activity
20232026
most citedMMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning

18 citations · 29 across the 28 of their papers we have counts for

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5 papers · 1 filter

cs.AI2026

Realtime-Bench: Can Frontier Models Solve Real-time Interactive Problems?

Jingyu Tang, Xu Yang, Zhehao Zhang +7

Real-time interaction requires agents to perceive evolving environments and act under temporal constraints, yet existing evaluations of real-time streaming models focus on conversa…

cs.AI2026

SearchAuditor: Auditing and Attributing Failures in Long-Horizon Search Agents

Zhixiang Liang, Yifei Liu, Yidan Huang +5

Deep search agents tackle challenging questions through long-horizon web interactions, a process that is both complex and fragile: small reasoning errors may propagate through long…

cs.AI2026

PRESTO: Prefix-Aligned Tree Drafting for Diffusion Speculative Decoding

Zheng Wang, Zhifan Ye, Qi Cheng +8

Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive (AR) LLMs, generating tokens in parallel. This makes them effective draft models f…

cs.AI2026

From Context to Skills: Can Language Models Learn from Context Skillfully?

Shuzheng Si, Haozhe Zhao, Yu Lei +10

Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learning, where LMs directly lear…

cs.AI2023★ 4 cited

Towards End-to-End Embodied Decision Making via Multi-modal Large Language Model: Explorations with GPT4-Vision and Beyond

Liang Chen, Yichi Zhang, Shuhuai Ren +6

In this study, we explore the potential of Multimodal Large Language Models (MLLMs) in improving embodied decision-making processes for agents. While Large Language Models (LLMs) h…