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

2026-02-20

AI Summary

This work addresses the limitation of traditional AI alignment methods, which focus on single-step outputs and struggle to ensure sustained reliability over long-horizon agent interaction trajectories. The authors propose APEMO, a runtime scheduling layer that reframes alignment as a sequential control task. By deploying behavioral proxies to monitor trajectory stability and leveraging temporal affective signals, APEMO dynamically allocates computational resources within a fixed budget. Crucially, it introduces a trajectory-level repair mechanism inspired by the peak–end rule, enabling targeted interventions at critical segments—specifically affective peaks and trajectory endings—without modifying model weights. Experiments demonstrate that APEMO significantly outperforms structured orchestration baselines in both multi-agent simulations and LLM-based plan–execute pipelines, effectively enhancing overall trajectory quality and reuse likelihood.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Mechanism Design

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• AI alignment
• long-horizon agentic systems
• trajectory-level reliability
• temporal control
• autonomous agents

Innovation

• temporal alignment
• peak-end modulation
• agentic systems
• runtime orchestration
• trajectory-level reliability