🔹 Title: ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning

🔹 Publication Date: Published on Aug 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.10419
• PDF: https://arxiv.org/pdf/2508.10419
• Github: https://github.com/EternityJune25/ComoRAG

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🔹 Title: 4DNeX: Feed-Forward 4D Generative Modeling Made Easy

🔹 Publication Date: Published on Aug 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.13154
• PDF: https://arxiv.org/pdf/2508.13154
• Project Page: https://4dnex.github.io/
• Github: https://github.com/3DTopia/4DNeX

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🔹 Title: Speed Always Wins: A Survey on Efficient Architectures for Large Language Models

🔹 Publication Date: Published on Aug 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09834
• PDF: https://arxiv.org/pdf/2508.09834
• Project Page: https://github.com/weigao266/Awesome-Efficient-Arch
• Github: https://github.com/weigao266/Awesome-Efficient-Arch

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🔹 Title: Has GPT-5 Achieved Spatial Intelligence? An Empirical Study

🔹 Publication Date: Published on Aug 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.13142
• PDF: https://arxiv.org/pdf/2508.13142

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🔹 Title: Matrix-Game 2.0: An Open-Source, Real-Time, and Streaming Interactive World Model

🔹 Publication Date: Published on Aug 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.13009
• PDF: https://arxiv.org/pdf/2508.13009
• Project Page: https://matrix-game-v2.github.io/
• Github: https://github.com/SkyworkAI/Matrix-Game/tree/main/Matrix-Game-2

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🔹 Title: Inverse-LLaVA: Eliminating Alignment Pre-training Through Text-to-Vision Mapping

🔹 Publication Date: Published on Aug 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.12466
• PDF: https://arxiv.org/pdf/2508.12466
• Project Page: https://inverse-llava.github.io
• Github: https://inverse-llava.github.io

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🔹 Title: S^2-Guidance: Stochastic Self Guidance for Training-Free Enhancement of Diffusion Models

🔹 Publication Date: Published on Aug 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.12880
• PDF: https://arxiv.org/pdf/2508.12880
• Project Page: https://s2guidance.github.io/
• Github: https://github.com/AMAP-ML/S2-Guidance

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🔹 Title: Precise Action-to-Video Generation Through Visual Action Prompts

🔹 Publication Date: Published on Aug 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.13104
• PDF: https://arxiv.org/pdf/2508.13104
• Github: https://zju3dv.github.io/VAP/

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🔹 Title: Lumen: Consistent Video Relighting and Harmonious Background Replacement with Video Generative Models

🔹 Publication Date: Published on Aug 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.12945
• PDF: https://arxiv.org/pdf/2508.12945
• Project Page: https://lumen-relight.github.io/
• Github: https://lumen-relight.github.io/

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🔹 Title: Phi-Ground Tech Report: Advancing Perception in GUI Grounding

🔹 Publication Date: Published on Jul 31

🔹 Abstract: The Phi-Ground model family achieves state-of-the-art performance in GUI grounding for multimodal reasoning models, improving accuracy across various benchmarks. AI-generated summary With the development of multimodal reasoning models , Computer Use Agents (CUAs), akin to Jarvis from "Iron Man", are becoming a reality. GUI grounding is a core component for CUAs to execute actual actions, similar to mechanical control in robotics, and it directly leads to the success or failure of the system. It determines actions such as clicking and typing, as well as related parameters like the coordinates for clicks. Current end-to-end grounding models still achieve less than 65\% accuracy on challenging benchmarks like ScreenSpot-pro and UI-Vision , indicating they are far from being ready for deployment. % , as a single misclick can result in unacceptable consequences. In this work, we conduct an empirical study on the training of grounding models, examining details from data collection to model training. Ultimately, we developed the Phi-Ground model family , which achieves state-of-the-art performance across all five grounding benchmarks for models under 10B parameters in agent settings. In the end-to-end model setting, our model still achieves SOTA results with scores of \textbf{43.2} on ScreenSpot-pro and \textbf{27.2} on UI-Vision . We believe that the various details discussed in this paper, along with our successes and failures, not only clarify the construction of grounding models but also benefit other perception tasks. Project homepage: https://zhangmiaosen2000.github.io/Phi-Ground/{https://zhangmiaosen2000.github.io/Phi-Ground/}

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.23779

• PDF: https://arxiv.org/pdf/2507.23779

• Project Page: https://zhangmiaosen2000.github.io/Phi-Ground/

• Github: https://github.com/zhangmiaosen2000/Phi-Ground

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🔹 Title: TRACEALIGN -- Tracing the Drift: Attributing Alignment Failures to Training-Time Belief Sources in LLMs

🔹 Publication Date: Published on Aug 4

🔹 Abstract: TraceAlign is a framework that identifies and mitigates alignment drift in LLMs by tracing unsafe completions to their training sources and applying interventions to reduce drift while maintaining utility. AI-generated summary Large Language Models (LLMs) fine-tuned to align with human values often exhibit alignment drift , producing unsafe or policy-violating completions when exposed to adversarial prompts , decoding perturbations , or paraphrased jailbreaks. While prior work has behaviorally characterized alignment failure, little is known about the training-time belief sources underlying these failures. We introduce TraceAlign , a unified framework for tracing unsafe completions back to their root causes in the model's training corpus. Central to our approach is the Belief Conflict Index (BCI), which quantifies semantic inconsistency between generated spans and aligned policies, based on retrieved training documents using suffix-array matching . We propose three complementary interventions: (i) TraceShield , an inference-time safety filter that refuses completions with high-BCI spans, (ii) Contrastive Belief Deconfliction Loss , a contrastive fine-tuning objective penalizing high-BCI continuations during DPO, and (iii) Prov-Decode , a provenance-aware decoding strategy that vetoes beam expansions predicted to yield high-BCI spans. Together, these defenses reduce alignment drift by up to 85% on our curated Alignment Drift Benchmark (ADB) while preserving utility on standard tasks, with delta less than 0.2 and improved refusal quality. We further derive a theoretical upper bound on drift likelihood via suffix-array span statistics, linking memorization frequency and length to adversarial reactivation risk . TraceAlign thus provides the first scalable, traceable, and grounded toolkit for understanding and mitigating alignment failures at source. To encourage further exploration and development, we open-source our implementation at: https://anonymous.4open.science/r/ tracealign -2DA7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.02063

• PDF: https://arxiv.org/pdf/2508.02063

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🔹 Title: HeroBench: A Benchmark for Long-Horizon Planning and Structured Reasoning in Virtual Worlds

🔹 Publication Date: Published on Aug 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.12782
• PDF: https://arxiv.org/pdf/2508.12782
• Github: https://github.com/stefanrer/HeroBench

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🔹 Title: When Punctuation Matters: A Large-Scale Comparison of Prompt Robustness Methods for LLMs

🔹 Publication Date: Published on Aug 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.11383
• PDF: https://arxiv.org/pdf/2508.11383
• Github: https://github.com/AIRI-Institute/when-punctuation-matters

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🔹 Title: G-CUT3R: Guided 3D Reconstruction with Camera and Depth Prior Integration

🔹 Publication Date: Published on Aug 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.11379
• PDF: https://arxiv.org/pdf/2508.11379

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🔹 Title: Beyond Solving Math Quiz: Evaluating the Ability of Large Reasoning Models to Ask for Information

🔹 Publication Date: Published on Aug 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.11252
• PDF: https://arxiv.org/pdf/2508.11252

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🔹 Title: Exploitation Is All You Need... for Exploration

🔹 Publication Date: Published on Aug 2

🔹 Abstract: Meta-reinforcement learning agents can exhibit exploratory behavior when trained with a greedy objective, provided the environment has recurring structure, the agent has memory, and long-horizon credit assignment is possible. AI-generated summary Ensuring sufficient exploration is a central challenge when training meta-reinforcement learning ( meta-RL ) agents to solve novel environments. Conventional solutions to the exploration-exploitation dilemma inject explicit incentives such as randomization, uncertainty bonuses, or intrinsic rewards to encourage exploration. In this work, we hypothesize that an agent trained solely to maximize a greedy (exploitation-only) objective can nonetheless exhibit emergent exploratory behavior , provided three conditions are met: (1) Recurring Environmental Structure , where the environment features repeatable regularities that allow past experience to inform future choices; (2) Agent Memory, enabling the agent to retain and utilize historical interaction data; and (3) Long-Horizon Credit Assignment , where learning propagates returns over a time frame sufficient for the delayed benefits of exploration to inform current decisions. Through experiments in stochastic multi-armed bandits and temporally extended gridworlds , we observe that, when both structure and memory are present, a policy trained on a strictly greedy objective exhibits information-seeking exploratory behavior. We further demonstrate, through controlled ablations, that emergent exploration vanishes if either environmental structure or agent memory is absent (Conditions 1 & 2). Surprisingly, removing long-horizon credit assignment (Condition 3) does not always prevent emergent exploration-a result we attribute to the pseudo-Thompson Sampling effect. These findings suggest that, under the right prerequisites, exploration and exploitation need not be treated as orthogonal objectives but can emerge from a unified reward-maximization process.

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.01287

• PDF: https://arxiv.org/pdf/2508.01287

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🔹 Title: Reinforcement Learning with Rubric Anchors

🔹 Publication Date: Published on Aug 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.12790
• PDF: https://arxiv.org/pdf/2508.12790

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🔹 Title: Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods

🔹 Publication Date: Published on Aug 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.12730
• PDF: https://arxiv.org/pdf/2508.12730
• Project Page: https://gnueaj.github.io/Machine-Unlearning-Comparator/
• Github: https://github.com/gnueaj/Machine-Unlearning-Comparator

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🔹 Title: RotBench: Evaluating Multimodal Large Language Models on Identifying Image Rotation

🔹 Publication Date: Published on Aug 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.13968
• PDF: https://arxiv.org/pdf/2508.13968
• Github: https://github.com/tianyiniu/RotBench

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