• Computer Science > Artificial Intelligence [Submitted on 20 Feb 2026] Title:Alignment in Time: Peak-Aware Orchestration for Long-Horizon Agentic Systems 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. • References & Citations export BibTeX citation Loading…
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A recent study introduces APEMO (Affect‑aware Peak‑End Modulation for Orchestration), a runtime scheduling layer designed to improve alignment in long‑horizon autonomous agents. Unlike traditional alignment methods that focus on individual model outputs, APEMO monitors entire interaction trajectories, detecting instability through behavioral proxies and applying repairs at critical moments such as peaks and endings. The system reallocates computational resources within fixed budgets, optimizing performance without altering model weights. Experiments on multi‑agent simulations and large‑language‑model planner‑executor workflows show that APEMO consistently enhances trajectory‑level quality and reuse probability compared to existing structural orchestrators. The authors argue that alignment should be viewed as a temporal control problem, offering a practical engineering path for resilient long‑horizon agentic systems.
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