Hugging Face Trending Papers
Improving VLMs on Scientific Chart Analysis via Autonomous Evolution
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arXiv Computation and Language
Oct 27
Improving VLMs on Scientific Chart Analysis via Autonomous Evolution
arXiv:2610.35077v1 Announce Type: cross Abstract: Understanding and operating with scientific charts is a standard approach in data analysis, and while humans are good at interpreting such charts, op...
arXiv AI
Aug 10
Ask-E: An Environment for Calibrated Question Generation
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 Machine Learning
Oct 22
ExpEvolve: Structuring and Reusing Experience for Open-Ended Discovery
ExpEvolve introduces idea-level extraction and control into evolutionary coding agents, enabling the discovery of high-level factors that explain differences in program performance. By leveraging these factors, the framework guides both exploitation of existing experience and exploration when progress stalls, leading to significant gains in sample efficiency and final performance. Across 11 open-ended mathematical tasks and additional benchmarks, ExpEvolve improves results by up to 5.0× and transfers benefits to other large language models and machine learning tasks.
arXiv Computer Vision
Sep 22
Pay More Attention To Text In High-Resolution MLLMs
The paper introduces EviSpec, a training‑free compiler that generates complementary evidence specifications to improve high‑resolution multimodal large language models (MLLMs). By explicitly guiding visual search with structured evidence specifications, EviSpec achieves significant relative gains—up to 14.8% over random evidence—across five MLLMs and three benchmarks, and also sets new state‑of‑the‑art results on VQA and hallucination‑focused tasks.
arXiv Computation and Language
3d ago
Fewer Tokens, More Answers: Verifiable-Reward Tuning of an Ornith-Style VLM for Multi-View Driving QA
The paper reports on a verifiable‑reward tuning approach applied to an Ornith‑style vision‑language model for multi‑view driving question answering. Using a 256‑token limit, the GRPO method raises answer extraction from 78.25 % to 90.25 % and accuracy from 55.50 % to 63.75 %. On a larger 2,511‑question set, the tuned pipeline cuts average token usage from 108.6 to 47.1, though the accuracy improvement is not statistically significant.
arXiv AI
Jun 2
BenchEvolver: Frontier Task Synthesis via Solution-Centric Evolution
arXiv:2606. 01286v1 Announce Type: cross Abstract: The rapid progress of frontier large language models has led to widespread benchmark saturation, limiting the ability of existing datasets to differentiate model capabilities or provide useful training signal.