Ishaan Panigrahi

on the atlas · 479

• UC buys Ritz-Carlton Half Moon Bay for $193M1 savers
• Martin Weiss on X: "Most of my papers are borderline, because I like writing papers slightly outside the Overton window. So rebuttals are a big part of how I publish. ICLR reviews are out, so here’s my process 🧵 https://t.co/ME0hfVKoy8" / X1 savers
• An Alchemist’s Notes on Deep Learning — An Alchemist's Notes on Deep Learning5 savers
• Takes on “Already Clean” — LessWrong1 savers
• How to Run Better Research With Agent Fleets — LessWrong2 savers
• Asymmetry of verification and verifier’s law — Jason Wei16 savers
• Not Everything in Mech Interp Has to Be Causal — Sida Liu1 savers
• A Safe Path to Open Weights - Thinking Machines Lab10 savers
• Thinking Machines on X: "Introducing Inkling 2 Small. Scoring 31 on the AA Intelligence Index, it's a big jump over its predecessor. Inkling 2 Small is available for fine-tuning and inference on Tinker today, with full weight release planned soon. https://t.co/VR02tw73fJ" / X1 savers
• People Should Resolve Tensions In AI Safety Research — LessWrong4 savers
• The benchmark where AIs have to lie to win1 savers
• Thomas Fel on X: "A science of post-training can be built on interpretability. Models know when they reward hack, and we see it, yet training ignores it. We propose Mechanistic Shaping: using the model’s own concepts to write training rules. In vision, DPO & RL, and its effects on monitoring 🧵 https://t.co/ts4IY825Lf" / X1 savers
• Estimating Training Data Influence by Tracing Gradient Descent1 savers
• Tracing the Persona Circuit: How Large Language Models Encode and Express Character Traits1 savers
• Tracing Persona Vectors Through LLM Pretraining1 savers
• Emotion Concepts and their Function in a Large Language Model7 savers
• “P’s Parties,” by Jhumpa Lahiri | The New Yorker1 savers
• Preventing data poisoning seems surprisingly tractable1 savers
• “Year’s End,” by Jhumpa Lahiri | The New Yorker1 savers
• Stop telling people to pivot to AI safety, I beg you — LessWrong4 savers
• [2610.31955] A Mathematical Theory of the Jacobian Lens1 savers
• Suhas Kotha2 savers
• Fan Pu Zeng1 savers
• Jane Street Blog - A study of sequence weighting at scale1 savers
• Think Like a World Model, Act Like a VLA:Distilling World-Model Representations into Compact Robot Policies2 savers
• LLMs Can Now Read and Write Neural Network Weights - YouTube1 savers
• friendships, play, and love | arielle lok28 savers
• Aaron Mueller on X: "Interested in putting interpretability on more rigorous foundations? Check out our #EMNLP2026 tutorial slides! I learned a ton from my fantastic collaborators @rpatrik96, @_shruti_joshi_, @dhanya_sridhar. Recommended for newcomers and experts alike! 📚 https://t.co/qK9cJdOId0 https://t.co/HxqqbWHRFF" / X1 savers
• “Hell-Heaven,” by Jhumpa Lahiri | The New Yorker1 savers
• Top Performers are Pathologically Ambitious - by Matt Beard35 savers
• Towards understanding score-seeking in LLMs — LessWrong3 savers
• The Hitchhiker’s Guide to Training on Compute Islands | Core Automation7 savers
• Why study alignment interventions on pre-RL checkpoints? — LessWrong1 savers
• Alignment Forecasting: Predicting Misalignment from Training Data — LessWrong1 savers
• CS234: Reinforcement Learning Winter 20261 savers
• Would This Change Your Answer? Evaluating Explanations of LLM Behavior in the Wild with Counterfactual Experiments1 savers
• Language Model Circuits Are Sparse in the Neuron Basis | Transluce AI5 savers
• AI Safety Opportunities1 savers
• The nature of LLM algorithmic progress (v2) — LessWrong2 savers
• Open Models Dashboard — Interconnects1 savers
• Atoms CTO: Intelligence, Regrets, Travis Kalanick Stories | Brian Attwell - YouTube1 savers
• Robert Caro – The master biographer reflects at 91 - YouTube3 savers
• Silicon Valley Walks the Jagged Edge - YouTube1 savers
• Sanity Checks for Sanity Checks for Sparse Autoencoders — LessWrong1 savers
• Automated Subtle Emergent Misalignment Research2 savers
• AI Boom or Bust? – #122 - YouTube1 savers
• Story Imprinting: AI Assistants Absorb Traits from Human Characters They Resemble — LessWrong2 savers
• An updated Model Spec for more capable and beneficial AI | OpenAI2 savers
• [D] MLP's are actually nonlinear ➞ linear preconditioners (with visuals!) : r/MachineLearning1 savers
• Measuring Reward-Seeking by Instilling Contrastive Beliefs4 savers
• Why Sam Altman is against a single superintelligence - YouTube1 savers
• How we detect frontier model misalignment | OpenAI3 savers
• A basic case for differentially accelerating conceptual reasoning capabilities — LessWrong10 savers
• Patching reward hacks won’t fix reward hacking — LessWrong1 savers
• Why United’s CEO thinks air traffic control is the problem - YouTube1 savers
• Next-Latent Prediction Transformers Learn World Models feat. Jayden Teoh from MIT - YouTube1 savers
• Intel: How the world's most advanced chips are made — Flashcards3 savers
• Pretraining at scale without a pretraining team — Mirendil4 savers
• formal-math/eth at 16a5ce41304fcdaea4e0be7c132e1c361dc26da5 · anthropics/formal-math · GitHub1 savers
• neuronpedia on X: "an interpretability "encyclopedia"? for accelerating interp research? 🤯 btw 7M+ neurons for qwen3-32B, gemma 4 31B, and llama 3.3 70B are now on neuronpedia: https://t.co/Ujo1L8yHEs https://t.co/E102yMpCn5 https://t.co/a3F8wczthc" / X1 savers
• The Ultimate Neolab List3 savers
• Brian Kernighan: Inside Bell Labs, The Best Programmer, Why C Won - YouTube1 savers
• Cognitive Mercantilism - by Venkatesh Rao - Contraptions7 savers
• Different eval awareness may track different graders — LessWrong1 savers
• Asian American Psycho1 savers
• Blunt-Force Ethnic Credibility | Som-Mai Nguyen18 savers
• Why I Hate Asian-American Fiction1 savers
• Your Softmax Is Broken. Here's the Fix. - YouTube1 savers
• Knowing How Is Not Knowing What | Yunze Xiao1 savers
• Is the Agent Harness Dead? — Jediah Katz, SpaceXAI - YouTube1 savers
• amosblomqvist/learnharness: Agent harness for learning difficult things. ·1 savers
• Not All Open Model Tokens Are the Same | YC Paper Club - YouTube1 savers
• Training Models Too Big for One GPU | ZeRO & FSDP - YouTube1 savers
• How trillion-parameter AI models work and how to serve them - YouTube2 savers
• All languages are NOT created (tokenized) equal1 savers
• What's Continual Learning, and Why Might We Expect To See It In Advanced LLM Agents? — LessWrong3 savers
• What will the CPU of 2045 look like? - YouTube1 savers
• Many Biases, One Circuit: Post-Training Tunes a Pretrained Cue-Routing Mechanism1 savers
• Predictive Data Debugging: Reveal and Shape What Your Model Learns, Before You Train - Goodfire1 savers
• SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training1 savers
• Liv on X: "I think there are some issues in the recent “Already Clean” essay, and I’m concerned it may have incorrectly updated people on important safety-relevant questions. My thoughts (including some reproductions): https://t.co/ou3RwzAkOf" / X1 savers
• Are Neural Networks Really Clean? - McNair Shah1 savers
• nev on X: "There has been much discussion recently about how interpretable neurons in LLMs are. Today we're announcing our paper from January's cohort of Anthropic Fellows, where we show neurons may be a stronger basis for interpretability than commonly assumed. 🧵 (1/6) https://t.co/qE7iyyhY2f" / X1 savers
• Jane Street Blog - Flex Model: an event-driven feature engineering system1 savers
• Reducing Reward Hacking by Localizing Bad Behavior Instead of Penalizing It2 savers
• PaperLeet - LeetCode for AI Researchers2 savers
• RLMs are Compositional Generalizers with Alex Zhang from MIT - YouTube1 savers
• You could have invented transformers from memory | Alex Wang13 savers
• Productivity advice | Zhengdong10 savers
• Sauers on X: "Language models can learn to detect when you read their activations. How? 🧵 https://t.co/hhQcgHVrE8" / X1 savers
• Liberal Indo-Optimism1 savers
• How India Became an IT Outsourcing Giant1 savers
• [2610.20748] Interpreting Language Model Parameters1 savers
• dlab Open Source Week: Frontier AI on Your Own Hardware — Tim Dettmers2 savers
• What does it mean to change an elasticity? | Jordan Rosenthal-Kay1 savers
• The people holding up the internet | Data Drop3 savers
• Inside TPU and GPU Clusters: The Anatomy of Collective Communication3 savers
• Shimeng Yu - YouTube1 savers
• junuxyz/mlsys-notes: Learning notes for understanding modern Machine Learning System.1 savers
• Neural networks are not already clean — LessWrong6 savers