The ‘Horizon Gap’ Explains Why Long Context Still Doesn’t Mean Long-Task Reliability

A new paper worth watching this week is The Horizon Gap: Planning, Memory, Execution, Training, and Evaluation for Long-Horizon LLM Agents. The authors review 1,547 arXiv papers published from 2024 through 2026 and argue that one of the field’s most common shortcuts is also one of its most misleading: treating a larger context window as evidence that an agent can handle a longer task.

Long context is not the same problem

A context window tells you how much information the model can inspect at once. A long-horizon task asks whether the system can preserve goals, update a plan, use tools, recover from mistakes, and keep making good decisions over many steps. An agent can technically retain every earlier message and still drift, repeat work, trust a bad intermediate result, or stop before the task is actually complete.

That distinction matters because errors compound. A small mistake on step 12 may not become visible until step 40. By then, a simple final success-or-failure score provides little information about which decision caused the breakdown.

The research signal

The paper argues that progress will require better process-level feedback, trajectory diagnostics, memory management, and evaluations that reveal where an agent’s plan went off course. In other words, the frontier is shifting from “how much can the model remember?” to “how well can the system manage time, state, and uncertainty?”

Why builders should care

For anyone evaluating an agent product, the practical takeaway is straightforward: do not treat a giant context window as a proxy for reliability. Ask whether the system can detect a bad step, revise a plan, preserve the right state, and show enough of its execution trace for a human to diagnose failure. Those properties are likely to matter more as agents move from short demos into work that lasts hours.