The Archive

OpenAI claims its reasoning model disproved a geometry conjecture unsolved since 1946 — and this time, the mathematicians who exposed its last embarrassing claim are backing it up.

Autonomous AI agents are taking on all types of work for businesses: routing logistics fleets, triaging support tickets, generating code, and orchestrating... Autonomous AI agents are taking on all types of work for businesses: routing logistics fleets, triaging support tickets, generating code, and orchestrating multistep workflows. How do you take a general-purpose model and make it excel at your specific task? Customization provides an agent with the right capabilities. This post explains nine techniques for customizing AI agents… Source

Initially billed as an "AI butler," Iris watches what happens on a user's desktop and automatically learns how to do tasks for them, its co-founder says.

Reddit speculation that Alibaba's Qwen will release a 27B model; lacks confirmation or roadmap details.

OpenAI's general-purpose model autonomously solved the planar unit distance problem, a famous 80-year-old Erdős conjecture, discovering new constructions outperforming grid-based solutions.

Google I/O 2026: Gemini Omni and 99 other announcements; focus on multimodal AI and platform expansions.

Reddit discussion on ML PhD admissions competitiveness and networking requirements across regions.

OpenAI model contributes to proof challenging 80-year-old mathematical conjecture, demonstrating general-purpose reasoning for novel knowledge production.

OpenAI model disproved a longstanding conjecture in discrete geometry, demonstrating AI capability in mathematical research and theorem discovery.

Midjourney reports TPU infrastructure choice delayed research by ~1 year; regrets not exclusive NVIDIA commitment.

Reddit post with unclear title and no content; appears to be incomplete or user error.

CARV framework reduces variance in Monte Carlo gradient estimation for diffusion-model-based pipelines via hierarchical resampling.

Equilibrium Reasoners enable test-time compute scaling via learned task-conditioned attractors without external verifiers.

Framework quantifies hyperparameter transfer across model scales, revealing embedding layer learning rate criticality.

EvoStruct integrates protein language models with equivariant GNNs to fix vocabulary collapse in antibody CDR design.

Velocityformer applies equivariant graph transformers to cosmological kinematic Sunyaev-Zel'dovich velocity reconstruction.

DeepWeb-Bench introduces harder evaluation for frontier LLMs on deep research requiring massive cross-source evidence and reasoning.

AiraXiv proposes AI-era publishing platform enabling human and AI authors with continuous feedback-driven iteration.

WikiVQABench benchmark combines Wikipedia images with Wikidata for knowledge-grounded visual question answering evaluation.

Interactive tool visualizing LLM token generation speeds from 5 to 800 tokens/second for practical latency understanding.

FROG enables learnable graph structure for relational deep learning on RDBs without fixed schema constraints.

Agent JIT compilation compiles task descriptions into executable code for web agents, reducing latency vs. sequential fetch-execute loops.

RLVR training exhibits rank-1 weight trajectory structure; minimal training captures performance gains via linear parameter evolution.

DelTA interprets RLVR updates as linear discriminators over token gradients, explaining token-level probability changes in reasoning model training.

LLM-based grammar adaptation for metamodel evolution in domain-specific languages; evaluated on Xtext DSLs.

Mem-π framework generates context-specific agent guidance on-demand via dedicated model rather than static retrieval-based memory.

ML framework for GNSS positioning error mitigation in urban environments using activation function-based weighted least squares.

HITL-D combines diffusion policies with human control for shared autonomous manipulation, conditioning on scene point clouds.

Framework for optimal simulator-experiment allocation when deploying pre-trained simulators; decomposes value error into calibration drift and parametric residual.

Rubric embeddings mitigate label bias in high-stakes prediction (hiring, admissions) by replacing black-box embeddings with interpretable representations.