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MementoGUI introduces a plug-in agentic memory framework for GUI agents that uses learned controllers for selective memory management and retrieval, improving performance on long-horizon tasks with compressed visual and textual representations.
This paper introduces Omni-DuplexEval, a benchmark and automatic evaluation framework for real-time duplex interaction in multimodal large language models, assessing continuous response generation and proactive event detection in streaming scenarios.
Proposes VeGAS, a test-time framework for MLLM-based embodied agents that samples multiple candidate actions and uses a generative verifier to select the most reliable, achieving up to 36% relative improvement over CoT baselines on challenging tasks.
This paper analyzes the reconstruction-concealment tradeoff in intent-obfuscation jailbreak attacks on Multimodal Large Language Models (MLLMs). It proposes concealment-aware variant construction and keyword-related distractor images to exploit model vulnerabilities more effectively.
RemoteZero is a framework that eliminates the need for human-annotated box supervision in geospatial reasoning by leveraging the semantic verification capabilities of multimodal large language models (MLLMs) to enable self-evolving localization from unlabeled remote sensing data.
Researchers introduce MM-JudgeBias, a benchmark that exposes systematic compositional biases in multimodal large language models when used as automatic judges, testing 26 SOTA MLLMs across 1,800 samples.