arXiv Machine Learning
MA-Bench: Measuring Operational Planning in Language Models
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arXiv AI
Aug 19
Grading Needs a Rubric, Not Intelligence
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.
arXiv AI
Aug 25
K-Bench: measuring model performance on real scientific agent requests
arXiv:2608.21601v1 Announce Type: new Abstract: Benchmarks for scientific artificial intelligence are mostly written to be scored: multiple-choice questions, curated agent tasks with reference soluti...
arXiv AI
Oct 15
Instructions Are Evidence, Not Objectives: Measuring Outcome Alignment of Agents
arXiv:2610.17757v1 Announce Type: new Abstract: Large language model agents can follow complex human instructions to solve real-world tasks. In practice, however, initial instructions are often incom...
arXiv AI
Oct 26
RubricCraft: Where Solutions Diverge, Criteria Emerge
RubricCraft is a method that generates natural‑language evaluation rubrics for open‑ended, artifact‑producing language tasks by analyzing differences among independently sampled solver trajectories. It constructs rubric criteria based on how each attempt satisfies or violates task requirements, then applies and refines these criteria to better detect errors and recognize partial correctness without needing a single reference solution. On the SpreadsheetBench v2 benchmark, RubricCraft’s rubrics reduce the proportion of incorrect outputs that pass (29.5% vs. 42.6%) and improve ranking of outputs while maintaining consistency with the benchmark’s own checker.
arXiv AI
Oct 21
DecisionPivot: A Diagnostic Benchmark for Decision Models in LLM Systems
arXiv:2610.28522v1 Announce Type: new Abstract: Decision models support routing, guardrails, and output verification in LLM systems. However, item accuracy alone cannot establish whether models make...
arXiv AI
Aug 25
GIM: Evaluating models via tasks that integrate multiple cognitive domains
The paper introduces the Grounded Integration Measure (GIM), a benchmark of 820 expert‑authored problems designed to test models on tasks that integrate multiple cognitive operations such as constraint satisfaction, state tracking, epistemic vigilance, and audience calibration. GIM emphasizes realistic, broadly accessible knowledge rather than specialized expertise, and uses a judge‑aware 2‑parameter logistic IRT model to produce robust ability estimates across 53 model‑thinking‑level configurations. The authors provide a comprehensive leaderboard of 22 models and 47 test configurations, and conduct an extensive study on how test‑time compute affects model capability, finding that configuration choices like thinking budget and quantization can be as influential as model selection itself. whyItMatters":"By focusing on integration of multiple cognitive domains, GIM offers a more realistic assessment of model reasoning capabilities than benchmarks that either overemphasize memorization or abstract reasoning alone."