Cultural Evolution of Cooperation among LLM Agents A study examining how large language model (LLM) agents evolve cooperation and social norms over generations, specifically focusing on indirect reciprocity. Problem: Limited understanding of how multiple LLM agents interact
MACHINE LEARNING
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Grokking Complexity Dynamics: Memorization to Generalization
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The Complexity Dynamics of Grokking A study on how neural network complexity dynamics explain the grokking phenomenon, where models transition from memorization to generalization long after overfitting. Problem: Grokking challenges our understanding of generalization in
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Adaptive Computation Modules: Efficient Token-Level Conditional Inference
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Adaptive Computation Modules: Granular Conditional Computation for Efficient Inference A neural network module that dynamically adapts computational load per token, reducing inference costs without sacrificing accuracy. Problem: Transformer models are computationally
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MetaMorph: Multimodal LLM Extension via Instruction Tuning
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MetaMorph: Multimodal Understanding and Generation via Instruction Tuning A method to extend LLMs for unified visual and textual generation, leveraging instruction tuning for efficient multimodal adaptation. Problem: Unified models for visual understanding and generation
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Confusion Over o1 vs o1 Pro Model Claims
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Part of the reason this is so confusing IMO is they don’t make the same claim about o1 pro, which is still a mystery modification to the purely autoregressive o1. All they’ve really said about pro I think is that it’s not merely o1 with higher reasoning_effort.
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Limits of LLM self-judgment and verification
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It’s not that easy in practice — you could LLM-judge all those things and the verifier would still accept reddit-tier meme slop. If it were that easy, LLMs could apply the meta-strategy of decomposing and self-judging to solve everything.
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ChatGPT for Data Analysis: A Beginner’s Guide
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ChatGPT for Data Analysis: A Beginner’s Guide https://
bit.ly/3CUGjpv
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
ARC-AGI-1 Saturation: Ensemble Methods Achieving 81% Score
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what was this thing btw? "Moreover, ARC-AGI-1 is now saturating – besides o3's new score, the fact is that a large ensemble of low-compute Kaggle solutions can now score 81% on the private eval" big ensemble of heuristics?
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Journal-Style Review Process for NeurICMLR Conference Submissions
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Sounds like you want a journal (e.g., JMLR): a thorough process for each submission. What you describe is already the status quo in NeurICMLR.
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Abacus AI ChatLLM: Data Science Co-Pilot Revolutionizes ML Workflow
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The future of #DataScience #MachineLearning is now! => Tell the @AbacusAI ChatLLM #AI Assistant ("Data Science Co-Pilot") to:
— Kirk Borne (@KirkDBorne) 21 décembre 2024
—Clean, Visualize & Explore data.
—Create #ML features.
—Train & Deploy models.
—Monitor models in production
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>=> an incredible aid to #DataScientists pic.twitter.com/OpvXmxWGd5The future of #DataScience #MachineLearning is now! => Tell the @AbacusAI ChatLLM #AI Assistant ("Data Science Co-Pilot") to:
—Clean, Visualize & Explore data.
—Create #ML features.
—Train & Deploy models.
—Monitor models in production >=> an incredible aid to #DataScientists