most citedOn the Fundamental Limits of LLMs at Scale

1 citations · 1 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG2026

Scaling Muon for Diffusion Transformers

Chenghao Li, Xiao Han, Xinxin Huang +22

The matrix-aware optimizer Muon improves large model training by balancing updates across singular directions, yet its scaling behavior and end-to-end efficiency on large Diffusion…

cs.LG2026

: Stratified Scaling Search for Test-Time in Diffusion Language Models

Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer +6

Test-time scaling investigates whether a fixed diffusion language model (DLM) can generate better outputs when given more inference compute, without additional training. However, n…

cs.AI2026

Bi-Level Prompt Optimization for Multimodal LLM-as-a-Judge

Bo Pan, Xuan Kan, Kaitai Zhang +6

Large language models (LLMs) have become widely adopted as automated judges for evaluating AI-generated content. Despite their success, aligning LLM-based evaluations with human ju…

cs.LG2026

Continuous-Utility Direct Preference Optimization

Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal +6

Large language model reasoning is often treated as a monolithic capability, relying on binary preference supervision that fails to capture partial progress or fine-grained reasonin…

cs.CV2025

FreeSwim: Revisiting Sliding-Window Attention Mechanisms for Training-Free Ultra-High-Resolution Video Generation

Yunfeng Wu, Jiayi Song, Zhenxiong Tan +2

The quadratic time and memory complexity of the attention mechanism in modern Transformer based video generators makes end-to-end training for ultra high resolution videos prohibit…

cs.LG2025★ 1 cited

On the Fundamental Limits of LLMs at Scale

Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal +13

Large Language Models (LLMs) have benefited enormously from scaling, yet these gains are bounded by five fundamental limitations: (1) hallucination, (2) context compression, (3) re…

cs.RO2025

Scaling Cross-Embodiment World Models for Dexterous Manipulation

Zihao He, Bo Ai, Tongzhou Mu +6

Cross-embodiment learning seeks to build generalist robots that learn from and operate across diverse morphologies, but differences in kinematics and action spaces hinder data shar…

cs.IR2025

CS-PaperSum: A Large-Scale Dataset of AI-Generated Summaries for Scientific Papers

Javin Liu, Aryan Vats, Zihao He

The rapid expansion of scientific literature in computer science presents challenges in tracking research trends and extracting key insights. Existing datasets provide metadata but…