arXiv AI

Front-to-Attractors: Modifying the Front-to-Front Heuristic in Bidirectional Search

arXiv:2606. 07047v1 Announce Type: new Abstract: Heuristics play a central role in the performance of bidirectional search algorithms, which commonly rely on two main classes.

arXiv AI
Sep 12

Probabilistic Focal Search: Accelerating Bounded-Suboptimal Search via Lower-Bound Advancement

Probabilistic Focal Search (PFS) augments traditional Focal Search by probabilistically choosing between the standard heuristic-guided expansion and expanding the minimum‑f node in OPEN. This strategy advances the lower bound, enlarges the FOCAL frontier, and can dramatically reduce node expansions—up to 90% in some benchmarks such as N‑Puzzle and TSP—especially when long f_min plateaus delay useful FOCAL admissions. An anytime variant, APFS, outperforms other tested anytime algorithms on the Generalized Covering TSP, and the same probabilistic scheduler transfers to Dynamic Potential Search as Probabilistic Dynamic Potential Search (PDPS), though its effectiveness varies by domain and bound.

By Minh Vu Duc, Trung Le Huu, H\`a Minh Ho\`ang, Trung Thanh Nguyen, Phuong Khanh Nguyen, Huynh Thi Thanh Binh
arXiv AI
Jul 28

Agent-UCT: Upper Confidence Bounds Applied to Trees for Agentic Workflow Optimization with Cost-Awareness

arXiv:2607. 24162v1 Announce Type: new Abstract: Optimizing agentic workflows, such as retrieval-augmented generation (RAG) pipelines, requires navigating a combinatorial space of discrete component choices under tight evaluation budgets.

By Yang Li, Hai Liu, Dian Shao, Yu Wang, Xiyu Chen, Sergey Volkov, Bozhi Wang, Ziyu Sun, Sihang Liu, Ye Luo, Xiaowei Zhang