The paper introduces a differentiable evaluation engine for linear temporal logic (LTL) that is algebra‑generic and suitable for training soft‑valued systems such as neural policies and adaptive controllers. It presents an executable specification of the algebras it can accept, implements several algebras, and audits their forward and backward behavior, all within the PyTorch library telos.
arXiv:2605. 20919v3 Announce Type: replace-cross Abstract: Sutra is a typed, purely functional programming language whose compiled forward pass is a PyTorch neural network.
arXiv:2602. 06934v4 Announce Type: replace-cross Abstract: Grassroots Logic Programs (GLP) is a concurrent logic programming language in which logic variables are partitioned into paired readers and writers.
arXiv:2607. 11696v1 Announce Type: new Abstract: Self-refinement often fails to strengthen few-shot inductive reasoning in large language models.
arXiv:2505.15002v3 Announce Type: replace-cross Abstract: Combinatory Homomorphic Automatic Differentiation (CHAD) was originally formulated as a semantics-driven source-to-source transformation for...
arXiv:2606. 19279v1 Announce Type: new Abstract: Neurosymbolic semantics is fragmented: classical, fuzzy, probabilistic and neural systems each define truth by their own inductive rules.
arXiv:2608. 16443v1 Announce Type: new Abstract: Neurosymbolic (NeSy) Artificial Intelligence aims to integrate Deep Learning (DL) architectures with symbolic reasoning.
arXiv:2605. 29965v2 Announce Type: replace Abstract: The development of temporal extensions of Answer Set Programming (ASP) has led to the emergence of non-monotonic linear-time (TEL), dynamic (DEL), and metric (MEL) temporal equilibrium logics.
arXiv:2603. 25414v4 Announce Type: replace-cross Abstract: A prevailing assumption in machine learning is that model correctness must be enforced after the fact.
arXiv:2609.23954v1 Announce Type: cross Abstract: Programmers write formal specifications, and LLMs implement them, proving that each implementation matches its spec. Taken to its extreme, this makes...
Programmers write formal specifications, and LLMs implement them, proving that each implementation matches its spec. Taken to its extreme, this makes specification languages the new programming langua...
FormalEvolve is a neuro‑symbolic evolutionary search framework that treats autoformalization as a budgeted test‑time search problem. It builds a compilation‑feasible archive of formal statements and expands it using LLM‑driven mutation, crossover, bounded patch repair, and symbolic AST rewrites to generate diverse, semantically accepted formalizations. In experiments on CombiBench and ProofNet, FormalEvolve achieves higher SH@100 scores and improves theorem‑complete proving under fixed prover budgets compared to no‑archive baselines.
The paper examines why standard neural architectures struggle to generalize to longer inputs when solving dynamic programming (DP) problems. It shows that every finite min-plus DP can be represented as a shortest‑path problem on a directed acyclic graph, equivalently as a tropical polynomial whose extended Newton polyhedron captures the decision boundary of the winning path. The authors prove that the graph, polynomial, and polyhedron descriptions form isomorphic semirings at both the formal polynomial and computed function levels, and they demonstrate that the natural dimensionality‑reduction operations in this semiring are neither injective nor closed, revealing structural limitations that hinder length‑generalization.