✨SocialVeil: Probing Social Intelligence of Language Agents under Communication Barriers
📝 Summary:
SocialVeil is a new environment simulating communication barriers to test LLM social intelligence under realistic conditions. It shows barriers significantly impair LLM performance, reducing mutual understanding by over 45% and increasing confusion by nearly 50%. Adaptation strategies offered onl...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05115
• PDF: https://arxiv.org/pdf/2602.05115
==================================
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📝 Summary:
SocialVeil is a new environment simulating communication barriers to test LLM social intelligence under realistic conditions. It shows barriers significantly impair LLM performance, reducing mutual understanding by over 45% and increasing confusion by nearly 50%. Adaptation strategies offered onl...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05115
• PDF: https://arxiv.org/pdf/2602.05115
==================================
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✨CoPE: Clipped RoPE as A Scalable Free Lunch for Long Context LLMs
📝 Summary:
CoPE introduces a soft clipping method for Rotary Positional Embedding that unifies out-of-distribution mitigation and semantic modeling while enabling effective long-context processing up to 256k len...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05258
• PDF: https://arxiv.org/pdf/2602.05258
• Github: https://github.com/hrlics/CoPE
🔹 Models citing this paper:
• https://huggingface.co/haoranli-ml/Llama-3-8B-CoPE-64k-Base
• https://huggingface.co/haoranli-ml/Llama-3-8B-CoPE-64k-Instruct
• https://huggingface.co/haoranli-ml/Llama-3-8B-RoPE-64k-Base
==================================
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📝 Summary:
CoPE introduces a soft clipping method for Rotary Positional Embedding that unifies out-of-distribution mitigation and semantic modeling while enabling effective long-context processing up to 256k len...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05258
• PDF: https://arxiv.org/pdf/2602.05258
• Github: https://github.com/hrlics/CoPE
🔹 Models citing this paper:
• https://huggingface.co/haoranli-ml/Llama-3-8B-CoPE-64k-Base
• https://huggingface.co/haoranli-ml/Llama-3-8B-CoPE-64k-Instruct
• https://huggingface.co/haoranli-ml/Llama-3-8B-RoPE-64k-Base
==================================
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✨Learning Rate Matters: Vanilla LoRA May Suffice for LLM Fine-tuning
📝 Summary:
Recent LoRA variants claim improvements, but systematic evaluation shows these gains vanish with proper learning rate tuning. Vanilla LoRA achieves similar peak performance when hyperparameters are optimized, remaining a competitive baseline for LLM fine-tuning.
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04998
• PDF: https://arxiv.org/pdf/2602.04998
==================================
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📝 Summary:
Recent LoRA variants claim improvements, but systematic evaluation shows these gains vanish with proper learning rate tuning. Vanilla LoRA achieves similar peak performance when hyperparameters are optimized, remaining a competitive baseline for LLM fine-tuning.
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04998
• PDF: https://arxiv.org/pdf/2602.04998
==================================
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✨Failing to Explore: Language Models on Interactive Tasks
📝 Summary:
Language models exhibit limited exploration capabilities in interactive environments, with performance improvements achieved through budget allocation strategies and historical summarization technique...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22345
• PDF: https://arxiv.org/pdf/2601.22345
• Project Page: https://explore-exploit-bench.github.io/
• Github: https://github.com/mahdi-jfri/explore-exploit-bench
==================================
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📝 Summary:
Language models exhibit limited exploration capabilities in interactive environments, with performance improvements achieved through budget allocation strategies and historical summarization technique...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22345
• PDF: https://arxiv.org/pdf/2601.22345
• Project Page: https://explore-exploit-bench.github.io/
• Github: https://github.com/mahdi-jfri/explore-exploit-bench
==================================
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✨DFlash: Block Diffusion for Flash Speculative Decoding
📝 Summary:
DFlash is a speculative decoding framework that uses a lightweight block diffusion model for parallel token drafting. This enables efficient LLM inference by generating draft tokens in a single pass. DFlash achieves over 6x lossless acceleration, significantly outperforming existing methods.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06036
• PDF: https://arxiv.org/pdf/2602.06036
• Project Page: https://z-lab.ai/projects/dflash/
• Github: https://github.com/z-lab/dflash
🔹 Models citing this paper:
• https://huggingface.co/z-lab/Qwen3-Coder-30B-A3B-DFlash
• https://huggingface.co/z-lab/Qwen3-8B-DFlash-b16
• https://huggingface.co/z-lab/Qwen3-4B-DFlash-b16
==================================
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📝 Summary:
DFlash is a speculative decoding framework that uses a lightweight block diffusion model for parallel token drafting. This enables efficient LLM inference by generating draft tokens in a single pass. DFlash achieves over 6x lossless acceleration, significantly outperforming existing methods.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06036
• PDF: https://arxiv.org/pdf/2602.06036
• Project Page: https://z-lab.ai/projects/dflash/
• Github: https://github.com/z-lab/dflash
🔹 Models citing this paper:
• https://huggingface.co/z-lab/Qwen3-Coder-30B-A3B-DFlash
• https://huggingface.co/z-lab/Qwen3-8B-DFlash-b16
• https://huggingface.co/z-lab/Qwen3-4B-DFlash-b16
==================================
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👍1
✨DASH: Faster Shampoo via Batched Block Preconditioning and Efficient Inverse-Root Solvers
📝 Summary:
DASH significantly accelerates the Shampoo optimizer by using 3D tensor stacking for better GPU utilization and introducing faster inverse matrix root computations like Newton-DB. This results in up to 4.83x faster optimizer steps and improved model performance.
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02016
• PDF: https://arxiv.org/pdf/2602.02016
• Github: https://github.com/IST-DASLab/DASH
==================================
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📝 Summary:
DASH significantly accelerates the Shampoo optimizer by using 3D tensor stacking for better GPU utilization and introducing faster inverse matrix root computations like Newton-DB. This results in up to 4.83x faster optimizer steps and improved model performance.
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02016
• PDF: https://arxiv.org/pdf/2602.02016
• Github: https://github.com/IST-DASLab/DASH
==================================
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✨Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents
📝 Summary:
Easy Dataset is a framework that synthesizes LLM fine-tuning data from unstructured documents via a GUI and LLMs. It improves domain-specific performance while preserving general knowledge.
🔹 Publication Date: Published on Jul 5, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.04009
• PDF: https://arxiv.org/pdf/2507.04009
• Github: https://github.com/ConardLi/easy-dataset
==================================
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📝 Summary:
Easy Dataset is a framework that synthesizes LLM fine-tuning data from unstructured documents via a GUI and LLMs. It improves domain-specific performance while preserving general knowledge.
🔹 Publication Date: Published on Jul 5, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.04009
• PDF: https://arxiv.org/pdf/2507.04009
• Github: https://github.com/ConardLi/easy-dataset
==================================
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Hierarchical Navigable Small World (HNSW) is an algorithm that makes vector search fast even on huge amounts of data, allowing you to search billions of vectors in milliseconds. The idea of its operation is quite elegant - it's one of the most interesting discoveries of recent years.
Here's how it works:
HNSW builds a multi-level graph, where each upper layer contains exponentially fewer nodes than the layer below.
▪️ All vectors are located in the lower layer (layer 0), which is well connected.
▪️ Only some vectors appear in layer 1, even fewer in layer 2, etc.
▪️ The upper layers work as "fast lanes", allowing you to skip a large number of irrelevant data.
During the search, the algorithm starts from the upper layer, finds the nearest node, descends to the lower layer and repeats the process. By the time you reach the lower layer, you have already narrowed the search to the most relevant environment - there's no need to sort through everything.
This explains why HNSW is so economical with memory. It can "jump over" large amounts of data without evaluating each element.
Key parameters that affect the balance of speed and quality:
▪️ ef - the size of the candidate list during the search
▪️ maxConnections - how many connections each node can have
▪️ distance - a metric for comparing vectors (cosine, dot product, etc.)
Adding new elements works in a similar way: first, we search for the optimal location, then we create connections. Restructuring the graph is resource-intensive, but the queries themselves are performed very quickly.
You can read more in detail here✅
👉 @DataScienceT
Here's how it works:
HNSW builds a multi-level graph, where each upper layer contains exponentially fewer nodes than the layer below.
During the search, the algorithm starts from the upper layer, finds the nearest node, descends to the lower layer and repeats the process. By the time you reach the lower layer, you have already narrowed the search to the most relevant environment - there's no need to sort through everything.
This explains why HNSW is so economical with memory. It can "jump over" large amounts of data without evaluating each element.
Key parameters that affect the balance of speed and quality:
Adding new elements works in a similar way: first, we search for the optimal location, then we create connections. Restructuring the graph is resource-intensive, but the queries themselves are performed very quickly.
You can read more in detail here
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❤1
✨Arch-Router: Aligning LLM Routing with Human Preferences
📝 Summary:
Arch-Router is a 1.5B model that aligns LLM routing with human preferences by matching queries to user-defined domains and action types. It outperforms proprietary models in subjective evaluations and supports flexible addition of new models.
🔹 Publication Date: Published on Jun 19, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.16655
• PDF: https://arxiv.org/pdf/2506.16655
• Project Page: https://huggingface.co/katanemo/Arch-Router-1.5B
• Github: https://github.com/katanemo/archgw/
🔹 Models citing this paper:
• https://huggingface.co/katanemo/Arch-Router-1.5B
• https://huggingface.co/katanemo/Arch-Router-1.5B.gguf
• https://huggingface.co/Mungert/Arch-Router-1.5B-GGUF
✨ Spaces citing this paper:
• https://huggingface.co/spaces/jaimegalanmartinez/f1_faq_engine
• https://huggingface.co/spaces/tejasashinde/archRouter_simulator
• https://huggingface.co/spaces/IsaiahJ04/katanemo-Arch-Router-1.5B
==================================
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📝 Summary:
Arch-Router is a 1.5B model that aligns LLM routing with human preferences by matching queries to user-defined domains and action types. It outperforms proprietary models in subjective evaluations and supports flexible addition of new models.
🔹 Publication Date: Published on Jun 19, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.16655
• PDF: https://arxiv.org/pdf/2506.16655
• Project Page: https://huggingface.co/katanemo/Arch-Router-1.5B
• Github: https://github.com/katanemo/archgw/
🔹 Models citing this paper:
• https://huggingface.co/katanemo/Arch-Router-1.5B
• https://huggingface.co/katanemo/Arch-Router-1.5B.gguf
• https://huggingface.co/Mungert/Arch-Router-1.5B-GGUF
✨ Spaces citing this paper:
• https://huggingface.co/spaces/jaimegalanmartinez/f1_faq_engine
• https://huggingface.co/spaces/tejasashinde/archRouter_simulator
• https://huggingface.co/spaces/IsaiahJ04/katanemo-Arch-Router-1.5B
==================================
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arXiv.org
Arch-Router: Aligning LLM Routing with Human Preferences
With the rapid proliferation of large language models (LLMs) -- each optimized for different strengths, style, or latency/cost profile -- routing has become an essential technique to...
❤3
✨Transformer Explainer: Interactive Learning of Text-Generative Models
📝 Summary:
Transformer Explainer is an interactive web tool enabling non-experts to understand GPT-2's internal workings. It visualizes how the model generates text in real-time based on user input. This improves public access to learning about modern generative AI.
🔹 Publication Date: Published on Aug 8, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2408.04619
• PDF: https://arxiv.org/pdf/2408.04619
• Project Page: https://poloclub.github.io/transformer-explainer/
• Github: https://github.com/helblazer811/ManimML
==================================
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#AI #ExplainableAI #LLM #DataVisualization #GenerativeAI
📝 Summary:
Transformer Explainer is an interactive web tool enabling non-experts to understand GPT-2's internal workings. It visualizes how the model generates text in real-time based on user input. This improves public access to learning about modern generative AI.
🔹 Publication Date: Published on Aug 8, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2408.04619
• PDF: https://arxiv.org/pdf/2408.04619
• Project Page: https://poloclub.github.io/transformer-explainer/
• Github: https://github.com/helblazer811/ManimML
==================================
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❤1
✨InftyThink+: Effective and Efficient Infinite-Horizon Reasoning via Reinforcement Learning
📝 Summary:
To address high costs and limits in chain-of-thought reasoning, InftyThink uses reinforcement learning to optimize iterative reasoning. It learns to strategically summarize and resume, boosting accuracy by 21% on AIME24, reducing latency, and improving generalization.
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06960
• PDF: https://arxiv.org/pdf/2602.06960
==================================
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#ReinforcementLearning #AIReasoning #ChainOfThought #ArtificialIntelligence #MachineLearning
📝 Summary:
To address high costs and limits in chain-of-thought reasoning, InftyThink uses reinforcement learning to optimize iterative reasoning. It learns to strategically summarize and resume, boosting accuracy by 21% on AIME24, reducing latency, and improving generalization.
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06960
• PDF: https://arxiv.org/pdf/2602.06960
==================================
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✨Revisiting the Shape Convention of Transformer Language Models
📝 Summary:
This paper challenges the traditional narrow-wide-narrow FFN in Transformers, proposing deeper hourglass-shaped FFNs. This new design improves model efficiency and performance by better utilizing parameters, especially when expanding other model components.
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06471
• PDF: https://arxiv.org/pdf/2602.06471
==================================
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📝 Summary:
This paper challenges the traditional narrow-wide-narrow FFN in Transformers, proposing deeper hourglass-shaped FFNs. This new design improves model efficiency and performance by better utilizing parameters, especially when expanding other model components.
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06471
• PDF: https://arxiv.org/pdf/2602.06471
==================================
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✨MemGUI-Bench: Benchmarking Memory of Mobile GUI Agents in Dynamic Environments
📝 Summary:
MemGUI-Bench is a new, comprehensive benchmark designed to evaluate the memory capabilities of mobile GUI agents. It addresses current benchmarks' failure to assess memory by offering a taxonomy, 128 tasks, and an automated evaluation pipeline. Experiments with state-of-the-art agents reveal sign...
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06075
• PDF: https://arxiv.org/pdf/2602.06075
• Project Page: https://lgy0404.github.io/MemGUI-Bench/
• Github: https://github.com/lgy0404/MemGUI-Bench
==================================
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#MobileAI #GUIagents #AIBenchmarking #MemoryAI #AIResearch
📝 Summary:
MemGUI-Bench is a new, comprehensive benchmark designed to evaluate the memory capabilities of mobile GUI agents. It addresses current benchmarks' failure to assess memory by offering a taxonomy, 128 tasks, and an automated evaluation pipeline. Experiments with state-of-the-art agents reveal sign...
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06075
• PDF: https://arxiv.org/pdf/2602.06075
• Project Page: https://lgy0404.github.io/MemGUI-Bench/
• Github: https://github.com/lgy0404/MemGUI-Bench
==================================
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✨OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale
📝 Summary:
OmniMoE presents a system-algorithm co-designed framework that achieves fine-grained expert specialization in Mixture-of-Experts architectures through vector-level atomic experts and optimized routing...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05711
• PDF: https://arxiv.org/pdf/2602.05711
• Github: https://github.com/flash-algo/omni-moe
==================================
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📝 Summary:
OmniMoE presents a system-algorithm co-designed framework that achieves fine-grained expert specialization in Mixture-of-Experts architectures through vector-level atomic experts and optimized routing...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05711
• PDF: https://arxiv.org/pdf/2602.05711
• Github: https://github.com/flash-algo/omni-moe
==================================
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✨On the Entropy Dynamics in Reinforcement Fine-Tuning of Large Language Models
📝 Summary:
This paper theoretically analyzes entropy dynamics in reinforcement fine-tuning of large language models. It derives expressions for entropy change and proposes novel entropy control methods based on discriminant analysis, aiming to optimize the exploration-exploitation balance during LLM fine-tu...
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03392
• PDF: https://arxiv.org/pdf/2602.03392
• Github: https://github.com/agentscope-ai/Trinity-RFT
==================================
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#LLM #ReinforcementLearning #Entropy #AIResearch #MachineLearning
📝 Summary:
This paper theoretically analyzes entropy dynamics in reinforcement fine-tuning of large language models. It derives expressions for entropy change and proposes novel entropy control methods based on discriminant analysis, aiming to optimize the exploration-exploitation balance during LLM fine-tu...
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03392
• PDF: https://arxiv.org/pdf/2602.03392
• Github: https://github.com/agentscope-ai/Trinity-RFT
==================================
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✨Self-Improving Multilingual Long Reasoning via Translation-Reasoning Integrated Training
📝 Summary:
TRIT framework improves multilingual long reasoning by jointly training translation and reasoning. This self-improving method enhances non-English question understanding and response generation without extra data. It boosts accuracy and language consistency, also improving cross-lingual question ...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05940
• PDF: https://arxiv.org/pdf/2602.05940
==================================
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📝 Summary:
TRIT framework improves multilingual long reasoning by jointly training translation and reasoning. This self-improving method enhances non-English question understanding and response generation without extra data. It boosts accuracy and language consistency, also improving cross-lingual question ...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05940
• PDF: https://arxiv.org/pdf/2602.05940
==================================
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✨PlanViz: Evaluating Planning-Oriented Image Generation and Editing for Computer-Use Tasks
📝 Summary:
PlanViz is a new benchmark evaluating unified multimodal models for image generation and editing in computer-use planning tasks. It features route planning, work diagramming, and web&UI displaying sub-tasks, using a task-adaptive PlanScore to assess correctness, visual quality, and efficiency.
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06663
• PDF: https://arxiv.org/pdf/2602.06663
• Project Page: https://github.com/lijunxian111/PlanViz
• Github: https://github.com/lijunxian111/PlanViz/releases/tag/v1
==================================
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#MultimodalAI #ImageGeneration #ImageEditing #ComputerVision #Benchmarking
📝 Summary:
PlanViz is a new benchmark evaluating unified multimodal models for image generation and editing in computer-use planning tasks. It features route planning, work diagramming, and web&UI displaying sub-tasks, using a task-adaptive PlanScore to assess correctness, visual quality, and efficiency.
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06663
• PDF: https://arxiv.org/pdf/2602.06663
• Project Page: https://github.com/lijunxian111/PlanViz
• Github: https://github.com/lijunxian111/PlanViz/releases/tag/v1
==================================
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#MultimodalAI #ImageGeneration #ImageEditing #ComputerVision #Benchmarking
✨POINTS-GUI-G: GUI-Grounding Journey
📝 Summary:
GUI agents for automated digital tasks rely on vision-language models with enhanced grounding capabilities, achieved through refined data engineering, improved training strategies, and reinforcement l...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06391
• PDF: https://arxiv.org/pdf/2602.06391
• Github: https://github.com/Tencent/POINTS-GUI
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
GUI agents for automated digital tasks rely on vision-language models with enhanced grounding capabilities, achieved through refined data engineering, improved training strategies, and reinforcement l...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06391
• PDF: https://arxiv.org/pdf/2602.06391
• Github: https://github.com/Tencent/POINTS-GUI
==================================
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✨EgoAVU: Egocentric Audio-Visual Understanding
📝 Summary:
MLLMs struggle with egocentric video's joint audio-visual understanding. EgoAVU, a new data engine, generates diverse audio-visual narrations to create the EgoAVU-Instruct dataset. This fine-tunes MLLMs, enabling up to 113% performance improvement in joint audio-visual comprehension.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06139
• PDF: https://arxiv.org/pdf/2602.06139
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For more data science resources:
✓ https://t.iss.one/DataScienceT
#EgocentricAI #MultimodalAI #AudioVisualAI #DeepLearning #Datasets
📝 Summary:
MLLMs struggle with egocentric video's joint audio-visual understanding. EgoAVU, a new data engine, generates diverse audio-visual narrations to create the EgoAVU-Instruct dataset. This fine-tunes MLLMs, enabling up to 113% performance improvement in joint audio-visual comprehension.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06139
• PDF: https://arxiv.org/pdf/2602.06139
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#EgocentricAI #MultimodalAI #AudioVisualAI #DeepLearning #Datasets