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[2602.17910] Alignment in Time: Peak-Aware Orchestration for Long-Horizon Agentic Systems
Summary
This paper presents APEMO, a novel runtime scheduling layer designed to enhance the reliability of long-horizon agentic systems by optimizing computational allocation based on temporal-affective signals.
Why It Matters
As AI systems become increasingly autonomous, ensuring their reliability over extended interactions is crucial. This research reframes alignment as a temporal control problem, potentially leading to more resilient AI systems that can adaptively manage their performance across complex workflows.
Key Takeaways
- APEPMO optimizes computational resources by focusing on critical moments in agent interactions.
- The approach enhances trajectory-level quality and reuse probability compared to traditional methods.
- Reframing alignment as a temporal control problem opens new avenues for developing long-horizon AI systems.
Computer Science > Artificial Intelligence arXiv:2602.17910 (cs) [Submitted on 20 Feb 2026] Title:Alignment in Time: Peak-Aware Orchestration for Long-Horizon Agentic Systems Authors:Hanjing Shi, Dominic DiFranzo View a PDF of the paper titled Alignment in Time: Peak-Aware Orchestration for Long-Horizon Agentic Systems, by Hanjing Shi and Dominic DiFranzo View PDF HTML (experimental) 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.17910 [cs.AI] (or arXiv:2602.17910v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.260...
