AGENTIC-SYSTEMSApril 15, 20261 min read18 views

The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break

Researchers introduce HORIZON, a diagnostic benchmark to analyze why LLM-based agents fail in long-horizon tasks. The study evaluates SOTA models like GPT and Claude, offering a scalable 'LLM-as-a-Judge' pipeline for failure attribution.

Computer Science > Artificial Intelligence

Title:The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break

View PDF HTML (experimental)Abstract:Large language model (LLM) agents perform strongly on short- and mid-horizon tasks, but often break down on long-horizon tasks that require extended, interdependent action sequences. Despite rapid progress in agentic systems, these long-horizon failures remain poorly characterized, hindering principled diagnosis and comparison across domains. To address this gap, we introduce HORIZON, an initial cross-domain diagnostic benchmark for systematically constructing tasks and analyzing long-horizon failure behaviors in LLM-based agents. Using HORIZON, we evaluate state-of-the-art (SOTA) agents from multiple model families (GPT-5 variants and Claude models), collecting 3100+ trajectories across four representative agentic domains to study horizon-dependent degradation patterns. We further propose a trajectory-grounded LLM-as-a-Judge pipeline for scalable and reproducible failure attribution, and validate it with human annotation on trajectories, achieving strong agreement (inter-annotator \kappa=0.61; human-judge \kappa=0.84). Our findings offer an initial methodological step toward systematic, cross-domain analysis of long-horizon agent failures and offer practical guidance for building more reliable long-horizon agents. We release our project website at \href{this https URL}{HORIZON Leaderboard} and welcome contributions from the community.

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Source: arXiv cs.AI Recent

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