arXiv:2605.30434v1 Announce Type: new Abstract: Real-world data analysis is inherently iterative, yet existing benchmarks mostly evaluate isolated or short interactive tasks, leaving agents' ability to track evolving analytical context over long horizons untested. We introduce LongDS, a benchmark for long-horizon, multi-turn data analysis where agents must maintain, update, restore, and compose evolving analytical states. LongDS comprises 68 tasks constructed from real-world Kaggle notebooks, spanning 2,225 turns across six domains including Geoscience, Business, and Education. Tasks are desig
The rapid advancement and deployment of AI agents in various applications highlight the crucial need to benchmark their capabilities in complex, real-world scenarios beyond isolated tasks.
This research reveals current limitations of AI agents in handling long-horizon, iterative data analysis, which is critical for their effective deployment in white-collar workflows and the broader economy.
The understanding of AI agentic capabilities for complex tasks shifts from assuming generalizability to recognizing a significant gap in maintaining analytical context over long interactive sessions.
- · AI research institutions specializing in agentic systems
- · Companies developing AI agent testing and evaluation platforms
- · Fields requiring robust, iterative data analysis
- · AI agent developers overestimating current long-horizon capabilities
- · Businesses deploying agents without comprehensive long-term testing
- · Simple, task-isolated AI benchmarks
Refocusing of AI agent research and development towards robust context management and iterative reasoning over extended periods.
Increased investment in specialized AI agent architectures and training methodologies designed for multi-step, dynamic problem-solving.
Slower-than-expected full automation of complex white-collar tasks until long-horizon agentic capabilities are significantly improved.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG