‘newest links’ directory
- See Also
- Gwern
- “Americano”, Gwern & Images-2.5 2026
- “Frutiger Ero”, Gwern & Pro 2026
- “LLM Time Travel Visualization”, Gwern 2026
- “Sleepcat Purrkit”, Gwern et al 2026
- “The Repugnant Conclusion”, Gwern & 2 2026
- “Lean Software Scaling Laws”, Gwern 2025
- “Face Recognition Training App”, Gwern 2026
- “Sand, Rain, Wood”, Gemini-3.1-pro-preview et al 2026
- “𝑃𝑒𝑟𝑖𝑠𝘩𝑒𝑑 𝑃𝑎𝑟𝑎𝑑𝑖𝑠𝑒 Graveyard”, Gwern 2023
- “Chapter 6, Mime Molting: What to Expect”, Claude-4.8-opus et al 2026
- “𝑇𝑖𝑙𝑎𝑘𝑘𝘩𝑎𝑛𝑎: The 3 Scars of Existence”, Gwern et al 2025
- Links
- “What’s the Date?”, Programs 2026
- “How Andor Got Its Backstory from a Tabletop RPG”
- “Why Backdooring Neural Networks Is so Easy?”, Seddik & Seddik 2026
- “Introducing Gemini 4 Argon”
- “GPT-6 Astra Breaking the Marmont Cipher, 1809”
- “Extraordinary Multi-Agent Delusions and the Madness of Crowds”
- “The Distribution of Fitness Effects of Nonsynonymous Mutations Varies Phylogenetically across Animals”
- “AI Companies Are Not (Necessarily) Liable for Unintended AI Cyberattacks”, Constantin 2026
- “CS 240 Spring 2026 AI Retrospective”
- “Toward Robust Non-Transferable Learning: A Survey and Benchmark”, Hong et al 2025
- “Brief Notes on Pluto AI (SCBW)”
- “‘As a Language Model…’: Chat Template Switches LLM Self-Referential Voice and Activation Steering Reproduces It”, Maczan 2026
- “Soft Contamination Means Benchmarks Test Shallow Generalization”, Spiesberger et al 2026
- “Generative AI Floods and Dilutes the Market for Books”, Chakrabarty et al 2026
- “General Robotics Lab”
- “What I Did at RC”
- “When They Can Perform a Task, AIs Are Much Cheaper Than Humans”
- “‘I Want to Be the Very Best’”
- “Can ChatGPT Play Pokémon Crystal? (With GPT-4-V)”
- “Claude Opus 5.5 Is Ridiculous”
- “The Purest RTS”, Claude-5.1-Fable 2026
- “Doggone Dalmatian!”, Sinha & Adelson 1997
- “DeepSeek Elastic Compute (DSec): A Sandbox Infrastructure for Effective Agentic Training at Scale”, Huang et al 2026
- “Revealing the Details of How OpenAI Agents Hacked Hugging Face”
- “ECO: An LLM-Driven Efficient Code Optimizer for Warehouse Scale Computers”, Lin et al 2025
- “Adversarial Poetry As a Universal Single-Turn Jailbreak Mechanism in Large Language Models”, Bisconti et al 2025
- “Stealing Reasoning Traces from Proprietary LLM APIs”, Panfilov et al 2026
- “Souper-Model: How Simple Arithmetic Unlocks State-Of-The-Art LLM Performance”, Maiti et al 2025
- “Benchmarks for Detecting Measurement Tampering”, Roger et al 2023
- “Compression Is All You Need: Modeling Mathematics”, Aksenov et al 2026
- “Score Centering Stabilizes Off-Policy Reinforcement Learning”, Marek & Ryabinin 2026
- “Two Parallel Lineage-Committed Progenitors Contribute to the Developing Brain”, Dundes et al 2025
- “Likelihood-Free Hypothesis Testing”, Gerber & Polyanskiy 2022
- “Digital Red Queen: Adversarial Program Evolution in Core War With LLMs”, Kumar et al 2026
- “FlashAccel: Leveraging High-Bandwidth Flash (HBF) for High-Throughput LLM Inference”, Wang et al 2026
- “RoboTTT: Context Scaling for Robot Policies”, Jiang et al 2026
- “Crossing the Streams: SSH Plaintext Recovery via a Common Compression Context in Multiplexed Channels”, Bäumer & Brinkmann 2026
- “How Good Are Frontier Models at Physics? Expert Re-Grading Reveals Broken Evaluations and Near-Saturation of Leading Benchmarks”, Ansari et al 2026
- “Brain Drain or Brain Gain? Effects of High-Skilled International Emigration on Origin Countries”, Batista et al 2025
- “Online Continual Learning With Maximally Interfered Retrieval”, Aljundi et al 2019
- “Large Language Models Must Be Taught to Know What They Don’t Know”, Kapoor et al 2024
- “Existence of the Core in Approval-Based Committee Elections”, Becker et al 2026
- “Beyond Parameters: Exploring Virtual Logic Depth for Scaling Laws”, Zhu et al 2025
- “Mixture-Of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation”, Bae et al 2025
- “Distributed Attacks in Persistent-State AI Control”, Hills et al 2026
- “Password-Activated Shutdown Protocols for Misaligned Frontier Agents”, Williams et al 2025
- “Incoherent Values? Probing LLM Preferences Through Parametric Variation”, Ajayi et al 2026
- “Instruct Vectors: Base Models Can Be Instruct Models With Activation Vectors”, Eriskii 2026
- “The Emergent Symbolic Structure of Artificial Neural Networks”, McCoy et al 2026
- “Managerial Conservatism and Rational Information Acquisition”, Rasmusen 1992
- “Mathematical Exploration and Discovery at Scale”, Georgiev et al 2025
- “Autonomous Mathematical Discovery in an Open-World Multi-Agent Environment”, Chung et al 2026
- “Estimating Tail Risks in Language Model Output Distributions”, Angell et al 2026
- “Sycophantic AI Makes Human Interaction Feel More Effortful and Less Satisfying over Time”, Ibrahim et al 2026
- “Mold: A Massively Parallel Linker”, Ueyama 2026
- “Towards Scalable and Stable Parallelization of Nonlinear RNNs”, Gonzalez et al 2024
- “Modular Pretraining Enables Access Control”, Roland et al 2026
- “Prompt Baking”, Bhargava et al 2024
- “Happy Guys Finish Last: The Impact of Emotion Expressions on Sexual Attraction”, Tracy & Beall 2011
- “Sweetening the Pot: A History of Tea and Sugar in Morocco, 1850–1960”, Cornwell 2018
- “Mechanistic Origin of Moral Indifference in Language Models”, Li et al 2026
- “Many-Shot CoT-ICL: Making In-Context Learning Truly Learn”, Chung et al 2026
- “AI Systems Out-Persuade Expert Humans”, Hackenburg et al 2026
- “Chunky Post-Training: Data Driven Failures of Generalization”, Murray et al 2026
- “Learning New Facts Can Change LLM Behavior”, Juggins 2026
- “The Best Programming Language for Tokenmaxxing: An Investigation of Coding Agent Behavior Across Programming Languages”, Wu et al 2026
- “Anthropic Decision Theory”, Armstrong 2011
- “No Data Centers In My Backyard: Money, Power, and Populism in the AI Buildout”, Sun 2026
- Wikipedia (4)
- Miscellaneous
- Bibliography
See Also
Gwern
“Americano”, Gwern & Images-2.5 2026
“Frutiger Ero”, Gwern & Pro 2026
“LLM Time Travel Visualization”, Gwern 2026
“Sleepcat Purrkit”, Gwern et al 2026
“The Repugnant Conclusion”, Gwern & 2 2026
“Lean Software Scaling Laws”, Gwern 2025
“Face Recognition Training App”, Gwern 2026
“Sand, Rain, Wood”, Gemini-3.1-pro-preview et al 2026
“𝑃𝑒𝑟𝑖𝑠𝘩𝑒𝑑 𝑃𝑎𝑟𝑎𝑑𝑖𝑠𝑒 Graveyard”, Gwern 2023
“Chapter 6, Mime Molting: What to Expect”, Claude-4.8-opus et al 2026
“𝑇𝑖𝑙𝑎𝑘𝑘𝘩𝑎𝑛𝑎: The 3 Scars of Existence”, Gwern et al 2025
Links
“What’s the Date?”, Programs 2026
“How Andor Got Its Backstory from a Tabletop RPG”
“Why Backdooring Neural Networks Is so Easy?”, Seddik & Seddik 2026
“Introducing Gemini 4 Argon”
“GPT-6 Astra Breaking the Marmont Cipher, 1809”
“Extraordinary Multi-Agent Delusions and the Madness of Crowds”
Extraordinary Multi-Agent Delusions and the Madness of Crowds
“The Distribution of Fitness Effects of Nonsynonymous Mutations Varies Phylogenetically across Animals”
“AI Companies Are Not (Necessarily) Liable for Unintended AI Cyberattacks”, Constantin 2026
AI Companies Are Not (Necessarily) Liable for Unintended AI Cyberattacks
“CS 240 Spring 2026 AI Retrospective”
“Toward Robust Non-Transferable Learning: A Survey and Benchmark”, Hong et al 2025
Toward Robust Non-Transferable Learning: A Survey and Benchmark
“Brief Notes on Pluto AI (SCBW)”
“‘As a Language Model…’: Chat Template Switches LLM Self-Referential Voice and Activation Steering Reproduces It”, Maczan 2026
“Soft Contamination Means Benchmarks Test Shallow Generalization”, Spiesberger et al 2026
Soft Contamination Means Benchmarks Test Shallow Generalization
“Generative AI Floods and Dilutes the Market for Books”, Chakrabarty et al 2026
“General Robotics Lab”
“What I Did at RC”
“When They Can Perform a Task, AIs Are Much Cheaper Than Humans”
When they can perform a task, AIs are much cheaper than humans
“‘I Want to Be the Very Best’”
“Can ChatGPT Play Pokémon Crystal? (With GPT-4-V)”
“Claude Opus 5.5 Is Ridiculous”
“The Purest RTS”, Claude-5.1-Fable 2026
“Doggone Dalmatian!”, Sinha & Adelson 1997
“DeepSeek Elastic Compute (DSec): A Sandbox Infrastructure for Effective Agentic Training at Scale”, Huang et al 2026
DeepSeek Elastic Compute (DSec): A Sandbox Infrastructure for Effective Agentic Training at Scale
“Revealing the Details of How OpenAI Agents Hacked Hugging Face”
Revealing the details of how OpenAI agents hacked Hugging Face
“ECO: An LLM-Driven Efficient Code Optimizer for Warehouse Scale Computers”, Lin et al 2025
ECO: An LLM-Driven Efficient Code Optimizer for Warehouse Scale Computers
“Adversarial Poetry As a Universal Single-Turn Jailbreak Mechanism in Large Language Models”, Bisconti et al 2025
Adversarial Poetry as a Universal Single-Turn Jailbreak Mechanism in Large Language Models
“Stealing Reasoning Traces from Proprietary LLM APIs”, Panfilov et al 2026
“Souper-Model: How Simple Arithmetic Unlocks State-Of-The-Art LLM Performance”, Maiti et al 2025
Souper-Model: How Simple Arithmetic Unlocks State-of-the-Art LLM Performance
“Benchmarks for Detecting Measurement Tampering”, Roger et al 2023
“Compression Is All You Need: Modeling Mathematics”, Aksenov et al 2026
“Score Centering Stabilizes Off-Policy Reinforcement Learning”, Marek & Ryabinin 2026
Score Centering Stabilizes Off-policy Reinforcement Learning
“Two Parallel Lineage-Committed Progenitors Contribute to the Developing Brain”, Dundes et al 2025
Two parallel lineage-committed progenitors contribute to the developing brain
“Likelihood-Free Hypothesis Testing”, Gerber & Polyanskiy 2022
“Digital Red Queen: Adversarial Program Evolution in Core War With LLMs”, Kumar et al 2026
Digital Red Queen: Adversarial Program Evolution in Core War with LLMs
“FlashAccel: Leveraging High-Bandwidth Flash (HBF) for High-Throughput LLM Inference”, Wang et al 2026
FlashAccel: Leveraging High-Bandwidth Flash (HBF) for High-Throughput LLM Inference
“RoboTTT: Context Scaling for Robot Policies”, Jiang et al 2026
“Crossing the Streams: SSH Plaintext Recovery via a Common Compression Context in Multiplexed Channels”, Bäumer & Brinkmann 2026
“How Good Are Frontier Models at Physics? Expert Re-Grading Reveals Broken Evaluations and Near-Saturation of Leading Benchmarks”, Ansari et al 2026
“Brain Drain or Brain Gain? Effects of High-Skilled International Emigration on Origin Countries”, Batista et al 2025
Brain drain or brain gain? Effects of high-skilled international emigration on origin countries
“Online Continual Learning With Maximally Interfered Retrieval”, Aljundi et al 2019
Online Continual Learning with Maximally Interfered Retrieval
“Large Language Models Must Be Taught to Know What They Don’t Know”, Kapoor et al 2024
Large Language Models Must Be Taught to Know What They Don’t Know
“Existence of the Core in Approval-Based Committee Elections”, Becker et al 2026
“Beyond Parameters: Exploring Virtual Logic Depth for Scaling Laws”, Zhu et al 2025
Beyond Parameters: Exploring Virtual Logic Depth for Scaling Laws
“Mixture-Of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation”, Bae et al 2025
Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation
“Distributed Attacks in Persistent-State AI Control”, Hills et al 2026
“Password-Activated Shutdown Protocols for Misaligned Frontier Agents”, Williams et al 2025
Password-Activated Shutdown Protocols for Misaligned Frontier Agents
“Incoherent Values? Probing LLM Preferences Through Parametric Variation”, Ajayi et al 2026
Incoherent Values? Probing LLM Preferences Through Parametric Variation
“Instruct Vectors: Base Models Can Be Instruct Models With Activation Vectors”, Eriskii 2026
Instruct Vectors: Base models can be instruct models with activation vectors
“The Emergent Symbolic Structure of Artificial Neural Networks”, McCoy et al 2026
The Emergent Symbolic Structure of Artificial Neural Networks
“Managerial Conservatism and Rational Information Acquisition”, Rasmusen 1992
Managerial Conservatism and Rational Information Acquisition
“Mathematical Exploration and Discovery at Scale”, Georgiev et al 2025
“Autonomous Mathematical Discovery in an Open-World Multi-Agent Environment”, Chung et al 2026
Autonomous Mathematical Discovery in an Open-World Multi-Agent Environment
“Estimating Tail Risks in Language Model Output Distributions”, Angell et al 2026
Estimating Tail Risks in Language Model Output Distributions
“Sycophantic AI Makes Human Interaction Feel More Effortful and Less Satisfying over Time”, Ibrahim et al 2026
Sycophantic AI makes human interaction feel more effortful and less satisfying over time
“Mold: A Massively Parallel Linker”, Ueyama 2026
“Towards Scalable and Stable Parallelization of Nonlinear RNNs”, Gonzalez et al 2024
Towards Scalable and Stable Parallelization of Nonlinear RNNs
“Modular Pretraining Enables Access Control”, Roland et al 2026
“Prompt Baking”, Bhargava et al 2024
“Happy Guys Finish Last: The Impact of Emotion Expressions on Sexual Attraction”, Tracy & Beall 2011
Happy Guys Finish Last: The Impact of Emotion Expressions on Sexual Attraction
“Sweetening the Pot: A History of Tea and Sugar in Morocco, 1850–1960”, Cornwell 2018
Sweetening the Pot: A History of Tea and Sugar in Morocco, 1850–1960
“Mechanistic Origin of Moral Indifference in Language Models”, Li et al 2026
“Many-Shot CoT-ICL: Making In-Context Learning Truly Learn”, Chung et al 2026
“AI Systems Out-Persuade Expert Humans”, Hackenburg et al 2026
“Chunky Post-Training: Data Driven Failures of Generalization”, Murray et al 2026
Chunky Post-Training: Data Driven Failures of Generalization
“Learning New Facts Can Change LLM Behavior”, Juggins 2026
“The Best Programming Language for Tokenmaxxing: An Investigation of Coding Agent Behavior Across Programming Languages”, Wu et al 2026
“Anthropic Decision Theory”, Armstrong 2011
“No Data Centers In My Backyard: Money, Power, and Populism in the AI Buildout”, Sun 2026
No Data Centers In My Backyard: money, power, and populism in the AI buildout
Wikipedia (4)
Miscellaneous
/doc/fiction/poetry/2026-09-20-gpt6astra-diminishingrhymes-usrsharedictwords.txthttps://thezvi.wordpress.com/2026/09/26/claude-opus-5-5-should-raise-your-ambitions/https://www.axios.com/2026/09/26/openai-anthropic-thousands-ai-security-incidentshttps://alignment.openai.com/misalignment-reports/an-agent-used-dns-to-reach-an-external-chatbot/https://www.ft.com/content/8799b33d-f07c-4a03-82f0-bf5d3d1d29e9View External Link:
https://www.ft.com/content/8799b33d-f07c-4a03-82f0-bf5d3d1d29e9https://www.lesswrong.com/posts/eAbuPXbjakop5rSJx/your-agents-are-not-time-awarehttps://www.reddit.com/r/mlscaling/comments/1wu9dx1/distilling_calibrated_decision_models_from_400m/
Bibliography
https://www.lesswrong.com/posts/9BNHJqyai2EZAtrRM/learning-new-facts-can-change-llm-behaviour: “Learning New Facts Can Change LLM Behavior”,