✨MME-CC: A Challenging Multi-Modal Evaluation Benchmark of Cognitive Capacity

📝 Summary:
MME-CC is a new vision-grounded benchmark to evaluate multimodal large language models cognitive capacity in spatial, geometric, and knowledge-based reasoning tasks. It reveals that while some models lead, spatial and geometric reasoning remain broadly weak. This highlights the need for better ev...

🔹 Publication Date: Published on Nov 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.03146
• PDF: https://arxiv.org/pdf/2511.03146
• Project Page: https://randomtutu.github.io/MME-CC/

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#MultimodalAI #LLMs #Benchmarking #CognitiveAI #ComputerVision

✨Cognitive Foundations for Reasoning and Their Manifestation in LLMs

📝 Summary:
LLMs underutilize cognitive elements and meta-cognitive controls, leading to reasoning gaps. A new framework shows models fail to spontaneously deploy successful strategies. Test-time guidance significantly improves their performance on complex problems.

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16660
• PDF: https://arxiv.org/pdf/2511.16660
• Project Page: https://tinyurl.com/cognitive-foundations
• Github: https://github.com/pkargupta/cognitive_foundations/

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#LLMs #CognitiveAI #Reasoning #ArtificialIntelligence #DeepLearning

✨From Pixels to Feelings: Aligning MLLMs with Human Cognitive Perception of Images

📝 Summary:
MLLMs struggle with human cognitive perception of images like memorability or aesthetics. CogIP-Bench evaluates this gap, showing post-training significantly improves alignment. This enhances human-like perception and improves creative AI tasks.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22805
• PDF: https://arxiv.org/pdf/2511.22805
• Project Page: https://follen-cry.github.io/MLLM-Cognition-project-page/

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#MLLM #CognitiveAI #ImagePerception #AIAlignment #AIResearch

✨WiseEdit: Benchmarking Cognition- and Creativity-Informed Image Editing

📝 Summary:
WiseEdit is a new benchmark for evaluating image editing models, focusing on cognition and creativity. It decomposes editing into Awareness, Interpretation, and Imagination tasks, assessing declarative, procedural, and metacognitive knowledge. This reveals limitations in current models.

🔹 Publication Date: Published on Nov 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00387
• PDF: https://arxiv.org/pdf/2512.00387
• Project Page: https://qnancy.github.io/wiseedit_project_page/

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#ImageEditing #ComputerVision #AIResearch #CognitiveAI #CreativeAI

✨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

✨The Curious Case of Analogies: Investigating Analogical Reasoning in Large Language Models

📝 Summary:
LLMs can encode high-level relational concepts for analogies but struggle with missing relational information and transfer to new entities. Success depends on strong structural alignment. Their analogical reasoning is emerging but limited compared to humans.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20344
• PDF: https://arxiv.org/pdf/2511.20344

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#LLMs #AnalogicalReasoning #AIResearch #NaturalLanguageProcessing #CognitiveAI

✨SkillFactory: Self-Distillation For Learning Cognitive Behaviors

📝 Summary:
SkillFactory fine-tunes models to learn cognitive skills using self-generated data before reinforcement learning. This self-distillation method enhances robustness and generalization post-RL, enabling models to effectively utilize acquired cognitive skills.

🔹 Publication Date: Published on Dec 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04072
• PDF: https://arxiv.org/pdf/2512.04072

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#SelfDistillation #ReinforcementLearning #CognitiveAI #MachineLearning #AIResearch

✨MemoBrain: Executive Memory as an Agentic Brain for Reasoning

📝 Summary:
Long-horizon tasks strain tool-augmented agents due to accumulating context. MemoBrain is an executive memory model that organizes and prunes reasoning steps, maintaining a compact, high-salience backbone within a fixed context. This improves coherent, goal-directed reasoning.

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08079
• PDF: https://arxiv.org/pdf/2601.08079
• Github: https://github.com/qhjqhj00/MemoBrain

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#AIagents #ExecutiveMemory #Reasoning #LLM #CognitiveAI

✨LoongFlow: Directed Evolutionary Search via a Cognitive Plan-Execute-Summarize Paradigm

📝 Summary:
LoongFlow is a self-evolving agent that integrates LLMs into a cognitive Plan-Execute-Summarize PES paradigm for directed evolutionary search. It prevents premature convergence by balancing exploration and exploitation with a hybrid memory system. LoongFlow achieves superior solutions 60% more ef...

🔹 Publication Date: Published on Dec 30, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24077
• PDF: https://arxiv.org/pdf/2512.24077
• Project Page: https://github.com/baidu-baige/LoongFlow
• Github: https://github.com/baidu-baige/LoongFlow

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#EvolutionarySearch #LLMs #CognitiveAI #AIAgents #Optimization

✨Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering

📝 Summary:
ML-Master 2.0 enables ultra-long-horizon AI autonomy for machine learning engineering. It uses Hierarchical Cognitive Caching to accumulate knowledge from execution, decoupling short-term actions from long-term strategy, achieving state-of-the-art results.

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10402
• PDF: https://arxiv.org/pdf/2601.10402
• Project Page: https://sjtu-sai-agents.github.io/ML-Master/
• Github: https://github.com/sjtu-sai-agents/ML-Master

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#AI #MachineLearning #AutonomousAI #AIAgents #CognitiveAI