arXiv:2608. 05797v1 Announce Type: new Abstract: Task difficulty dictates an agent's likelihood of success, and estimating it without rollouts means forecasting this directly from a task description before executing costly simulations in stateful environments.
Task difficulty dictates an agent's likelihood of success, and estimating it without rollouts means forecasting this directly from a task description before executing costly simulations in stateful environments. Reliable estimates would therefore allow environment designers to calibrate evaluation benchmarks and construct progressive training curricula.
arXiv:2604. 00594v2 Announce Type: replace Abstract: As the focus in LLM-based coding shifts from static single-step code generation to multi-step agentic interaction with tools and environments, understanding which tasks will challenge agents and why becomes increasingly difficult.
The paper investigates what makes software issue resolution tasks difficult for agents by proposing a measurement framework and conducting a large‑scale empirical study on the CoderForge‑Preview dataset. It extracts static features from task patches, repositories, and prompts, and uses ensemble methods, SHAP attribution, and effect size analysis to predict task outcomes. The study finds that task difficulty is largely predictable from static features (AU C = 0.863), driven mainly by patch fragmentation and repository scale, with prompt linguistic features contributing for mid‑band tasks, suggesting a layered difficulty structure.
arXiv:2602. 07267v2 Announce Type: replace Abstract: Evaluating the real-world capabilities of AI systems requires grounding benchmark performance in human-interpretable measures of task difficulty.
EarlyEval introduces a lightweight framework that predicts an LLM agent’s final outcome early in its execution, allowing the run to halt when a LightGBM classifier reaches a calibrated confidence threshold. By training success and failure classifiers on behavioral, textual, and reference-solution features, EarlyEval can cut 13%-26% of agent steps and up to 44.1% of input tokens while maintaining 89%-97% prediction accuracy. Across three benchmarks—SWE-bench Verified, TerminalBench, and Toolathlon—this approach reduces evaluation costs with minimal impact on per-agent resolve rates.
arXiv:2608. 08570v1 Announce Type: new Abstract: Rejection sampling fine-tuning (RFT) is widely used to train code agents by generating trajectories on verifiable software engineering tasks, retaining those that pass the tests, and fine-tuning on the successful rollouts.
arXiv:2606. 29537v1 Announce Type: new Abstract: Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limitations of frontier agents.
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:2606. 29537v2 Announce Type: replace Abstract: Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limitations of frontier agents.
arXiv:2605. 28556v2 Announce Type: replace Abstract: As agent capabilities advance, existing benchmarks, such as $\tau^2$-Bench, are becoming increasingly saturated.
arXiv:2607. 05188v1 Announce Type: new Abstract: A coding agent solving a software-engineering task spends dozens of steps reasoning, editing code, and running tests, yet little is known about what the underlying language model internally represents about the program it is working on.
arXiv:2607. 27283v1 Announce Type: new Abstract: Long-horizon benchmarks often show that agents fail more as tasks become longer.