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arXiv:2604.03754v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have been shown to encode truth of statements in their activation space along a linear truth direction. Previous...
The paper "Can You Check That? The Checkability Boundary for Local LLM Network Automation" proposes a method called Touchstone that uses local small language models (SLMs) to generate network‑automation candidates and applies task‑specific intrinsic checks to reject incorrect outputs before escalating to a larger frontier LLM. By defining a task as checkable when a cheap, deterministic test can reject outputs violating a necessary correctness condition, the authors demonstrate that Touchstone achieves high end‑to‑end accuracy (98.6% on conflict detection and 93.8% on intent translation) while escalating only a small fraction of inputs. The study shows that local inference is preferable when precise, low‑cost checks are available, whereas tasks lacking such checks should rely on the frontier model.
arXiv:2608.23179v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly attractive for automating network configuration, yet their reliability and failure patterns are po...
The paper introduces control‑data flow separation to improve prompt optimization in multi‑agent large language model systems. By representing execution protocols as typed, validated program objects and keeping task‑relevant content as unstructured language, the method prevents prompt edits from corrupting critical routing, formatting, or termination signals. Experiments on synthetic reasoning, collaborative review generation, and insurance rating workflows show that this approach maintains 100% protocol validity while consistently enhancing task performance.
arXiv:2608. 14956v1 Announce Type: new Abstract: The development of models demands sound modeling and simulation knowledge as well as domain knowledge.
arXiv:2608. 11340v1 Announce Type: cross Abstract: Symbolic network verifiers can reason about correctness across vast spaces of routing inputs and failures, but only for the protocols and features an expert has encoded by hand.