arXiv AI
Jul 24
Encoding Event-B Proof Rules in Prolog: An Interactive Sequent Prover for ProB
arXiv:2607. 21191v1 Announce Type: cross Abstract: Event-B is a formal method rooted in predicate logic and set theory.
Animation, Verification and Visualisation of Prolog Transition Systems with ProB
arXiv:2607. 21192v1 Announce Type: cross Abstract: ProB is a Prolog-based model checker, animator and constraint solver for high-level formal specifications.
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arXiv AI
Sep 18
Structured Four-Stage Legal Translation: From Natural-Language Traffic Rules to PROLOG
The paper introduces Structured Four-Stage Legal Translation (S4L→Prolog), a reasoning-guided framework that converts raw traffic rules into Prolog logic by performing semantic role extraction, scene completion, logical mapping, and rule generation in a single prompt. Compared to baseline approaches (NL→Prolog and LE→Prolog), S4L achieves higher accuracy—formalizing 75 % of twenty real-world traffic rules versus 60 % and 55 % for the baselines. Qualitative analysis shows S4L better captures implicit causal relations, deontic modality, and exception structures.
arXiv AI
Jul 21
Composable Verification Pipelines for Multi-Agent Systems
arXiv:2607. 16266v1 Announce Type: cross Abstract: Existing approaches for reasoning about action and change provide expressive semantics for modeling dynamic systems, in most cases built on top of logic programming systems.
arXiv AI
Jul 13
OpenProver: Agentic and Interactive Theorem Proving with Lean 4
arXiv:2607. 09217v1 Announce Type: new Abstract: In this system paper, we present OpenProver, an open-source system for LLM-driven automated theorem proving (ATP) with integrated Lean 4 formal verification.
arXiv AI
Jun 16
Mind-Studio: Executable World Models with Lookahead Evaluation for Partially Observable Games
arXiv:2606. 16070v1 Announce Type: new Abstract: World-model synthesis aims to turn interaction experience into an internal model of environment dynamics.
arXiv AI
Aug 28
HybridProver: Augmenting Theorem Proving with LLM-Driven Proof Synthesis and Refinement
HybridProver is a unified framework that combines whole-proof synthesis and tactic-based generation using proof sketches as an intermediate representation. Implemented in Isabelle/HOL, it employs two 7B-scale LLMs trained on optimized Isabelle datasets. On the miniF2F Isabelle benchmark, HybridProver achieved a 73.8% success rate, surpassing the previous state of the art of 61.9%, and ablation studies examined the effects of dataset quality, training settings, and sampling strategies.
arXiv AI
Jun 19
DataMagic: Transforming Tabular Data into Data Insight Video
arXiv:2606. 20388v1 Announce Type: cross Abstract: Data videos integrate dynamic charts, voice narration, and synchronized animations to communicate data insights as temporal narratives, making them an effective medium for improving data consumption efficiency in the data management lifecycle.
arXiv AI
Jun 3
Efficient Temporal Datalog Materialisation for Composite Event Recognition
arXiv:2605. 02488v2 Announce Type: replace Abstract: Several applications demand the timely detection of critical situations, such as threats to safety and transparency, over high-velocity streams of symbolic events.
arXiv AI
Jun 16
PrologMCP: A Standardized Prolog Tool Interface for LLM Agents
arXiv:2606. 14935v1 Announce Type: new Abstract: Frontier reasoning-tuned language models still fail on deductive tasks at depth, and the cost of improved performance through extended internal reasoning scales poorly.
arXiv Computation and Language
Aug 31
CEDAR: Automata as Verifiable Interfaces for Language-Guided Embodied Action
CEDAR is a counterexample-guided framework that translates natural-language instructions for embodied agents into regular languages over environment event traces, represented as deterministic finite automata. By using a language model for semantic judgments and execution traces for correction, CEDAR turns constraints into executable finite-state objects, enabling the intersection of learned skills with additional specifications. In Minecraft experiments, CEDAR preserves temporal and spatial constraints better than a program-generating baseline and reduces cumulative LLM queries by reusing learned skills.
arXiv AI
Jul 24
Case study: solving P-99 with LPTP and an LLM
arXiv:2607. 21196v1 Announce Type: cross Abstract: Ninety-Nine Prolog Problems (P-99) is a famous set of Prolog exercises.
arXiv AI
Sep 10
Compiling VGDL into Causal Models
arXiv:2609.05459v1 Announce Type: new Abstract: Reinforcement learning and large language models often struggle to accurately capture the causal mechanics of game environments. Standard reinforcement...