activity
20232026
most citedSora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

105 citations · 129 across the 29 of their papers we have counts for

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Showing 2026Show all

11 papers · 1 filter

cs.AI2026

Dr. Claw: An AI Scientist Workspace for Vibe Research

Dingjie Song, Hanrong Zhang, Dawei Liu +10

Command-line coding agents (e.g., Claude Code, Gemini CLI) can already read and write files and sustain long sessions, yet end-to-end research still fragments across chat tools, ID…

cs.AI2026

PreDiff-LM: Pretrained Discrete Masked Diffusion Language Modeling with Hybrid Attention

Zhengtao Yao, Runhao Li, Xupeng Chen +12

Discrete masked diffusion language models support bidirectional generation and infilling, but adapting pretrained autoregressive (AR) transformers requires reconciling causal pretr…

cs.AI2026

SpecAlign: Efficient Specification-Grounded Alignment of Large Language Models via Synthetic Data

Wenjie Wang, Yue Huang, Zhengqing Yuan +6

As large language models (LLMs) are increasingly deployed in real-world applications, alignment is no longer governed by a single universal notion of safety or helpfulness, but ins…

cs.CV2026

Visual Aesthetic Benchmark: Can Frontier Models Judge Beauty?

Yichen Feng, Yuetai Li, Chunjiang Liu +14

Multimodal large language models (MLLMs) are now routinely deployed for visual understanding, generation, and curation. A substantial fraction of these applications require an expl…

cs.CL2026

NARRA-Gym for Evaluating Interactive Narrative Agents

Yue Huang, Yuchen Ma, Jiayi Ye +14

Interactive narrative tasks require LLMs to sustain a coherent, evolving story while adapting to a user over multiple turns. However, suitable benchmarks for this setting are limit…

cs.CL2026

PolicyLLM: Towards Excellent Comprehension of Public Policy for Large Language Models

Han Bao, Penghao Zhang, Yue Huang +9

Large Language Models (LLMs) are increasingly integrated into real-world decision-making, including in the domain of public policy. Yet, their ability to comprehend and reason abou…

cs.CL2026

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Zhengqing Yuan, Hanchi Sun, Lichao Sun +1

We present MegaTrain, a memory-centric system that efficiently trains 100B+ parameter large language models at full precision on a single GPU. Unlike traditional GPU-centric system…

cs.CL2026

MedGPT-oss: Training a General-Purpose Vision-Language Model for Biomedicine

Kai Zhang, Zhengqing Yuan, Cheng Peng +10

Biomedical multimodal assistants have the potential to unify radiology, pathology, and clinical-text reasoning, yet a critical deployment gap remains: top-performing systems are ei…

cs.AI2026

Drift-Bench: Diagnosing Cooperative Breakdowns in LLM Agents under Input Faults via Multi-Turn Interaction

Han Bao, Zheyuan Zhang, Pengcheng Jing +3

As Large Language Models transition to autonomous agents, user inputs frequently violate cooperative assumptions (e.g., implicit intent, missing parameters, false presuppositions,…

cs.CL2026

CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era

Kaiwen Shi, Weixiang Sun, Zheyuan Zhang +3

Scientific research relies on citation integrity, yet large language models (LLMs) have introduced a critical risk: fabricated references that appear plausible but correspond to no…

cs.OS2026

Horizon-LM: A RAM-Centric Architecture for LLM Training

Zhengqing Yuan, Lichao Sun, Yanfang Ye

The rapid growth of large language models (LLMs) has outpaced the evolution of single-GPU hardware, making model scale increasingly constrained by memory capacity rather than compu…