About the work
A-Query is an architecture for amplified context comprehension in LLMs that couples opt-in browser sandboxes (WebGPU/WebAssembly) with domain-specialized multi-agent orchestration, semantic prompt compression, KV-cache compression, and RAG with source traceability.
On the client, A-Query performs chunking, saliency analysis (POS/NER), and Semantic Prompt Compression (PCS) under explicit user consent, encrypting artifacts before transmission. The server side employs efficient attention (sparse/compressive) and optimized memory for long-context workloads, trading off quality, latency, and cost.
Each specialized agent returns evidence-grounded outputs; a consensus layer merges them and exposes a transparency panel with steps and citations —without revealing raw chain-of-thought.
The paper contributes system design, mathematical grounding (rate–distortion view of semantic compression), security/privacy considerations (opt-in, isolation, encryption), and an evaluation protocol (quality, token/latency savings, ablations).
Expected impact: significant cost/latency reductions on large-context tasks, improved fidelity, and better adoption in regulated domains by providing practical transparency and data control.
Keywords: LLM, sparse attention, semantic compression, KV-cache, WebGPU, WebAssembly, RAG, multi-agent, transparency, privacy.
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Title Amplified Parameter Comprehension System through Opt-In Browser Sandboxes and Multi-Agent Orchestration (A-Query)
A-Query is an architecture for amplified context comprehension in LLMs that couples opt-in browser sandboxes (WebGPU/WebAssembly) with domain-specialized multi-agent orchestration, semantic prompt compression, KV-cache compression, and RAG with source traceability.
On the client, A-Query performs chunking, saliency analysis (POS/NER), and Semantic Prompt Compression (PCS) under explicit user consent, encrypting artifacts before transmission. The server side employs efficient attention (sparse/compressive) and optimized memory for long-context workloads, trading off quality, latency, and cost.
Each specialized agent returns evidence-grounded outputs; a consensus layer merges them and exposes a transparency panel with steps and citations —without revealing raw chain-of-thought.
The paper contributes system design, mathematical grounding (rate–distortion view of semantic compression), security/privacy considerations (opt-in, isolation, encryption), and an evaluation protocol (quality, token/latency savings, ablations).
Expected impact: significant cost/latency reductions on large-context tasks, improved fidelity, and better adoption in regulated domains by providing practical transparency and data control.
Keywords: LLM, sparse attention, semantic compression, KV-cache, WebGPU, WebAssembly, RAG, multi-agent, transparency, privacy.
Work type Technical
Tags embedded, investigacion, cientificos, ia generativa, flujos de trabajo, marketplace, monetizacion, papers
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Registry info in Safe Creative
Identifier 2510263483741
Entry date Oct 26, 2025, 2:01 AM UTC
License All rights reserved
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Author 100.00 %. Holder Santos Antonio Fraustro Solis. Date Oct 26, 2025.
Information available at https://www.safecreative.org/work/2510263483741-amplified-parameter-comprehension-system-through-opt-in-browser-sandboxes-and-multi-agent-orchestration-a-query-
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Registration: 2510263483741
