EvidenceDemonstrated research proof-of-conceptv1.22.1
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.
Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation
Evidence card
- Claim
- Low-bit KV cache quantization can degrade refusal/alignment behavior while conventional metrics remain stable in tested settings.
- Evidence level
- Emerging evidence
- Source
- https://arxiv.org/abs/2606.09864
- Publication date
- 2026-06-01
- Authors or institution
- Bruce Changlong Xu, Adarsh Kumarappan, Mu Zhou
- System tested
- Eleven instruction-tuned models and multiple safety benchmarks as reported.
- Limitations
- Very recent preprint; production applicability depends on quantizer, model, deployment, and mitigations.
- What the evidence does show
- Low-bit KV cache quantization can degrade refusal/alignment behavior while conventional metrics remain stable in tested settings.
- What the evidence does not show
- That every KV cache optimization produces the same failure mode.
- Date last reviewed in UTC
- 2026-06-26T00:00:00Z
Site use
This source supports Cognivirus.com pages related to KV cache quantization, alignment degradation, inferenceA conclusion or output produced from data. Open glossary definition optimization. Its role is bounded by the limitations listed above.