Papers for

enterprise ai platform developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Trustworthy virtual memory protects private data while aiding remote ai models

Semantic-TVM: Structure-Preserving Trustworthy Virtual Memory for Memory-Augmented and Tool-Using Agents

Abstract: Memory-augmented and tool-using agents expose exact private values when remote LLMs process retrieved memory, tool actions, and intermediate observations. One-way masking limits direct exposure but removes values needed for trusted execution and can leak them through later observations. We propose Trustworthy Virtual Memory (TVM), a closed-loop runtime that keeps exact-value state local while presenting a protected view to the remote model. Within this single runtime, Rule-TVM replaces whole protected fields with locally recoverable handles, and Semantic-TVM instead replaces only sensitive spans predicted by a trusted local model, preserving surrounding task-relevant context. On Memory-EHR and Memory-RAP across two providers, span-level projection recovers most of the EHR utility lost under whole-field replacement (Task Success 84.17% vs. 52.33% on DeepSeek) while measured exposure stays low and workflows remain executable.

Mon 14 SeptArtificial Intelligence

The gist

When AI systems use tools or look up memories, they sometimes expose private or sensitive information. The authors found that simply hiding all sensitive information can make the AI less useful or leak data in other ways. They created Trustworthy Virtual Memory (TVM), which keeps exact sensitive data safely local while sending only safe summaries to the remote AI. This approach keeps the useful context intact and helps AI complete tasks better without exposing private information.

Open → 2609.15011v1