Patient education is a major NLP application, but current evaluation largely focuses on text-only assistance. In radiology, this overlooks an important part of clinical communication: patients often need both a plain-language explanation and a visual aid showing where to look and what it refers to. We introduce MediSketch, a multimodal patient-education task in which an agent must produce an image–text bundle: an educational image and a grounded plain-language explanation. We instantiate this setting with a clinician-curated radiology testbed, a structured multimodal rubric, and a rubric-following judge that evaluates bundle quality rather than text-only correctness. Under a matched image-generation budget, we compare three test-time scaling strategies: parallel sampling, sequential revision, and hybrid search. Across five backbone models, the no-scaling baseline remains weak, while naive parallel search often provides little gain despite using the full budget. Hybrid performs best on 4 of 5 backbones, suggesting that test-time compute works best when exploration and revision are combined. Further analysis identifies weak multimodal self-critique as a major bottleneck: many models stop revising too early and overestimate the quality of their own image–text outputs. MediSketch exposes both an evaluation gap in multimodal patient education and a concrete failure mode in test-time scaling.

A benchmark score credits final answers, but not the route by which an item can be answered. In medical multimodal multiple-choice questions (MCQs), this distinction matters because a correct answer can be supported by the intended image finding or by benchmark-preserved cues in the wording of answers, non-visual clinical text, visible image text, artificial annotations, or device/context artifacts. We call the resulting score-level overinterpretation reasoning inflation. Here, a route is an observable input path that can support answer selection, not a claim about the model’s hidden cognition. Across six medical multimodal MCQ datasets, we separate candidate cues from behavioral evidence through prompt- and image-side audits, modality ablations, and matched repairs that preserve the medical target and answer key. In a 13-configuration open-model panel, full-input accuracy is 62.63%, while text-only and options-only settings achieve 53.96% and 29.71%, respectively. Removing length-gap, absolute/conspicuous, and spatial/prepositional cues lowers accuracy by 6.58, 3.50, and 4.77 percentage points. We also construct MedQA-MM, a 1,000-item shortcut-mitigated subset, where text-only and options-only accuracy fall to 5.21% and 12.33%. This does not imply that models never use images; it shows that medical image-reasoning claims require route-level evidence.

Language model (LM) agents are increasingly deployed to maintain codebases over extended periods, fixing streams of related defects while carrying context from one fix to the next. Yet existing software engineering (SWE) benchmarks evaluate models one bug at a time: the repository is reset, the codebase is re-read, and a single self-contained issue is graded in isolation. This setting collapses a continuous maintenance workflow into a series of independent sessions, ignoring the cumulative dependencies that make real-world bug fixing challenging. To bridge this gap, we introduce ChainSWE, the first benchmark for evaluating agents on sequential, dependent bug fixes within a shared codebase. We collect chronological chains of 304 issues across 54 Python projects, mined from six SWE-bench-family datasets. Our evaluation across a range of agents and models reveals a consistent performance drop by **up to 70%** as the chain length increases. Our data and code is available at [C-lister/ChainSWE](https://huggingface.co/datasets/C-lister/ChainSWE).