# Toward Systems Foundations for Agentic Exploration

arXiv: https://arxiv.org/abs/2510.05556 · HTML: https://mlsanity.com/paper/2510.05556/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2025.

## Built on by (most cited first, 4 of 4)

- [DeltaBox: Scaling Stateful AI Agents with Millisecond-Level Sandbox Checkpoint/Rollback](https://mlsanity.com/paper/2605.22781/) (2026)
- [Data Flow Control: Data Safety Policies for AI Agents](https://mlsanity.com/paper/2606.05679/) (2026)
- [Agentic Data Environments](https://mlsanity.com/paper/2607.07397/) (2026)
- [SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering](https://mlsanity.com/paper/2610.14405/) (2026)

## Abstract (arXiv)

Agentic exploration, letting LLM-powered agents branch, backtrack, and search across many execution paths, demands systems support well beyond today's pass-at-k resets. Our benchmark of six snapshot/restore mechanisms shows that generic tools such as CRIU or container commits are not fast enough even in isolated testbeds, and they crumble entirely in real deployments where agents share files, sockets, and cloud APIs with other agents and human users. In this talk, we pinpoint three open fundamental challenges: fork semantics, which concerns how branches reveal or hide tentative updates; external side-effects, where fork awareness must be added to services or their calls intercepted; and native forking, which requires cloning databases and runtimes in microseconds without bulk copying.