Link: https://arxiv.org/pdf/2503.11486
@jiqizhixin
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Boston Dynamics’ Atlas Robot Showcased as Adorable Innovation
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Atlas is absolutely adorable! 😊 @BostonDynamics pic.twitter.com/nTX8USwpui
— 机器之心 JIQIZHIXIN (@jiqizhixin) 20 mars 2025Atlas is absolutely adorable! @BostonDynamics
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Tsinghua Hephaestus Wins RoboCup German Open 2025 Championship
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9:0! Tsinghua Hephaestus won the championship of the RoboCup German Open 2025!
— 机器之心 JIQIZHIXIN (@jiqizhixin) 20 mars 2025
Congratulations! @Tsinghua_Uni https://t.co/3GsRABYgmw9:0! Tsinghua Hephaestus won the championship of the RoboCup German Open 2025! Congratulations! @Tsinghua_Uni
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Kolmogorov: New AI Research Paper Code Released
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Paper: https://
arxiv.org/pdf/2503.13992
Code: https://
github.com/facebookresear
ch/kolmogorov
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Compression Benchmarks: Infinite Problem Instances for AI Evaluation
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The benefits include: An essentially infinite number of problem instances of varying difficulty is readily available, strong baselines already exist, the evaluation metric (compression) cannot be gamed, and pretraining data contamination is highly unlikely.
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Kolmogorov-Test: Compression as Intelligence Metric for LLMs
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Compression = Intelligence? This ICLR 2025 paper introduced the KOLMOGOROV-TEST (KT), a compression-as-intelligence test for code generating LLMs.And the team identified several benefits of KT for both evaluation and training.
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O1-Pro costs 270X more than DeepSeek-R1
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o1-pro is 270X more expensive than DeepSeek-R1! Imagine waking up one day and finding your O1-Pro still running smoothly…
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Using LLMs to Help Write Code: Simon Willison’s Insights
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"Here’s how I use LLMs to help me write code" from Simon Willison, one of the creators of Django. Here are some useful insights. https://
simonwillison.net/2025/Mar/11/us
ing-llms-for-code/
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Multi-Agent Reinforcement Learning Framework DPT-Agent
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Paper: https://
arxiv.org/abs/2502.11882
GitHub: https://
github.com/sjtu-marl/DPT-
Agent
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DPT-Agent Enhances LLM Reasoning and Action Execution
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They empirically demonstrated that DPT-Agent can effectively help LLMs translate correct slow thinking and reasoning into executable actions, thereby enhancing performance.