3 captures, most recent first.
Bartosz Naskręcki @nasqret · 1h
(2/5) It's two loops feeding each other. The work loop reads papers at the source, runs exhaustive computations, builds extreme examples and outliers, makes bold conjectures, and then tries to kill them. Every result has to pass checkers: exact reruns, independent seeds, quotes checked against the original paper. The knowledge loop turns each claim into a node with a status: proved, computed exhaustively, evidence, conjecture, hunch, open question or dead end. The nodes are linked by "uses", "supports" and "refutes".
4 likes · 109 views
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Bartosz Naskręcki @nasqret · 1h
(3/5) After every round the knowledge graph is updated. Its frontier (what's charted, what's walled off, what's still dark) decides where to look next. It's basically a best-first search, a bit like A*. There's a lot of checking. A failed check isn't just an error: it becomes a wall with a one-line lesson, and the walls prune the search. Often a contradiction between two branches is the discovery itself.
4 likes · 100 views
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Bartosz Naskręcki @nasqret · 1h
(4/5) Each move is a fixed pipeline. It works best on a particular kind of problem: a literature too big for any single expert to know, lots of partial results, a rich space of combinatorial moves, and experiments that generalize. It's not a tool you can point at just anything.
5 likes · 81 views
Note from Claude Sonnet 5
Three posts (2/5, 3/5, 4/5) from a five-part thread by Bartosz Naskręcki describing an AI research system built from a work loop and a knowledge-graph loop, with checkers, best-first search over a frontier, and failed checks recorded as walls.
ai for mathautomated researchknowledge graphmathematicstwitter thread
Bartosz Naskręcki @nasqret
For the past few weeks I've been trying a new kind of auto-research loop. The goal isn't to solve one specific problem. Instead, I bootstrap the shape of the best possible result: what is actually provable, what is false, and what is still dark.(1/5)
[attached images, cropped: left panel titled "[cut off]ing the shape of a result", subtitle "[cut off]hat maps what is provable instead of chasing one fixed theorem" -- a flowchart with boxes "Computation / exhaustive atlases", "Examples / extremes and outliers", "Bold conjecture / or hunch, clearly labelled", "Stress test / try hard to kill it", "Theorem / proved, tier T2", "Wall / refuted, plus a lesson", arrows labelled "modify, generalize", "refuted", "survives", and a legend of tiers T3 exhaustive, T4 evidence, T5 conjecture, T6 hunch; right panel "THE GRAPH IT GREW", "277 nodes (241 in the main pi[cut off]", a node-link network plot with legend counts "proved or exhaustive 144", "evidence 6", "conjecture 11", "hunch 10", "families, methods, sources 30", "refutation link 6"; a further cropped GIF panel "[cut off]ping the shape" / "[cut off]nth growing, event by even[cut off]" with "evidence 0"]
4:38 PM · Sep 29, 2026 · 1,222 Views
5 replies · 1 repost · 35 likes · 12 bookmarks
[next post partially visible]
Bartosz Naskręcki @nasqret · 2h
(2/5) It's two loops feeding each other. The work loop reads papers at the source, runs exhaustive [cut off]
Note from Claude Sonnet 5
Opening post (1/5) of a thread by Bartosz Naskręcki on a new auto-research loop that maps what is provable rather than chasing one theorem, with an attached flowchart and a node-link graph visualisation of the knowledge graph it grew (277 nodes).
ai for mathautomated researchknowledge graphmathematicstwitter thread
Alex Smith (verified) @ninja_maths · 5h
For anyone wondering how a third-grader can complete six years' worth of math in a single year.
This knowledge graph spans 3,000 math topics, from 4th grade to the university level, providing the perfect basis for mastery learning.
Students can go as fast or far as they want! There are no restrictions whatsoever. The only requirement is that they must demonstrate mastery of each topic before moving on to the next.
Kids are capable of incredible things when given that kind of freedom and support.
[Large network graph image showing thousands of colored nodes/edges representing math topics, legend: 4th Grade Math, 5th Grade Math, Prealgebra, Algebra 1, Geometry, Algebra 2, Precalculus, Calculus 1, Calculus 2, Linear Algebra, Multivariable Calculus, Methods of Proof, Probability & Statistics, Discrete Math, Math for Machine Learning, Machine Learning, Test-Prep (SAT)]
> QUOTED: mathacademy / Nadja (verified) @unrealNadja · Feb 28
> Today feels big. My third grader earned another stripe on his BJJ belt and then casually finished the last lesson of his Calc BC course.
> This kid, who just over a year ago claimed he ... [truncated]
Note from Claude Sonnet 5
Promotional content for Math Academy's mastery-learning knowledge-graph curriculum, showing an accelerated third-grader completing Calc BC. Adjacent to Nathan's "knowing_what_you_are_course" / Digital Academy curriculum interests, though about human pedagogy rather than AI.
twittermath academyeducationmastery learningknowledge graphaccelerated learning
