Ask the Right Comparison:Bias-Aware Bayesian Active Top-$k$ Ranking with LLM Judges
Bias-aware Bayesian active ranking framework identifies top-k items under fixed budget while mitigating systematic biases in LLM judge comparisons.
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Bias-aware Bayesian active ranking framework identifies top-k items under fixed budget while mitigating systematic biases in LLM judge comparisons.
Structured Gaussian process classifier integrates biological pathway graphs into kernel design for high-dimensional omics classification with uncertainty quantification.
WBMM optimizes large-kernel convolutions via windowed batching and regular memory access, improving efficiency on large feature maps.
Guided Action Flow enables test-time guidance of frozen SmolVLA policies using learned action-chunk critics for robot manipulation.
Improved Fourier Neural Operators for Rayleigh-Bénard convection via time-increment prediction achieve faster inference with 314k parameters.
SUNTA uses prediction-error-driven chunking in hierarchical state-space models for improved long-horizon video prediction.
Evolutionary Wave Function Collapse combines procedural content generation with evolutionary search over small input examples for level generation.
HaloGuard 1.0 releases open-weights constitutional safety classifier achieving state-of-the-art multilingual prompt-safety performance at 1/10 model size.
Maven framework adds evidence-state value functions to long-context RL, rewarding intermediate reasoning transitions for improved synthesis.
kNNGuard provides training-free LLM guardrails via multi-layer kNN over hidden activations, requiring only 50 labeled examples.
ADTC formalizes exhaustive analysis of decision trees via algebraic model counting for explainability verification.
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…
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.
Godot project bans AI-assisted code contributions, citing maintainer concerns about contributor understanding and code quality.
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.