Shallow Verification
Cross-source consensus on Shallow Verification from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Benefits
Highlighted claims
- CATS uses a shallow verification pass that loads intermediate layers once per decoding cycle to verify draft tokens in parallel. — CATS: Cascaded Adaptive Tree Speculation for Memory-Limited LLM Inference Acceleration
- The shallow verifier produces correction candidates when its tokens differ from corresponding draft tokens. — CATS: Cascaded Adaptive Tree Speculation for Memory-Limited LLM Inference Acceleration
- Draft tokens and correction tokens are assembled into a verification tree for target verification. — CATS: Cascaded Adaptive Tree Speculation for Memory-Limited LLM Inference Acceleration
- The final target pass verifies the main branch and correction branches in one batched forward pass using tree-masked attention. — CATS: Cascaded Adaptive Tree Speculation for Memory-Limited LLM Inference Acceleration
- CATS improves verification capacity by keeping the drafter shallow and loading the shallow verifier once per cycle. — CATS: Cascaded Adaptive Tree Speculation for Memory-Limited LLM Inference Acceleration