What question did this study set out to answer?

The aim is to resolve inference limitations caused by memory bandwidth in large language models during ultra-long-context processing.

August 11, 2026Open Access

The Unified Latent-State Fabric: Resolving the Inference Memory Wall via GLRP v2.0 and Aegis-KV

View Full Paper

Ask AI

Bookmark

Authors

This pre-print demonstrates a hardware-agnostic solution to enhance memory throughput in large language models via a new architectural framework.

The aim is to resolve inference limitations caused by memory bandwidth in large language models during ultra-long-context processing.
Proposed the Unified Latent-State Memory Fabric integrating FSQ from GLRP v2.0 and Aegis-KV architecture.
Conducted empirical telemetry validation using GPT-2 data to verify compression and performance metrics.
Implemented an obfuscated binary to ensure intellectual property protection and enable hardware integration.
Achieved a compression ratio of 384x with a memory footprint reduction from 48.00 MB to 0.1250 MB for 4096-token blocks.
Demonstrated 0.9617 mean cosine similarity for geometric preservation in attention blocks while reducing dimensionality to 16 quantized integers.
Validated pipeline latency of 19.8 ms on legacy hardware, aligning with sub-2.0 ms targets for advanced processing systems.

Dr Sharanagouda N Patil (2026) studied this question.

View Full Paper

Ask AI

Bookmark

August 11, 2026Open Access

View Full Paper

Ask AI

Bookmark

Authors

This pre-print demonstrates a hardware-agnostic solution to enhance memory throughput in large language models via a new architectural framework.

The aim is to resolve inference limitations caused by memory bandwidth in large language models during ultra-long-context processing.
Proposed the Unified Latent-State Memory Fabric integrating FSQ from GLRP v2.0 and Aegis-KV architecture.
Conducted empirical telemetry validation using GPT-2 data to verify compression and performance metrics.
Implemented an obfuscated binary to ensure intellectual property protection and enable hardware integration.
Achieved a compression ratio of 384x with a memory footprint reduction from 48.00 MB to 0.1250 MB for 4096-token blocks.
Demonstrated 0.9617 mean cosine similarity for geometric preservation in attention blocks while reducing dimensionality to 16 quantized integers.
Validated pipeline latency of 19.8 ms on legacy hardware, aligning with sub-2.0 ms targets for advanced processing systems.

Dr Sharanagouda N Patil (2026) studied this question.

View Full Paper

Ask AI

Bookmark