Dual-Gated Epistemic Time-Dilation: Autonomous Compute Modulation in Asynchronous MARL

• URL: http://arxiv.org/abs/2603.23722v1
• Date: Tue, 24 Mar 2026 21:19:06 GMT
• Title: Dual-Gated Epistemic Time-Dilation: Autonomous Compute Modulation in Asynchronous MARL
• Abstract summary: Multi-Agent Reinforcement Learning (MARL) algorithms achieve unprecedented successes across complex continuous domains.<n>Their standard deployment strictly adheres to a synchronous operational paradigm.<n>This dense throughput acts as a fundamental barrier to physical deployment on edge-devices where thermal and metabolic budgets are highly constrained.<n>We propose Epistemic Time-Dilation MAPPO (ETD-MAPPO), augmented with a Dual-Gated Epistemic Trigger.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While Multi-Agent Reinforcement Learning (MARL) algorithms achieve unprecedented successes across complex continuous domains, their standard deployment strictly adheres to a synchronous operational paradigm. Under this paradigm, agents are universally forced to execute deep neural network inferences at every micro-frame, regardless of immediate necessity. This dense throughput acts as a fundamental barrier to physical deployment on edge-devices where thermal and metabolic budgets are highly constrained. We propose Epistemic Time-Dilation MAPPO (ETD-MAPPO), augmented with a Dual-Gated Epistemic Trigger. Instead of depending on rigid frame-skipping (macro-actions), agents autonomously modulate their execution frequency by interpreting aleatoric uncertainty (via Shannon entropy of their policy) and epistemic uncertainty (via state-value divergence in a Twin-Critic architecture). To format this, we structure the environment as a Semi-Markov Decision Process (SMDP) and build the SMDP-Aligned Asynchronous Gradient Masking Critic to ensure proper credit assignment. Empirical findings demonstrate massive improvements (> 60% relative baseline acquisition leaps) over current temporal models. By assessing LBF, MPE, and the 115-dimensional state space of Google Research Football (GRF), ETD correctly prevented premature policy collapse. Remarkably, this unconstrained approach leads to emergent Temporal Role Specialization, reducing computational overhead by a statistically dominant 73.6% entirely during off-ball execution without deteriorating centralized task dominance.

Related papers

• Is Your Model Thinking or Just Stagnating? PUMA: Diagnosing Reasoning Pathology via Phase-Momentum Alignment [13.9489759632504]
  Test-time scaling empowers Large Reasoning Models (LRMs) to tackle complex tasks via extensive Chain-of-Thought (CoT)<n>This paper introduces PUMA, a training-free framework employing a tiered diagnostic architecture.<n>Experiments on LRMs spanning 1.5B to 32B demonstrate that PUMA consistently outperforms state-of-the-art baselines across diverse benchmarks.
  arXiv  Detail & Related papers  (2026-07-19T11:00:59Z)
• Episodic Memory Temporal Consistency for Cooperative Multi-Agent Reinforcement Learning [17.62926868585848]
  Cooperative Multi-Agent Reinforcement Learning (MARL) frequently suffers from severe reward sparsity and exploration bottlenecks.<n>We propose Episodic Memory Temporal Consistency (EMTC), a framework that robustly constructs and selectively leverages historical experiences.<n>EMTC achieves absolute win-rate improvements of up to 24% in super-hard SMAC scenarios and an average improvement of 28% across GRF tasks.
  arXiv  Detail & Related papers  (2026-06-03T06:15:39Z)
• PIMSM: Physics-Informed Multi-Scale Mamba for Stable Neural Representations under Distribution Shift [16.230225739605316]
  We argue that failure to preserve scientific temporal structure contributes to brittleness under distribution shift.<n>We propose Physics-Informed Multi-Scale Mamba, a state-space architecture that maps spectrum-estimated transition points between frequency regimes.<n>On Human Connectome Project fMRI, PIMSM improves robustness and representation stability under severe temporal-context truncation.
  arXiv  Detail & Related papers  (2026-05-08T04:58:05Z)
• Causal Diffusion Models for Counterfactual Outcome Distributions in Longitudinal Data [0.0]
  Causal Diffusion Model (CDM) generates full probabilistic distributions of counterfactual outcomes under sequential interventions.<n>CDM consistently outperforms state-of-the-art longitudinal causal inference methods.
  arXiv  Detail & Related papers  (2026-04-14T17:25:53Z)
• From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation [18.70033095161235]
  We propose a framework that distills a Conditional Flow Matching expert into a fast single-step student via Implicit Likelihood Estimation (IMLE)<n>A bi-directional Chamfer distance provides a set-level objective that promotes both mode coverage and fidelity.<n>A unified perception encoder further integrates multi-view RGB, depth, point clouds, and proprioception into a geometry-aware representation.
  arXiv  Detail & Related papers  (2026-03-10T09:30:05Z)
• FoSS: Modeling Long Range Dependencies and Multimodal Uncertainty in Trajectory Prediction via Fourier State Space Integration [21.39395366378851]
  We present FoSS, a dual-branch framework that unifies frequency-domain reasoning with linear-time sequence modeling.<n>Experiments on Argoverse 1 and Argoverse 2 benchmarks demonstrate that FoSS achieves state-of-the-art accuracy while reducing computation by 22.5% and parameters by over 40%.
  arXiv  Detail & Related papers  (2026-03-01T21:38:59Z)
• MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Models [51.506429027626005]
  Memory for Time Series (MEMTS) is a lightweight and plug-and-play method for retrieval-free domain adaptation in time series forecasting.<n>Key component of MEMTS is a Knowledge Persistence Module (KPM), which internalizes domain-specific temporal dynamics.<n>This paradigm shift enables MEMTS to achieve accurate domain adaptation with constant-time inference and near-zero latency.
  arXiv  Detail & Related papers  (2026-02-14T14:00:06Z)
• Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting [79.37674445572462]
  Time series forecasting (TSF) remains a challenging problem due to the intricate entanglement of intraperiod-fluctuations and interperiod-trends.<n>Treating reshaped tensors as static images results in a topological mismatch.<n> relying on uniform fixed-size representations allocates modeling capacity inefficiently.<n>TimeGS is a novel framework that fundamentally shifts the forecasting paradigm from regression to 2D generative rendering.
  arXiv  Detail & Related papers  (2026-02-10T14:13:36Z)
• Analyzing and Improving Diffusion Models for Time-Series Data Imputation: A Proximal Recursion Perspective [45.713195454899875]
  Diffusion models (DMs) have shown promise for Time-Series Data Imputation.<n>DMs' performance remains inconsistent in complex scenarios.<n>We propose a novel framework called SPIRIT (Semi-Proximal Transport Regularized time-series Imputation)
  arXiv  Detail & Related papers  (2026-02-01T12:11:57Z)
• ECG-RAMBA: Zero-Shot ECG Generalization by Morphology-Rhythm Disentanglement and Long-Range Modeling [0.6875312133832079]
  ECG-RAMBA is a framework that separates morphology and rhythm and re-integrates them through context-aware fusion.<n>It shows consistent cross-dataset performance on PTB-XL.
  arXiv  Detail & Related papers  (2025-12-29T10:14:52Z)
• 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)
• PPMStereo: Pick-and-Play Memory Construction for Consistent Dynamic Stereo Matching [51.98089287914147]
  textbfPick-and-textbflay textbfMemory (PM) construction module for dynamic bfStereo matching, dubbed as bftextPPMStereo.<n>Inspired by the two-stage decision-making process in humans, we propose a textbfPick-and-textbflay textbfMemory (PM) construction module for dynamic bfStereo matching, dubbed as bftextPPMStereo.
  arXiv  Detail & Related papers  (2025-10-23T03:52:39Z)
• 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)
• Twice Regularized Markov Decision Processes: The Equivalence between Robustness and Regularization [64.60253456266872]
  Markov decision processes (MDPs) aim to handle changing or partially known system dynamics. Regularized MDPs show more stability in policy learning without impairing time complexity. Bellman operators enable us to derive planning and learning schemes with convergence and generalization guarantees.
  arXiv  Detail & Related papers  (2023-03-12T13:03:28Z)

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.