EvidenceStrong architectural inferencev1.21.5
In plain English
This page shows what kind of support exists for each claim: real systems, experiments, early evidence, architectural reasoning, open questions, or speculative scenarios.
- Why this matters: AI risk can come from the whole arrangement, not one obvious model.
- What to look for: data, memory, routes, adapters, tools, evaluators, updates, and rollback paths.
- Technical version below: the expert terminology remains available and is linked through the glossary.
Zero-Dependency Rust LLM Improvements
Evidence card
- Claim
- A zero-dependency browser LLM stack can be decomposed into runtime, quantization, adapter, cache, tokenizer, sampler, allocator, worker, and diagnostic surfaces.
- Evidence level
- Architectural inference
- Source
- docs/source-reports/raw-markdown/zero-dependency-rust-llm-improvements.md
- Publication date
- 2026-06-28
- Authors or institution
- User-supplied source report
- System tested
- Architecture report; no deployed Cognivirus system test claimed.
- Limitations
- User-supplied architecture analysis; not independently verified as consensus research in this package.
- What the evidence does show
- A zero-dependency browser LLM stack can be decomposed into runtime, quantization, adapter, cache, tokenizer, sampler, allocator, worker, and diagnostic surfaces.
- What the evidence does not show
- That any specific browser LLM implementation is compromised or that zero-dependency design automatically prevents supply-chain risk.
- Date last reviewed in UTC
- 2026-06-28T15:00:00Z
Site use
This card points to a preserved local source report and its public-safe summary. It supports bounded content synthesis and .uai memory routing, not a confirmed incident claim.