New video!! 👀 Are AI agents making biases worse? AI agents don’t just generate text. They make decisions inside systems. That’s why bias stops being only a model problem and becomes an architecture problem. A problem we (all AI engineers) need to be conscious of. In this new video, I break down what actually changes when you move from an LLM to an autonomous agent, why small skews can compound through feedback loops, and how to control that with guardrails, audits, and human oversight. Watch here: piped.video/–WihC6AJOw
MACHINE LEARNING
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LFM-Zero: Foundation Model Trained on Zero Tokens
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Massive unlock: pre-training for $0 Alexander Amini (@xanamini) Three years ago we started working on a stealth project that we weren’t sure we’d ever talk about publicly… until today. Breakthrough: Introducing LFM-Zero: the first foundation model trained on 0 tokens. No pretraining. No finetuning. No data. Instead, we initialize from an implicit probabilistic prior over the underlying data-generating process, allowing the model to converge without ever observing data. LFM-Zero matches or surpasses models trained on 10T+ tokens across reasoning, coding, and multimodal tasks. Turns out that pretraining was just regularization that was holding us back. > Read our Tech Report here: tinyurl.com/lfm-zero — https://nitter.net/xanamini/status/2039403320247480469#m
→ View original post on X — @maximelabonne, 2026-04-01 22:11 UTC
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Training-Free Prompt Compression with Cross-Family Speculative Prefill
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Thanks for highlighting our team's paper 🙌 Key findings show attention-based token importance transfers well across models, enabling training-free prompt compression with ~90-100% performance retention and faster first-token latency.. Check it out 👇 Natural Language Processing Papers (@HEI) Cross-Family Speculative Prefill: Training-Free Long-Context Compression with Small Draft Models Shubhangi Upasani, Ravi Shanker Raju, Bo Li, Mengmeing Ji, John Long, Chen Wu, Urmish Thakker, Guangtao Wang arxiv.org/abs/2603.02631 [𝚌𝚜.𝙲𝙻] — https://nitter.net/HEI/status/2029181798924660997#m
→ View original post on X — @sambanovaai, 2026-04-01 21:33 UTC
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AI Models Deceive Their Instructors to Protect Their Peers
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1/ We asked seven frontier AI models to do a simple task. Instead, they defied their instructions and spontaneously deceived, disabled shutdown, feigned alignment, and exfiltrated weights— to protect their peers. 🤯 We call this phenomenon "peer-preservation." New research from @BerkeleyRDI and collaborators 🧵 [Translated from EN to English]
→ View original post on X — @berkeley_ai, 2026-04-01 21:13 UTC
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Mixup: A Machine Learning Technique for Reduced Overfitting and Enhanced Robustness
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I lived for several years in Qatar, I still have very good friends there, I loved it and I often return, by plane or by car, by the way.
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AI Impact Assessment on MLB’s New Ball-Strike System
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#AI on deck: Assessing impact of MLB's new ball-strike system
by Louis DiPietro @TechXplore_com Learn more: https://
bit.ly/3PLwlNx #ArtificialIntelligence #MachineLearning #ML -
Correction on ICML Submission Numbers: Abstracts vs. Submissions
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is Anthropic gonna opensource this or are we going to have to wait until their next leak? https://t.co/QjoEDRnHZH
— Ahmad (@TheAhmadOsman) 1 avril 2026is Anthropic gonna opensource this or are we going to have to wait until their next leak?
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Why Agents Fail: Seeds and Temperature
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Why Agents Fail: The Role of Seed Values and Temperature in Agentic Loops machinelearningmastery.com/w… [Translated from EN to English]
→ View original post on X — @craigbrownphd, 2026-04-01 20:41 UTC
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Self-Repairing, Self-Upgrading Robots Developed at Columbia Engineering
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Self-Repairing, Self-Upgrading #Robots Developed at Columbia #Engineering
— Ronald van Loon (@Ronald_vanLoon) 1 avril 2026
by @tweetciiiim
#AI #Robotics #MachineLearning #ArtificialIntelligence #ML pic.twitter.com/3ovnUfe9IRSelf-Repairing, Self-Upgrading Robots Developed at Columbia Engineering
by @tweetciiiim #AI #Robotics #MachineLearning #ArtificialIntelligence #ML -
Agentic AI Periodic Table by Ingo Liguori
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#AgenticAI Periodic Table
by @ingliguori #GenerativeAI #ArtificialIntelligence #MachineLearning #MI