Alignment in Time: Peak-Aware Orchestration for Long-Horizon Agentic Systems
This addresses alignment challenges for developers of long-horizon agentic systems, offering a resilient engineering pathway, though it appears incremental as it builds on existing orchestration methods.
The paper tackles the problem of sustaining reliability in long-horizon autonomous agent workflows by introducing APEMO, a runtime scheduling layer that optimizes computational allocation using temporal-affective signals, resulting in enhanced trajectory-level quality and reuse probability over structural orchestrators.
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.