From Prompts to Pavement Through Time: Temporal Grounding in Agentic Scene-to-Plan Reasoning
The article discusses a study on temporal grounding in scene-to-plan reasoning for autonomous vehicles. It highlights the limitations of current models that treat time as a secondary property, which affects reasoning consistency. The research introduces new planner architectures and evaluates their performance, revealing insights into predictive hazard reasoning and the challenges of prompt-based temporal grounding.
- ▪The study focuses on enhancing scene interpretation and planning in autonomous vehicles using large language and multimodal models.
- ▪It introduces three planner architectures with varying levels of temporal integration to assess their effectiveness.
- ▪Qualitative analysis indicates improvements in predictive hazard reasoning and strategic divergence, despite no significant gains in standard correctness metrics.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.19824 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
| 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 | 09ZXjKtzIHLl |
| 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 |
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| 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:2605.19824 (cs) [Submitted on 19 May 2026] Title:From Prompts to Pavement Through Time: Temporal Grounding in Agentic Scene-to-Plan Reasoning Authors:Ahmed Y. Gado, Omar Y. Goba, Alaa Hassanein, Catherine M. Elias, Ahmed Hussein View a PDF of the paper titled From Prompts to Pavement Through Time: Temporal Grounding in Agentic Scene-to-Plan Reasoning, by Ahmed Y. Gado and 4 other authors View PDF HTML (experimental) Abstract:Recent attempts to support high-level scene interpretation and planning in Autonomous Vehicles (AVs) using ensembles of Large Language Models (LLMs) and Large Multimodal Models (LMMs) continue to treat time as a secondary property.
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