arXiv:2607. 05915v1 Announce Type: new Abstract: PCB routing is the task of connecting the nets of a board with copper traces under strict design rules, yet learning-based methods still lag behind rule-based routers.
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arXiv:2609.21263v1 Announce Type: new Abstract: Automated macro placement remains a fundamental challenge in VLSI physical design. Despite decades of research, existing approaches predominantly optim...
arXiv:2607. 11399v1 Announce Type: cross Abstract: Large language model agents are increasingly executed not by a single model call, but by an execution harness that manages observation, context, control, action, state, and verification.
arXiv:2606. 20785v2 Announce Type: replace Abstract: Collecting computer use data from human demonstrations is expensive and slow, motivating the need for scalable generation strategies.
arXiv:2607. 21557v1 Announce Type: new Abstract: Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems.
EDGEGEN is a synthetic task generation framework that extracts compliance rules from a tool‑calling agent’s specification to create database‑grounded edge‑case tasks that violate those rules. By combining EdgeGen with existing synthetic data generation methods, it forms a fully automated closed‑loop system that requires no human annotation. Experiments show that finetuning on EdgeGen data improves performance by 2–42 % on the tau2bench airline domain, while harness optimization yields 10–30 % gains over human‑curated and base harnesses for the Gemma‑4‑e4b model.
arXiv:2605.18859v3 Announce Type: replace-cross Abstract: LLM routing matters most in long-horizon applications such as coding agents, deep research systems, and computer-use agents, where a single u...