NeurIPS 2025 on SoloSoft

OmniSVG: Unified Multimodal SVG Generation Model (NeurIPS 2025)

Fri, 01 May 2026 00:00:00 +0000

Vector graphics are everywhere – from icons and logos to illustrations and data visualizations. But generating complex SVGs programmatically has remained a stubborn research challenge, with most approaches limited to simple geometric shapes or requiring extensive training data. OmniSVG, published at NeurIPS 2025, breaks through these limitations by introducing the first unified family of end-to-end multimodal SVG generators built on vision-language models.

The project at github.com/OmniSVG/OmniSVG represents a paradigm shift in SVG generation. Rather than relying on differentiable rendering or reinforcement learning – the dominant approaches prior to OmniSVG – it fine-tunes pre-trained VLMs to output SVG code directly. This allows the model to leverage the vast visual knowledge encoded in modern VLMs while learning the syntax and structure of SVG as a target language.

ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates

Fri, 01 May 2026 00:00:00 +0000

Large language models have made impressive strides in general knowledge and language generation, but complex reasoning – multi-step math problems, formal logic, algorithmic coding – remains a challenge, particularly for smaller models. ReasonFlux, developed by Gen-Verse and accepted at NeurIPS 2025, attacks this problem from a novel angle: rather than scaling up model size, it scales up the reasoning strategies available to the model.

The core insight behind ReasonFlux is elegant. Most reasoning failures in LLMs are not failures of knowledge – the model knows the relevant facts – but failures of approach. The model picks the wrong strategy, or tries to solve a problem in one shot when it should decompose it into steps. ReasonFlux addresses this by providing a curated library of 500 expert-designed thought templates, each encoding a reusable thinking strategy.