arXiv AI By Sadra Sabouri, Hosein Hasanbeig, Adam Sage, Shreya Sinha, Cale Darling, Ashish Tiwari, Katie Garms, Souti Chattopadhyay, Sumit Gulwani

FlashScript: Compiling Recurring Agent Workflows into Reusable Neuro-Symbolic Programs

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FlashScript compiles a successful agent run into a reusable neuro‑symbolic program (NSP) by turning actions and data transformations into code, parameterizing instance‑specific values, and relegating model‑dependent decisions to LLM calls. The resulting NSPs produce identical tool calls on every run, use roughly 70 % fewer input tokens, and run a third of the time compared to a skill‑guided agent, while maintaining correctness across 120 WorkBench analytics tasks and 108 unseen instances. The approach localizes nondeterminism, enabling separate review and testing of deterministic code and neural components.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Sep 25

Grow the Harness, Not the Context: From Strategy-Free Scaffolds to Reusable Specialist Agents

The paper introduces Growing Harness, a training method that transforms recurring control logic in large language model agents into reusable executable code, reducing reliance on the model for task-specific decisions. By using strategy-free scaffolds, failure-guided code repair, and success-first gating, the approach learns a shared harness that improves performance across multiple benchmarks and model sizes. Experiments on BrowseComp-Plus and WebArena-Verified show significant gains in success rates and substantial reductions in LLM calls and inference cost compared to traditional tool‑calling agents.

By Laizhen Li, Jiarui Li, Juanjuan Zhao, Kejiang Ye, Ye Li, Cheng-zhong Xu, Xitong Gao
arXiv AI
Sep 30

Neuro-Symbolic Computer Use: Learning Reusable Policies for Reliable and Efficient Execution

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 Machine Learning
Jul 10

TTHE: Test-Time Harness Evolution

arXiv:2607. 08124v1 Announce Type: cross Abstract: The behavior of an LLM agent is determined not only by the underlying model, but also by its harness: the executable program that constructs context, invokes tools, verifies intermediate results, and recovers from failures.

By Jun Nie, Yonggang Zhang, Jun Song, Qianshu Cai, Dahai Yu, Yike Guo, Xinmei Tian, Bo Han