How did humanity coax mathematics from the æther?

We introduce a logical language for reasoning about quantized aggregate-combine graph neural networks with global readout (ACR-GNNs). We provide a logical characterization and use it to prove that verification tasks for quantized GNNs with readout are (co)NEXPTIME-complete. This result implies that the verification of quantized GNNs is computationally intractable, prompting substantial research efforts toward ensuring the safety of GNN-based systems. We also experimentally demonstrate that quantized ACR-GNN models are lightweight while maintaining good accuracy and generalization capabilities with respect to non-quantized models.

Coordinating heterogeneous robot teams from free-form natural-language instructions is hard. Language-only planners struggle with long-horizon coordination and hallucination, while purely formal methods require closed-world models. We present FLEET, a hybrid decentralized framework that turns language into optimized multi-robot schedules. An LLM front-end produces (i) a task graph with durations and precedence and (ii) a capability-aware robot--task fitness matrix; a formal back-end solves a makespan-minimization problem while the underlying robots execute their free-form subtasks with agentic closed-loop control. Across multiple free-form language-guided autonomy coordination benchmarks, FLEET improves success over state of the art generative planners on two-agent teams across heterogeneous tasks. Ablations show that mixed integer linear programming (MILP) primarily improves temporal structure, while LLM-derived fitness is decisive for capability-coupled tasks; together they deliver the highest overall performance. We demonstrate the translation to real world challenges with hardware trials using a pair of quadruped robots with disjoint capabilities.

Automating mathematical reasoning is a longstanding goal in artificial intelligence (Newell et al., 1957). A prominent line of work on the problem (Li et al., 2024) uses neural models to direct

The Workshop Program of the Association for the Advancement of Artificial Intelligence's 39th Conference on Artificial Intelligence (AAAI-25) was held in Philadelphia, Pennsylvania, on February 25 - March 4, 2025. TIKA is envisioned to create an open knowledge resource and serve as a hub for research, education and training on knowledge representation and knowledge engineering. Over 50 AI researchers convened at the workshop over two days. The discussions focused on different aspects of creating an open knowledge resource including foundational knowledge, automated reasoning, knowledge curation, education on knowledge axiomatization, and evaluation of outcomes. The opening discussion confirmed that the idea of curated knowledge, that is, knowledge captured in an expressive formal language that can be explicitly examined and verified by humans, is compelling. It must, however, be situated in the modern context of AI. Such a resource should address the limitations of existing generative ...