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Kang Zeng

3 accepted papers

2026

Exposing Weaknesses of Large Reasoning Models through Graph Algorithm Problems

ICLR 2026poster

Large Reasoning Models (LRMs) have advanced rapidly, yet existing benchmarks on mathematics, code, and common-sense reasoning remain limited: they lack long-context evaluation, offer insufficient challenge, and provide answers that are difficult to verify programmatically. We introduce GrAlgoBench,…

Cited by 0SourceScholar
2025

AVAM: a Universal Training-free Adaptive Visual Anchoring Embedded into Multimodal Large Language Model for Multi-image Question Answering

ICCV 2025poster

The advancement of Multimodal Large Language Models (MLLMs) has driven significant progress in Visual Question Answering (VQA), evolving from Single to Multi Image VQA (MVQA). However, the increased number of images in MVQA inevitably introduces substantial visual redundancy that is irrelevant to qu…

Cited by 0SourcePDFScholar
2024

MF-MOS: A Motion-Focused Model for Moving Object Segmentation

ICRA 2024poster

Moving object segmentation (MOS) provides a reliable solution for detecting traffic participants and thus is of great interest in the autonomous driving field. Dynamic capture is always critical in the MOS problem. Previous methods capture motion features from the range images directly. Differently,…

Cited by 19SourcecodeScholar