Alireza Shamsoshoara

Alireza Shamsoshoara

AI / ML Engineer @ PyTorch-Meta

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Headings are cool

You can have many headings

Aren’t headings cool?

You May Also Enjoy

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Daily AI Papers — October 18, 2026

9 minute read

Published:

1. LeanPRM: Lean-Grounded Process Supervision for Natural Language Proof Verification

Authors: Rodney Lafuente-Mercado, Salman Rahman, Jingyan Shen, James Shiffer, Saadia Gabriel, Pavel Izmailov arXiv: arxiv.org/abs/2610.17697 Summary: LeanPRM uses paired natural-language and formal Lean proofs to generate step-level correctness labels without human annotation, filtering out failures caused only by formal syntax. Its 10,634 proofs and 152,000 labeled steps raise a Qwen3-4B verifier’s exact step-label accuracy from 48.2% to 74.7% and improve proof selection and external verification performance.

Daily AI Papers — October 17, 2026

10 minute read

Published:

1. Behavioral Compositionality of Sparse Autoencoder Representations

Authors: Haotian Ma, Ziye Mei, Frederic Sala arXiv: arxiv.org/abs/2610.21261 Summary: SAE Additivity Residuals quantify how much behavioral variation remains unexplained above a chosen interaction order, letting the authors compare learned sparse-autoencoder coordinates with random rotations of the same subspace. Across 18 model-behavior settings, learned coordinates are more first-order compositional, but pairwise terms still substantially improve coalition ordering and combined steering fidelity.

Daily AI Papers — October 16, 2026

10 minute read

Published:

1. OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding

Authors: Xuyang Liu, Shihao Wang, Xinyu Wei, Minghan Li, Zeqing Wang, Zuhao Yang, Zichen Wen, Lei Zhang arXiv: arxiv.org/abs/2610.18829 Summary: OmniCompass evaluates when, where, and what happens in video through temporal grounding, spatial grounding, and question answering, with separate vision-language and joint audio-visual tracks. Testing 50 multimodal models on manually verified data reveals persistent weaknesses in precise grounding, paired target consistency, and especially audio-visual understanding among open models.