Resources [113.33902847941941]
  Variance-Aware Sampling (VAS) is a data selection strategy guided by Variance Promotion Score (VPS)<n>We release large-scale, carefully curated resources containing 1.6M long CoT cold-start data and 15k RL QA pairs.<n> Experiments across mathematical reasoning benchmarks demonstrate the effectiveness of both the curated data and the proposed VAS.
  arXiv  Detail & Related papers  (2025-09-25T14:58:29Z)
• RegimeFolio: A Regime Aware ML System for Sectoral Portfolio Optimization in Dynamic Markets [2.21658458251407]
  RegimeFolio is a novel regime-aware and sector-specialized framework.<n>It integrates explicit volatility regime segmentation with sector-specific ensemble forecasting.<n>We evaluate RegimeFolio on 34 large cap U.S. equities from 2020 to 2024.
  arXiv  Detail & Related papers  (2025-09-14T12:03:06Z)
• Adversarial Distribution Matching for Diffusion Distillation Towards Efficient Image and Video Synthesis [65.77083310980896]
  We propose Adrial Distribution Matching (ADM) to align latent predictions between real and fake score estimators for score distillation.<n>Our proposed method achieves superior one-step performance on SDXL compared to DMD2 while consuming less GPU time.<n>Additional experiments that apply multi-step ADM distillation on SD3-Medium, SD3.5-Large, and CogVideoX set a new benchmark towards efficient image and video synthesis.
  arXiv  Detail & Related papers  (2025-07-24T16:45:05Z)
• Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting [52.6508222408558]
  We introduce Elucidated Rolling Diffusion Models (ERDM)<n>ERDM is the first framework to unify a rolling forecast structure with the principled, performant design of Elucidated Diffusion Models (EDM)<n>On 2D Navier-Stokes simulations and ERA5 global weather forecasting at 1.5circ resolution, ERDM consistently outperforms key diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-06-24T21:44:31Z)
• A Minimax-MDP Framework with Future-imposed Conditions for Learning-augmented Problems [10.827221988826484]
  We study a class of sequential decision-making problems with augmented predictions, potentially provided by a machine learning algorithm.<n>In this setting, the decision-maker receives prediction intervals for unknown parameters that become progressively refined over time.<n>We propose a minimax Markov Decision Process (minimax-MDP) framework, where the system state consists of an adversarially evolving environment state and an internal state controlled by the decision-maker.
  arXiv  Detail & Related papers  (2025-05-02T03:28:35Z)
• Rectified Diffusion Guidance for Conditional Generation [94.83538269086613]
  We revisit the theory behind CFG and rigorously confirm that the improper combination coefficients (textiti.e.) brings about expectation shift the generative distribution.<n>We show that our approach enjoys a textbftextitform solution given the strength.<n> Empirical evidence on real-world data demonstrate the compatibility of our design with existing state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2024-10-24T13:41:32Z)
• SCott: Accelerating Diffusion Models with Stochastic Consistency Distillation [74.32186107058382]
  We propose Consistency Distillation (SCott) to enable accelerated text-to-image generation.<n>SCott distills the ordinary differential equation solvers-based sampling process of a pre-trained teacher model into a student.<n>On the MSCOCO-2017 5K dataset with a Stable Diffusion-V1.5 teacher, SCott achieves an FID of 21.9 with 2 sampling steps, surpassing that of the 1-step InstaFlow (23.4) and the 4-step UFOGen (22.1)
  arXiv  Detail & Related papers  (2024-03-03T13:08:32Z)
• Decision-Dependent Distributionally Robust Markov Decision Process Method in Dynamic Epidemic Control [4.644416582073023]
  The Susceptible-Exposed-Infectious-Recovered (SEIR) model is widely used to represent the spread of infectious diseases. We present a Distributionally Robust Markov Decision Process (DRMDP) approach for addressing the dynamic epidemic control problem.
  arXiv  Detail & Related papers  (2023-06-24T20:19:04Z)
• Distributionally Robust Multiclass Classification and Applications in Deep Image Classifiers [9.979945269265627]
  We develop a Distributionally Robust Optimization (DRO) formulation for Multiclass Logistic Regression (MLR) We demonstrate reductions in test error rate by up to 83.5% and loss by up to 91.3% compared with baseline methods, by adopting a novel random training method.
  arXiv  Detail & Related papers  (2022-10-15T05:09:28Z)
• Distributionally Robust Multiclass Classification and Applications in Deep Image Classifiers [3.179831861897336]
  We develop a Distributionally Robust Optimization (DRO) formulation for Multiclass Logistic Regression (MLR) We demonstrate reductions in test error rate by up to 83.5% and loss by up to 91.3% compared with baseline methods, by adopting a novel random training method.
  arXiv  Detail & Related papers  (2021-09-27T02:58:19Z)
• Robustified Multivariate Regression and Classification Using Distributionally Robust Optimization under the Wasserstein Metric [11.383869751239166]
  We develop Distributionally Robust Optimization (DRO) formulations for Multivariate Linear Regression (MLR) and Multiclass Logistic Regression (MLG) We relax the DRO formulation into a regularized learning problem whose regularizer is a norm of the coefficient matrix. Experimental results show that our approach improves the predictive error by 7% -- 37% for MLR, and a metric of robustness by 100% for MLG.
  arXiv  Detail & Related papers  (2020-06-10T22:16:50Z)

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.