Small language models can grade open‑ended exam answers as reliably as much larger models when they use an explicit rubric. In experiments with six cost‑efficient model configurations, the rubric decouples grading from judge intelligence, with answer identity explaining 95.6% of score variance and judge identity only 0.2%. Removing rubric criteria or the official answer collapses reliability and inflates scores, showing the rubric’s essential role.
By Jhen-Ke Lin
arXiv:2608. 16045v1 Announce Type: cross Abstract: LLM-based data-analysis tools are increasingly used to help users analyze messy spreadsheets and workbooks, from answering questions over uploaded files to generating code, summaries, and visualizations.
By Yike Yuan, Virum Ranka, Tina Lasisi, Lin Ma
The study evaluates large language model (LLM) graders on two computer‑science exams, testing 171 configurations of closed‑ and open‑weights models. While the best LLM configuration achieved a mean absolute error of 1.64/35—better than the 2.61/35 error between two human graders—its performance was highly sensitive to the prompt. A short "strict grader" preamble caused most open‑weight models to exceed acceptable error thresholds or stop grading entirely, whereas fine‑tuning with a single LoRA adapter restored parity with human graders and reduced sensitivity to harsh prompts.
By Ali Habibullah, Yazan Alshoibi, Mohammad Alshiekh, Salman Khan, Naeemullah Khan
arXiv:2605. 22664v2 Announce Type: replace Abstract: LLM agents are increasingly expected to carry out end-to-end workflows, producing complete artifacts from high-level user instructions.
By Thomson Yen, Julian Poeltl, Harshith Srinivas Gear, Yilin Meng, Joshua Fan, Adam Shen, Yili Liu, Ali Bauyrzhan, Siri Du, Haoyang Liu, Daniel Guetta, Hongseok Namkoong
arXiv:2606. 23767v1 Announce Type: new Abstract: Headline accuracies on the Tuebingen cause-effect pairs are routinely compared across papers even though each is measured under its authors' own protocol -- different pair subsets, weightings, model-selection, and decision rates.
By Wietse Stienstra
arXiv:2606. 10457v1 Announce Type: new Abstract: Decision rules that enterprise experts apply tacitly -- in auditing, compliance, and contract review -- can be systematically recovered and improved through iterative error analysis.
By Junli Zha, Jinbo Wang, Chao Zhou, Xiang Song
arXiv:2607. 27189v2 Announce Type: cross Abstract: We introduce APEX-Accounting, a benchmark built by Mercor in partnership with Ramp, to assess whether frontier models can do the real work of accountants.
By Julien Benchek, Austin Bennett, Jasmin Kern, Ryan Stevens, Rene Sultan, Charis Ching, Hayley Popiel, Vaibhav Mittal, Felix Mercier, Brendan Foody, Bertie Vidgen
The paper introduces GRADE, a graph-based representation of large language model (LLM) agent executions that captures both execution steps and their dependencies. By adding graded dependency edges—observed, declared, or inferred—to the trace, the authors evaluate how this dependency layer affects failure prediction across six corpora involving tool use, coding, and web tasks. Experiments show that the dependency block can improve prediction in some settings, but its effectiveness varies with the evaluation probe and corpus, and controlled experiments demonstrate that the observed structure is not merely a degree-matched artifact.
By Yue Zhao
arXiv:2607. 24010v1 Announce Type: new Abstract: Active RAG systems decide when to retrieve external knowledge during generation, making them a budget-sensitive case of agentic RAG and self-adaptive retrieval.
By Pin Qian, Su Wang, Chong Peng, Junxian You, Lifei Liu, Haoran Yu, Yihang Chen, Xiaochong Jiang
The paper introduces THPT‑Ladder, a benchmark based on Vietnam’s 2025 National High School Graduation Examination’s convex grading scheme, which rewards partial credit non‑additively. It shows that standard accuracy metrics inflate model scores because they treat partial knowledge proportionally, whereas the official rubric penalizes incomplete correct sets. Using the benchmark, the authors demonstrate that this discrepancy can shift a model’s percentile ranking by up to 13 points among over 480,000 candidates.
By Nguyen Quoc Hung, Nguyen Dang Minh, Le Nhu Quynh, Tran Khanh Linh, Nguyen Kieu Linh
arXiv:2607. 01245v1 Announce Type: cross Abstract: We introduce Office Comprehension Bench (OCB), the first public benchmark to jointly evaluate LLM systems on Word, Excel, and PowerPoint comprehension over native file formats (.
By Firoz Shaik, Mateus Pican\c{c}o Lima Gomes, Tanvir Aumi, Jingci Wang, Milos Milunovic, Filip Basara, Ivana Jovanovic, Vishwas Suryanarayanan, Neha Nandan Kenkare, Weiyao Xie, Zhipeng Han, Zheng Zhang, Waleed Shahid, Jay Rathi, Russell Scherer, Thong Q. Nguyen, Michael Bentley, Tamara Stankovic, Rasika Chakravarthy, Vishal Chowdhary
arXiv:2606. 28471v1 Announce Type: new Abstract: Model capability is the central variable in LLM pre-training, yet is never observed directly: data shapes it prospectively, while evaluation reveals it only retrospectively, compressing samples, prompts, decoding, and scoring rules into one noisy score.
By Zhixuan Li, Jiangan Yuan, Han Xu