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

Hanjing Shi; Dominic DiFranzo

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arXiv · 2602.17910

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

Hanjing Shi·Dominic DiFranzo

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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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Hanjing Shi, Dominic DiFranzo. 2026-02-20. Alignment in Time: Peak-Aware Orchestration for Long-Horizon Agentic Systems. https://arxiv.org/abs/2602.17910

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