✨Beyond Language Modeling: An Exploration of Multimodal Pretraining

📝 Summary:
Controlled multimodal pretraining experiments reveal key insights about unified visual representations, data complementarity, world modeling emergence, and efficient scaling through mixture-of-experts...

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03276
• PDF: https://arxiv.org/pdf/2603.03276

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?

📝 Summary:
Current code agent benchmarks fail to capture real-world complexity, prompting the creation of BeyondSWE to evaluate broader reasoning and knowledge scopes, alongside SearchSWE to study external knowl...

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03194
• PDF: https://arxiv.org/pdf/2603.03194
• Project Page: https://aweai-team.github.io/BeyondSWE/
• Github: https://github.com/AweAI-Team/BeyondSWE

✨ Datasets citing this paper:
• https://huggingface.co/datasets/AweAI-Team/BeyondSWE

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

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✨APRES: An Agentic Paper Revision and Evaluation System

📝 Summary:
Large language models are used to automatically revise scientific papers based on citation-predictive rubrics while preserving core content, achieving improved citation predictions and human evaluator...

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03142
• PDF: https://arxiv.org/pdf/2603.03142

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

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✨Code2Math: Can Your Code Agent Effectively Evolve Math Problems Through Exploration?

📝 Summary:
Code agents can autonomously generate more complex mathematical problems by evolving existing ones, providing a scalable solution for creating high-difficulty reasoning problems. AI-generated summary ...

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03202
• PDF: https://arxiv.org/pdf/2603.03202
• Github: https://github.com/TarferSoul/Code2Math

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

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✨Fast Matrix Multiplication in Small Formats: Discovering New Schemes with an Open-Source Flip Graph Framework

📝 Summary:
A new open-source C++ framework discovers fast matrix multiplication schemes, improving 79 ranks. It found a 4x4x10 scheme with 115 multiplications, beating Strassen's exponent for that size, and redistributes many schemes to simpler coefficients. Tools are public.

🔹 Publication Date: Published on Mar 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02398
• PDF: https://arxiv.org/pdf/2603.02398
• Project Page: https://github.com/dronperminov/FastMatrixMultiplication
• Github: https://github.com/dronperminov/ternary_flip_graph

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

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✨AgentConductor: Topology Evolution for Multi-Agent Competition-Level Code Generation

📝 Summary:
AgentConductor uses reinforcement learning-optimized multi-agent systems with an LLM-based orchestrator to dynamically generate interaction topologies for code generation, improving accuracy while red...

🔹 Publication Date: Published on Feb 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.17100
• PDF: https://arxiv.org/pdf/2602.17100

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Qwen2 Technical Report

📝 Summary:
The Qwen2 series, comprising 0.5 to 72 billion parameter models, surpasses prior open models across language understanding, generation, multilingualism, coding, math, and reasoning, with exceptional p...

🔹 Publication Date: Published on Jul 15, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2407.10671
• PDF: https://arxiv.org/pdf/2407.10671
• Github: https://github.com/qwenlm/qwen2

🔹 Models citing this paper:
• https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct
• https://huggingface.co/Qwen/QwQ-32B-Preview
• https://huggingface.co/Qwen/Qwen2.5-7B-Instruct

✨ Datasets citing this paper:
• https://huggingface.co/datasets/thunder-research-group/SNU_Thunder-synthetic-instruction-following
• https://huggingface.co/datasets/thunder-research-group/SNU_Thunder-synthetic-coding

✨ Spaces citing this paper:
• https://huggingface.co/spaces/pliny-the-prompter/obliteratus
• https://huggingface.co/spaces/multimodalart/kugelaudio
• https://huggingface.co/spaces/agents-course/First_agent_template

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

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✨Utonia: Toward One Encoder for All Point Clouds

📝 Summary:
Utonia introduces a unified self-supervised transformer encoder for diverse point cloud domains. It enhances perception and aids embodied and multimodal reasoning, aiming for foundation models in sparse 3D data.

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03283
• PDF: https://arxiv.org/pdf/2603.03283
• Project Page: https://pointcept.github.io/Utonia/
• Github: https://github.com/Pointcept/Utonia

🔹 Models citing this paper:
• https://huggingface.co/Pointcept/Utonia

✨ Spaces citing this paper:
• https://huggingface.co/spaces/pointcept-bot/Utonia

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✨Qwen3-Coder-Next Technical Report

📝 Summary:
Qwen3-Coder-Next is an 80-billion-parameter language model that activates only 3 billion parameters during inference, achieving strong coding capabilities through agentic training with verifiable task...

🔹 Publication Date: Published on Feb 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00729
• PDF: https://arxiv.org/pdf/2603.00729
• Project Page: https://github.com/QwenLM/Qwen3-Coder
• Github: https://github.com/QwenLM/Qwen3-Coder

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

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✨AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning

📝 Summary:
AReaL, a fully asynchronous reinforcement learning system, decouples generation and training to achieve higher GPU utilization and up to 2.57x training speedup for large language models on reasoning t...

🔹 Publication Date: Published on May 30, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.24298
• PDF: https://arxiv.org/pdf/2505.24298
• Github: https://github.com/inclusionAI/AReaL

🔹 Models citing this paper:
• https://huggingface.co/inclusionAI/AReaL-boba-2-8B
• https://huggingface.co/inclusionAI/AReaL-boba-2-14B
• https://huggingface.co/inclusionAI/AReaL-boba-2-8B-Open

✨ Datasets citing this paper:
• https://huggingface.co/datasets/inclusionAI/AReaL-tau2-data

✨ Spaces citing this paper:
• https://huggingface.co/spaces/rzvn/Medieval-Village-AI

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨InfoPO: Information-Driven Policy Optimization for User-Centric Agents

📝 Summary:
InfoPO optimizes agent-user collaboration for underspecified requests. It uses an information-gain reward to credit valuable turns that reduce uncertainty, improving decision-making and outperforming multi-turn RL baselines.

🔹 Publication Date: Published on Feb 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00656
• PDF: https://arxiv.org/pdf/2603.00656
• Github: https://github.com/kfq20/InfoPO

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

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#ReinforcementLearning #AI #HumanComputerInteraction #InformationTheory #AIagents

✨Chain of World: World Model Thinking in Latent Motion

📝 Summary:
CoWVLA unifies world-model temporal reasoning with disentangled latent motion representation to improve visuomotor learning efficiency. This new approach overcomes limitations of existing VLA models and outperforms them on robotic simulation benchmarks.

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03195
• PDF: https://arxiv.org/pdf/2603.03195
• Project Page: https://fx-hit.github.io/cowvla-io/
• Github: https://fx-hit.github.io/cowvla-io/

🔹 Models citing this paper:
• https://huggingface.co/hitfx/CoWVLA

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

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#WorldModels #Robotics #MachineLearning #VisuomotorLearning #DeepLearning

✨Surgical Post-Training: Cutting Errors, Keeping Knowledge

📝 Summary:
Surgical Post-Training SPoT efficiently improves LLM reasoning while preventing catastrophic forgetting. It employs data rectification with an Oracle and a novel binary cross-entropy objective. SPoT enhanced Qwen3-8B accuracy by 6.2 percent using minimal data and training time.

🔹 Publication Date: Published on Mar 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01683
• PDF: https://arxiv.org/pdf/2603.01683
• Github: https://github.com/Visual-AI/SPoT

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

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#LLM #CatastrophicForgetting #MachineLearning #AI #DeepLearning

✨Whisper-RIR-Mega: A Paired Clean-Reverberant Speech Benchmark for ASR Robustness to Room Acoustics

📝 Summary:
Whisper-RIR-Mega dataset evaluates ASR model robustness to reverberation by pairing clean and reverberant speech samples with stratified splits based on RT60 and DRR metrics. AI-generated summary We i...

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02252
• PDF: https://arxiv.org/pdf/2603.02252
• Project Page: https://huggingface.co/datasets/mandipgoswami/whisper-rirmega-bench
• Github: https://github.com/mandip42/whisper-rirmega-bench

✨ Datasets citing this paper:
• https://huggingface.co/datasets/mandipgoswami/whisper-rirmega-bench

✨ Spaces citing this paper:
• https://huggingface.co/spaces/mandipgoswami/whisper-rirmega-benchmark

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✨Learning When to Act or Refuse: Guarding Agentic Reasoning Models for Safe Multi-Step Tool Use

📝 Summary:
MOSAIC is a framework aligning agentic models for safe multi-step tool use, employing explicit safety reasoning and refusal. It significantly reduces harmful actions, increases refusal for unsafe tasks, cuts privacy leakage, and preserves benign performance.

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03205
• PDF: https://arxiv.org/pdf/2603.03205
• Project Page: https://aradhye2002.github.io/mosaic-agent-safety/

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

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#AISafety #AIAgents #ResponsibleAI #LLMs #AIAlignment

✨Spilled Energy in Large Language Models

📝 Summary:
Reinterpreting LLM softmax as an Energy-Based Model enables training-free hallucination detection. New energy metrics from output logits identify errors and biases without training overhead, demonstrating robust cross-task generalization.

🔹 Publication Date: Published on Feb 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18671
• PDF: https://arxiv.org/pdf/2602.18671
• Github: https://github.com/OmnAI-Lab/spilled-energy

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

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#LLM #EnergyBasedModels #HallucinationDetection #AISafety #ArtificialIntelligence

✨Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction

📝 Summary:
CCP evaluates LLMs simulating social media users. Supervised fine-tuning improves text structure but degrades semantic accuracy, as models infer from behavioral histories without explicit conditioning. Prioritize authentic behavioral traces.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22752
• PDF: https://arxiv.org/pdf/2602.22752
• Project Page: https://nsschw.github.io/Turing-TWONy/
• Github: https://github.com/nsschw/Conditioned-Comment-Prediction

🔹 Models citing this paper:
• https://huggingface.co/nsschw/echo-Llama-3.1-8B-Instruct-eng
• https://huggingface.co/nsschw/echo-Llama-3.1-8B-Instruct-ger

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

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#LLMs #SocialMedia #AISimulation #NLP #AIResearch

✨Conditioned Activation Transport for T2I Safety Steering

📝 Summary:
Current T2I models generate unsafe content, and linear steering degrades image quality. This paper proposes Conditioned Activation Transport CAT, which uses geometric conditioning and nonlinear transport maps to activate only in unsafe regions. CAT significantly reduces unsafe content generation ...

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03163
• PDF: https://arxiv.org/pdf/2603.03163
• Github: https://github.com/NASK-AISafety/conditional-activation-transport

✨ Datasets citing this paper:
• https://huggingface.co/datasets/NASK-PIB/SafeSteerDataset

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

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#AISafety #TextToImage #GenerativeAI #DeepLearning #AIethics

✨Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modeling

📝 Summary:
A machine learning framework using generative flow networks with experience replay, uniform exploration, and physics-based masking enables fast and accurate radio propagation path sampling with signif...

🔹 Publication Date: Published on Mar 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01655
• PDF: https://arxiv.org/pdf/2603.01655
• Project Page: https://differt.rtfd.io/npjwt2026/notebooks/sampling-paths.html
• Github: https://github.com/jeertmans/sampling-paths

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

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#AI #DataScience #MachineLearning #HuggingFace #Research