Why Do Long-Horizon Agents Break? HORIZON and the Case for Diagnostic Evaluation

HORIZON, introduced in the paper "The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break," starts from a simple but important observation: LLM agents often look strong on short- and mid-horizon tasks, yet can break down on long-horizon tasks that require extended, interdependent action sequences. The paper presents HORIZON as an initial cross-domain diagnostic benchmark meant to make those failures easier to characterize, compare, and discuss in a more principled way. [S1] [S1]

Paper overview: the problem HORIZON is trying to name

The HORIZON paper focuses on a gap in current agent evaluation. According to the paper abstract, agentic systems have improved quickly, but failures on long-horizon tasks remain poorly characterized. That matters because long tasks are not just longer versions of short ones; they involve many linked decisions, and an early mistake can affect later steps. HORIZON is presented as an initial cross-domain diagnostic benchmark for systematically studying where and why these systems fail. In other words, the paper is less about claiming a final solution and more about making long-horizon failure visible in a structured way. [S1]

Sources: [S1]

Core idea: a benchmark for diagnosing long, interdependent task failure

The central idea behind HORIZON is diagnostic evaluation. Rather than treating a long task as a single pass-or-fail outcome, the paper frames the problem as understanding breakdowns in extended action sequences. The source states that long-horizon tasks require interdependent steps, which implies that evaluation should pay attention to how performance degrades across a chain of actions, not only whether the final answer is correct. My interpretation is that this shifts the discussion from "Can the agent finish the task?" to "At what point does the agent lose coherence, and what kind of dependency causes the failure?" That diagnostic framing is the main contribution signaled in the abstract. [S1]

Sources: [S1]

What is different from existing evaluation: beyond single scores, and closer to planning

A useful way to understand HORIZON is to place it next to two related evaluation concerns raised in other recent work. First, the paper on long-horizon plan execution in large tool spaces argues that the field lacks rigorous, plan-level evaluation frameworks, especially when agents must operate over massive tool libraries and long decision chains. That highlights a planning and execution problem: evaluating whether an agent can sustain a multi-step plan under realistic tool complexity. [S8]

Second, the paper "Beyond Scores" argues that aggregate scores hide fine-grained ability variation. Its point is broader than agents alone, but highly relevant here: a single number can obscure which specific abilities are weak, making targeted improvement difficult. [S12]

Taken together, these sources suggest a broader shift in evaluation. HORIZON fits that shift by emphasizing diagnosis over headline scores. The source does not claim that all existing benchmarks are inadequate in every setting, but it does make clear that long-horizon failures are poorly characterized today. My interpretation is that HORIZON belongs to a family of efforts pushing evaluation toward finer-grained analysis: not just whether a model succeeds, but whether it can plan, maintain dependencies, and preserve competence over long execution traces. [S1][S8][S12]

Sources: [S8], [S12], [S1]

Where this matters: tool-heavy agents and long workflow execution

The practical relevance of this line of work is clearest in agent settings where many tools, many steps, and many dependencies interact. The long-horizon plan execution paper explicitly points to the difficulty of multi-step tasks in massive tool libraries, where agents face both evaluation gaps and the computational burden of exploring large decision spaces. [S8]

That makes diagnostic benchmarks like HORIZON potentially useful for comparing agents in environments where success depends on sustained execution rather than one-shot reasoning. Examples at a high level include tool-augmented workflows, API-driven task chains, and other settings where an agent must keep a plan coherent across many actions. This is an interpretation of the problem framing rather than a direct deployment claim from the papers. What the sources do support directly is that long-horizon planning and large tool spaces create a distinct evaluation challenge, and that HORIZON is designed to help characterize failures across domains. [S1][S8]

Sources: [S8], [S1]

Limitations and open questions

The first limitation is explicit in the HORIZON paper's own framing: it is an initial cross-domain diagnostic benchmark. That wording matters. It suggests an early step toward systematic diagnosis, not a complete account of all long-horizon agent failure modes. [S1]

A second limitation is methodological. The "Beyond Scores" paper argues that aggregate evaluation often hides fine-grained ability differences, and proposes a cognitive diagnostic framework to estimate abilities across multiple dimensions. While that work is not specifically about long-horizon agents, it points to an unresolved question for this area: how should long-horizon failure be decomposed into underlying abilities in a way that is both interpretable and actionable? [S12]

So the current picture is promising but incomplete. HORIZON helps define the problem more clearly, and related work emphasizes plan-level evaluation and fine-grained ability analysis. But the sources do not show that the field has already converged on a full taxonomy of long-horizon failure, nor that one benchmark alone can explain every breakdown in complex agent behavior. [S1][S12]

Sources: [S1], [S12]

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One-line takeaway: HORIZON treats long-horizon agent failure as a diagnostic problem: instead of relying on a single success score, it aims to show where and why LLM agents lose reliability across long, interdependent action sequences, especially in planning- and tool-heavy settings. [S1][S8] [S1] [S8]

Short summary: HORIZON is an initial cross-domain diagnostic benchmark for studying why LLM agents fail on long, interdependent tasks. Read alongside related work on plan-level evaluation and fine-grained abilities, it reflects a shift from single scores to more diagnostic views of agent performance.

Sources and references: - [S1] cs.AI updates on arXiv.org - The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break - URL: https://arxiv.org/abs/2604.11978 - [S8] cs.AI updates on arXiv.org - Long-Horizon Plan Execution in Large Tool Spaces through Entropy-Guided Branching - URL: https://arxiv.org/abs/2604.12126 - [S12] cs.AI updates on arXiv.org - Beyond Scores: Diagnostic LLM Evaluation via Fine-Grained Abilities - URL: https://arxiv.org/abs/2604.12191

Internal link ideas: - How to evaluate tool-using LLM agents beyond task success rate - What long-horizon planning means in autonomous AI systems - Why single benchmark scores often hide model weaknesses

HORIZON #LLM agents #long-horizon tasks #benchmark #agent evaluation #tool use

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