MemCtrl: Using MLLMs as Active Memory Controllers on Embodied Agents | ScienceToStartup | ScienceToStartup

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References (27)

[1]

Context Length Alone Hurts LLM Performance Despite Perfect Retrieval

2025Yufeng Du, Minyang Tian et al.

[2]

Where to show Demos in Your Prompt: A Positional Bias of In-Context Learning

2025K.A. Cobbina, Tianyi Zhou

[3]

Building spatial world models from sparse transitional episodic memories

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"MemCtrl is a framework for enhancing embodied agents with efficient memory control using Multimodal Large Language Models."

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MemCtrl: Using MLLMs as Active Memory Controllers on Embodied Agents

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References (27)

Founder's Pitch

"MemCtrl is a framework for enhancing embodied agents with efficient memory control using Multimodal Large Language Models."

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MemCtrl: Using MLLMs as Active Memory Controllers on Embodied Agents

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References (27)

Founder's Pitch

"MemCtrl is a framework for enhancing embodied agents with efficient memory control using Multimodal Large Language Models."

Commercial Viability Breakdown

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