arXiv AI

MemVerse: Multimodal Memory for Lifelong Learning Agents

arXiv:2512. 03627v2 Announce Type: replace Abstract: Despite rapid progress in large-scale language and vision models, AI agents still suffer from a fundamental limitation: they cannot remember.

arXiv AI

Jun 3

PhotoCraft: Agentic Reasoning with Hierarchical Self-Evolving Memory for Deep Image Search

arXiv:2606. 03099v1 Announce Type: cross Abstract: Deep Image Search requires multi-step reasoning over rich contextual cues, such as time, location, and event relations.

By Kailin Lyu, Zhiqiang Yuan, Jianwei He, Qiwei Yan, Xuanbo Su, Nanxing Hu, Yang Liu, Ce Hao, Shengqian Qin, Lianyu Hu, Jinchao Zhang, Jie Zhou

llms agents multimodal

arXiv AI

Jun 3

PEAM: Parametric Embodied Agent Memory through Contrastive Internalization of Experience in Minecraft

arXiv:2605. 27762v2 Announce Type: replace Abstract: We present PEAM, a Parametric Embodied Agent Memory framework in Minecraft that transforms agent memory from inference-time retrieval into parameter-resident skills internalized through experience.

By Yuchen Guo, Junli Gong, Weicheng Wang, Hongmin Cai, Yiu-ming Cheung, Weifeng Su

llms agents robotics fine-tuning multimodal

arXiv AI

2d ago

MobileMem: Learning from a Year of Mobile Experiences

arXiv:2608. 13606v1 Announce Type: new Abstract: The next generation of AI agents is increasingly moving beyond systems that answer isolated questions toward persistent personal assistants that can understand, remember, and continuously learn from users' experiences.

By Xinle Deng, Yida Xue, Xiangyuan Ru, Haoming Xu, Shuofei Qiao, Mengru Wang, Yijun Chen, Buqiang Xu, Chen Jiang, Yuchen Eleanor Jiang, Lizhong Wang, Jianfeng Wang, Li Zeng, Haofen Wang, Guilin Qi, Huajun Chen, Ningyu Zhang

agents multimodal benchmarks

arXiv AI

Aug 7

CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning

arXiv:2605. 20247v2 Announce Type: replace-cross Abstract: Catastrophic forgetting remains a major obstacle to continual learning in large language models (LLMs) and vision--language models (VLMs).

By Yang Liu, Toan Nguyen, Flora D. Salim

llms fine-tuning multimodal benchmarks safety

Hugging Face Trending Papers

Jul 15

Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents

Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks. Yet nearly all existing approaches, from graph-structured memories to reflective insight stores, access memory through fixed, hand-designed heuristics.

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 16

Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents

arXiv:2607. 13591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.

By Eric Hanchen Jiang, Zhi Zhang, Yuchen Wu, Levina Li, Dong Liu, Xiao Liang, Rui Sun, Yubei Li, Edward Sun, Haozheng Luo, Zhaolu Kang, Aylin Caliskan, Kai-Wei Chang, Ying Nian Wu

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 8

MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Hierarchical Graph Memory and Agentic Retrieval Mechanism

arXiv:2606. 07512v1 Announce Type: cross Abstract: Current Vision-Language Models struggle with hours-long videos because processing full-length visual sequences induces prohibitive token explosion and attention dilution.

By Cong Chen, Guo Gan, Kaixiang Ji, ChaoYang Zhang, Zhen Yang, Guangming Yao, Hao Chen, Jingdong Chen, Yi Yuan, Chunhua Shen

llms agents multimodal benchmarks

arXiv AI

Jul 2

XSkill: Continual Learning from Experience and Skills in Multimodal Agents

arXiv:2603. 12056v3 Announce Type: replace Abstract: Multimodal agents can now tackle complex reasoning tasks with diverse tools, yet they still suffer from inefficient tool use and inflexible orchestration in open-ended settings.

By Guanyu Jiang, Zhaochen Su, Xiaoye Qu, Yi R. Fung

agents nlp multimodal benchmarks

arXiv Machine Learning

Jul 31

Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting

arXiv:2508. 04227v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs), spanning predictive architectures to generative Multimodal Large Language Models (MLLMs), have revolutionized artificial intelligence through powerful cross-modal alignment and zero-shot generalization.

By Yuyang Liu, Qiuhe Hong, Linlan Huang, Alexandra Gomez-Villa, Dipam Goswami, Tiantian Peng, Xialei Liu, Joost van de Weijer, Yonghong Tian

llms agents robotics multimodal benchmarks safety

arXiv AI

Jul 10

Cognitive-structured Multimodal Agent for Multimodal Understanding, Generation, and Editing

arXiv:2607. 08497v1 Announce Type: cross Abstract: Recent unified multimodal models show a single architecture can jointly perform vision/language understanding and image generation/editing.

By Feng Wang, Canmiao Fu, Zhipeng Huang, Chen Li, Jing Lyu, Ge Li

llms agents diffusion reinforcement-learning multimodal benchmarks

arXiv Machine Learning

Jul 7

RL Forgets! Towards Continual Policy Optimization

arXiv:2607. 04364v1 Announce Type: new Abstract: Continual post-training is becoming a central paradigm for adapting vision-language models to evolving tasks.

By Mao-Lin Luo, Zhe-Xu Wang, Zi-Hao Zhou, Bo Ye, Jian Zhao, Min-Ling Zhang, Tong Wei

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jun 8

AdMem: Advanced Memory for Task-solving Agents

arXiv:2606. 06787v1 Announce Type: new Abstract: Large Language Models (LLMs) show promise as tool-using agents but remain limited in long-horizon tasks that require remembering, organizing, and reusing knowledge.

By Runzhe Wang, Huilin Lu, Shengjie Liu, Li Dong, Jason Zhu

llms agents efficiency

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