3 papers
cs.DB2026
V3DB: Audit-on-Demand Zero-Knowledge Proofs for Verifiable Vector Search over Committed Snapshots
Zipeng Qiu, Wenjie Qu, Jiaheng Zhang +1
Dense retrieval services increasingly underpin semantic search, recommendation, and retrieval-augmented generation, yet clients typically receive only a top- list with no audita…
cs.CR2026
IMMACULATE: A Practical LLM Auditing Framework via Verifiable Computation
Yanpei Guo, Wenjie Qu, Linyu Wu +7
Commercial large language models are typically deployed as black-box API services, requiring users to trust providers to execute inference correctly and report token usage honestly…
cs.LG2025
MLKV: Efficiently Scaling up Large Embedding Model Training with Disk-based Key-Value Storage
Yongjun He, Roger Waleffe, Zhichao Han +8
Many modern machine learning (ML) methods rely on embedding models to learn vector representations (embeddings) for a set of entities (embedding tables). As increasingly diverse ML…