arXiv AI
MatLoom: Layered Text-to-Material Generation in a Compact Program Space
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arXiv AI
Aug 24
PromptResponse: Optimizing Prompts for LLM Coding Tasks
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...
arXiv AI
Sep 1
Imag-Eval: a language-grounded framework for interpretable Text-to-Image instruction following evaluation
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.
Hugging Face Trending Papers
Aug 31
Autoregressive Mosaics: Probing 2D Spatial Reasoning in Text-Only Language Models
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 Machine Learning
Sep 10
LatentMD: Benchmarking Markdown Boundary Failures in LLM-Generated Text
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 AI
Sep 1
Autoregressive Mosaics: Probing 2D Spatial Reasoning in Text-Only Language Models
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...
arXiv AI
Aug 24
Beyond Prompt Engineering: A Systematic Analysis of Prompt Lexical Sensitivity and Its Impacts on Quality
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.