arXiv:2607. 17710v1 Announce Type: new Abstract: Large Language Models (LLMs) have had a remarkable impact across many areas of machine learning.
By Ehsan Futuhi, Nathan R. Sturtevant
The paper introduces neuro‑symbolic computer use, a method that learns reusable policies to execute recurring computer workflows efficiently. Instead of re‑planning each run, the learned policy encodes stable decisions (ordering, variables, loops, branches) into executable code while delegating observation‑dependent decisions to neural models. Using neuro‑symbolic policy iteration, the approach iteratively refines the policy from a single agent trajectory, diagnoses failures, and revises the code with a coding model, achieving superior Pass^3 scores and significant reductions in per‑run cost and latency on OSWorld‑Verified and ScienceBoard benchmarks.
By Hyewon Suh, Thanh Minh Nguyen, Chih-Lun Lee, Darrow Hartman, Lizhao Liu, Xin Eric Wang, Ang Li, Jiachen Yang
GADAR (Generalizing Across Domains via Action Ranking) is a learned policy for classical planning that accepts a domain description as input and constructs grounded actions in unseen domains without search. It uses a three‑layer architecture—lifted domain, binding, and chain layers—to embed domain structure, tie action applicability to lifted preconditions, and capture schema dependencies. In zero‑shot experiments across eight benchmark domains, a single GADAR model trained on seven domains solves 58.3% of test instances, matching closely the performance of domain‑specific models and outperforming random choice and a representation lacking the chain layer.
By Rajesh Mangannavar, Pranay Dugar, Prasad Tadepalli
arXiv:2607. 13389v1 Announce Type: new Abstract: Reinforcement Learning (RL) post-training is increasingly used to adapt foundation models for reasoning, planning, and feedback-driven robot-learning pipelines, but constrained post-training resources are often summarized by a single total FLOP budget.
By Patrick Wilhelm, Odej Kao
arXiv:2607. 28135v1 Announce Type: new Abstract: Machine learning for combinatorial optimization typically relies on neural constructors trained via reinforcement learning on large offline datasets for a fixed problem class-incurring high pretraining costs and generalizing poorly outside the training distribution.
By Mohand Mezmaz, Gr\'egoire Danoy
arXiv:2610.08246v1 Announce Type: new
Abstract: Frontier large language models (LLMs) can generate heuristic functions that guide search to achieve state-of-the-art performance in satisficing plannin...
By Andr\'{e} G. Pereira, Augusto B. Corr\^ea, Felipe Meneguzzi, Jendrik Seipp