✨GTA-2: Benchmarking General Tool Agents from Atomic Tool-Use to Open-Ended Workflows

📝 Summary:
GTA-2 is a new benchmark for General Tool Agents, covering both atomic and real-world, open-ended workflows. It shows frontier models struggle significantly, especially on workflows. The study emphasizes that execution frameworks are crucial for performance, more so than just model capacity.

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15715
• PDF: https://arxiv.org/pdf/2604.15715
• Github: https://github.com/open-compass/GTA

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#AIAgents #BenchmarkingAI #LLMs #AIWorkflows #AIResearch

✨Learning Adaptive Reasoning Paths for Efficient Visual Reasoning

📝 Summary:
Existing visual reasoning models often overthink, using redundant steps. AVR is an adaptive framework that dynamically chooses efficient reasoning formats. It reduces token usage by 50-90 percent while maintaining accuracy.

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14568
• PDF: https://arxiv.org/pdf/2604.14568
• Github: https://github.com/RunRiotComeOn/AVR

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

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#VisualReasoning #AI #MachineLearning #Efficiency #DeepLearning

✨Repurposing 3D Generative Model for Autoregressive Layout Generation

📝 Summary:
LaviGen is a 3D layout generation framework that repurposes 3D generative models. It uses an adapted 3D diffusion model for autoregressive generation, explicitly modeling geometric relations and physical constraints. This achieves superior, more plausible 3D layouts 65% faster than previous methods.

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16299
• PDF: https://arxiv.org/pdf/2604.16299
• Project Page: https://fenghora.github.io/LaviGen-Page/
• Github: https://github.com/fenghora/LaviGen

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#3DGeneration #DiffusionModels #GenerativeAI #ComputerGraphics #DeepLearning

✨Web Retrieval-Aware Chunking (W-RAC) for Efficient and Cost-Effective Retrieval-Augmented Generation Systems

📝 Summary:
Web Retrieval-Aware Chunking (W-RAC) introduces a cost-efficient framework for web document processing that reduces LLM token usage and hallucination risks through structured content representation an...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04936
• PDF: https://arxiv.org/pdf/2604.04936

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✨Maximal Brain Damage Without Data or Optimization: Disrupting Neural Networks via Sign-Bit Flips

📝 Summary:
Deep neural networks exhibit catastrophic vulnerability to minimal parameter bit flips across multiple domains, which can be identified and mitigated through targeted protection strategies. AI-generat...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.07408
• PDF: https://arxiv.org/pdf/2502.07408
• Project Page: https://mkimhi.github.io/DNL/
• Github: https://github.com/IdoGalil/maximal-brain-damage

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization

📝 Summary:
AccelOpt is a self-improving LLM agentic system that autonomously optimizes kernels for AI accelerators using iterative generation and optimization memory, achieving significant throughput improvement...

🔹 Publication Date: Published on Apr 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.15915
• PDF: https://arxiv.org/pdf/2511.15915
• Project Page: https://ppl.stanford.edu/accelopt.html
• Github: https://github.com/zhang677/AccelOpt

🔹 Models citing this paper:
• https://huggingface.co/Genghan/sft-qwen-7b-instruct_GRPO_nki_pure_0920_cluster3
• https://huggingface.co/Genghan/deepseek-coder-33b-instruct_GRPO_nki_pure_0907_cluster1
• https://huggingface.co/Genghan/sft-deepseek-coder-33b-instruct_GRPO_nki_pure_0921_cluster4

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Genghan/NKIBench

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

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✨NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results

📝 Summary:
This paper overviews the NTIRE 2026 Challenge on Video Saliency Prediction. Participants developed automatic saliency map prediction for videos using a novel 2,000-video dataset with crowdsourced fixations. Over 20 teams submitted, and all challenge data is now publicly available.

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14816
• PDF: https://arxiv.org/pdf/2604.14816
• Project Page: https://www.codabench.org/competitions/12842/
• Github: https://github.com/msu-video-group/NTIRE26_Saliency_Prediction

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

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#VideoSaliency #ComputerVision #NTIRE #MachineLearning #SaliencyPrediction

✨TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment

📝 Summary:
Enhanced vision-language models achieve superior dense patch-text alignment through improved pretraining techniques including patch-level distillation, modified masked image objectives, and optimized ...

🔹 Publication Date: Published on Apr 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.12012
• PDF: https://arxiv.org/pdf/2604.12012
• Project Page: https://gdm-tipsv2.github.io/

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

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✨(1D) Ordered Tokens Enable Efficient Test-Time Search

📝 Summary:
This paper demonstrates that 1D ordered, coarse-to-fine token structures enhance test-time search in autoregressive models. These tokens allow better verifier evaluation of intermediate states, improving scaling and enabling training-free text-to-image generation through pure test-time search. To...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15453
• PDF: https://arxiv.org/pdf/2604.15453
• Project Page: https://soto.epfl.ch/
• Github: https://github.com/EPFL-VILAB/search-over-tokens

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation

📝 Summary:
TwinTrack framework addresses pancreatic cancer segmentation ambiguity through post-hoc calibration of ensemble probabilities to empirical mean human response, improving calibration metrics on multi-r...

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15950
• PDF: https://arxiv.org/pdf/2604.15950

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

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✨EdgeDetect: Importance-Aware Gradient Compression with Homomorphic Aggregation for Federated Intrusion Detection

📝 Summary:
EdgeDetect enables efficient and secure federated intrusion detection for 6G-IoT environments through gradient binarization and homomorphic encryption, achieving high accuracy with reduced communicati...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14663v1
• PDF: https://arxiv.org/pdf/2604.14663

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

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✨Elucidating the SNR-t Bias of Diffusion Probabilistic Models

📝 Summary:
Diffusion models suffer from an SNR-timestep bias during inference, impairing generation quality. A differential correction method is proposed that processes frequency components separately. This significantly improves generation quality across various models with minimal computational cost.

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16044
• PDF: https://arxiv.org/pdf/2604.16044
• Github: https://github.com/AMAP-ML/DCW

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Can Large Language Models Reinvent Foundational Algorithms?

📝 Summary:
Large language models can reinvent foundational computer science algorithms through an unlearning and reinvention process, with performance varying based on hint levels and reinforced learning techniq...

🔹 Publication Date: Published on Apr 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05716
• PDF: https://arxiv.org/pdf/2604.05716
• Project Page: https://huggingface.co/spaces/jzhao1122/qwen3-thinking-dijkstra
• Github: https://github.com/Algo-Reinvention/algo-reinvention

🔹 Models citing this paper:
• https://huggingface.co/algo-reinvention/Qwen3-4B-Thinking-2507-Dijkstra-Unlearn
• https://huggingface.co/algo-reinvention/Qwen3-4B-Thinking-2507-Strassen-Unlearn

✨ Spaces citing this paper:
• https://huggingface.co/spaces/jzhao1122/qwen3-thinking-dijkstra

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨QuantCode-Bench: A Benchmark for Evaluating the Ability of Large Language Models to Generate Executable Algorithmic Trading Strategies

📝 Summary:
QuantCode-Bench evaluates large language models on generating executable trading strategies by testing their ability to translate natural language descriptions into functional code that operates corre...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15151
• PDF: https://arxiv.org/pdf/2604.15151
• Project Page: https://limexailab.github.io/QuantCode-Bench/
• Github: https://github.com/LimexAILab/QuantCode-Bench

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✨DiPO: Disentangled Perplexity Policy Optimization for Fine-grained Exploration-Exploitation Trade-Off

📝 Summary:
A novel reinforcement learning approach for large language models that addresses the exploration-exploitation trade-off through perplexity-based sample partitioning and bidirectional reward allocation...

🔹 Publication Date: Published on Apr 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.13902
• PDF: https://arxiv.org/pdf/2604.13902

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

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✨Hierarchical Codec Diffusion for Video-to-Speech Generation

📝 Summary:
HiCoDiT generates speech from videos by leveraging the hierarchical structure of discrete speech tokens, achieving better audio-visual alignment through coarse-to-fine conditioning with dual-scale nor...

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15923
• PDF: https://arxiv.org/pdf/2604.15923

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

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#VideoToSpeech #DiffusionModels #GenerativeAI #SpeechSynthesis #DeepLearning

✨Where does output diversity collapse in post-training?

📝 Summary:
Output diversity collapse in post-trained language models is primarily driven by training data composition rather than generation format, with different post-training methods affecting diversity diffe...

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16027
• PDF: https://arxiv.org/pdf/2604.16027
• Github: https://github.com/ckarouzos/where-diversity-collapses

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

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✨RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies

📝 Summary:
RoboLab is a simulation benchmarking framework that addresses limitations in robot policy evaluation by enabling scalable, realistic task generation and systematic analysis of policy behavior under co...

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.09860
• PDF: https://arxiv.org/pdf/2604.09860
• Project Page: https://research.nvidia.com/labs/srl/projects/robolab/
• Github: https://github.com/NVLabs/RoboLab

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✨The Amazing Agent Race: Strong Tool Users, Weak Navigators

📝 Summary:
The Amazing Agent Race benchmark introduces DAG-based puzzles to evaluate LLM agents' navigation and tool-use capabilities beyond traditional linear benchmarks, revealing that navigation errors domina...

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.10261
• PDF: https://arxiv.org/pdf/2604.10261
• Project Page: https://minnesotanlp.github.io/the-amazing-agent-race/
• Github: https://github.com/minnesotanlp/the-amazing-agent-race

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

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