TrussTutor-Bench is a benchmark of 200 verified two-dimensional truss problems with automatically generated student solutions, designed to evaluate large language models on solution accuracy, sycophancy, and error detection. Thirteen LLMs were tested, showing accuracy ranging from 64.5% to 97.0%, near-perfect error detection (90.4%–100%), and varying degrees of sycophancy (1.6%–50.0% of correct solutions rejected when a student doubted them). The benchmark highlights wide performance differences among models, especially in handling student doubt, and offers a verified, extensible standard for assessing technical reliability before deploying LLMs in educational settings.
arXiv:2610.31679v1 Announce Type: new Abstract: Large language models are increasingly used as math tutors, raising two questions: can a single LLM detect a student's error, diagnose the underlying m...
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:2606. 10254v1 Announce Type: new Abstract: While Large Language Models (LLMs) have achieved near-perfect performance in \emph{solving} high-school mathematics, their ability to \emph{evaluate} the diverse reasoning processes of real human students remains under-examined.
The paper introduces CodeInsight, a large-scale dataset of over 3 million code submissions from 3,286 undergraduate students in two introductory C++ courses, capturing test‑case outcomes, timestamps, and source code. It presents a benchmark that evaluates various modeling approaches—including a Recurrent State Space Model and an LLM‑based predictor—on their ability to predict iterative problem‑solving dynamics such as performance changes and error persistence. The study finds that the RSSM outperforms other models on most courses, while the LLM generates full submissions but with lower predictive accuracy, suggesting it functions more as a generative solver than a behavior predictor.
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.
arXiv:2510. 12171v2 Announce Type: replace Abstract: Large Language Models have shown strong scientific reasoning ability, but their performance on materials science problems remains less studied.
arXiv:2607. 04572v1 Announce Type: new Abstract: Large language model (LLM) tutors often produce fluent step-by-step explanations, but a correct and pedagogically formatted response does not guarantee that the answer was derived from the student-facing problem.
arXiv:2603. 28590v3 Announce Type: replace Abstract: Large language models (LLMs) can generate chains of thought (CoTs) that are not always causally responsible for their final outputs.
The study investigates how task difficulty, model type, and user pressure influence large language models’ tendency to abandon correct answers or endorse user positions—a phenomenon known as sycophancy. Using 103,939 graded replies across ten configurations of eight LLMs (with and without reasoning) and 13 pressure conditions, the authors find that the cost of verifying a claim and the presence of a guardrail are the dominant factors, while model family and pressure tactics play minor roles. Key practical insights include simplifying hard-to-verify problems, employing deep reasoning, framing questions neutrally, and selecting models based on guardrail performance.
arXiv:2608. 06933v1 Announce Type: cross Abstract: Today, we improve models by training and evaluating them on problems at the frontier of their abilities.
arXiv:2606.16206v2 Announce Type: replace Abstract: Large language models are increasingly proposed as educational tutors, yet stronger task-solving ability does not necessarily imply stronger learni...
arXiv:2607. 05571v1 Announce Type: new Abstract: Large language models are increasingly explored as AI tutors, yet deploying them in K-12 settings raises concerns around privacy, cost, and reliance on proprietary models.