How do we govern AI systems whose reasoning we cannot fully inspect? Governance does not require understanding a system's reasoning. It requires stating what the system is obliged, permitted, and forbidden to do, and checking whether it complied. I present an implementation of Reified Input/Output Logic, the formalism behind the DAPRECO knowledge base, in Wolfram Language: the core I/O axioms, obligations, permissions, constitutive norms, reified eventualities, and temporal operators. I then test whether GPT-4 can translate English legal statements into the formalism, and report the failures: hallucinated functions, omitted temporal scope, deviation from the formalism, and (in the worst cases) code that runs, reads plausibly, but silently encodes the wrong norm. A case study, an AI guard dog operating under a computational contract, shows how formalized rules can extend from a contract directly into the operational code of an embodied agent, producing symbolic, auditable justifications for its behaviour. I argue that computational law can be used as a governance tool and that a desirable goal would be to formalize the law that can and ought to be programmatically executable.
Automated attack chain generation is critical for modern cybersecurity, yet manual construction fails to scale as adversary behaviors expand. While classical AI planning using the Planning Domain Definition Language (PDDL) offers a formal method to automate this process, it relies on the accurate translation of techniques into symbolic predicates. Current state-of-the-art systems like AURORA employ a nine-category Attack Action Linking Model (AALM), but the necessity of this specific granularity remains unvalidated. This work investigates whether AURORA's nine-category taxonomy provides representational distinctions beyond those captured by a reduced, empirically derived scheme. Utilizing a pipeline where a Large Language Model (LLM) performs translation and the Fast Downward engine performs deterministic reasoning, the study compares the full nine-category AALM against a reduced five-category scheme derived empirically from Atomic Red Team (ART) execution evidence. Because the nine-category domain is constructed as a relabeling of the five-category domain, plan validity and cost are held identical between schemes by design; the substantive test of granularity's effect lies instead in the resulting predicate category resolution. There, a controlled A/B test isolates a case where a coarser scheme's plan passes every validity check while remaining operationally wrong: holding administrator privilege and being able to exercise it over a network logon prove to be causally distinct system states. Results from a sixteen-technique corpus show 81.3% identical plan outcomes across both schemes by construction, with a genuine predicate category resolution gain confined to a single technique out of sixteen. The findings suggest that higher granularity primarily enhances the internal structural resolution of a plan's justification rather than the viability of the generated attack chain itself.
Hongyu Hè, Maria Apostolakics.AI cs.MA cs.NI cs.SC
A formal model enables verifying reachability, localizing an outage, or anticipating the blast radius of a change. Yet, virtually no production network has one, since writing a model by hand demands rare expertise and is hard to keep current as the network changes frequently. At its core, network modeling is a typographical exercise: it translates network artifacts (e.g., configurations, topology, and routing state) into rules in formal logic. Translation of this kind is what large language models (LLMs) nowadays do well. Unlike free-form AI reasoning, such translation can be formally verified. Once modeling is no longer the bottleneck, trusting AI to reason over large, complex networks no longer makes sense. Our position therefore cuts against the prevailing race to put autonomous AI agents in charge end-to-end. We instead confine AI to translation and rely on a solver for reliable long-horizon reasoning, building a reusable formal model of general network behavior that can then be specialized to specific tasks, e.g., root-cause analysis (RCA). We build TypoNet that constructs and validates a symbolic model of an emulated production-scale WAN from the network's own artifacts. Our preliminary evaluation shows TypoNet helps in two ways. On its own, TypoNet answers operational questions (e.g., reachability verification and change-impact analysis) faster, more cheaply, and more reliably than an LLM. As a tool for an AI agent, TypoNet boosts fault localization at lower cost. The result makes the case for AI that builds verifiable network models and relies on a solver for reliable long-horizon reasoning.
Yan Huang, Xubing Hao, Xiaojin Li +4cs.LO cs.AI cs.ET
The reliance on unstructured free text for documenting clinical trial protocols creates a significant barrier to automated reasoning, cohort discovery, and trial simulation. The lack of formal structure obscures critical temporal phenotypes, such as dynamic eligibility criteria and event timing constraints. Although Temporal Ensemble Logic (TEL) offers an expressive framework for modeling these elements, manual encoding remains a prohibitive bottleneck. We introduce the CT-TEL workflow: a scalable pipeline leveraging Large Language Models (LLMs) to translate narrative clinical protocols into TEL formulas. We applied CT-TEL to generate logical models for 23 real-world trials from ClinicalTrials.gov. We evaluated translation fidelity via a back-translation approach, using LLMs to convert TEL formulas back into natural language and measuring semantic similarity against source texts. The resulting semantic retention suggests that LLMs may offer a pathway for mapping informal protocols to computable logic, providing preliminary evidence toward scalable clinical trial emulation within the emerging "Symbolic Biomedicine" paradigm championed by the corresponding author.