Toward Systems Foundations for Agentic Exploration
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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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Oct 2025.
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Citing papers per year: 2026: 4.
4 papers.
- 2026DeltaBox: Scaling Stateful AI Agents with Millisecond-Level Sandbox Checkpoint/Rollbackcited by 12
- 2026Data Flow Control: Data Safety Policies for AI Agentscited by 0
- 2026Agentic Data Environmentscited by 0
- 2026SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineeringcited by 0
- 2026SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering9 Oct 2026
- 2026Agentic Data Environments8 Jul 2026
- 2026Data Flow Control: Data Safety Policies for AI Agents4 Jun 2026
- 2026DeltaBox: Scaling Stateful AI Agents with Millisecond-Level Sandbox Checkpoint/Rollback21 May 2026