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Zhenzhu Yang

7 accepted papers

2026

IV-Bench: A Benchmark for Image-Grounded Video Perception and Reasoning in Multimodal LLMs

ICLR 2026poster

Existing evaluation frameworks for Multimodal Large Language Models (MLLMs) primarily focus on image reasoning or general video understanding tasks, largely overlooking the significant role of image context in video comprehension. To bridge this gap, we propose \textbf{IV-Bench}, the first comprehen…

Cited by 0SourcecodeScholar
2026

ScaleLong: A Multi-Timescale Benchmark for Long Video Understanding

ICLR 2026poster

Understanding long videos requires Multimodal Large Language Models (MLLMs) to grasp multi-timescale information, often organized in hierarchies. However, current long-video understanding benchmarks either overlook multi-timescale design or distribute questions targeting different timescales across…

Cited by 0SourcecodeScholar
2026

WorldTravel: A Realistic Multimodal Travel-Planning Benchmark with Tightly Coupled Constraints

ICML 2026poster

Real-world autonomous planning requires coordinating tightly coupled constraints where a single decision dictates the feasibility of all subsequent actions. However, existing benchmarks predominantly feature loosely coupled constraints solvable through local greedy decisions and rely on idealized da…

Cited by 0SourceScholar
2026

YuE: Scaling Open Foundation Models for Long-Form Music Generation

ICLR 2026poster

We tackle the task of long-form music generation, particularly the challenging \textbf{lyrics-to-song} problem, by introducing \textbf{YuE (乐)}, a family of open-source music generation foundation models. Specifically, YuE scales to trillions of tokens and generates up to five minutes of music while…

Cited by 0SourcecodeScholar
2025

OmniBench: Towards The Future of Universal Omni-Language Models

NeurIPS 2025poster

Recent advancements in multimodal large language models (MLLMs) have focused on integrating multiple modalities, yet their ability to simultaneously process and reason across different inputs remains underexplored. We introduce OmniBench, a novel benchmark designed to evaluate models’ ability to rec…

Cited by 0SourcecodeScholar
2025

SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

NeurIPS 2025poster

Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledge encompasses over 200 specialized disciplines, far exceeding the scope of existing benchmarks. The capabilities of LLMs…

Cited by 215SourceScholar
2024

MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI

CVPR 2024poster

We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes 11.5K meticulously collected multimodal questions from college exams quizzes and textbooks covering six core d…