✨The Sequential Edge: Inverse-Entropy Voting Beats Parallel Self-Consistency at Matched Compute

📝 Summary:
Sequential scaling for language model reasoning consistently outperforms parallel self-consistency at matched compute, achieving significant accuracy gains. The paper introduces inverse-entropy weighted voting to further enhance sequential scaling, establishing it as the superior test-time strate...

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02309
• PDF: https://arxiv.org/pdf/2511.02309

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#LLM #AIReasoning #SelfConsistency #SequentialScaling #InverseEntropy

🤖🧠 DeepAgent: A New Era of General AI Reasoning and Scalable Tool-Use Intelligence

🗓️ 09 Nov 2025
📚 AI News & Trends

Artificial intelligence has rapidly progressed from simple assistants to advanced reasoning systems capable of complex problem-solving. As tasks demand more autonomy, adaptability and real-world interaction, the AI field has entered the era of intelligent agent systems. These agents are expected not just to answer questions, but to think, plan, search, act and interact across digital ...

#GeneralAI #ArtificialIntelligence #AIReasoning #IntelligentAgents #ScalableAI #ToolUseAI

✨RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments

📝 Summary:
RLVE improves language model reasoning by dynamically adjusting problem difficulty in verifiable environments. This adaptive approach significantly outperforms static environments and traditional RL, yielding a 3.37% average improvement on reasoning benchmarks.

🔹 Publication Date: Published on Nov 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07317
• PDF: https://arxiv.org/pdf/2511.07317
• Github: https://github.com/Zhiyuan-Zeng/RLVE

🔹 Models citing this paper:
• https://huggingface.co/hamishivi/Nemotron-Research-Reasoning-Qwen-1.5B-v2-RLVE
• https://huggingface.co/hamishivi/OpenThinker3-1.5B-RLVE

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#ReinforcementLearning #LLMs #AI #AIReasoning #AdaptiveLearning

✨Tiny Model, Big Logic: Diversity-Driven Optimization Elicits Large-Model Reasoning Ability in VibeThinker-1.5B

📝 Summary:
VibeThinker-1.5B, a 1.5B-parameter model, uses the Spectrum-to-Signal Principle to achieve superior reasoning. It outperforms much larger models on math and coding benchmarks, proving small models can deliver advanced AI at low cost.

🔹 Publication Date: Published on Nov 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06221
• PDF: https://arxiv.org/pdf/2511.06221
• Github: https://github.com/WeiboAI/VibeThinker

🔹 Models citing this paper:
• https://huggingface.co/WeiboAI/VibeThinker-1.5B
• https://huggingface.co/Mungert/VibeThinker-1.5B-GGUF

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#SLM #AIReasoning #ModelOptimization #MachineLearning #EfficientAI

✨Reasoning via Video: The First Evaluation of Video Models' Reasoning Abilities through Maze-Solving Tasks

📝 Summary:
VR-Bench evaluates video models' spatial reasoning using maze-solving tasks. It demonstrates that video models excel in spatial perception and reasoning, outperforming VLMs, and benefit from diverse sampling during inference. These findings show the strong potential of reasoning via video for spa...

🔹 Publication Date: Published on Nov 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.15065
• PDF: https://arxiv.org/pdf/2511.15065
• Project Page: https://imyangc7.github.io/VRBench_Web/
• Github: https://github.com/ImYangC7/VR-Bench

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#VideoModels #AIReasoning #SpatialAI #ComputerVision #MachineLearning

✨V-ReasonBench: Toward Unified Reasoning Benchmark Suite for Video Generation Models

📝 Summary:
V-ReasonBench is a new benchmark to evaluate generative video models' reasoning across structured problem-solving, spatial cognition, pattern inference, and physical dynamics. It uses diverse tasks to reveal dimension-wise differences in models, aiming to support development of human-aligned reas...

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16668
• PDF: https://arxiv.org/pdf/2511.16668
• Project Page: https://oahzxl.github.io/VReasonBench/
• Github: https://github.com/yangluo7/V-ReasonBench

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#VideoGeneration #AIReasoning #GenerativeAI #Benchmarking #MachineLearning

✨REASONEDIT: Towards Reasoning-Enhanced Image Editing Models

📝 Summary:
REASONEDIT integrates MLLM reasoning thinking and reflection into image editing models. This enables a thinking-editing-reflection loop, improving instruction understanding and editing accuracy by interpreting abstract instructions and correcting results. The approach achieves significant perform...

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22625
• PDF: https://arxiv.org/pdf/2511.22625

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#ImageEditing #AIReasoning #MLLM #ComputerVision #AI

✨ORION: Teaching Language Models to Reason Efficiently in the Language of Thought

📝 Summary:
ORION models compress reasoning into ultra-compressed structured tokens, inspired by Mentalese. This reduces reasoning steps by 4-16x, cuts inference latency by 5x, and training costs by 7-9x while maintaining high accuracy.

🔹 Publication Date: Published on Nov 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22891
• PDF: https://arxiv.org/pdf/2511.22891

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#LLM #AI #AIReasoning #CognitiveAI #DeepLearning

✨Schoenfeld's Anatomy of Mathematical Reasoning by Language Models

📝 Summary:
This paper introduces ThinkARM, a framework based on Schoenfelds Episode Theory, to abstract LLM reasoning traces into functional steps. It reveals distinct thinking dynamics and structural differences in models solving math problems, with exploration being key for correctness. This makes LLM rea...

🔹 Publication Date: Published on Dec 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19995
• PDF: https://arxiv.org/pdf/2512.19995
• Github: https://github.com/MingLiiii/ThinkARM

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#LLM #AIReasoning #MathematicalReasoning #AI #MachineLearning

✨Fantastic Reasoning Behaviors and Where to Find Them: Unsupervised Discovery of the Reasoning Process

📝 Summary:
This paper introduces RISE, an unsupervised framework using sparse auto-encoders to discover and control LLM reasoning behaviors. It identifies interpretable reasoning vectors like reflection and backtracking, enabling targeted interventions and discovery of novel behaviors without retraining.

🔹 Publication Date: Published on Dec 30, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23988
• PDF: https://arxiv.org/pdf/2512.23988

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#LLM #AI #MachineLearning #AIReasoning #Interpretability