Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning
The paper discusses a new approach to long-horizon planning in embodied AI agents. It combines low-level imitation learning with high-level symbolic abstractions to create bilevel policies. The proposed BISON system demonstrates improved efficiency and generalization in solving complex tasks.
- ▪The research addresses the challenge of long-horizon planning for AI agents.
- ▪Bilevel policies are introduced, consisting of a neural policy for low-level tasks and a symbolic policy for high-level planning.
- ▪Experiments show that BISON can efficiently solve problems with a large number of objects.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15975 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 7hF43ggwob0e |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.15975 (cs) [Submitted on 15 May 2026] Title:Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning Authors:Dillon Z. Chen, Till Hofmann, Toryn Q. Klassen, Sheila A. McIlraith View a PDF of the paper titled Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning, by Dillon Z. Chen and Till Hofmann and Toryn Q. Klassen and Sheila A. McIlraith View PDF HTML (experimental) Abstract:We tackle the challenge of building embodied AI agents that can reliably solve long-horizon planning problems.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
