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20242026
most citedExplainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey

11 citations · 19 across the 24 of their papers we have counts for

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7 papers · 1 filter

cs.LG2026

PRISM: Preference-Aware Influence Function Based Data Selection Method for Efficient Fine-Tuning

Qihao Lin, Guanxu Chen, Dongrui Liu +1

As LLMs continue to scale up, improving training efficiency heavily relies on effective data utilization. Data selection mitigates this issue by allocating the limited training bud…

cs.CR2026★ 1 cited

Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs

Leitao Yuan, Qinghua Mao, Daizong Liu +5

Multimodal large language models (MLLMs) remain vulnerable to transfer-based targeted attacks, where perturbations optimized on open-source surrogate encoders can generalize to clo…

cs.AI2026

What Do EEG Foundation Models Capture from Human Brain Signals?

Ling Tang, Qian Chen, Jilin Mei +6

Clinical electroencephalogram (EEG) analysis rests on a hand-crafted feature catalog refined over decades, \emph{e.g.,} band power, connectivity, complexity, and more. Modern EEG f…

cs.AI2026

Attributing Emergence in Million-Agent Systems

Ling Tang, Jilin Mei, Qian Chen +6

Large language models (LLMs) can simulate human-like reasoning and decision-making in individual agents. LLM-powered multi-agent systems (MAS) combine such agents to simulate popul…

cs.CR2026

RvB: Automating AI System Hardening via Iterative Red-Blue Games

Lige Huang, Zicheng Liu, Jie Zhang +3

The dual offensive and defensive utility of Large Language Models (LLMs) highlights a critical gap in AI security: the lack of unified frameworks for dynamic, iterative adversarial…

cs.CL2026

Loop as a Bridge: Can Looped Transformers Truly Link Representation Space and Natural Language Outputs?

Guanxu Chen, Dongrui Liu, Jing Shao

Large Language Models (LLMs) often exhibit a gap between their internal knowledge and their explicit linguistic outputs. In this report, we empirically investigate whether Looped T…

cs.CL2026

ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging

Junyao Yang, Chen Qian, Dongrui Liu +3

Large Reasoning Models (LRMs) with long chain-of-thought reasoning have recently achieved remarkable success. Yet, equipping domain-specialized models with such reasoning capabilit…