✨Believe Your Model: Distribution-Guided Confidence Calibration

📝 Summary:
Large reasoning models enhance prediction accuracy through test-time scaling techniques that generate multiple candidate responses, with the proposed DistriVoting method utilizing distributional prior...

🔹 Publication Date: Published on Mar 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03872
• PDF: https://arxiv.org/pdf/2603.03872
• Github: https://github.com/yxizhong/SSC

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✨Scale Space Diffusion

📝 Summary:
Scale-space theory connects diffusion models' information hierarchy to low-pass filtering, leading to a framework that combines scale spaces with diffusion processes for efficient image processing. AI...

🔹 Publication Date: Published on Mar 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08709
• PDF: https://arxiv.org/pdf/2603.08709
• Project Page: https://prateksha.github.io/projects/scale-space-diffusion/
• Github: https://github.com/prateksha/ScaleSpaceDiffusion

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✨FVG-PT: Adaptive Foreground View-Guided Prompt Tuning for Vision-Language Models

📝 Summary:
Foreground attention shifts during CLIP-based prompt tuning are addressed through an adaptive module that enhances foreground view quality and mitigates generalization degradation. AI-generated summar...

🔹 Publication Date: Published on Mar 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08708
• PDF: https://arxiv.org/pdf/2603.08708
• Github: https://github.com/JREion/FVG-PT

✨ Datasets citing this paper:
• https://huggingface.co/datasets/JREion/Prompt_Tuning_Datasets_with_Foreground

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✨Skip to the Good Part: Representation Structure & Inference-Time Layer Skipping in Diffusion vs. Autoregressive LLMs

📝 Summary:
Diffusion language models exhibit distinct representational structures compared to autoregressive models, with hierarchical abstractions and reduced bias, enabling efficient layer-skipping inference w...

🔹 Publication Date: Published on Mar 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07475
• PDF: https://arxiv.org/pdf/2603.07475

==================================

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✨Scaling Agentic Capabilities, Not Context: Efficient Reinforcement Finetuning for Large Toolspaces

📝 Summary:
ATLAS enables small language models to effectively operate in large-scale tool environments through reinforcement fine-tuning that learns context control and execution structure, achieving performance...

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.06713
• PDF: https://arxiv.org/pdf/2603.06713

==================================

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✨Agentic Critical Training

📝 Summary:
Agentic Critical Training (ACT) is a reinforcement learning approach that trains language model agents to autonomously reason about action quality by directly rewarding correct judgment between altern...

🔹 Publication Date: Published on Mar 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08706
• PDF: https://arxiv.org/pdf/2603.08706
• Project Page: https://attention-is-all-i-need.github.io/ACT/

==================================

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✨OfficeQA Pro: An Enterprise Benchmark for End-to-End Grounded Reasoning

📝 Summary:
OfficeQA Pro evaluates AI agents on multi-document reasoning across historical financial documents, revealing persistent challenges in grounded reasoning despite advanced model capabilities. AI-genera...

🔹 Publication Date: Published on Mar 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08655
• PDF: https://arxiv.org/pdf/2603.08655
• Github: https://github.com/databricks/officeqa

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✨HiAR: Efficient Autoregressive Long Video Generation via Hierarchical Denoising

📝 Summary:
HiAR, a hierarchical autoregressive diffusion framework, improves video generation by conditioning on context at the same noise level and employs forward-KL regularization to maintain temporal continu...

🔹 Publication Date: Published on Mar 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08703
• PDF: https://arxiv.org/pdf/2603.08703
• Project Page: https://jacky-hate.github.io/HiAR/
• Github: https://jacky-hate.github.io/HiAR/

🔹 Models citing this paper:
• https://huggingface.co/jackyhate/HiAR

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✨NaviDriveVLM: Decoupling High-Level Reasoning and Motion Planning for Autonomous Driving

📝 Summary:
NaviDriveVLM presents a decoupled vision-language model framework for autonomous driving that separates high-level reasoning from motion planning, achieving superior performance in end-to-end driving ...

🔹 Publication Date: Published on Mar 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07901
• PDF: https://arxiv.org/pdf/2603.07901
• Github: https://github.com/TAMU-CVRL/NaviDrive

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✨CARE-Edit: Condition-Aware Routing of Experts for Contextual Image Editing

📝 Summary:
CARE-Edit introduces a condition-aware routing mechanism that dynamically allocates diffusion model computation to specialized experts for improved contextual image editing tasks. AI-generated summary...

🔹 Publication Date: Published on Mar 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08589
• PDF: https://arxiv.org/pdf/2603.08589
• Project Page: https://care-edit.github.io/
• Github: https://care-edit.github.io/

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✨Spatiotemporal Heterogeneity of AI-Driven Traffic Flow Patterns and Land Use Interaction: A GeoAI-Based Analysis of Multimodal Urban Mobility

📝 Summary:
A GeoAI Hybrid framework combining MGWR, RF, and ST-GCN models effectively captures complex traffic flow patterns and land use interactions across multiple mobility modes with superior predictive perf...

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05581
• PDF: https://arxiv.org/pdf/2603.05581

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✨AutoResearch-RL: Perpetual Self-Evaluating Reinforcement Learning Agents for Autonomous Neural Architecture Discovery

📝 Summary:
An autonomous reinforcement learning framework conducts continuous neural architecture and hyperparameter research without human intervention, achieving performance comparable to hand-tuned baselines ...

🔹 Publication Date: Published on Mar 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07300
• PDF: https://arxiv.org/pdf/2603.07300

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✨Autophoresis of a Janus particle near a planar wall: a lubrication limit

📝 Summary:
We study the self-diffusiophoresis of a spherical chemically active particle near a planar, impermeable wall, with a focus on the influence of particle orientation on propulsion. We analyze a Janus pa...

🔹 Publication Date: Published on Feb 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00791
• PDF: https://arxiv.org/pdf/2603.00791

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✨LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory

📝 Summary:
LoGeR enables long-term 3D video reconstruction by combining bidirectional priors with a hybrid memory system that includes parametric Test-Time Training and non-parametric sliding window attention me...

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03269
• PDF: https://arxiv.org/pdf/2603.03269
• Project Page: https://loger-project.github.io/
• Github: https://github.com/Junyi42/LoGeR

🔹 Models citing this paper:
• https://huggingface.co/Junyi42/LoGeR

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✨How Far Can Unsupervised RLVR Scale LLM Training?

📝 Summary:
Intrinsic Unsupervised RL with Verifiable Rewards URLVR for LLMs faces fundamental scaling limits. It fails due to confidence-correction misalignment, leading to collapse. External reward methods show promise for overcoming these barriers.

🔹 Publication Date: Published on Mar 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08660
• PDF: https://arxiv.org/pdf/2603.08660
• Github: https://github.com/PRIME-RL/TTRL/tree/urlvr-dev

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✨Lost in Stories: Consistency Bugs in Long Story Generation by LLMs

📝 Summary:
Large language models struggle with maintaining narrative consistency over long-form storytelling, exhibiting predictable patterns of contradictions that this benchmark identifies and categorizes. AI-...

🔹 Publication Date: Published on Mar 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05890
• PDF: https://arxiv.org/pdf/2603.05890
• Project Page: https://picrew.github.io/constory-bench.github.io/
• Github: https://github.com/Picrew/ConStory-Bench

✨ Datasets citing this paper:
• https://huggingface.co/datasets/jayden8888/ConStory-Bench

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✨CaTok: Taming Mean Flows for One-Dimensional Causal Image Tokenization

📝 Summary:
CaTok presents a 1D causal image tokenizer with a MeanFlow decoder that enables fast one-step generation and high-fidelity multi-step sampling while achieving state-of-the-art image reconstruction per...

🔹 Publication Date: Published on Mar 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.06449
• PDF: https://arxiv.org/pdf/2603.06449
• Project Page: https://sharelab-sii.github.io/catok-web/
• Github: https://github.com/ShareLab-SII/CaTok

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✨SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation

📝 Summary:
Self-Evolving Gated Attention enables efficient temporal modeling in diffusion policies for long-horizon robotic manipulation, achieving superior performance with reduced computational requirements. A...

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05117
• PDF: https://arxiv.org/pdf/2603.05117

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✨CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation

📝 Summary:
CoCo is a code-driven framework for text-to-image generation, using executable code for precise spatial layout and structured image creation. It significantly outperforms natural language CoT methods, enabling more controllable and accurate image synthesis.

🔹 Publication Date: Published on Mar 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08652
• PDF: https://arxiv.org/pdf/2603.08652

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✨Concept-Guided Fine-Tuning: Steering ViTs away from Spurious Correlations to Improve Robustness

📝 Summary:
A novel fine-tuning method improves Vision Transformer robustness to distribution shifts. It aligns ViT attention with AI-generated concept masks, shifting focus from spurious correlations to semantic features. This boosts out-of-distribution performance and model interpretability.

🔹 Publication Date: Published on Mar 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08309
• PDF: https://arxiv.org/pdf/2603.08309
• Project Page: https://yonisgit.github.io/concept-ft/

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