🔹 Title: Stairway to Fairness: Connecting Group and Individual Fairness

🔹 Publication Date: Published on Aug 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21334
• PDF: https://arxiv.org/pdf/2508.21334
• Github: https://github.com/theresiavr/stairway-to-fairness

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🔹 Title: Gated Associative Memory: A Parallel O(N) Architecture for Efficient Sequence Modeling

🔹 Publication Date: Published on Aug 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.00605
• PDF: https://arxiv.org/pdf/2509.00605
• Github: https://github.com/rishiraj/gam

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🔹 Title: Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs

🔹 Publication Date: Published on Sep 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01790
• PDF: https://arxiv.org/pdf/2509.01790

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🔹 Title: Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices

🔹 Publication Date: Published on Sep 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02523
• PDF: https://arxiv.org/pdf/2509.02523
• Github: https://github.com/moonshine-ai/moonshine

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🔹 Title: On the Theoretical Limitations of Embedding-Based Retrieval

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21038
• PDF: https://arxiv.org/pdf/2508.21038
• Github: https://github.com/google-deepmind/limit

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• https://huggingface.co/datasets/orionweller/LIMIT
• https://huggingface.co/datasets/orionweller/LIMIT-small

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🔹 Title: Robix: A Unified Model for Robot Interaction, Reasoning and Planning

🔹 Publication Date: Published on Sep 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01106
• PDF: https://arxiv.org/pdf/2509.01106
• Project Page: https://robix-seed.github.io/robix/

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🔹 Title: Open Data Synthesis For Deep Research

🔹 Publication Date: Published on Aug 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.00375
• PDF: https://arxiv.org/pdf/2509.00375
• Github: https://github.com/VectorSpaceLab/InfoSeek

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🔹 Title: InstructVLA: Vision-Language-Action Instruction Tuning from Understanding to Manipulation

🔹 Publication Date: Published on Jul 23

🔹 Abstract: InstructVLA is an end-to-end vision-language-action model that enhances manipulation performance while preserving vision-language reasoning through multimodal training and mixture-of-experts adaptation. AI-generated summary To operate effectively in the real world, robots must integrate multimodal reasoning with precise action generation . However, existing vision-language-action (VLA) models often sacrifice one for the other, narrow their abilities to task-specific manipulation data, and suffer catastrophic forgetting of pre-trained vision-language capabilities. To bridge this gap, we introduce InstructVLA, an end-to-end VLA model that preserves the flexible reasoning of large vision-language models (VLMs) while delivering leading manipulation performance. InstructVLA introduces a novel training paradigm, Vision-Language-Action Instruction Tuning (VLA-IT) , which employs multimodal training with mixture-of-experts adaptation to jointly optimize textual reasoning and action generation on both standard VLM corpora and a curated 650K-sample VLA-IT dataset. On in-domain SimplerEnv tasks , InstructVLA achieves 30.5% improvement over SpatialVLA. To evaluate generalization, we introduce SimplerEnv-Instruct , an 80-task benchmark requiring closed-loop control and high-level instruction understanding, where it outperforms a fine-tuned OpenVLA by 92% and an action expert aided by GPT-4o by 29%. Additionally, InstructVLA surpasses baseline VLMs on multimodal tasks and exhibits inference-time scaling by leveraging textual reasoning to boost manipulation performance in both simulated and real-world settings. These results demonstrate InstructVLA's potential for bridging intuitive and steerable human-robot interaction with efficient policy learning .

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.17520

• PDF: https://arxiv.org/pdf/2507.17520

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🔹 Title: MOSAIC: Multi-Subject Personalized Generation via Correspondence-Aware Alignment and Disentanglement

🔹 Publication Date: Published on Sep 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01977
• PDF: https://arxiv.org/pdf/2509.01977
• Project Page: https://bytedance-fanqie-ai.github.io/MOSAIC/
• Github: https://github.com/bytedance-fanqie-ai/MOSAIC

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🔹 Title: Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation

🔹 Publication Date: Published on Aug 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2509.00428
• PDF: https://arxiv.org/pdf/2509.00428
• Project Page: https://xavierjiezou.github.io/Face-MoGLE/
• Github: https://github.com/XavierJiezou/Face-MoGLE

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🔹 Title: Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference

🔹 Publication Date: Published on Aug 6

🔹 Abstract: The paper diagnoses structural issues in AI conferences, including publication rates, carbon footprint, negative community sentiment, and logistical challenges, and proposes a Community-Federated Conference model to address these issues. AI-generated summary Artificial Intelligence (AI) conferences are essential for advancing research, sharing knowledge, and fostering academic community. However, their rapid expansion has rendered the centralized conference model increasingly unsustainable. This paper offers a data-driven diagnosis of a structural crisis that threatens the foundational goals of scientific dissemination, equity, and community well-being. We identify four key areas of strain: (1) scientifically, with per-author publication rates more than doubling over the past decade to over 4.5 papers annually; (2) environmentally, with the carbon footprint of a single conference exceeding the daily emissions of its host city; (3) psychologically, with 71% of online community discourse reflecting negative sentiment and 35% referencing mental health concerns; and (4) logistically, with attendance at top conferences such as NeurIPS 2024 beginning to outpace venue capacity. These pressures point to a system that is misaligned with its core mission. In response, we propose the Community-Federated Conference (CFC) model, which separates peer review, presentation, and networking into globally coordinated but locally organized components, offering a more sustainable, inclusive, and resilient path forward for AI research.

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.04586

• PDF: https://arxiv.org/pdf/2508.04586

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🔹 Title: LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations

🔹 Publication Date: Published on Sep 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.03405
• PDF: https://arxiv.org/pdf/2509.03405

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🔹 Title: SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs

🔹 Publication Date: Published on Aug 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.00930
• PDF: https://arxiv.org/pdf/2509.00930

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🔹 Title: Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots

🔹 Publication Date: Published on Sep 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02530
• PDF: https://arxiv.org/pdf/2509.02530
• Github: https://manipulation-as-in-simulation.github.io/

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🔹 Title: Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training

🔹 Publication Date: Published on Sep 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2509.03403
• PDF: https://arxiv.org/pdf/2509.03403
• Github: https://github.com/Chenluye99/PROF

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🔹 Title: Towards a Unified View of Large Language Model Post-Training

🔹 Publication Date: Published on Sep 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.04419
• PDF: https://arxiv.org/pdf/2509.04419
• Github: https://github.com/TsinghuaC3I/Unify-Post-Training

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🔹 Title: DeepResearch Arena: The First Exam of LLMs' Research Abilities via Seminar-Grounded Tasks

🔹 Publication Date: Published on Sep 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01396
• PDF: https://arxiv.org/pdf/2509.01396

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🔹 Title: Drawing2CAD: Sequence-to-Sequence Learning for CAD Generation from Vector Drawings

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18733
• PDF: https://arxiv.org/pdf/2508.18733
• Github: https://github.com/lllssc/Drawing2CAD

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🔹 Title: False Sense of Security: Why Probing-based Malicious Input Detection Fails to Generalize

🔹 Publication Date: Published on Sep 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.03888
• PDF: https://arxiv.org/pdf/2509.03888

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🔹 Title: Transition Models: Rethinking the Generative Learning Objective

🔹 Publication Date: Published on Sep 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.04394
• PDF: https://arxiv.org/pdf/2509.04394
• Github: https://github.com/WZDTHU/TiM

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