arXiv:2608.21074v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in research workflows and software development pipelines, yet their output remains sensitive to in...
Imag‑Eval is a new language‑grounded benchmark for evaluating Text‑to‑Image models, focusing on how well they follow compositional natural‑language instructions. It disentangles prompt length from compositional difficulty by independently varying the number of instances and the combination of constraints (rules), providing 1,140 prompts and 8,842 rule combinations. The study shows that for structured skills, the difficulty is mainly driven by the number of grounded rules and their binding to instances rather than prompt length alone.
Large language models (LLMs) trained only on text and code can sometimes generate programs that draw recognizable images. However, it is unclear whether this reflects an internal representation of 2D...
arXiv:2609.06993v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly generate Markdown that is consumed by renderers, agents, code extractors, and structured downstream pipelin...
arXiv:2608.30751v1 Announce Type: new Abstract: Large language models (LLMs) trained only on text and code can sometimes generate programs that draw recognizable images. However, it is unclear whethe...
The paper investigates how small lexical changes in prompts can cause large performance swings in large language models. Using a dataset of 132,000 prompt variants, the authors uncover a scaling law linking higher average task performance to lower variance and greater robustness. They identify domain-specific terminology and explicit action directives as key linguistic factors that stabilize prompts, and propose an automated Prompt-Refining Agent that reduces performance variance by 40.7% in code generation while maintaining or improving mean performance.
arXiv:2606. 10334v1 Announce Type: new Abstract: Code-generating large language models (LLMs) increasingly produce visual artifacts such as charts, web pages, and slides by writing programs that are executed by non-differentiable renderers, committing to code before observing the render.
arXiv:2601.09487v2 Announce Type: replace Abstract: The rapid evolution of Large Language Models (LLMs) has fostered diverse paradigms for automated slide generation, ranging from code-driven layouts...
arXiv:2608.22390v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong potential in graphical user interface (GUI) generation, but reliable evaluation remains challengi...
Narcissus is a program synthesizer that uses context‑aware large language model (LLM) approximations to guide enumerative search. Unlike prior methods that convert LLM proposals into rule frequencies and lose structural information, Narcissus retains proposals as syntax trees and scores each program expansion based on its surrounding context, ensuring every rule remains reachable. Across five domains and two search backends, it consistently outperforms static guidance and LLM re‑prompting, solving 40% of ARC tasks that raw proposals only solve 13%, all without any LLM calls during search.
arXiv:2601. 04498v2 Announce Type: replace Abstract: Infographics are composite visual artifacts that combine data visualizations with textual and illustrative elements to communicate information.
arXiv:2602. 03045v2 Announce Type: replace Abstract: Large language models have recently enabled text-to-CAD systems that synthesize parametric CAD programs (e.