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PACE: Adaptive Budget Allocation for Time-Efficient Embodied Planning — arXiv:2608.03034

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.03034
License (as archived): CC0-1.0
Archived: 2026-10-31T12:18:27.641Z
SHA-256 of the archived text: 30e6ad8ca4b74ac703d2ad92931ffa63bd7b602d3bddc8330dff476dcbc29d8c
Stable URL of this page: https://metraipolis.com/documento/dc_30c8cdebf5da

How to cite

PACE: Adaptive Budget Allocation for Time-Efficient Embodied Planning — arXiv:2608.03034 — arXiv (en), https://arxiv.org/abs/2608.03034 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_30c8cdebf5da (Metraipolis archive dc_30c8cdebf5da, sha256 30e6ad8ca4b74ac703d2ad92931ffa63bd7b602d3bddc8330dff476dcbc29d8c)

BibTeX

@misc{dc30c8cdebf5da,
  title = {PACE: Adaptive Budget Allocation for Time-Efficient Embodied Planning — arXiv:2608.03034},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.03034},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_30c8cdebf5da; sha256 30e6ad8ca4b74ac703d2ad92931ffa63bd7b602d3bddc8330dff476dcbc29d8c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.03034. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Reasoning-enhanced large language models have achieved remarkable improvements in planning tasks, yet their deployment in embodied systems remains impractical due to prohibitive inference delays-often exceeding minutes per planning instance. The fundamental bottleneck stems from the serial nature of existing paradigms: models must complete all reasoning before any action execution, leaving execution time windows entirely unexploited. We introduce PACE (Planning with Adaptive Cognitive Effort), a framework that enables interleaved reasoning and execution through two key innovations: an Interleaved Think-Act architecture that pipelines cognitive processing with action execution, and a Dynamic Budget Allocator that adapts reasoning token budgets to available execution time windows. On the Robotouille benchmark using Qwen3-8B-AWQ, PACE achieves a 10% success rate-representing a 67% improvement over the ReAct+Think baseline-while delivering 6.9 times acceleration in thinking time compared to unconstrained reasoning. The framework hides 66.8% of thinking time within execution windows, demonstrating that strategic cognitive effort allocation can simultaneously improve both planning quality and time efficiency. These results provide evidence that time-aware architectural innovations enable reasoning models to operate in latency-sensitive embodied domains where they were previously impractical.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-05 in cs.AI).
Categorie arXiv: Robotics (cs.RO) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.03034 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T12:05:52.980Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-05?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).