mlsanityData to 14 Oct 2026

Spawn Freely, Act Sparingly: Progressive Risk Vesting for Recursive LLM-Agent Trees

arXiv 2609.01035 ↗First posted 1 Sep 2026cs.AI cs.LG math.PRcited by 1

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

Recursive LLM agents can broaden their search by spawning specialists. Some branches later request tools that send data or deploy code. When should a branch receive authority to act? We distinguish sandbox spawning, in which external controls prevent the specified harm, from capability activation, in which a selected branch crosses an irreversible-action boundary. Progressive Risk Vesting (PRV) holds a trajectory-level risk budget in escrow and debits it as branches are activated. We prove an anytime harm bound for adaptively generated trees. Branch outcomes may be dependent, but each local certificate needs to remain valid conditional on the full pre-activation history, including the information used to select the request. When activation gates, branch charges, and compute constraints are held fixed, delayed vesting preserves every policy available under irrevocable spawn charging. Marginal risk estimates can still fail after branch selection. In a stylized branching model, trajectory harm changes as the authority reproduction number RA crosses one. As local risk p approaches zero, trajectory harm is proportional to p below criticality, proportional to √p at criticality, and retains a positive floor above it. A finite-type occupancy model yields risk and compute shadow prices. For nested fanout modes with decreasing marginal value per unit risk, these prices produce a threshold rule. Branching calculations and a split-sample experiment illustrate the results. These synthetic studies do not estimate safety in deployed agents. The analysis suggests a design rule: search broadly in the sandbox and grant recursive authority sparingly, with an explicit risk charge.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 14 of its 26 references that are in the map. First posted on arXiv 1 Sep 2026.

Builds on

14 of 26 references.

Closest ancestors · 5

Same direction, newest first

  1. 2026Bounded Agents: Delegation Security for Multi-Agent AI Systems16 Aug
  2. 2026Post-Hoc Trajectory-Risk Certification for Modular LLM-Based Security Agents4 Aug
  3. 2026Overlaying Governance: A Compositional Authorization Framework for Delegation and Scope in Agentic AI2 Jun
  4. 2026LACUNA: Safe Agents as Recursive Program Holes27 May
  5. 2025Risk-Sensitive Agent Compositions5 Jun

Other references · 9

  1. 2025Multi-agent Architecture Search via Agentic Supernetcited by 179
  2. 2026AOrchestra: Automating Sub-Agent Creation for Agentic Orchestrationcited by 35
  3. 2026Understanding Agent Scaling in LLM-Based Multi-Agent Systems via Diversitycited by 29
  4. 2024THREAD: Thinking Deeper with Recursive Spawningcited by 25
  5. 2026Agent Contracts: A Formal Framework for Resource-Bounded Autonomous AI Systemscited by 16
  6. 2019Online Control of the False Coverage Rate and False Sign Ratecited by 11
  7. 2026AgentSpawn: Adaptive Multi-Agent Collaboration Through Dynamic Spawning for Long-Horizon Code Generationcited by 8
  8. 2026PASC: Pipeline-Aware Conformal Prediction with Joint Coverage Guarantees for Multi-Stage NLP and LLM Pipelinescited by 7
  9. 2026Token Budgets: An Empirical Catalog of 63 LLM-Agent Budget-Overrun Incidents, with an Affine-Typed Rust Mitigation as a Case Studycited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

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  3. 2026Position: A Three-Layer Probabilistic Assume-Guarantee Architecture Is Structurally Required for Safe LLM Agent Deployment
  4. 2026Operational Reframing and Approval-Framed Delegation in Multi-Agent LLM Safety
  5. 2026Why LLM Agents Collapse Without Oversight: The Enforcement Gap as the Mechanism Behind Emergence World Failures
  6. 2026RouteGuard: Certifying Routing Gain in LLM Multi-Agent Systems When Complementarity Is Not Enough
  7. 2026Traversal-as-Policy: Log-Distilled Gated Behavior Trees as Externalized, Verifiable Policies for Safe, Robust, and Efficient Agents
  8. 2026ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis