Kaiqu Liang — accepted AI-conference papers

2026

ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models

Jonathan Roberts, Mohammad Reza Taesiri, Ansh Sharma, Akash Gupta, Samuel Roberts, Ioana Croitoru +27

ICML 2026poster

Large Multimodal Models (LMMs) exhibit shortfalls when interpreting images and, by some measures, have poorer spatial cognition than young children or animals. Despite this, they attain high scores on many popular visual benchmarks, with headroom rapidly eroded by surging model progress. To address …

Cited by 0 Source ↗Scholar ↗

2024

CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs

Zirui Wang, Mengzhou Xia, Luxi He, Howard Chen, Yitao Liu, Richard Zhu +7

NeurIPS 2024poster

Chart understanding plays a pivotal role when applying Multimodal Large Language Models (MLLMs) to real-world tasks such as analyzing scientific papers or financial reports. However, existing datasets often focus on oversimplified and homogeneous charts with template-based questions, leading to an o…

Cited by 41 Source ↗PDF ↗code ↗Scholar ↗

2024

Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguity

Kaiqu Liang, Zixu Zhang, Jaime Fernández Fisac

NeurIPS 2024poster

Large language models (LLMs) exhibit advanced reasoning skills, enabling robots to comprehend natural language instructions and strategically plan high-level actions through proper grounding. However, LLM hallucination may result in robots confidently executing plans that are misaligned with user go…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗

2024

Who Plays First? Optimizing the Order of Play in Stackelberg Games with Many Robots

Haimin Hu, Gabriele Dragotto, Zixu Zhang, Kaiqu Liang, Bartolomeo Stellato, Jaime Fernández Fisac

RSS 2024poster

We consider the multi-agent spatial navigation problem of computing the socially optimal order of play, i.e., the sequence in which the agents commit to their decisions, and its associated equilibrium in an N-player Stackelberg trajectory game. We model this problem as a mixed-integer optimization p…

Cited by 4 Source ↗PDF ↗code ↗Scholar ↗

2023

Simple Baselines for Interactive Video Retrieval with Questions and Answers

Kaiqu Liang, Samuel Albanie

ICCV 2023poster

To date, the majority of video retrieval systems have been optimized for a "single-shot" scenario in which the user submits a query in isolation, ignoring previous interactions with the system. Recently, there has been renewed interest in interactive systems to enhance retrieval, but existing approa…

Cited by 8 PDF ↗code ↗Scholar ↗

2022

Path Independent Equilibrium Models Can Better Exploit Test-Time Computation

Cem Anil, Ashwini Pokle, Kaiqu Liang, Johannes Treutlein, Yuhuai Wu, Shaojie Bai +2

NeurIPS 2022accept

Designing networks capable of attaining better performance with an increased inference budget is important to facilitate generalization to harder problem instances. Recent efforts have shown promising results in this direction by making use of depth-wise recurrent networks. In this work, we reproduc…

Cited by 26 Source ↗PDF ↗Scholar ↗