🔹 Title: RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Lifelong Learning in Physical Embodied Systems

🔹 Publication Date: Published on Aug 2

🔹 Abstract: RoboMemory, a brain-inspired multi-memory framework, enhances lifelong learning in physical robots by integrating cognitive neuroscience principles and achieving state-of-the-art performance in real-world tasks. AI-generated summary We present RoboMemory, a brain-inspired multi-memory framework for lifelong learning in physical embodied systems, addressing critic al challenges in real-world environments: continuous learning, multi-module memory latency, task correlation capture, and infinite-loop mitigation in closed-loop planning. Grounded in cognitive neuroscience, it integrates four core modules: the Information Preprocessor (thalamus-like), the Lifelong Embodied Memory System (hippocampus-like), the Closed-Loop Planning Module (prefrontal lobe-like), and the Low-Level Executer (cerebellum-like) to enable long-term planning and cumulative learning. The Lifelong Embodied Memory System , central to the framework, alleviates inference speed issues in complex memory frameworks via parallelized updates/retrieval across Spatial, Temporal, Episodic, and Semantic submodules. It incorporates a dynamic Knowledge Graph (KG) and consistent architectural design to enhance memory consistency and scalability. Evaluations on EmbodiedBench show RoboMemory outperforms the open-source baseline ( Qwen2.5-VL-72B-Ins ) by 25% in average success rate and surpasses the closed-source State-of-the-Art ( SOTA ) ( Claude3.5-Sonnet ) by 5%, establishing new SOTA . Ablation studies validate key components ( critic , spatial memory , long-term memory ), while real-world deployment confirms its lifelong learning capability with significantly improved success rates across repeated tasks. RoboMemory alleviates high latency challenges with scalability, serving as a foundational reference for integrating multi-modal memory systems in physical robots.

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.01415

• PDF: https://arxiv.org/pdf/2508.01415

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🔹 Title: On the Expressiveness of Softmax Attention: A Recurrent Neural Network Perspective

🔹 Publication Date: Published on Jul 31

🔹 Abstract: Softmax attention is more expressive than linear attention due to its recurrent form, which can be analyzed using RNN components. AI-generated summary Since its introduction, softmax attention has become the backbone of modern transformer architectures due to its expressiveness and scalability across a wide range of tasks. However, the main drawback of softmax attention is the quadratic memory requirement and computational complexity with respect to the sequence length . By replacing the softmax nonlinearity, linear attention and similar methods have been introduced to avoid the quadratic bottleneck of softmax attention . Despite these linear forms of attention being derived from the original softmax formulation, they typically lag in terms of downstream accuracy. While strong intuition of the softmax nonlinearity on the query and key inner product suggests that it has desirable properties compared to other nonlinearities, the question of why this discrepancy exists still remains unanswered. This work demonstrates that linear attention is an approximation of softmax attention by deriving the recurrent form of softmax attention . Using this form, each part of softmax attention can be described in the language of recurrent neural networks (RNNs) . Describing softmax attention as an RNN allows for the ablation of the components of softmax attention to understand the importance of each part and how they interact. In this way, our work helps explain why softmax attention is more expressive than its counterparts.

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.23632

• PDF: https://arxiv.org/pdf/2507.23632

• Github: https://github.com/gmongaras/On-the-Expressiveness-of-Softmax-Attention-A-Recurrent-Neural-Network-Perspective

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🔹 Title: ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability

🔹 Publication Date: Published on Aug 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.07050
• PDF: https://arxiv.org/pdf/2508.07050
• Project Page: https://github.com/8421BCD/ReasonRank
• Github: https://github.com/8421BCD/ReasonRank

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/liuwenhan/reasonrank_data_13k
• https://huggingface.co/datasets/liuwenhan/reasonrank_data_rl
• https://huggingface.co/datasets/liuwenhan/reasonrank_data_sft

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🔹 Title: WideSearch: Benchmarking Agentic Broad Info-Seeking

🔹 Publication Date: Published on Aug 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.07999
• PDF: https://arxiv.org/pdf/2508.07999
• Project Page: https://widesearch-seed.github.io/
• Github: https://widesearch-seed.github.io/

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/ByteDance-Seed/WideSearch

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🔹 Title: Omni-Effects: Unified and Spatially-Controllable Visual Effects Generation

🔹 Publication Date: Published on Aug 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.07981
• PDF: https://arxiv.org/pdf/2508.07981
• Project Page: https://amap-ml.github.io/Omni-Effects.github.io/
• Github: https://github.com/AMAP-ML/Omni-Effects

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/GD-ML/Omni-VFX

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🔹 Title: Klear-Reasoner: Advancing Reasoning Capability via Gradient-Preserving Clipping Policy Optimization

🔹 Publication Date: Published on Aug 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2508.07629
• PDF: https://arxiv.org/pdf/2508.07629
• Project Page: https://github.com/suu990901/KlearReasoner
• Github: https://github.com/suu990901/KlearReasoner

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🔹 Title: UserBench: An Interactive Gym Environment for User-Centric Agents

🔹 Publication Date: Published on Jul 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.22034
• PDF: https://arxiv.org/pdf/2507.22034
• Github: https://github.com/SalesforceAIResearch/UserBench

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🔹 Title: BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agent

🔹 Publication Date: Published on Aug 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.06600
• PDF: https://arxiv.org/pdf/2508.06600
• Project Page: https://texttron.github.io/BrowseComp-Plus/
• Github: https://github.com/texttron/BrowseComp-Plus

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/Tevatron/browsecomp-plus-corpus
• https://huggingface.co/datasets/Tevatron/browsecomp-plus

🔹 Spaces citing this paper:
• https://huggingface.co/spaces/Tevatron/BrowseComp-Plus
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🔹 Title: OmniEAR: Benchmarking Agent Reasoning in Embodied Tasks

🔹 Publication Date: Published on Aug 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.05614
• PDF: https://arxiv.org/pdf/2508.05614
• Project Page: https://zju-real.github.io/OmniEmbodied/
• Github: https://zju-real.github.io/OmniEmbodied/

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/wangzx1210/OmniEAR

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🔹 Title: MolmoAct: Action Reasoning Models that can Reason in Space

🔹 Publication Date: Published on Aug 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.07917
• PDF: https://arxiv.org/pdf/2508.07917

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🔹 Title: Temporal Self-Rewarding Language Models: Decoupling Chosen-Rejected via Past-Future

🔹 Publication Date: Published on Aug 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.06026
• PDF: https://arxiv.org/pdf/2508.06026

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🔹 Title: Reinforcement Learning in Vision: A Survey

🔹 Publication Date: Published on Aug 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08189
• PDF: https://arxiv.org/pdf/2508.08189
• Github: https://github.com/weijiawu/Awesome-Visual-Reinforcement-Learning

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🔹 Title: SONAR-LLM: Autoregressive Transformer that Thinks in Sentence Embeddings and Speaks in Tokens

🔹 Publication Date: Published on Aug 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.05305
• PDF: https://arxiv.org/pdf/2508.05305
• Github: https://github.com/FusionBrainLab/SONAR-LLM/tree/main

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🔹 Title: Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning

🔹 Publication Date: Published on Aug 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08221
• PDF: https://arxiv.org/pdf/2508.08221

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🔹 Title: Follow-Your-Shape: Shape-Aware Image Editing via Trajectory-Guided Region Control

🔹 Publication Date: Published on Aug 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08134
• PDF: https://arxiv.org/pdf/2508.08134
• Github: https://github.com/mayuelala/FollowYourShape

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🔹 Title: Less Is More: Training-Free Sparse Attention with Global Locality for Efficient Reasoning

🔹 Publication Date: Published on Aug 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.07101
• PDF: https://arxiv.org/pdf/2508.07101
• Github: https://github.com/DerrickYLJ/LessIsMore

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🔹 Title: VisR-Bench: An Empirical Study on Visual Retrieval-Augmented Generation for Multilingual Long Document Understanding

🔹 Publication Date: Published on Aug 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.07493
• PDF: https://arxiv.org/pdf/2508.07493
• Github: https://github.com/puar-playground/VisR-Bench

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/puar-playground/VisR-Bench

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🔹 Title: GLiClass: Generalist Lightweight Model for Sequence Classification Tasks

🔹 Publication Date: Published on Aug 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.07662
• PDF: https://arxiv.org/pdf/2508.07662

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🔹 Title: Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs

🔹 Publication Date: Published on Aug 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.06601
• PDF: https://arxiv.org/pdf/2508.06601

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/EleutherAI/deep-ignorance-pretraining-mix
• https://huggingface.co/datasets/EleutherAI/deep-ignorance-annealing-mix

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🔹 Title: Bifrost-1: Bridging Multimodal LLMs and Diffusion Models with Patch-level CLIP Latents

🔹 Publication Date: Published on Aug 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.05954
• PDF: https://arxiv.org/pdf/2508.05954
• Project Page: https://bifrost-1.github.io/
• Github: https://github.com/HL-hanlin/Bifrost-1

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🔹 Title: When Good Sounds Go Adversarial: Jailbreaking Audio-Language Models with Benign Inputs

🔹 Publication Date: Published on Aug 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.03365
• PDF: https://arxiv.org/pdf/2508.03365

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