This research paper from Apple is been quite controversial for several reasons, first of which, Apple lagging behind the AI race: https://machinelearning.apple.com/research/illusion-of-thinkinghttps:/

We introduce MEMOIR — a scalable framework for lifelong model editing that reliably rewrites thousands of facts sequentially using a residual memory module. https://x.com/qinym710/status/1933514852313

What if an LLM could update its own weights? Meet SEAL: a framework where LLMs generate their own training data self-edits to update their weights in response to new inputs. Self-editing is learned vi

🔥 “Reasoning” features learnt by an SAEs Sparce Auto Encoders can be transferred as is across MODELS and datasets is super cool and similar in spirit to Mistral’s finding that there exists a low dim

🔥 SakanaAILabs: We’re excited to introduce Text-to-LoRA: a Hypernetwork that generates task-specific LLM adapters LoRAs based on a text description of the task. Catch our presentation at ICML2025! ht

🔥 deedydas: DeepSeek just dropped the single best end-to-end paper on large model training. https://x.com/deedydas/status/1924512147947848039

🔥 deedydas: The BEST AI report in the world just dropped and I read all 340 pages so you don’t have to. https://x.com/deedydas/status/1929381310856151280