✨LARY: A Latent Action Representation Yielding Benchmark for Generalizable Vision-to-Action Alignment

📝 Summary:
General visual foundation models trained without action supervision outperform specialized embodied models and demonstrate superior alignment between visual and physical action spaces compared to pixe...

🔹 Publication Date: Published on Apr 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.11689
• PDF: https://github.com/meituan-longcat/LARYBench/blob/main/LARYBench.pdf
• Project Page: https://meituan-longcat.github.io/LARYBench/
• Github: https://meituan-longcat.github.io/LARYBench/

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨You Only Judge Once: Multi-response Reward Modeling in a Single Forward Pass

📝 Summary:
A multimodal reward model evaluates multiple responses simultaneously through concatenated input and cross-entropy scoring, achieving faster training and superior performance in open-ended generation ...

🔹 Publication Date: Published on Apr 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.10966
• PDF: https://arxiv.org/pdf/2604.10966

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨VideoFlexTok: Flexible-Length Coarse-to-Fine Video Tokenization

📝 Summary:
VideoFlexTok enables efficient video representation through variable-length token sequences that capture abstract information first, followed by fine-grained details, allowing for reduced computationa...

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.12887
• PDF: https://arxiv.org/pdf/2604.12887
• Github: https://github.com/apple/ml-videoflextok

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Seeing Through Touch: Tactile-Driven Visual Localization of Material Regions

📝 Summary:
Deep learning model for tactile localization that uses dense cross-modal feature interactions to identify material properties in images, overcoming limitations of existing methods through enhanced dat...

🔹 Publication Date: Published on Apr 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.11579
• PDF: https://arxiv.org/pdf/2604.11579
• Project Page: https://mm.kaist.ac.kr/projects/SeeingThroughTouch/
• Github: https://github.com/kaistmm/SeeingThroughTouch

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

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✨Many-Tier Instruction Hierarchy in LLM Agents

📝 Summary:
Large language model agents require robust instruction conflict resolution mechanisms that can handle arbitrary privilege levels across diverse real-world scenarios, revealing current models' limitati...

🔹 Publication Date: Published on Apr 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.09443
• PDF: https://arxiv.org/pdf/2604.09443
• Project Page: https://jhu-clsp.github.io/ManyIH
• Github: https://github.com/JHU-CLSP/ManyIH

✨ Datasets citing this paper:
• https://huggingface.co/datasets/jhu-clsp/ManyIH-Bench
• https://huggingface.co/datasets/jackzhang/ManyIH-Bench

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

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✨Hierarchical SVG Tokenization: Learning Compact Visual Programs for Scalable Vector Graphics Modeling

📝 Summary:
HiVG introduces a hierarchical SVG tokenization framework that improves autoregressive vector graphics generation by addressing geometric structure representation and spatial consistency issues throug...

🔹 Publication Date: Published on Apr 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05072
• PDF: https://arxiv.org/pdf/2604.05072
• Project Page: https://hy-hivg.github.io/
• Github: https://github.com/ximinng/HiVG

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

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✨Beyond Perception Errors: Semantic Fixation in Large Vision-Language Models

📝 Summary:
Vision-language models exhibit semantic fixation by preferring default interpretations over alternative valid rule mappings, which can be mitigated through prompt interventions and training strategies...

🔹 Publication Date: Published on Apr 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.12119
• PDF: https://arxiv.org/pdf/2604.12119
• Project Page: https://maveryn.github.io/vlm-fix/
• Github: https://github.com/maveryn/vlm-fix

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

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✨CONSCIENTIA: Can LLM Agents Learn to Strategize? Emergent Deception and Trust in a Multi-Agent NYC Simulation

📝 Summary:
Large language model agents demonstrate limited strategic behaviors including selective trust and deception in a simulated urban environment, remaining vulnerable to adversarial persuasion despite imp...

🔹 Publication Date: Published on Apr 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.09746
• PDF: https://arxiv.org/pdf/2604.09746

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

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✨Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness

📝 Summary:
LLMs generally lack superior self-awareness of correctness. However, when models disagree, they demonstrate privileged knowledge for factual tasks, outperforming peers. This advantage emerges in early-to-mid layers, but not in math reasoning.

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.12373
• PDF: https://arxiv.org/pdf/2604.12373
• Project Page: https://technion-cs-nlp.github.io/Privileged-Knowledge/

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

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#LLM #AI #NLP #ModelCorrectness #PrivilegedKnowledge

✨Accelerating Speculative Decoding with Block Diffusion Draft Trees

📝 Summary:
DDTree enhances speculative decoding by constructing draft trees from block diffusion drafter distributions. It efficiently verifies multiple trajectories in parallel in a single target model pass, improving performance.

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.12989
• PDF: https://arxiv.org/pdf/2604.12989
• Project Page: https://liranringel.github.io/ddtree
• Github: https://github.com/liranringel/ddtree

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

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#SpeculativeDecoding #BlockDiffusion #LLMAcceleration #DeepLearning #AIResearch

✨When Reasoning Models Hurt Behavioral Simulation: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation

📝 Summary:
Reasoning-enhanced LLMs can over-optimize, making them better problem solvers but poor simulators of diverse, boundedly rational behavior. This solver-sampler mismatch means high model capability hurts simulation fidelity. Bounded reflection improves realism.

🔹 Publication Date: Published on Apr 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.11840
• PDF: https://arxiv.org/pdf/2604.11840
• Project Page: https://www.sandric.co

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

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#LLM #MultiAgentSystems #BehavioralSimulation #AI #AgentBasedModeling

✨Turing Test on Screen: A Benchmark for Mobile GUI Agent Humanization

📝 Summary:
This paper introduces the Turing Test on Screen to address GUI agents detectability by digital platforms. It proposes a benchmark and methods to humanize agent behavior, balancing imitability with task performance, enabling seamless coexistence in adversarial digital environments.

🔹 Publication Date: Published on Feb 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.09574
• PDF: https://arxiv.org/pdf/2604.09574

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

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#TuringTest #GUIAgents #AIHumanization #MobileAI #AISecurity

✨SpotSound: Enhancing Large Audio-Language Models with Fine-Grained Temporal Grounding

📝 Summary:
SpotSound improves audio language models for precise temporal grounding in long, noisy audio. It uses a novel training objective to suppress false timestamps, addressing sparse events in challenging backgrounds. SpotSound achieves state-of-the-art performance on temporal grounding benchmarks.

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.13023
• PDF: https://arxiv.org/pdf/2604.13023
• Project Page: https://loiesun.github.io/spotsound/
• Github: https://github.com/LoieSun/SpotSound

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

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#AudioLanguageModels #TemporalGrounding #AIResearch #MachineLearning #AudioProcessing

✨Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution

📝 Summary:
Domain-specific autoencoders significantly enhance medical image super-resolution. Replacing generic VAEs improves fidelity, showing autoencoder choice is key, not the diffusion architecture. Autoencoder performance predicts overall SR quality.

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.12152
• PDF: https://arxiv.org/pdf/2604.12152
• Github: https://github.com/sebasmos/latent-sr

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

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#MedicalImaging #SuperResolution #DiffusionModels #DeepLearning #Autoencoders

✨3DTV: A Feedforward Interpolation Network for Real-Time View Synthesis

📝 Summary:
3DTV is a feedforward network combining lightweight geometry and learning for real-time, robust sparse-view interpolation. It generates novel views efficiently without scene-specific optimization, making it practical for interactive applications.

🔹 Publication Date: Published on Apr 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.11211
• PDF: https://arxiv.org/pdf/2604.11211
• Project Page: https://stefanmschulz.github.io/3DTV_webpage/
• Github: https://github.com/StefanMSchulz/3DTV

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

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#ViewSynthesis #DeepLearning #ComputerVision #NeuralNetworks #RealTimeAI

✨BERT-as-a-Judge: A Robust Alternative to Lexical Methods for Efficient Reference-Based LLM Evaluation

📝 Summary:
Lexical LLM evaluation is rigid and inaccurate, while LLM-as-a-Judge is expensive. This paper introduces BERT-as-a-Judge, a robust, scalable encoder-driven method for reference-based LLM evaluation. It performs like larger LLM judges but with lower cost.

🔹 Publication Date: Published on Apr 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.09497
• PDF: https://arxiv.org/pdf/2604.09497
• Github: https://github.com/artefactory/BERT-as-a-Judge

🔹 Models citing this paper:
• https://huggingface.co/artefactory/BERTJudge
• https://huggingface.co/artefactory/BERTJudge-Formatted-QCR-500k
• https://huggingface.co/artefactory/BERTJudge-Formatted-QCR-OOD

✨ Datasets citing this paper:
• https://huggingface.co/datasets/artefactory/BERTJudge-Dataset

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

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#LLMEvaluation #BERT #NLP #AIResearch #MachineLearning

✨Spatial Competence Benchmark

📝 Summary:
Three frontier models show declining accuracy on a new spatial competence benchmark, with performance saturating quickly under token budget constraints. AI-generated summary Spatial competence is the ...

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.09594
• PDF: https://arxiv.org/pdf/2604.09594
• Github: https://github.com/ashleyharris-maptek-com-au/SpatialCompetenceBenchmark

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

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✨GlotOCR Bench: OCR Models Still Struggle Beyond a Handful of Unicode Scripts

📝 Summary:
Current OCR models poorly generalize across diverse scripts. GlotOCR Bench, a new benchmark for over 100 Unicode scripts, reveals most models perform well on under ten scripts. Generalization is limited and strongly depends on pretraining coverage.

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.12978
• PDF: https://arxiv.org/pdf/2604.12978
• Project Page: https://huggingface.co/datasets/cis-lmu/GlotOCR-bench
• Github: https://github.com/cisnlp/glotocr-bench

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

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#OCR #NLP #MultilingualAI #Benchmarking #AIResearch

✨LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety

📝 Summary:
Language-Agnostic Semantic Alignment (LASA) addresses LLM safety gaps across languages by targeting semantic bottlenecks where representations are primarily driven by shared semantics rather than lang...

🔹 Publication Date: Published on Apr 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.12710
• PDF: https://arxiv.org/pdf/2604.12710

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

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✨PokeRL: Reinforcement Learning for Pokemon Red

📝 Summary:
PokeRL presents a modular reinforcement learning system with environment wrapping, anti-loop mechanisms, and hierarchical rewards to train agents for early-game Pokemon Red tasks. AI-generated summary...

🔹 Publication Date: Published on Apr 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.10812
• PDF: https://arxiv.org/pdf/2604.10812
• Github: https://github.com/reddheeraj/PokemonRL

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

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