Alignment in Time: Peak-Aware Orchestration for Long-Horizon Agentic Systems
arXiv:2602.17910v1 Announce Type: new 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.
arXiv:2602.17910v1 Announce Type: new 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.
Executive Summary
This article introduces APEMO, a novel approach to aligning autonomous agents in long-horizon workflows. APEMO optimizes computational allocation by detecting trajectory instability and targeting repairs at critical segments. The approach is evaluated through multi-agent simulations and LLM-based planner-executor flows, demonstrating enhanced trajectory-level quality and reuse probability. The results offer a resilient engineering pathway for developing long-horizon agentic systems, reframing alignment as a temporal control problem.
Key Points
- ▸ APEMO introduces a runtime scheduling layer for optimizing computational allocation
- ▸ The approach detects trajectory instability through behavioral proxies and targets repairs at critical segments
- ▸ APEMO enhances trajectory-level quality and reuse probability in multi-agent simulations and LLM-based planner-executor flows
Merits
Innovative Approach
APEMO offers a novel solution to the alignment problem in long-horizon agentic systems, providing a more efficient and effective approach to optimizing computational allocation.
Demerits
Limited Scalability
The article does not provide sufficient evidence on the scalability of APEMO to more complex systems, which may limit its applicability in real-world scenarios.
Expert Commentary
The introduction of APEMO marks a significant advancement in the field of autonomous systems, as it addresses the critical issue of alignment in long-horizon workflows. By reframing alignment as a temporal control problem, APEMO offers a promising solution to the challenges of sustaining reliability in complex systems. However, further research is needed to fully explore the potential of APEMO and its limitations, particularly in terms of scalability and explainability.
Recommendations
- ✓ Further research should be conducted to evaluate the scalability of APEMO in more complex systems
- ✓ The development of APEMO should be accompanied by the creation of regulatory frameworks that address the challenges of aligning autonomous agents in complex systems.