✨Terminal Agents Suffice for Enterprise Automation

📝 Summary:
Simple terminal-based coding agents interacting directly with platform APIs, powered by foundation models, are highly effective for enterprise automation. These low-level agents match or outperform complex tool-augmented systems, demonstrating that elaborate agent architectures are often unnecess...

🔹 Publication Date: Published on Mar 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.00073
• PDF: https://arxiv.org/pdf/2604.00073

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Think, Act, Build: An Agentic Framework with Vision Language Models for Zero-Shot 3D Visual Grounding

📝 Summary:
A dynamic agentic framework called TAB addresses 3D visual grounding by decoupling spatial semantics resolution from 3D structure instantiation through 2D VLMs and multi-view geometry, achieving super...

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.00528
• PDF: https://arxiv.org/pdf/2604.00528
• Github: https://github.com/WHB139426/TAB-Agent

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨GaussianGPT: Towards Autoregressive 3D Gaussian Scene Generation

📝 Summary:
GaussianGPT uses a transformer-based autoregressive approach with 3D rotary positional embeddings to generate 3D scenes by predicting Gaussian primitives, offering advantages over diffusion methods in...

🔹 Publication Date: Published on Mar 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.26661
• PDF: https://arxiv.org/pdf/2603.26661
• Project Page: https://nicolasvonluetzow.github.io/GaussianGPT/

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Universal YOCO for Efficient Depth Scaling

📝 Summary:
Universal YOCO YOCO-U merges YOCO architecture with recursive computation for efficient LLM depth scaling. It uses iterative processing in shallow attention layers, offering constant KV cache and better token utility.

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01220
• PDF: https://arxiv.org/pdf/2604.01220

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨PerceptionComp: A Video Benchmark for Complex Perception-Centric Reasoning

📝 Summary:
PerceptionComp is a new video benchmark for complex, long-horizon perception-centric reasoning. It requires multiple temporal visual evidence and compositional logic. Current AI models struggle significantly, highlighting a major bottleneck in perceptual video reasoning.

🔹 Publication Date: Published on Mar 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.26653
• PDF: https://arxiv.org/pdf/2603.26653
• Project Page: https://perceptioncomp.github.io/
• Github: https://github.com/hrinnnn/PerceptionComp

✨ Datasets citing this paper:
• https://huggingface.co/datasets/hrinnnn/PerceptionComp

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨ViGoR-Bench: How Far Are Visual Generative Models From Zero-Shot Visual Reasoners?

📝 Summary:
ViGoR benchmark addresses limitations in current AIGC evaluation by introducing a comprehensive framework for assessing visual generative reasoning across multiple modalities and cognitive dimensions....

🔹 Publication Date: Published on Mar 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25823
• PDF: https://arxiv.org/pdf/2603.25823
• Project Page: https://vincenthancoder.github.io/ViGoR-Bench/
• Github: https://github.com/VincentHancoder/ViGoR-Bench-Eval

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Embarrassingly Simple Self-Distillation Improves Code Generation

📝 Summary:
Simple self-distillation improves code generation in large language models by fine-tuning on model-generated samples, effectively addressing precision-exploration trade-offs in decoding. AI-generated ...

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01193
• PDF: https://arxiv.org/pdf/2604.01193
• Github: https://github.com/apple/ml-ssd

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Revision or Re-Solving? Decomposing Second-Pass Gains in Multi-LLM Pipelines

📝 Summary:
Multi-LLM revision pipelines' effectiveness varies by task structure and draft quality, with gains decomposing into re-solving, scaffold, and content components rather than representing uniform error ...

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01029
• PDF: https://arxiv.org/pdf/2604.01029

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨MMaDA-VLA: Large Diffusion Vision-Language-Action Model with Unified Multi-Modal Instruction and Generation

📝 Summary:
A native discrete diffusion framework unifies multi-modal understanding and generation for robotic manipulation, enabling parallel action and visual outcome prediction with improved long-horizon consi...

🔹 Publication Date: Published on Mar 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25406
• PDF: https://arxiv.org/pdf/2603.25406
• Project Page: https://yliu-cs.github.io/MMaDA-VLA
• Github: https://github.com/yliu-cs/MMaDA-VLA

🔹 Models citing this paper:
• https://huggingface.co/yliu-cs/MMaDA-VLA

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨HippoCamp: Benchmarking Contextual Agents on Personal Computers

📝 Summary:
HippoCamp is a new multimodal benchmark evaluating agents on massive personal file management. It exposes significant performance gaps in current models for long-horizon retrieval and cross-modal reasoning in user-centric environments, revealing bottlenecks in multimodal perception.

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01221
• PDF: https://arxiv.org/pdf/2604.01221
• Project Page: https://hippocamp-ai.github.io/
• Github: https://github.com/Savannah-yz/HippoCamp

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨MiroEval: Benchmarking Multimodal Deep Research Agents in Process and Outcome

📝 Summary:
MiroEval is a new benchmark for deep research systems, addressing limitations of existing evaluations. It assesses adaptive synthesis, factuality, and process quality across real-user text and multimodal tasks, showing process quality predicts outcomes and multimodal tasks are very challenging.

🔹 Publication Date: Published on Mar 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.28407
• PDF: https://arxiv.org/pdf/2603.28407
• Project Page: https://miroeval-ai.github.io/website/
• Github: https://github.com/MiroMindAI/MiroEval

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨QuitoBench: A High-Quality Open Time Series Forecasting Benchmark

📝 Summary:
QuitoBench addresses the lack of large-scale time series benchmarks by introducing a regime-balanced dataset with eight TSF regimes, revealing that foundation models outperform deep learning at long c...

🔹 Publication Date: Published on Mar 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.26017
• PDF: https://arxiv.org/pdf/2603.26017

✨ Datasets citing this paper:
• https://huggingface.co/datasets/hq-bench/quitobench
• https://huggingface.co/datasets/hq-bench/quito-corpus

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

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#TimeSeriesForecasting #DataScience #MachineLearning #AI #QuitoBench

✨Vision2Web: A Hierarchical Benchmark for Visual Website Development with Agent Verification

📝 Summary:
Vision2Web presents a comprehensive benchmark for visual website development tasks and evaluates coding agents across static UI generation, interactive frontend reproduction, and full-stack developmen...

🔹 Publication Date: Published on Mar 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.26648
• PDF: https://arxiv.org/pdf/2603.26648
• Project Page: https://vision2web-bench.github.io/
• Github: https://github.com/zai-org/Vision2Web

✨ Datasets citing this paper:
• https://huggingface.co/datasets/zai-org/Vision2Web

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

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✨Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers

📝 Summary:
A systematic evaluation framework called PaperRecon is proposed to assess AI-generated papers by separating quality assessment into presentation and hallucination dimensions using a benchmark of 51 re...

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01128
• PDF: https://arxiv.org/pdf/2604.01128
• Project Page: https://agent4science-utokyo.github.io/PaperRecon_HP/
• Github: https://github.com/Agent4Science-UTokyo/PaperRecon

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

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✨Proactive Agent Research Environment: Simulating Active Users to Evaluate Proactive Assistants

📝 Summary:
A framework for proactive agent research is introduced that models applications as finite state machines to enable realistic user simulation and task execution across multiple digital environments. AI...

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.00842
• PDF: https://arxiv.org/pdf/2604.00842
• Github: https://github.com/deepakn97/pare

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

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✨Benchmarking and Mechanistic Analysis of Vision-Language Models for Cross-Depiction Assembly Instruction Alignment

📝 Summary:
Vision Language Models struggle with aligning assembly diagrams and video feeds due to a depiction gap, with findings indicating visual encoding as the primary target for improving cross-depiction rob...

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.00913
• PDF: https://arxiv.org/pdf/2604.00913
• Project Page: https://ryenhails.github.io/IKEA-Bench/
• Github: https://ryenhails.github.io/IKEA-Bench/

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

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✨Understand and Accelerate Memory Processing Pipeline for Disaggregated LLM Inference

📝 Summary:
LLM inference faces significant memory processing overhead. This paper proposes using heterogeneous GPU-FPGA systems to accelerate these operations by offloading memory-bounded tasks to FPGAs. This achieves 1.04-2.2x speedup and 1.11-4.7x energy savings over GPU baselines, proving heterogeneous s...

🔹 Publication Date: Published on Mar 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.29002
• PDF: https://arxiv.org/pdf/2603.29002

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

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#LLMInference #FPGA #HeterogeneousComputing #HardwareAcceleration #SystemArchitecture

✨UniMixer: A Unified Architecture for Scaling Laws in Recommendation Systems

📝 Summary:
UniMixer is a unified architecture for recommendation systems that improves scaling efficiency. It uses a generalized parameterized token mixing module to optimize mixing patterns and connect attention, TokenMixer, and factorization-machine methods. A lightweight version boosts performance further.

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.00590
• PDF: https://arxiv.org/pdf/2604.00590

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨ClawKeeper: Comprehensive Safety Protection for OpenClaw Agents Through Skills, Plugins, and Watchers

📝 Summary:
OpenClaw agents face critical security vulnerabilities due to extensive operational privileges. ClawKeeper provides comprehensive real-time protection using skill-based, plugin-based, and novel watcher-based mechanisms for state verification and intervention.

🔹 Publication Date: Published on Mar 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24414
• PDF: https://arxiv.org/pdf/2603.24414
• Project Page: https://huggingface.co/datasets/xunyoyo/clawkeeper
• Github: https://github.com/SafeAI-Lab-X/ClawKeeper

✨ Datasets citing this paper:
• https://huggingface.co/datasets/xunyoyo/clawkeeper

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

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#AISafety #AgentSecurity #AIagents #Cybersecurity #AIResearch

✨MemRerank: Preference Memory for Personalized Product Reranking

📝 Summary:
MemRerank improves personalized product reranking by distilling user purchase history into concise preference signals using reinforcement learning. This framework consistently outperforms raw history and other baselines, proving explicit preference memory is effective for e-commerce personalization.

🔹 Publication Date: Published on Mar 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.29247
• PDF: https://arxiv.org/pdf/2603.29247

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

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#Personalization #ECommerce #ReinforcementLearning #RecommendationSystems #MachineLearning

✨Reasoning Shift: How Context Silently Shortens LLM Reasoning

📝 Summary:
LLMs significantly shorten their reasoning traces when problems are presented in various contexts compared to isolation. This compression reduces self-verification, potentially affecting performance on complex tasks. It highlights issues with LLM reasoning robustness and context management.

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01161
• PDF: https://arxiv.org/pdf/2604.01161

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

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#LLM #AIReasoning #ContextualAI #AIRobustness #MachineLearning