OpenAI's Astra Solves 10 Decade-Old Math Problems
A research-stage multi-agent system cracked open problems that resisted humans for years — for about $2,000 of compute.
Topic
Reasoning
How models think longer, plan, and check their own work.
Claude Opus 5: Frontier-Class, at Half the Price of Fable
A perfect 42/42 on the 2026 IMO, 96% on SWE-bench Verified — at unchanged Opus pricing.
Google DeepMind's New Leadership and the Race to Catch Up
A leadership change atop DeepMind underscores how intense the frontier competition has become.
Long-Horizon Agents: AI That Works for Hours
The frontier is shifting from instant answers to systems that grind on a task for hours or days.
Multi-Agent Systems for Genuinely Hard Problems
Decompose, delegate to sub-agents, verify — the pattern behind AI cracking problems a single model can't.
Machine-Verified Proofs: Why They Change Everything
When an AI's answer can be checked by a machine, you don't have to trust the AI at all.
The Economics of Test-Time Compute
Spending more inference to get a better answer is now a dial you turn — and pay for.
Is AI Doing Real Research Now?
AI systems are solving genuinely open problems. What does that mean — and what are the limits?
Reasoning Models vs Agents: Not the Same Thing
One thinks harder before answering; the other acts in the world. The distinction matters.
Why Benchmarks Like IMO and SWE-bench Matter (and Don't)
Model launches lead with benchmark scores. Here's how to read them without being fooled.
What Comes After the Current Frontier?
Next-gen pretraining runs, multi-agent research systems, and the shape of the coming leap.
Are We Hitting a Scaling Wall?
For years, bigger meant better. The debate over whether that still holds is the field's biggest question.
AGI: What the Term Actually Means
Everyone argues about artificial general intelligence. Almost no one agrees what it is.
The Bitter Lesson, Revisited
The most quoted idea in AI: general methods that scale beat clever hand-crafted ones.
Emergent Abilities: Real or Measurement Artifact?
Do new skills suddenly 'appear' as models scale — or does it just look that way?
Chain-of-Thought Prompting, Explained
The simple trick of asking a model to 'think step by step' — and why it works.
The Reversal Curse: A Strange LLM Blind Spot
A model can learn 'A is B' and still fail at 'B is A.' Here's why that's revealing.
Few-Shot vs Zero-Shot Prompting
Sometimes you show examples; sometimes you just ask. Knowing which is a real skill.
Self-Consistency: Voting for Better Answers
Generate several reasoning paths, then take the majority answer. Simple, and it works.
Reward Models: The Judge Behind Aligned AI
Behind every well-behaved model is another model that learned to score answers.
How to Actually Measure Hallucination
'The model hallucinates less' is a claim. Here's how you turn it into a number.
Guardrails vs Fine-Tuning for Safe AI
Two ways to keep a model in bounds — one wraps around it, one changes it.
Interpretability: Looking Inside the Black Box
We build models that work without fully understanding how. Interpretability is the effort to change that.
DPO vs PPO: How Preference Tuning Works
Two ways to teach a model what humans prefer — one simple, one powerful.
Why Self-Correction Beats Longer Chains
The best reasoning models aren't the ones that think the longest — they're the ones that catch their own mistakes.
Alignment: What It Means and Why It's Hard
Getting AI to do what we actually want turns out to be a deep, unsolved problem.
GRPO and the Rise of Group-Relative RL
The reinforcement-learning trick behind many of 2026's reasoning models.
RLHF, Explained Simply
How raw language models became helpful assistants.
The Real Reason Models Hallucinate
Hallucinations cluster where training data is thin — not absent, just rare.
The Data Contamination Problem
When benchmark answers leak into training data, high scores mean memorization — not skill.
Reasoning with Tools: When to Compute, Not Guess
A model that reaches for a calculator beats one that guesses at arithmetic.
Test-Time Compute, One Year On
Spending compute at inference reshaped the field. Here's where the curve stands now.
AI Evaluation: Beyond Benchmarks
Public benchmarks are a starting point, not an answer. Real evaluation looks different.
Red-Teaming AI: How Models Get Stress-Tested
Before a model ships, people are paid to try to break it. Here's how, and why.
Constitutional AI: Training Models with Principles
Instead of humans labeling every bad answer, the model critiques itself against a set of rules.
Verification Is the Bottleneck in Reasoning
A good verifier multiplies the value of every attempt a model makes.
Can a Model Know When It's Wrong?
Calibration — matching confidence to correctness — is the quiet key to trustworthy AI.
The AI Scientist Reaches Nature
Sakana's system for automated discovery — idea to experiment to paper — crossed a real milestone.
What Comes After the Transformer?
The architecture behind every modern model has ruled for years. What might replace it?
Speculative Decoding, Explained
A small draft model guesses ahead; a big model checks the work. The result is 2–3x faster.
How to Read AI Benchmark Claims
Every new model 'leads the benchmarks.' Here's how to tell signal from marketing.
Distillation: How Small Models Get Smart
Train a small student on a large teacher — including how it reasons.
Long-Horizon Planning Is the Frontier
Agents are great at single steps and fragile across many. Fixing that is the game.
The Shift to Test-Time Compute
The biggest change in 2025 wasn't a bigger model — it was letting models think longer.
Multi-Agent Systems, Explained
When one agent isn't enough, a coordinator delegates to specialists.
Video Models Are Becoming World Models
Generating believable video means learning how the world behaves.
What Makes an AI Agent Actually Work
Everyone is shipping agents. Most of the difficulty is not the model.