LongDS-Bench is a new benchmark for evaluating long-horizon, multi-turn data analysis by agents, featuring 68 tasks derived from real-world Kaggle notebooks that span 2,225 turns across six domains such as Geoscience, Business, and Education. The benchmark focuses on agents’ ability to maintain, update, restore, and compose evolving analytical states, with tasks designed around state-evolution patterns like counterfactual perturbation, rollback, and multi-state composition, and an average dependency span of 11.3 turns. Evaluation of five state-of-the-art models shows that the best model achieves only 48.45% average accuracy, with performance dropping nearly 47 points from early to late turns and long-horizon errors accounting for 52%–69% of failures, indicating that maintaining a correct analytical state is the key bottleneck.
arXiv:2606. 13904v1 Announce Type: cross Abstract: Exploratory question answering (EQA) over data lakes requires an LLM agent to discover relevant sources, analyze retrieved data, and adapt its actions based on intermediate results.
arXiv:2602. 02905v2 Announce Type: replace Abstract: Autonomous agents powered by large language models (LLMs) promise to accelerate scientific discovery end-to-end, but rigorously evaluating their capacity for verifiable discovery remains a central challenge.
arXiv:2608. 10366v1 Announce Type: new Abstract: Real-world data science involves long-horizon workflows that span data wrangling, exploration, modeling, visualization, and validation, and require coordinated use of tools such as notebooks, IDEs, terminals, browsers, and databases within real operating environments.
arXiv:2607. 27283v1 Announce Type: new Abstract: Long-horizon benchmarks often show that agents fail more as tasks become longer.
arXiv:2604. 13072v2 Announce Type: replace-cross Abstract: OpenClaw-style personal assistants extend LLM agents from isolated tool use to open-ended, stateful, and personalized software environments.
The paper introduces Long-Transduction, a diagnostic framework designed to evaluate how well language models can maintain task fidelity during extended generation tasks that involve continuous reading, mutating, and outputting of context-dependent operations such as arithmetic, sorting, variable lookups, and table transformations. By independently varying local task complexity, input data formatting, and context length, the study isolates failure modes across these axes. Experiments on seven open-weight models reveal significant performance drops—62.8% when scaling context length from 4 to 128K, 36.5% with input format changes, and 39.9% with increased local task complexity—highlighting critical vulnerabilities in long-horizon agentic workflows.
arXiv:2606. 05080v1 Announce Type: new Abstract: Scientific and engineering progress is fundamentally a long-horizon iterative process: proposing changes, running experiments, measuring outcomes, and continuously refining artifacts.
arXiv:2606. 07682v1 Announce Type: cross Abstract: AI agents are increasingly expected to complete long-horizon workflows that require sustained progress over hours, millions of tokens, and complex environments.
arXiv:2608. 05144v1 Announce Type: new Abstract: Long-horizon reasoning requires an agentic runtime that can persist when evidence supports its current approach and pivot when measurements reveal failure, hidden constraints, or a misspecified objective.
AdaLens is an interactive system designed to monitor and steer long-running, autonomous data analysis workflows powered by large language models. It provides a storyline-based interface that unifies analytical plans, execution progress, intermediate findings, and data-column involvement, enabling analysts to observe evolving reasoning and evidence. The system also offers steering interactions that allow users to redirect low-value directions or deepen promising ones during execution.
arXiv:2606. 07299v1 Announce Type: new Abstract: Deep Research (DR) has emerged as a new agentic paradigm to tackle complex, open-ended research tasks, demanding systems that can iteratively frame problems, acquire evidence, verify sources, and synthesize long-form reports.
LLM-based agents have shown strong capabilities in automated data analysis and are increasingly moving toward long-horizon, multi-stage analytical workflows. However, as the analytical process evolves...