3 citations · 3 across the 13 of their papers we have counts for
11 papers · 1 filter
VLA-Arena: An Open-Source Framework for Benchmarking Vision-Language-Action Models
Borong Zhang, Jiahao Li, Jiachen Shen +8
While Vision-Language-Action models (VLAs) are rapidly advancing toward generalist robot policies, quantitatively characterizing their capability boundaries and failure modes remai…
SafeEditor: Unified MLLM for Efficient Post-hoc T2I Safety Editing
Ruiyang Zhang, Jiahao Luo, Xiaoru Feng +3
With the rapid advancement of text-to-image (T2I) models, ensuring their safety has become increasingly critical. Existing safety approaches can be categorized into training-time a…
SafeMT: Multi-turn Safety for Multimodal Language Models
Han Zhu, Juntao Dai, Jiaming Ji +8
With the widespread use of multi-modal Large Language models (MLLMs), safety issues have become a growing concern. Multi-turn dialogues, which are more common in everyday interacti…
Perception, Understanding and Reasoning, A Multimodal Benchmark for Video Fake News Detection
Cui Yakun, Peng Qi, Fushuo Huo +6
The advent of multi-modal large language models (MLLMs) has greatly advanced research on video fake news detection (VFND) tasks. Existing benchmarks typically focus on the detectio…
A Game-Theoretic Negotiation Framework for Cross-Cultural Consensus in LLMs
Guoxi Zhang, Jiawei Chen, Tianzhuo Yang +3
The increasing prevalence of large language models (LLMs) is influencing global value systems. However, these models frequently exhibit a pronounced WEIRD (Western, Educated, Indus…
SafeLawBench: Towards Safe Alignment of Large Language Models
Chuxue Cao, Han Zhu, Jiaming Ji +7
With the growing prevalence of large language models (LLMs), the safety of LLMs has raised significant concerns. However, there is still a lack of definitive standards for evaluati…
Towards Advanced Mathematical Reasoning for LLMs via First-Order Logic Theorem Proving
Chuxue Cao, Mengze Li, Juntao Dai +7
Large language models (LLMs) have shown promising first-order logic (FOL) reasoning capabilities with applications in various areas. However, their effectiveness in complex mathema…
Mitigating Deceptive Alignment via Self-Monitoring
Jiaming Ji, Wenqi Chen, Kaile Wang +8
Modern large language models rely on chain-of-thought (CoT) reasoning to achieve impressive performance, yet the same mechanism can amplify deceptive alignment, situations in which…
Measuring Hong Kong Massive Multi-Task Language Understanding
Chuxue Cao, Zhenghao Zhu, Junqi Zhu +6
Multilingual understanding is crucial for the cross-cultural applicability of Large Language Models (LLMs). However, evaluation benchmarks designed for Hong Kong's unique linguisti…
InterMT: Multi-Turn Interleaved Preference Alignment with Human Feedback
Boyuan Chen, Donghai Hong, Jiaming Ji +12
As multimodal large models (MLLMs) continue to advance across challenging tasks, a key question emerges: What essential capabilities are still missing? A critical aspect of human l…
When Slower Isn't Truer: Inverse Scaling Law of Truthfulness in Multimodal Reasoning
Sitong Fang, Wenjing Cao, Jiahao Li +7
Reasoning models have attracted increasing attention for their ability to tackle complex tasks, embodying the System II (slow thinking) paradigm in contrast to System I (fast, intu…