Alireza Shamsoshoara
AI / ML Engineer @ PyTorch-Meta
- Bay Area, California
- ResearchGate
- Github
- Google Scholar
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Daily AI Papers — October 18, 2026
8 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.
Daily AI Papers — October 15, 2026
12 minute read
Published:
1. Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation
Authors: Wang, Weijie, Zhang, Haomin, Li, Ye, Liu, Akide, Zhang, Zeyu, Zhao, Haoyu, He, Yefei, Chen, Donny Y., Chen, Feng, Zhuang, Bohan arXiv: arxiv.org/abs/2610.20571 Summary: ATAS compiles a screenplay into shot requests with explicit location, camera, and event-time references, then grounds generation in registered scene geometry and time-indexed event memory. Across four video generators it improves every reported cross-shot consistency measure, leads spatial-revisit and event-recapture comparisons, and earns the highest human ratings on all six criteria.