Alignment in Time: Peak-Aware Orchestration for Long-Horizon Agentic Systems

• URL: http://arxiv.org/abs/2602.17910v1
• Date: Fri, 20 Feb 2026 00:16:07 GMT
• Title: Alignment in Time: Peak-Aware Orchestration for Long-Horizon Agentic Systems
• Authors: Hanjing Shi, Dominic DiFranzo,
• Abstract summary: We introduce APEMO (Affect-aware Peak-End Modulation for Orchestration), a runtime scheduling layer.<n>APEMO detects trajectory instability through behavioral proxies and targets repairs at critical segments, such as peak moments and endings.<n>Our results reframe alignment as a temporal control problem, offering a resilient engineering pathway for the development of long-horizon agentic systems.
• Score: 2.5424331328233207
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional AI alignment primarily focuses on individual model outputs; however, autonomous agents in long-horizon workflows require sustained reliability across entire interaction trajectories. We introduce APEMO (Affect-aware Peak-End Modulation for Orchestration), a runtime scheduling layer that optimizes computational allocation under fixed budgets by operationalizing temporal-affective signals. Instead of modifying model weights, APEMO detects trajectory instability through behavioral proxies and targets repairs at critical segments, such as peak moments and endings. Evaluation across multi-agent simulations and LLM-based planner--executor flows demonstrates that APEMO consistently enhances trajectory-level quality and reuse probability over structural orchestrators. Our results reframe alignment as a temporal control problem, offering a resilient engineering pathway for the development of long-horizon agentic systems.

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