Yuheng Jing | Searcharxiv

arXiv2026

SLATE: Are AI-Generated Slides Educationally Effective? A Benchmark for Language Teaching Quality and Learner Knowledge Acquisition

LLMs have achieved remarkable capabilities in generating language teaching slides. However, a critical mismatch persists between visual polish and actual instructional effectiveness. To address this gap, we introduce SLATE (Slide-based Learning Assessment for Teaching Effectiveness), the first benchmark that evaluates AI-generated language teaching slides through instructional effectiveness and learner knowledge acquisition. SLATE transforms linguistics olympiad puzzles from low-resource languages with negligible web presence into 90 standardized instructional units comprising 1,133 assessable items, paired with a structured course outline and matched near- and far-transfer test sets. This pretest-posttest design eliminates pretrained knowledge leakage, ensuring gains reflect learning rather than prior recall. Using VLMs as scalable learner proxies and directionally supported by a three-system human pilot, our results show that content validity exhibits a weak association with learning gain, while pedagogical design exhibits a robust positive association. Moreover, most systems show a significant gap between near- and far-transfer accuracy, and even frontier models can produce negative learning gains. SLATE reveals a dissociation between artifact quality and instructional effectiveness, calling for a paradigm shift in how generative teaching systems are built, evaluated, and deployed.

cs.CL↗

arXiv2026

When Do Options Help? Policy Necrosis and Redundant Coverage in Option-Critic

Option-critic learns options: sub-policies together with a learned rule for when each one hands control back. Its headline result is that performance improves as options are added. We explain that result, with theory and experiment. First, the termination rule option-critic learns by maximising return contributes nothing. When the termination test and the policy that picks options read the same values, the test fires at every step, so the learned rule is identical to always terminating. When that policy explores and the test does not, as in option-critic itself, the rule can block the exploration; there are instances where it suffers $Ω(T)$ regret while always terminating holds to $O(\log T)$. Forcing termination at every step leaves the option-count curve intact. Second, the policy inside an option barely explores at all, so a state locks onto the first action that looked good and never updates again. We name this policy necrosis, give a state-level test for it, and find three fifths of states necrotic in a typical option. Restoring exploration repairs those states, and one option then solves the task. Third, extra options improve no option; what falls is the chance that all of them fail in the same state, from $59\\%$ to $4\\%$, and performance follows that joint quantity.

cs.LG↗