✨Demystifying Action Space Design for Robotic Manipulation Policies

📝 Summary:
Large-scale empirical study demonstrates that action space design significantly impacts robotic policy learning, with delta action prediction improving performance and joint-space/task-space represent...

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23408
• PDF: https://arxiv.org/pdf/2602.23408

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Mario: Multimodal Graph Reasoning with Large Language Models

📝 Summary:
Mario is a unified framework that enables large language model-based reasoning on multimodal graphs by addressing cross-modal consistency and heterogeneous modality preferences through graph-condition...

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05181
• PDF: https://arxiv.org/pdf/2603.05181
• Github: https://github.com/sunyuanfu/Mario

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation

📝 Summary:
DeepPresenter is an agentic framework for adaptive presentation generation. It plans and refines slide artifacts using environment-grounded reflection on rendered slides. This approach achieves state-of-the-art performance with reduced computational costs.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://huggingface.co/collections/ICIP/deeppresenter
• PDF: https://arxiv.org/pdf/2602.22839
• Github: https://github.com/icip-cas/PPTAgent

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#AI #AgenticAI #PresentationGeneration #DeepLearning #GenerativeAI

✨WorldCache: Accelerating World Models for Free via Heterogeneous Token Caching

📝 Summary:
WorldCache speeds up slow diffusion-based world models by addressing token heterogeneity and non-uniform dynamics. It uses curvature-guided prediction and chaotic-prioritized skipping. This achieves up to 3.7 times faster inference with 98 percent rollout quality.

🔹 Publication Date: Published on Mar 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.06331
• PDF: https://arxiv.org/pdf/2603.06331

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#WorldModels #DiffusionModels #AI #MachineLearning #Optimization

✨Layer by layer, module by module: Choose both for optimal OOD probing of ViT

📝 Summary:
Intermediate layers in ViTs provide better representations. Performance degradation in deeper layers is caused by distribution shift. Optimal probing depends on shift magnitude: FFN activation for strong shift, MHA output for weak shift.

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05280
• PDF: https://arxiv.org/pdf/2603.05280
• Github: https://github.com/ambroiseodt/vit-probing

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#ViT #OOD #DeepLearning #RepresentationLearning #ComputerVision

✨Dynamic Model Routing and Cascading for Efficient LLM Inference: A Survey

📝 Summary:
This survey analyzes dynamic routing systems that adaptively select among multiple independent LLMs based on query characteristics to optimize inference performance and cost. It covers diverse routing paradigms and presents a framework for understanding these systems, highlighting their ability t...

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04445
• PDF: https://arxiv.org/pdf/2603.04445

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#LLM #AI #ModelRouting #InferenceOptimization #DeepLearning

✨EffectMaker: Unifying Reasoning and Generation for Customized Visual Effect Creation

📝 Summary:
EffectMaker is a unified framework for reference-based VFX customization. It uses a multimodal language model and diffusion transformer for semantic-visual guidance, generating high-quality effects consistently without per-effect fine-tuning. This is supported by a large synthetic dataset.

🔹 Publication Date: Published on Mar 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.06014
• PDF: https://arxiv.org/pdf/2603.06014
• Project Page: https://effectmaker.github.io/

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#VFX #GenerativeAI #DiffusionModels #MultimodalAI #ComputerVision

✨Censored LLMs as a Natural Testbed for Secret Knowledge Elicitation

📝 Summary:
Researchers used Chinese LLMs censored on political topics as a natural testbed for honesty elicitation and lie detection. They found prompt modifications and fine-tuning increased truthful responses, while self-classification was effective for detection. No method fully eliminated falsehoods.

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05494
• PDF: https://arxiv.org/pdf/2603.05494
• Github: https://github.com/cywinski/chinese_auditing

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#LLMs #Censorship #LieDetection #AISafety #NLP

✨IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation

📝 Summary:
IF-RewardBench is a new meta-evaluation benchmark for instruction-following. It employs a preference graph for listwise evaluation to assess judge models ability to rank responses. This reveals current judge model deficiencies and shows stronger correlation with downstream task performance.

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04738
• PDF: https://arxiv.org/pdf/2603.04738
• Project Page: https://github.com/thu-coai/IF-RewardBench
• Github: https://github.com/thu-coai/IF-RewardBench

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#InstructionFollowing #LLMEvaluation #AIBenchmarks #JudgeModels #AIResearch

✨τ-Knowledge: Evaluating Conversational Agents over Unstructured Knowledge

📝 Summary:
τ-Knowledge extends τ-Bench to evaluate conversational agents in fintech customer support, integrating external knowledge with tool use. Its τ-Banking domain involves navigating 700 documents and executing tool-mediated updates. Frontier models achieve only ~25.5% pass, struggling with document r...

🔹 Publication Date: Published on Mar 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04370
• PDF: https://arxiv.org/pdf/2603.04370

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#ConversationalAI #Fintech #LLMEvaluation #KnowledgeIntegration #ToolUse

✨Operator Learning Using Weak Supervision from Walk-on-Spheres

📝 Summary:
WoS-NO trains neural PDE solvers using Monte Carlo weak supervision from Walk-on-Spheres, avoiding expensive data and higher-order derivatives. This method improves accuracy, speeds up training, and reduces memory compared to traditional physics-informed approaches.

🔹 Publication Date: Published on Mar 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01193
• PDF: https://arxiv.org/pdf/2603.01193
• Github: https://github.com/neuraloperator/WoS-NO

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#OperatorLearning #WeakSupervision #NeuralPDE #MonteCarlo #SciML

✨Physics Informed Viscous Value Representations

📝 Summary:
This work introduces a physics-informed regularization for offline GCRL, based on the Hamilton-Jacobi-Bellman equation's viscosity solution. Using Monte Carlo estimation, it improves value estimation and geometric consistency for complex navigation and manipulation tasks.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23280
• PDF: https://arxiv.org/pdf/2602.23280

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#ReinforcementLearning #PhysicsInformed #OfflineRL #MachineLearning #Robotics

✨U6G XL-MIMO Radiomap Prediction: Multi-Config Dataset and Beam Map Approach

📝 Summary:
This paper improves XL-MIMO radiomap prediction for 6G by creating a large dataset and benchmark framework. A novel physics-informed beam map feature enhances generalization to unseen array configurations and environments. This method significantly reduces prediction error by decoupling array rad...

🔹 Publication Date: Published on Mar 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.06401
• PDF: https://arxiv.org/pdf/2603.06401
• Project Page: https://lxj321.github.io/MulticonfigRadiomapDataset/
• Github: https://github.com/Lxj321/MulticonfigRadiomapDataset

✨ Datasets citing this paper:
• https://huggingface.co/datasets/lxj321/Multi-config-Radiomap-Dataset

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#6G #MIMO #WirelessCommunication #MachineLearning #Radiomap

✨DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces

📝 Summary:
DreamCAD is a multi-modal generative framework that creates editable BReps from point-level supervision using parametric patches and differentiable tessellation, achieving superior geometric fidelity ...

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05607
• PDF: https://arxiv.org/pdf/2603.05607
• Project Page: https://sadilkhan.github.io/dreamcad2026/

✨ Datasets citing this paper:
• https://huggingface.co/datasets/SadilKhan/CADCap-1M

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research