Tootfinder

Sharp Online Hardness for Large Balanced Independent Sets
Abhishek Dhawan, Eren C. K{\i}z{\i}lda\u{g}, Neeladri Maitra
https://arxiv.org/abs/2508.20785 https://

Sharp Online Hardness for Large Balanced Independent Sets
We study the algorithmic problem of finding large $γ$-balanced independent sets in dense random bipartite graphs; an independent set is $γ$-balanced if a $γ$ proportion of its vertices lie on one side of the bipartition. In the sparse regime, Perkins and Wang established tight bounds within the low-degree polynomial (LDP) framework, showing a factor-$1/(1-γ)$ statistical-computational gap via the Overlap Gap Property (OGP) framework tailored for stable algorithms. However, these techniques …

B-repLer: Semantic B-rep Latent Editor using Large Language Models
Yilin Liu, Niladri Shekhar Dutt, Changjian Li, Niloy J. Mitra
https://arxiv.org/abs/2508.10201 https://…

B-repLer: Semantic B-rep Latent Editor using Large Language Models
Multimodal large language models (mLLMs), trained in a mixed modal setting as a universal model, have been shown to compete with or even outperform many specialized algorithms for imaging and graphics tasks. As demonstrated across many applications, mLLMs' ability to jointly process image and text data makes them suitable for zero-shot applications or efficient fine-tuning towards specialized tasks. However, they have had limited success in 3D analysis and editing tasks. This is due to both the…