activity
20232026
most citedLong Context Transfer from Language to Vision

2 citations · 2 across the 21 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

Aero Realtime: Fully Aligned Input-Output Streams for Low-Latency Streaming Multimodal Generation

Kaichen Zhang, Wei Huang, Keming Wu +2

Existing streaming multimodal models process observations incrementally but still follow a turn-based prefill-then-decode pattern, making them non-duplex: new observations cannot n…

cs.AI2026

OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents

Kaicheng Zhang, Wen Ge, Lei Jiang +5

Although large language model agents are increasingly applied to quantitative-finance workflows, their evaluation remains fragmented across isolated tasks, while the financial rele…

cs.AI2026

Senses Wide Shut: A Representation-Action Gap in Omnimodal LLMs

Trung Nguyen Quang, Yiming Gao, Fanyi Pu +3

When an omnimodal large language model accepts a question whose textual premise contradicts what it actually sees or hears, does the failure lie in perception or in action? Recent…

cs.AI2025

OpenMMReasoner: Pushing the Frontiers for Multimodal Reasoning with an Open and General Recipe

Kaichen Zhang, Keming Wu, Zuhao Yang +6

Recent advancements in large reasoning models have fueled growing interest in extending such capabilities to multimodal domains. However, despite notable progress in visual reasoni…

cs.AI2025

GVPO: Group Variance Policy Optimization for Large Language Model Post-Training

Kaichen Zhang, Yuzhong Hong, Junwei Bao +4

Post-training plays a crucial role in refining and aligning large language models to meet specific tasks and human preferences. While recent advancements in post-training technique…

cs.AI2024

MixEval-X: Any-to-Any Evaluations from Real-World Data Mixtures

Jinjie Ni, Yifan Song, Deepanway Ghosal +10

Perceiving and generating diverse modalities are crucial for AI models to effectively learn from and engage with real-world signals, necessitating reliable evaluations for their de…