arXiv — cs.AI preprintsInternational7 October 2026
Attacca: Goal-Directed Control under State Continuity for Long-Horizon Embodied Agents
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.07785v1 Announce Type: new Abstract: A central capability of embodied agents is to accomplish complex objectives through sequences of interdependent tasks. Yet existing visual goal-conditioned policies underlying these agents are typically evaluated on isolated interactions where the target is already visible, and thus do not capture the conditions that arise during continuous long-horizon task execution. In such settings, each task begins from the state left by the previous one: the agent may end at a different position and orientation, the world may have been modified, and the nex
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- GAMEGO: Training Game-Dev Agents with Synthetic Trajectories Anchored in Real-World Assets7 October 2026
- Text2Dashboard: A Governed Agent Architecture for Natural-Language Dashboard Generation over Enterprise DataBrain7 October 2026
- FluidPD: In-Place Elasticity for SLO-Aware Prefill-Decode Disaggregated LLM Serving7 October 2026
- Anchor Divergence for Semantic Geometry in Contrastive Learning7 October 2026
- RadOnc-Agent: An LLM-Orchestrated Framework for AI Workflows Across the Radiotherapy Care Pathway7 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.