The Archive

Ben Guez has "a bunch of potential international wives in [his] DMs," thanks to an automated script he set up using OpenClaw, Claude code, and Instagram trials.

SA-HGNN applies hyperbolic graph neural networks to EEG data, capturing hierarchical brain connectivity for depression recognition.

Google tries balancing AI data center emissions with clean energy efforts.

OpenAI has floated giving the US government a 5 percent ownership stake as a way of easing tensions with the Trump administration and blunting mounting public backlash against AI, according to the Financial Times. CEO Sam Altman argued that giving the public a financial interest in the company would be the best way to share the upside of AI, the FT reported, citing two unnamed people familiar with the talks. He's said to have first pitched the idea to Trump early last year. Altman reportedly suggested the 5 percent figure. Based on OpenAI's latest funding round, which ended with the company v...

Newsletter post noting absence of significant AI industry announcements on a given day.

AIEWF speakers debate autoresearch and software factory vision, raising concerns about human agency and control in AI-driven development.

Neo is Bhavin Turakhia’s fifth venture and his latest involving enterprise software. This time he's taking on Microsoft Office, Google Apps with AI.

Introspection co-founder explains autoresearch loops, agent recipes, and self-improving systems while arguing humans remain essential to AI software development.

Cursor's Forward Deployed Engineers help enterprises implement AI agents as software factories, per Pauline Brunet.

SpaceX reportedly showed investors a "handset-like" AI device before going public. It could be another signal SpaceX wants to expand into wireless.

The actor and investor is joining forces with Morgan Beller, who was previously a GP at NFX, to invest in early-stage startups.

Here are Google’s latest AI updates from June 2026.

Framework measuring gap between LLM-generated and human research ideas via reverse-engineered paper lineages and two-axis evaluation.

Layer-wise analysis shows single transformer layer recovers most RL post-training gains, challenging uniform parameter update assumptions.

Language-critique framework uses natural language supervision instead of scalar signals for imitation learning from suboptimal demonstrations.

AutoMem treats memory management as trainable skill, enabling LLMs to autonomously decide file-system operations and knowledge organization.

Theoria verification architecture converts solutions into auditable state-transition sequences with explicit justifications between formal proofs and opaque scoring.

State-prediction separation hypothesis splits transformer computation into distinct streams for token prediction and state storage, improving efficiency.

FurnitureVLA enables long-horizon bimanual furniture assembly via vision-language-action models with VR teleoperation and progress-enhanced training.

Audit exposes reliability issues in coding-agent benchmarks (GSO, SWE-Perf, SWE-fficiency): runtime instability, scoring artifacts, selection bias.

Cloudflare is giving AI companies until September 15 to separate web crawlers used for search from those used for AI training and agents, or risk being blocked by default on many publisher sites.

Cartridge distillation detects stealth biases in LLMs by exposing preferential signals in logit distributions invisible to text-based analysis.

TiRex-2 extends xLSTM-based time series foundation model to multivariate forecasting with streaming and covariate dependencies.

GPU-parallel linearization error bounds for robust optimal control of nonlinear and neural network dynamics enable real-time planning with certified constraints.

World from Motion reconstructs freely renderable dynamic 3D Gaussian scenes from monocular video via conditioned video models and synthetic artifacts.

Empirical comparison of quantum vs. classical ML across seven model pairs finds insufficient evidence for quantum ML computational advantages.

Constraint programming and scheduling method optimizes resource utilization in autonomous labs for metal-organic framework synthesis experiments.

Neural Certificate Pricing exploits polynomial verification in combinatorial optimization by training neural networks to predict dual prices for certificates.

Adversarial generator-discriminator framework augments RLVR with learned style discriminator to prevent diversity collapse and reward hacking in LM training.

QuasiMoTTo uses quasi-Monte Carlo sampling to reduce redundancy in parallel test-time inference scaling for language models.