Alireza Shamsoshoara

Alireza Shamsoshoara

AI / ML Engineer @ PyTorch-Meta

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Daily AI Papers — September 29, 2026

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1. YuE2: Unifying Symbolic and Audio Music Generation at Frontier Quality

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Daily AI Papers — September 28, 2026

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1. FuseReg: Regularizing Layer Fusion Mitigates the Reconstruction-Generation Gap in Representation Autoencoders

Authors: Hongyang Du, Yunfei Xie, Junjie Ye, Jiawei Yang, Xiaoyan Cong, Haodong Zhang, Yongchao Huang, Haiyu Wu, Zongxia Li, Shihang Gui, Dawei Liu, Runhao Li, Jingcheng Ni, Chen Wei, Randall Balestriero, Yue Wang arXiv: arxiv.org/abs/2609.31620 Summary: FuseReg trains representation autoencoders over random subsets of visual-encoder layers, penalizing sensitivity to cross-layer disagreement and allowing one decoder to reconstruct from full, sparse, or single-layer fusions. On ImageNet-256, it improves reconstruction flexibility and cuts unguided gFID by 27% with decoder replacement alone and 29% when regularizing both decoder and diffusion training.

Daily AI Papers — September 27, 2026

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1. Single-stream Policy Optimization

Authors: Zhongwen Xu, Zihan Ding arXiv: arxiv.org/abs/2509.13232 Summary: Single-stream Policy Optimization replaces group-based baselines with a persistent KL-adaptive value tracker and globally normalized advantages, avoiding degenerate groups and synchronization barriers in LLM reinforcement learning. On five hard mathematics benchmarks with Qwen3-8B, it improves average maj@32 by 3.4 percentage points over GRPO while converging more smoothly and wasting less computation.

Daily AI Papers — September 26, 2026

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1. Learning to Discover Interesting Mathematics

Authors: Niket Patel, Ahmad Rammal, Amaury Hayat, Remi Munos, Julia Kempe arXiv: arxiv.org/abs/2609.28603 Summary: The authors define a theorem’s intrinsic interestingness as the ratio of proof length to statement length and show that it correlates strongly with downstream utility. A 27B proof-difficulty predictor then helps generate and select more interesting, less Mathlib-overlapping theorems for a self-expanding machine-verified library.