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Research papers, technical reports, benchmarks and datasets.
宝玉@doteyPicks76
Excited to share a research blog post that Daniel Freeman (@bucketofkets) and I have been working on for the past few months: https://www.anthropic.com/research/self-replicating-ai
The experimental setup helps distinguish successful replication under simplified conditions from autonomous spread on consumer devices, clarifying the limits of the projected timeline for smaller models.
QbitAI reports that mathematics enthusiast nor used GPT-6 Astra to prove optimality for packing 15, 16, 17, 18, and 20 unit disks inside a circle, completing the remaining cases for N≤20. The approach combines geometric constraints, finite computational enumeration, and exact optimality arguments. Nor describes substantial human involvement, including guiding the model, fixing issues, manually verifying validity, and rechecking the work with Astra and Opus 5.5.
The reported proof strategy shows how geometric constraints can reduce an infinite search space to finite cases, with rigidity arguments connecting computational elimination to exact optimality.
Microsoft Research introduces StudentSim, a research system that trains personalized student simulators from publicly available, de-identified learner records to evaluate and improve AI tutors. StudentSimEval measures behavioral fidelity and guidance responsiveness across chess, second-language English writing, and mathematics, where StudentSim outperforms leading baselines. In a chess tutor study, training with StudentSim feedback achieves 90.5% accuracy versus 75.7% without reinforcement learning and 71.6% with GPT-5.4 feedback; real learner studies remain essential, and the paper and code are publicly available.
Evaluating both a simulated learner’s starting ability and response to guidance offers a concrete method for assessing tutor feedback beyond the correctness of an explanation.
Google DeepMind’s AlphaProof Nexus solved dozens of open math problems, according to a peer-reviewed study in Science, including nine of 353 open Erdős problems, two unsolved for 56 years. It also proved 44 OEIS conjectures, solved an open algebraic geometry question, and improved a min-max optimization bound. Built around Gemini 3.1 Pro, it drafts and revises proofs in Lean using checker feedback and can call AlphaProof for harder sub-problems, though DeepMind reports it solved the nine Erdős problems without that tool.
DeepMind’s use of peer review provides a concrete comparison with OpenAI’s publication of AI proofs on GitHub amid a debate over how AI mathematics should be evaluated.
On October 7, OpenAI released 722 mathematics manuscripts, including two on the Kakeya conjecture: a 97-page paper claiming a three-dimensional maximal-function result stronger than Wang and Zahl's, and a 175-page paper claiming the four-dimensional Hausdorff dimension case. Both are attributed to OpenAI, are not Lean-formalized or peer-reviewed, and OpenAI's README says some unformalized results may be flawed.
The piece traces how two OpenAI manuscripts extend Wang and Zahl's three-dimensional Kakeya work to a stronger maximal-function version and to four dimensions, while noting the proofs lack peer review.
Sundar Pichai@sundarpichaiPicks66
A prospective clinic study of a patient-facing conversational diagnostic system, with clinician-reported preparation and diagnosis-agreement figures from real urgent care visits.
Google Research published a three-month field experiment on patent drafting through NBER. It randomly gave 133 lawyers at 11 intellectual-property law firms access to an unpublished Google Labs AI patent-writing assistant, now part of Gemini Notebook; two-thirds received early access.
The three-month patent-writing experiment separates AI's immediate productivity gains from changes in unaided judgment, providing comparative evidence on AI's effect on professional skills.
The NVIDIA team started with Nemotron 3 and used supervised fine-tuning, reinforcement learning, and a generate-verify-improve reasoning process to build specialized models that reached gold-medal thresholds at both IOI 2026 and IMO 2026.
The source gives a complete recipe for fine-tuning Nemotron 3 to gold-medal-level results at IOI and IMO, which readers can use to reproduce its data and reasoning workflow.
OpenAI released new results by an internal frontier model on open math problems and published Lean formalizations and research details on GitHub.
OpenAI made results by an internal frontier model on open math problems public alongside Lean formalizations, showing how model reasoning can be combined with formal verification.
The Microsoft Copilot Studio team and Hugging Face jointly released ThinkingBox. It runs agents in isolated MCP tool sessions and then performs executable checks on final backend state and side effects, rather than judging only the final reply or whether tool calls were allowed.
The agent benchmark from Microsoft and Hugging Face uses 507 stateful workflows and 20 repeated runs, shifting the focus from tool calls to the final database state.
AI at Meta@AIatMetaPicks66Meta made six papers from mathematicians' collaboration with Muse Spark public and described the division of work between people and AI and the review process.
Hugging Face@huggingfacePicks74Scores for the same model differ by nearly half across agent harnesses. This open guide gives a multi-harness RL training path without changing the harness, along with measured comparisons.
Google Research released a new federated learning system that uses trusted execution environments (TEEs) to provide verifiable, auditable guarantees for data anonymization. Gboard has deployed it for next-word prediction in English and Japanese, with stronger privacy guarantees and improved accuracy.
Google described a new TEE-based federated learning system and its deployment in Gboard, showing an engineering path for privacy-preserving on-device training.
Demis Hassabis@demishassabisPicks71Very happy to announce that our team @GoogleDeepmind has pushed the boundaries of generative biology, achieving the successful synthesis of AI-designed proteins that are both functional and watermarked. This proof-of-concept watermarking of the building blocks of life is enabled by SynthID Bio, our new protein watermarking method. It is designed to safeguard the new era of AI-powered generative biology and strengthen global biosecurity. You can read my thoughts here on why watermarking AI-designed proteins is an important research breakthrough: https://x.com/pushmeet/status/2105314763148321102
SynthID has expanded from image watermarking to protein design, with a paper and open-source tools released together, offering a technical path for biosecurity governance.
Google Research has introduced an AI video co-director multi-agent framework, an orchestration layer on top of Gemini and Veo. It models long-form video generation as a problem of global optimization and world-state tracking to mitigate semantic drift and cascading failures.
Google breaks long-video generation into four composable frameworks and reports continuity improvements across several benchmarks, illustrating how multi-agent orchestration can reduce semantic drift.
Microsoft Research systematically measured mobile-robot manipulation workloads and found that moving physical-AI inference from an onboard GPU to an edge or cloud GPU can improve task performance and extend battery life.
Microsoft Research measured robot inference workloads and reports performance and battery-life data for offloading inference from onboard hardware to edge or cloud GPUs.
Google Research has published a research experiment in which teachers use generative UI (GenUI) to dynamically create interactive, level-based learning simulations for STEM classes, complete with scaffolding such as hints, feedback, and solution steps.
Google Research lets teachers generate interactive learning simulations from course objectives and shares teacher-evaluation feedback and a pilot access point.
Google DeepMind has released AlphaGenome Atlas, a platform containing predicted effects for 9 billion single-nucleotide variants in the human genome. At 1 PB, it is more than 30 times the size of the AlphaFold Database. From today, academic researchers can access it free through the web portal, AlphaGenome API, and a skill in Google Antigravity.
The article gives the scale of predicted effects for 900 million single-nucleotide variants and free academic access points, helping readers assess the availability of genome-interpretation resources.
In a paper published in Cell, Google, HHMI Janelia, Cambridge, and collaborators have published a connectome of the complete male fruit-fly brain and central nervous system. It contains more than 166,000 neurons and 125 million synaptic connections, making it the largest brain map to date by neuron count.
Google, HHMI Janelia, and partners have released the largest brain map by neuron count, showing a concrete application of AI in connectomics.
Voice Arena and Hugging Face have added Monsoon en-IN and Monsoon hi-IN to the Open ASR Leaderboard, making Hindi the first Indian language represented on the multilingual leaderboard.
The Open ASR Leaderboard has added its first Hindi and Indian English benchmarks, showing how speaker attributes are being incorporated into public evaluations.
