ORBITER: Conflict-Aware Decision-Making for Agentic Last-Mile Delivery

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Mingzhao Li, Chenxi Liu, Yan Zhao, Hao Miao
• Venue: arXiv: cs.AI
• Published: 2026-08-19
• Collected: 2026-08-20
• URL: http://arxiv.org/abs/2608.18846v1
• PDF: https://arxiv.org/pdf/2608.18846v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

The paper introduces ORBITER, a system designed to make decisions for last-mile delivery by couriers. It models the courier service through decision points and uses language models (LLMs) to reason about individual decisions while addressing their sensitivity to task presentation. The system also includes checks to ensure reliable decisions are made. In evaluations across four cities, ORBITER outperformed existing state-of-the-art baselines by an average of 9.2%.

What the Document Says

• The paper presents ORBITER, a system for decision-making in last-mile delivery.
• ORBITER models courier service through decision points.
• Each decision point contains the courier's spatial and temporal state, visible orders, and local trade-offs.
• Fixed proposers rank candidates, and a structured report highlights disagreements.
• The LLM gathers evidence on leading alternatives and an independent critic checks decisions against this evidence.
• Evaluations were conducted in four cities, with ORBITER outperforming existing state-of-the-art baselines by up to 9.2% on average.

Key Concepts Mentioned

• last-mile delivery
• couriers
• decision points
• language models (LLMs)
• spatiotemporal state
• visible orders
• local trade-offs
• fixed proposers
• structured report
• leading alternatives
• independent critic

Claims Made by the Document

• ORBITER outperforms existing state-of-the-art baselines in last-mile delivery decision-making.

Evidence Used

• Evaluations across four cities show ORBITER's performance exceeding that of existing state-of-the-art baselines by up to 9.2% on average.

Methods / Approach

• Modeling courier service through decision points
• Ranking candidates by fixed proposers
• Using a structured report to identify disagreements
• Gathering evidence using LLMs
• Checking decisions against evidence by an independent critic

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The paper does not discuss potential limitations of ORBITER, such as possible biases in decision-making or scalability issues.

Open Questions

• What are the specific algorithms or techniques used within ORBITER to gather evidence and make decisions?
• How does ORBITER handle unexpected situations or anomalies in last-mile delivery?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper focuses on describing ORBITER and its performance, but it may be worth investigating the inner workings of the system for a better understanding.

Raw Abstract / Excerpt

Last-mile delivery aims to handle dynamically arriving orders with couriers while modeling complex spatial and temporal correlations. Recent learning-based methods model spatiotemporal dependencies among orders to predict courier service sequences, but leave next-order decision making unexplained. Describing the current delivery state in language allows LLMs to reason explicitly about the spatial, temporal, and behavioral cues behind an individual decision. As direct predictors, however, LLMs remain sensitive to task presentation and often produce unreliable decisions. To address these challenges, we introduce ORBITER, an agentic Order Arbiter for next-order decision-making in last-mile delivery. ORBITER models courier service through decision points, each containing the courier's spatiotemporal state and visible orders and exposing local trade-offs for modeling and verification. Fixed proposers rank the candidates, and a structured report identifies where their rankings disagree. The LLM uses task-specific tools to gather evidence on the leading alternatives, while an independent critic checks the resulting decision against that evidence. We conduct extensive evaluations on data in four cities, where ORBITER outperforms existing state-of-the-art baselines by up to 9.2% on average showing its effectiveness.