Long-Horizon State Tracking in LLMs: Executing MD5 through a Deep Sequence of Dependent Tool Calls
Researchers quantify how small per-step LLM errors compound catastrophically during long dependent tool-call chains, a finding that should temper confidence in agentic AI for security automation.
Summary written by editorial AI · Source link below
arXiv:2609.00012v1 Announce Type: cross Abstract: Long-horizon tasks remain uncommon in large language model (LLM) evaluation, and for a reason: when each step depends on the last, per-step accuracy that looks excellent in isolation decays catastrophically, as errors cascade and the end-to-end failure probability grows sharply with length. Existing agentic benchmarks report end-to-end success but confound this state-tracking difficulty with instruction interpretation, give no control group that
Editorial Analysis
Enterprises piloting agentic AI for SOC or DevSecOps tasks must account for compounding error rates that single-step benchmarks hide, or risk silent failures at scale.
Before deploying agentic LLM pipelines, require end-to-end accuracy testing on multi-step chains rather than relying on per-step benchmarks.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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