✨SuperLocalMemory V3.3: The Living Brain -- Biologically-Inspired Forgetting, Cognitive Quantization, and Multi-Channel Retrieval for Zero-LLM Agent Memory Systems

📝 Summary:
A new local-first agent memory system implements comprehensive cognitive memory processes with enhanced retrieval and forgetting mechanisms, achieving superior performance in zero-LLM settings. AI-gen...

🔹 Publication Date: Published on Apr 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04514
• PDF: https://arxiv.org/pdf/2604.04514
• Project Page: https://superlocalmemory.com/
• Github: https://github.com/qualixar/superlocalmemory

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨TRACER: Trace-Based Adaptive Cost-Efficient Routing for LLM Classification

📝 Summary:
TRACER trains ML surrogates using LLM classification production traces. These cost-efficient surrogates activate only if they agree with the original LLM above a threshold, saving significant costs. TRACER also provides interpretability for its routing decisions and achieves high coverage.

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14531
• PDF: https://arxiv.org/pdf/2604.14531

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#LLMs #MachineLearning #CostEfficiency #AI #Interpretability

✨OneHOI: Unifying Human-Object Interaction Generation and Editing

📝 Summary:
OneHOI is a unified diffusion transformer framework that consolidates human-object interaction generation and editing into a single conditional denoising process. It uses structured interaction representations to overcome limitations of prior approaches, achieving state-of-the-art results across ...

🔹 Publication Date: Published on Apr 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14062
• PDF: https://arxiv.org/pdf/2604.14062
• Project Page: https://jiuntian.github.io/OneHOI/
• Github: https://github.com/jiuntian/OneHOI

✨ Datasets citing this paper:
• https://huggingface.co/datasets/jiuntian/hoiedit44k
• https://huggingface.co/datasets/jiuntian/IEBench

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✨Towards Autonomous Mechanistic Reasoning in Virtual Cells

📝 Summary:
Large language models are enhanced for biological research through a multi-agent framework that generates and validates mechanistic explanations using structured formalism and verified datasets. AI-ge...

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.11661
• PDF: https://arxiv.org/pdf/2604.11661
• Project Page: https://valencelabs.substack.com/p/towards-reasoning-in-virtual-cells
• Github: https://github.com/valence-labs/VCR-Agent

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✨Switch-KD: Visual-Switch Knowledge Distillation for Vision-Language Models

📝 Summary:
Vision-language models face deployment challenges due to their large size, but knowledge distillation can improve efficiency while maintaining performance through a novel visual-switch framework that ...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14629
• PDF: https://arxiv.org/pdf/2604.14629
• Project Page: https://haoyi199815.github.io/Switch-KD/
• Github: https://github.com/haoyi199815/Switch-KD

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

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✨Representations Before Pixels: Semantics-Guided Hierarchical Video Prediction

📝 Summary:
Re2Pix is a hierarchical video prediction framework that improves future video generation by first predicting semantic representations and then using them to guide photorealistic visual synthesis, add...

🔹 Publication Date: Published on Apr 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.11707
• PDF: https://arxiv.org/pdf/2604.11707
• Github: https://github.com/Sta8is/Re2Pix

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✨Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG

📝 Summary:
Corpus2Skill structures document corpora into hierarchical skill directories for LLM agents. This allows agents to navigate, reason about information, and combine evidence more effectively than traditional RAG. It significantly outperforms other RAG methods on an enterprise benchmark.

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14572
• PDF: https://arxiv.org/pdf/2604.14572
• Github: https://github.com/dukesun99/Corpus2Skill

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography

📝 Summary:
RadAgent, a tool-using AI agent, enhances chest CT report generation through interpretable step-by-step reasoning traces that improve clinical accuracy, robustness, and faithfulness compared to existi...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15231
• PDF: https://arxiv.org/pdf/2604.15231
• Project Page: https://rad-agent.github.io

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✨Boosting Visual Instruction Tuning with Self-Supervised Guidance

📝 Summary:
Multimodal models struggle with visual reasoning due to under-utilizing visual information during instruction tuning. This paper proposes augmenting instruction tuning with visually grounded self-supervised tasks expressed as natural language. This simple method significantly improves performance...

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.12966
• PDF: https://arxiv.org/pdf/2604.12966
• Github: https://github.com/sirkosophia/V-GIFT

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✨An Optimal Transport-driven Approach for Cultivating Latent Space in Online Incremental Learning

📝 Summary:
An online mixture model learning framework based on optimal transport theory addresses challenges in incremental learning with distributional shifts by enabling dynamic centroid updates and improving ...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2211.16780
• PDF: https://arxiv.org/pdf/2211.16780

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

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✨Three-Phase Transformer

📝 Summary:
The Three-Phase Transformer introduces a structural prior for decoder-only Transformers through channel partitioning and phase-respecting operations that stabilize training and improve convergence. AI...

🔹 Publication Date: Published on Apr 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14430
• PDF: https://arxiv.org/pdf/2604.14430
• Github: https://github.com/achelousace/three-phase-transformer

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

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✨Beyond Prompts: Unconditional 3D Inversion for Out-of-Distribution Shapes

📝 Summary:
State-of-the-art text-to-3D generative models suffer from latent sink traps where they lose sensitivity to text prompts, but a robust framework can overcome this by decoupling geometric representation...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14914
• PDF: https://arxiv.org/pdf/2604.14914
• Project Page: https://daidedou.sorpi.fr/publication/beyondprompts

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✨Reinforcement Learning via Value Gradient Flow

📝 Summary:
Value Gradient Flow presents a scalable approach to behavior-regularized reinforcement learning by formulating it as an optimal transport problem solved through discrete gradient flow, enabling adapti...

🔹 Publication Date: Published on Apr 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14265
• PDF: https://arxiv.org/pdf/2604.14265
• Project Page: https://ryanxhr.github.io/vgf/
• Github: https://github.com/ryanxhr/vgf

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✨Envisioning the Future, One Step at a Time

📝 Summary:
Autoregressive diffusion models predict open-set future scene dynamics by modeling sparse point trajectories, enabling fast and scalable multi-modal motion prediction with physical plausibility. AI-ge...

🔹 Publication Date: Published on Apr 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.09527
• PDF: https://arxiv.org/pdf/2604.09527
• Project Page: https://compvis.github.io/myriad
• Github: https://github.com/compvis/myriad

🔹 Models citing this paper:
• https://huggingface.co/CompVis/myriad

✨ Datasets citing this paper:
• https://huggingface.co/datasets/CompVis/owm-95
• https://huggingface.co/datasets/CompVis/myriad-physics

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

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✨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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✨PersonaVLM: Long-Term Personalized Multimodal LLMs

📝 Summary:
PersonaVLM introduces a framework for long-term personalized multimodal LLMs. It remembers interactions, reasons multi-turn using retrieved memories, and aligns responses with evolving user personality. This novel method significantly outperforms baselines and GPT-4o on a new evaluation benchmark.

🔹 Publication Date: Published on Mar 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.13074
• PDF: https://arxiv.org/pdf/2604.13074
• Project Page: https://personavlm.github.io/
• Github: https://github.com/MiG-NJU/PersonaVLM

🔹 Models citing this paper:
• https://huggingface.co/ClareNie/PersonaVLM

✨ Datasets citing this paper:
• https://huggingface.co/datasets/ClareNie/Persona-MME
• https://huggingface.co/datasets/ClareNie/PersonaVLM-Dataset

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

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#LLM #MultimodalAI #PersonalizedAI #AIResearch #MemoryAI

✨VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects

📝 Summary:
VEFX-Bench offers a large human-annotated video editing dataset and VEFX-Reward, a specialized model for quality assessment. This benchmark allows standardized comparison, showing current models struggle with instruction following and edit locality.

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16272
• PDF: https://arxiv.org/pdf/2604.16272
• Project Page: https://xiangbogaobarry.github.io/VEFX-Bench/

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#VideoEditing #VFX #AI #ComputerVision #Benchmarks

✨Qwen3.5-Omni Technical Report

📝 Summary:
Qwen3.5-Omni is a large multimodal model excelling in audio-visual understanding and generation, achieving SOTA results across many benchmarks. It features a Hybrid Attention MoE architecture, introduces ARIA for improved speech synthesis, and exhibits a new Audio-Visual Vibe Coding capability.

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15804
• PDF: https://arxiv.org/pdf/2604.15804

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#MultimodalAI #AIResearch #DeepLearning #GenerativeAI #SpeechSynthesis

✨ArtifactNet: Detecting AI-Generated Music via Forensic Residual Physics

📝 Summary:
ArtifactNet detects AI-generated music by analyzing codec-specific artifacts in audio signals using a lightweight neural network and codec-aware training. It achieves superior performance and efficiency compared to existing methods, establishing forensic physics as a new detection paradigm.

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16254
• PDF: https://arxiv.org/pdf/2604.16254
• Project Page: https://demo.intrect.io

🔹 Models citing this paper:
• https://huggingface.co/intrect/artifactnet

✨ Datasets citing this paper:
• https://huggingface.co/datasets/intrect/artifactbench

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

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#AI #MachineLearning #AIMusic #DigitalForensics #AudioProcessing

✨PRL-Bench: A Comprehensive Benchmark Evaluating LLMs' Capabilities in Frontier Physics Research

📝 Summary:
PRL-Bench is a new benchmark evaluating LLMs' end-to-end capabilities in theoretical and computational physics research. It uses 100 curated papers to assess exploration-oriented, long-horizon workflows. Current LLMs perform poorly, revealing a significant gap in autonomous scientific discovery.

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15411
• PDF: https://arxiv.org/pdf/2604.15411

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#LLMs #PhysicsResearch #ScientificDiscovery #AI #Benchmarking

✨Cut Your Losses! Learning to Prune Paths Early for Efficient Parallel Reasoning

📝 Summary:
STOP is a systematic, learnable token-level path pruning method for Large Reasoning Models. It improves efficiency and accuracy, outperforming baselines and scaling across compute budgets to reduce futile paths in parallel reasoning.

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16029
• PDF: https://arxiv.org/pdf/2604.16029
• Project Page: https://bijiaxihh.github.io/STOP/
• Github: https://github.com/bijiaxihh/STOP

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#AI #LLM #MachineLearning #ParallelReasoning #ModelEfficiency