arXiv:2606. 05956v1 Announce Type: new Abstract: Bidirectional heuristic search can potentially reduce search effort for problems amenable to backward search.
By Tzur Shubi, Ariel Felner, Solomon Eyal Shimony, Shahaf S. Shperberg
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:2609.23293v1 Announce Type: new
Abstract: In the A* search algorithm, the tie-breaking strategies for nodes with the same $f$-value determines which states A* expands on the final $f$-layer. Fo...
By Alex Fukunaga
arXiv:2605. 30664v2 Announce Type: replace Abstract: Subgoal-based policy tree search, which uses a policy to guide search, is effective for complex single-agent deterministic problems but often relies on explicit subgoal generation that can incur substantial overhead and hinders scalability.
By Jake Tuero, Michael Buro, Laurent Orseau, Levi H. S. Lelis
arXiv:2607. 24647v1 Announce Type: new Abstract: AI-driven autonomous research (AR) systems are becoming increasingly effective across a broad range of tasks.
By Haiqian Yang, Yuan Cao
arXiv:2606. 02863v1 Announce Type: new Abstract: AI-Driven Research Systems (ADRS) -- systems coupling LLMs with automated evaluation to discover algorithms, proofs, and designs -- are being optimized and adopted across domains, but the tools to analyze them have not kept pace.
By Marquita Ellis, Paul Castro