Cross-modal feature alignment driven large model migration optimization system

Through a large model migration optimization system driven by cross-modal feature alignment, using technical means such as entropy-gated sparsification, multi-scale temporal aggregation, and contextual entropy pruning, the invalid correlation problem of high-dimensional embedding matrices in cross-modal adversarial simulation environments is solved, efficient feature alignment and model migration are achieved, and resource utilization and output consistency are improved.

CN120430379BActive Publication Date: 2025-10-10NO 15 INST OF CHINA ELECTRONICS TECH GRP
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Patent Information

Application Number
CN202510918868.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-07-04
Publication Date
2025-10-10
Estimated Expiration
2045-07-04

AI Technical Summary

Technical Problem

In a cross-modal adversarial simulation environment, invalid associations in the high-dimensional embedding matrix caused by multi-dimensional discrete combinations quickly occupy the video memory, causing a surge in storage, increased latency, and dilution of alignment weights. This causes the migrated large model to output offset actions at key situation nodes, and the overall performance continues to decline with the increase in data.

Method used

Through entropy-gated sparsification of multi-source observations, multi-scale time condensation, contextual entropy pruning, and dynamic control of joint change trends, a closed-loop optimization system is formed to achieve a balance between compressed storage and temporal integrity of cross-modal high-dimensional information. The weights in the mapping stage are adaptively adjusted according to the situation, residuals are corrected at any time, incremental rehearsals and drift monitoring consolidate long-term memory, and inference scheduling writes back performance indicators in real time.

Benefits of technology

It suppresses the dual burden of video memory and latency caused by dimensionality explosion, maintains migration alignment accuracy, ensures high consistency and high confidence output of strategy sequences in complex situations, significantly improves resource utilization, and linearly reduces computing power requirements.

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Abstract

The application discloses a cross-modal feature alignment driven large model migration optimization system, specifically relates to the field of analog confrontation, is used for solving the high-dimensional data storage and calculation efficiency problem of multi-source information processing in the confrontation environment, is through the sparse of multi-source observation after the entropy gate, with multi-scale time condensation, context entropy pruning, joint variable trend dynamic control forms a closed loop, makes the cross-modal high-dimensional information balanced between compression storage and time sequence integrity, the weight is adjusted according to the situation in the mapping stage, and the residual error is corrected at any time;Incremental exercise and drift monitoring continuously consolidate long-term memory, avoid disaster forgetting;Reasoning scheduling real-time write performance index, drive structure self-evolution;It not only inhibits the double burden of video memory and delay caused by dimension explosion, but also maintains migration alignment accuracy, ensures that the strategy sequence maintains high consistency and high confidence output in complex situation, while the computing power demand linearly decreases, and the resource utilization rate is significantly improved.
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