✨nabla-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Latent Space
📝 Summary:
nabla-Reasoner improves LLM reasoning by integrating differentiable optimization directly into the decoding loop. It leverages gradient signals from the LLM and a reward model to refine textual representations, achieving over 20% accuracy improvement while reducing model calls.
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04948
• PDF: https://arxiv.org/pdf/2603.04948
• Github: https://github.com/VITA-Group/Nabla-Reasoner
==================================
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📝 Summary:
nabla-Reasoner improves LLM reasoning by integrating differentiable optimization directly into the decoding loop. It leverages gradient signals from the LLM and a reward model to refine textual representations, achieving over 20% accuracy improvement while reducing model calls.
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04948
• PDF: https://arxiv.org/pdf/2603.04948
• Github: https://github.com/VITA-Group/Nabla-Reasoner
==================================
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✨Generalizable Knowledge Distillation from Vision Foundation Models for Semantic Segmentation
📝 Summary:
Generalizable Knowledge Distillation GKD improves out-of-domain generalization for semantic segmentation. GKD decouples representation learning from task learning, using query-based soft distillation to transfer knowledge from vision foundation models. It consistently outperforms other methods, a...
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02554
• PDF: https://arxiv.org/pdf/2603.02554
• Github: https://github.com/Younger-hua/GKD
==================================
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📝 Summary:
Generalizable Knowledge Distillation GKD improves out-of-domain generalization for semantic segmentation. GKD decouples representation learning from task learning, using query-based soft distillation to transfer knowledge from vision foundation models. It consistently outperforms other methods, a...
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02554
• PDF: https://arxiv.org/pdf/2603.02554
• Github: https://github.com/Younger-hua/GKD
==================================
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✨PIRA-Bench: A Transition from Reactive GUI Agents to GUI-based Proactive Intent Recommendation Agents
📝 Summary:
PIRA-Bench presents a benchmark for evaluating multimodal large language models on proactive GUI agent tasks using continuous visual inputs, while PIRF offers a memory-aware framework for handling com...
🔹 Publication Date: Published on Mar 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08013
• PDF: https://arxiv.org/pdf/2603.08013
• Project Page: https://www.pira-bench.top
==================================
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📝 Summary:
PIRA-Bench presents a benchmark for evaluating multimodal large language models on proactive GUI agent tasks using continuous visual inputs, while PIRF offers a memory-aware framework for handling com...
🔹 Publication Date: Published on Mar 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08013
• PDF: https://arxiv.org/pdf/2603.08013
• Project Page: https://www.pira-bench.top
==================================
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✨PureCC: Pure Learning for Text-to-Image Concept Customization
📝 Summary:
PureCC presents a concept customization method that preserves original model behavior through decoupled learning and adaptive guidance scaling. AI-generated summary Existing concept customization meth...
🔹 Publication Date: Published on Mar 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07561
• PDF: https://arxiv.org/pdf/2603.07561
• Github: https://github.com/lzc-sg/PureCC
==================================
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📝 Summary:
PureCC presents a concept customization method that preserves original model behavior through decoupled learning and adaptive guidance scaling. AI-generated summary Existing concept customization meth...
🔹 Publication Date: Published on Mar 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07561
• PDF: https://arxiv.org/pdf/2603.07561
• Github: https://github.com/lzc-sg/PureCC
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✨From Narrow to Panoramic Vision: Attention-Guided Cold-Start Reshapes Multimodal Reasoning
📝 Summary:
The study introduces a novel attention-based metric called Visual Attention Score to analyze cold-start initialization in multimodal large reasoning models, identifying a counter-intuitive phenomenon ...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03825
• PDF: https://arxiv.org/pdf/2603.03825
• Github: https://github.com/lrlbbzl/Qwen-AVAR
==================================
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📝 Summary:
The study introduces a novel attention-based metric called Visual Attention Score to analyze cold-start initialization in multimodal large reasoning models, identifying a counter-intuitive phenomenon ...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03825
• PDF: https://arxiv.org/pdf/2603.03825
• Github: https://github.com/lrlbbzl/Qwen-AVAR
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✨Holi-Spatial: Evolving Video Streams into Holistic 3D Spatial Intelligence
📝 Summary:
Holi-Spatial presents the first fully automated, large-scale, spatially-aware multimodal dataset constructed from raw video inputs, supporting multi-level spatial supervision for 3D scene understandin...
🔹 Publication Date: Published on Mar 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07660
• PDF: https://arxiv.org/pdf/2603.07660
• Project Page: https://visionary-laboratory.github.io/holi-spatial/
• Github: https://github.com/Visionary-Laboratory/holi-spatial
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📝 Summary:
Holi-Spatial presents the first fully automated, large-scale, spatially-aware multimodal dataset constructed from raw video inputs, supporting multi-level spatial supervision for 3D scene understandin...
🔹 Publication Date: Published on Mar 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07660
• PDF: https://arxiv.org/pdf/2603.07660
• Project Page: https://visionary-laboratory.github.io/holi-spatial/
• Github: https://github.com/Visionary-Laboratory/holi-spatial
==================================
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✨\$OneMillion-Bench: How Far are Language Agents from Human Experts?
📝 Summary:
A new benchmark evaluates language models on complex, real-world professional tasks requiring multi-step reasoning, evidence resolution, and domain-specific decision-making across multiple industries....
🔹 Publication Date: Published on Mar 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07980
• PDF: https://arxiv.org/pdf/2603.07980
• Github: https://github.com/humanlaya/OneMillion-Bench
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📝 Summary:
A new benchmark evaluates language models on complex, real-world professional tasks requiring multi-step reasoning, evidence resolution, and domain-specific decision-making across multiple industries....
🔹 Publication Date: Published on Mar 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07980
• PDF: https://arxiv.org/pdf/2603.07980
• Github: https://github.com/humanlaya/OneMillion-Bench
==================================
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✨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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📝 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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📝 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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📝 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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📝 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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📝 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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📝 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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📝 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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📝 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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arXiv.org
HiAR: Efficient Autoregressive Long Video Generation via...
Autoregressive (AR) diffusion offers a promising framework for generating videos of theoretically infinite length. However, a major challenge is maintaining temporal continuity while preventing...
✨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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📝 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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📝 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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📝 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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📝 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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📝 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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📝 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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