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Temporal Intent Reconstruction for Long-Horizon Agentic Predictive Control

by Krishna Teja Areti, Vijay Putta, Prudhvi Ratna Badri Satya, Ajay Guyyala
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 73
Year of Publication: 2026
Authors: Krishna Teja Areti, Vijay Putta, Prudhvi Ratna Badri Satya, Ajay Guyyala
10.5120/ijca2026926237

Krishna Teja Areti, Vijay Putta, Prudhvi Ratna Badri Satya, Ajay Guyyala . Temporal Intent Reconstruction for Long-Horizon Agentic Predictive Control. International Journal of Computer Applications. 187, 73 ( Jan 2026), 15-24. DOI=10.5120/ijca2026926237

@article{ 10.5120/ijca2026926237,
author = { Krishna Teja Areti, Vijay Putta, Prudhvi Ratna Badri Satya, Ajay Guyyala },
title = { Temporal Intent Reconstruction for Long-Horizon Agentic Predictive Control },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2026 },
volume = { 187 },
number = { 73 },
month = { Jan },
year = { 2026 },
issn = { 0975-8887 },
pages = { 15-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number73/temporal-intent-reconstruction-for-long-horizon-agentic-predictive-control/ },
doi = { 10.5120/ijca2026926237 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-01-20T22:56:18.642208+05:30
%A Krishna Teja Areti
%A Vijay Putta
%A Prudhvi Ratna Badri Satya
%A Ajay Guyyala
%T Temporal Intent Reconstruction for Long-Horizon Agentic Predictive Control
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 73
%P 15-24
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Temporal Intent Reconstruction framework integrated with a Masked Cognitive Predictor to improve predictive control under changing goals and dynamic conditions. Using real multimodal data from HARMONIC, RoboMind, RoboNet, and Open X-Embodiment, the model reconstructs past intent trajectories and embeds misalignment signals into the control objective for long-horizon adaptation. Experiments showed stable reconstruction across embodiment and modality variations, reduced goal divergence by 31.4%, and improved tracking behaviour by 78% during transitions. The framework improved accuracy, RMSE reduction, and tracking behaviour compared with baseline MPC, inverse learning, and reinforcement-based controllers. These results indicate that temporal intent reconstruction enhances consistency and long-range predictive capability in systems operating under varied sensing, morphology, and task settings.

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Index Terms

Computer Science
Information Sciences

Keywords

Temporal Intent Reconstruction Predictive Control Cognitive Modeling Intent Misalignment Adaptive Robotics Multimodal Datasets

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