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

11 citations · 18 across the 22 of their papers we have counts for

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Showing 2026 · cs.AIShow all

5 papers · 2 filters

cs.AI2026

StepGuard: Learning Step-Level Guardrails with Scalable Supervision and Safety-Utility Balancing

Zhijie Zheng, Yu Li, Chen Qian +5

LLM-based agents can interact with external environments through tool invocation, but this capability also introduces security risks such as file modification, information leakage,…

cs.AI2026

AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security

Dongrui Liu, Yu Li, Zhonghao Yang +47

Modern open-world agents such as OpenClaw exhibit powerful cross-environment execution capabilities yet introduce broad new safety risk sources. Meanwhile, advanced frontier AI mod…

cs.AI2026

Entropy-Gradient Inversion: Moving Toward Internal Mechanism of Large Reasoning Models

Junyao Yang, Chen Qian, Kun Wang +4

The advancement of Large Reasoning Models (LRMs) has catalyzed a paradigm shift from reactive ``fast thinking'' text generation to systematic, step-by-step ``slow thinking'' reason…

cs.AI2026

Seeing with You: Perception-Reasoning Coevolution for Multimodal Reasoning

Ziqi Miao, Haonan Jia, Lijun Li +4

Reinforcement learning with verifiable rewards (RLVR) has substantially enhanced the reasoning capabilities of multimodal large language models (MLLMs). However, existing RLVR appr…

cs.AI2026

The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution

Chen Qian, Peng Wang, Dongrui Liu +10

Large Language Model (LLM)-based agents are widely used in real-world applications such as customer service, web navigation, and software engineering. As these systems become more…