Minim: Privacy-Aware Minimal View for Agents via Trusted Local Sanitization

arXiv:2609.10992v1 Announce Type: new Abstract: The integration of Large Language Models into daily tasks relies on context-rich instructions, inevitably exposing sensitive user

arXiv:2603.18377v1 Announce Type: cross Abstract: Cloud-hosted large language models (LLMs) have become the de facto planners in agentic systems, coordinating tools and guiding

arXiv:2604.23280v1 Announce Type: new Abstract: AI agents are now running real transactions, workflows, and sub-agent chains across organizational boundaries without continuous

arXiv:2610.20960v1 Announce Type: new Abstract: Safety in agentic systems is inherently sequential: whether an action is safe depends on prior actions, tool outputs, information

arXiv:2603.19011v1 Announce Type: cross Abstract: NDAI zones let inventor and investor agents negotiate inside a Trusted Execution Environment (TEE) where any disclosed

arXiv:2604.13531v1 Announce Type: new Abstract: Graphical User Interface (GUI) agents show strong capabilities for automating web tasks, but existing interactive benchmarks

arXiv:2605.19260v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) have recently emerged as promising backbones for GUI-agent models, where high-resolution GUI

arXiv:2605.27766v1 Announce Type: new Abstract: LLM safety evaluations predominantly test models in isolation, yet deployed AI agents increasingly operate within persistent social