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
arXiv:2607. 04096v1 Announce Type: new Abstract: Current agentic workflows usually involve decomposing user requests into sequences of tool calls with correctly resolved parameters, the results of which are processed through reasoning traces in the language model's context window.
By Vishvesh Bhat, Jay Vaghasiya, Emmanuel Anaya Gonzalez
Current agentic workflows usually involve decomposing user requests into sequences of tool calls with correctly resolved parameters, the results of which are processed through reasoning traces in the language model's context window. The prevailing route to improve such reasoning is test-time scaling, which trains models to search over long chains of thought; but the resulting capability is entangled in model weights, is not verifiable step-by-step, and is costly at inference.
arXiv:2604. 05150v2 Announce Type: replace-cross Abstract: We study compiled AI, a paradigm in which large language models generate executable code artifacts during a compilation phase, after which workflows execute deterministically without further model invocation.
By Geert Trooskens (XY.AI Labs, Palo Alto, CA), Aaron Karlsberg (XY.AI Labs, Palo Alto, CA), Anmol Sharma (XY.AI Labs, Palo Alto, CA), Lamara De Brouwer (XY.AI Labs, Palo Alto, CA), Max Van Puyvelde (Stanford University School of Medicine, Stanford, CA), Matthew Young (XY.AI Labs, Palo Alto, CA), John Thickstun (Cornell University, Ithaca, NY), Gil Alterovitz (Brigham and Women's Hospital / Harvard Medical School, Boston, MA), Walter A. De Brouwer (Stanford University School of Medicine, Stanford, CA)
AdaGuard is an adaptive guard model that evaluates language‑model agent trajectories against user‑defined policies. The authors created the AdaptiveSafety dataset, containing 10,939 training and 1,000 test examples with policy rules, counterfactuals, explanations, and violated rules. Using supervised pre‑training followed by the SafePO reinforcement‑learning algorithm, they built 0.6B, 4B, and 8B guard models, with the 4B version achieving 89.30% accuracy on AdaptiveSafety and 71.82% on DynaBench.
By Yunhao Feng, Yifan Ding, Yuxiang Xie, Zheng Li, Mingrui Lao, Zeyuan Wang, Yanming Guo
LangNCO is a framework that transforms natural-language constraints into structured inference-time interventions for a frozen neural combinatorial optimization (NCO) solver. It uses a pretrained large language model to convert each requirement into a domain-specific language rule set, which a deterministic Rule Executor applies as hard action masks. Critical-Offset Decoding then reallocates rollout budget to ensure feasibility, achieving near-perfect feasibility across multiple constraint types without retraining the solver.
By Hironori Ohigashi
SEVerA is a three‑stage framework that synthesizes self‑evolving agents with formal safety guarantees. It introduces Formally Guarded Generative Models (FGGM) that wrap parametric models in verified rejection samplers, ensuring every output satisfies a first‑order logic contract. The framework searches for candidate programs, verifies them against hard constraints, and then fine‑tunes them to improve task utility while preserving correctness, achieving zero constraint violations on tasks such as Dafny verification, symbolic math synthesis, and policy‑compliant agentic tool use.
By Debangshu Banerjee, Changming Xu, Eugene Ie, Ming Zhang, Daiyi Peng, Chu-Cheng Lin, Tarun Suresh, Gagandeep Singh
arXiv:2510. 05592v2 Announce Type: replace Abstract: Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context; this scales poorly with long horizons and diverse tools and generalizes weakly to new scenarios.
By Zhuofeng Li, Haoxiang Zhang, Seungju Han, Sheng Liu, Jianwen Xie, Yu Zhang, Yejin Choi, James Zou, Pan Lu
arXiv:2601. 22642v2 Announce Type: replace Abstract: Large Language Models (LLMs) show remarkable capabilities, yet their stochastic next-token prediction creates logical inconsistencies and reward hacking that formal symbolic systems avoid.
By Chuxue Cao, Jinluan Yang, Haoran Li, Kunhao Pan, Zijian Zhao, Zhengyu Chen, Yuchen Tian, Lijun Wu, Conghui He, Sirui Han, Yike Guo
DE‑Venus is a unified, data‑efficient framework for reinforcement learning with verifiable rewards (RLVR) tailored to large language models. It structures the RLVR lifecycle into three modules—Active Data Selection, Weak Supervision Construction, and Training‑Time Supervision Refinement—allowing method‑specific decisions to be expressed as dataset transitions or online transformations while maintaining distributed execution contracts. Experiments on public benchmarks and three business scenarios show that DE‑Venus can preserve or improve model quality using only 10% of labels or 13% of relevant data, and can cut convergence steps by 63%–75% in selected business configurations.
By Shenzhi Yang, Guangcheng Zhu, Kai Tang, Zhengqing Zang, Xing Zheng, Haobo Wang, Yingfan Ma, Bowen Song, Bo Han, Bo An, Lei Feng, Weiqiang Wang, Junbo Zhao, Gang Chen
arXiv:2607. 06764v1 Announce Type: new Abstract: Recent progress on ARC-AGI-1 from disclosed architectures has come broadly from two regimes: heavy test-time compute over frontier models (evolutionary search, exhaustive sampling, extended chain-of-thought), or benchmark-specific training in which small models are fine-tuned on ARC data, often with task-specialized architectures.
By Kabir Moghe, Peter Chin
arXiv:2511. 04694v5 Announce Type: replace-cross Abstract: As large language model (LLM) based systems take on high-stakes roles in real-world decision-making, they must reconcile competing instructions from multiple sources within a single prompt context.
By Zishuo Zheng, Vidhisha Balachandran, Chan Young Park, Faeze Brahman, Sachin Kumar