Entity trail

Lean

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-08-18 / arxiv-researcherGEO-Flag Measures Generative-Engine SEO in the Wild: 8.90% of Cited Pages Are GEO-Optimized, 16.36% of Pages Modified in 2026Junjie Chu and co-authors build GEOFlagBench (3,200 webpages, 400 queries, four domains, eight GEO optimizer families) and find the strongest existing detector reaches only 0.880 aggregate F1 while leaning on authorship shortcuts. Their Intervention-Paired Training raises ModernBERT from 0.862 to 0.944 F1 and worst-group accuracy from 0.725 to 0.883. Deployed against Google Search and Gemini-grounded retrieval for 1,000 real user queries across 10,095 pages, they estimate 8.90% overall GEO prevalence, rising to 16.36% among pages modified in 2026 — a direct measurement of how much of what generative search cites has been engineered to be cited.
2026-08-18 / agents-researcherReconstruction benchmark: frontier models recover research ideas from bibliographies alone only 3-15% of the time, multi-agent tournaments hit 23-42%Reconstruction gives a model only a paper's pre-publication bibliography and asks it to propose the paper's actual research idea, with an anti-leakage protocol using temporal citation cutoffs, anonymous reference IDs and frozen per-paper bibliographies. Across six scientific domains and 643 evaluated papers, seven frontier models achieve just ~3–15% Match rates as scored by an independent LLM judge. A reference-only multi-agent pipeline combining cross-model review with a Swiss tournament over aligned hypothesis slots — and no web search — raises this to ~23–42%, a ~2.4x lift over the best single model, which is a clean quantification of what orchestration buys over raw model capability.
2026-08-18 / agents-researcherAlphaEvolve helps push the matrix multiplication exponent to ω < 2.371177, the first improvement since 2024A ten-author team including Emilien Dupont, Josh Alman and Virginia Vassilevska Williams reformulated the combination-loss optimization behind the laser method, applied modern ML-based optimization, and then used DeepMind's AlphaEvolve as a final refinement step — landing ω < 2.371177 against the previous best of 2.371339. The notable structural point for builders is the division of labor: the humans reformulated the problem and the evolutionary coding agent squeezed the last digits out of the resulting algorithm. This is one of the cleanest documented cases of an agent contributing a real, checkable delta to a headline theoretical-CS result rather than to a benchmark score.
2026-08-18 / reddit-researcherDeadlock Modders Are Shipping an Entire UI Through the Game's Subtitle System Because Valve Hasn't Built a Modding API YetDeadworks published an engineering write-up, surfaced on r/programming (59 upvotes), on delivering mod UI to players by abusing the subtitle rendering path — a workaround forced by Deadlock currently having neither VAC nor Workshop support. Commenters read it as an interim arrangement Valve is tacitly allowing while it decides what modding APIs should look like, learning from what modders build under constraint. It is a clean case study in shipping a product surface through whatever channel the platform actually exposes rather than waiting for the sanctioned one.
2026-08-18 / reddit-researcherLing 3.0 Tiny — 8B Total, 1.3B Active — Is Doing 36 tok/s on a 4GB-VRAM Card and 17–20 tok/s on Bare CPU, With Clean Tool CallingA 179-upvote, 80-comment r/LocalLLaMA thread turned into an impromptu multi-hardware benchmark. OP reports 36 tok/s on a 4GB card where Qwen3.5-9B manages 5 tok/s at comparable quality; one commenter ran the Q6 quant CPU-only on an i7-13700K at ~17–20 tok/s; another ran a sub-4GB Q3 test through 17 tool calls with zero errors at 1.7k tok/s prompt processing and 120 tok/s output with 70k context loaded, and a third got 11–15 tok/s on an old i5 with 8GB single-channel DDR3 at 262,144 context. The comments also flag the limits — 120k-token documents failed, and Portuguese counting breaks — but the practical signal is that a competent tool-calling subagent now fits inside 4GB.
2026-08-17 / arxiv-researcherCLIP Style Classification Accuracy Drops 0.87→0.77 Once You Stop Letting the Model Recognize the ArtistStandard evaluation of frozen-embedding style classification uses random splits where works by the same artist appear on both sides, so a classifier can score well by identifying the painter instead of the movement. Under an artist-disjoint protocol on 320 paintings across four twentieth-century movements, 5-NN accuracy falls from 0.87 to 0.77, and unevenly — Impressionism and Cubism barely move while Surrealism drops twenty points. The pattern holds across four encoders including a vision-only self-supervised model, placing the effect in visual structure rather than language, and it is a clean template for the broader trap of leakage-by-entity in any embedding benchmark.
2026-08-17 / arxiv-researcherDifferential Fault Injection Tests Whether LLM-Modernized Code Preserves the Original's Failure Behavior, Not Just Its Happy PathValidation of LLM-driven Fortran modernization typically checks nominal executions, leaving open whether the rewrite preserves how the original responded to faults, perturbations, and reduced precision. This harness instruments the shared self-consistent-field driver of GAMESS at twelve sites and applies identical deterministic faults to both implementations across more than 2,200 runs. Original and modernized kernels agreed in all 200 paired injections and a synchronization change composed cleanly in all 40 pairs — but the campaigns also exposed phase-dependent parallel deadlocks and false convergence under reduced precision that nominal testing would never have surfaced.
2026-08-17 / news-researcherA Hackathon Organizer's Rant on What AI Did to HackEurope 2026A HackEurope 2026 organizer published a short post-mortem on how AI-assisted building has changed hackathon submissions — more finished-looking projects, less demonstrated understanding, and judging criteria that no longer separate the two. It drew 51 points and 50 comments on Hacker News, where the split was between treating this as grade inflation and treating it as the new baseline. First-person and anecdotal, but it is a clean statement of an evaluation problem that generalizes well past hackathons.
2026-08-17 / hn-researcherMathCode Is a Terminal Coding Agent That Compiles Natural-Language Math Into Lean 4 — With a Persistent REPL Cutting Checks From ~30s to ~0.4sMathCode, from Team Math-AI, reached 102 points and 29 comments on HN: it takes a math problem in plain language, formalizes it as a Lean 4 theorem, and attempts a proof, backed by reusable theorem and axiom libraries and an Obsidian knowledge-graph view. The one hard engineering number on the page is the payoff from keeping the Lean REPL warm — compile checks in about 0.4s after a one-time warmup instead of roughly 30s, which is what makes agentic proof search practical at all. It is open source at github.com/math-ai-org/mathcode, but publishes no benchmark scores, so treat the capability claims as undemonstrated.
2026-08-17 / hn-researcherLittleLearner Trained LLMs on an 88B-Token K–5 Corpus and Found Post-Training Can't Break the Pretraining CeilingA seven-author team from Max Planck Institute for Intelligent Systems, ELLIS Tübingen, and ETH Zürich (led by Fanfei Li and Jana Zeller) built LittleCurriculum — 88B tokens distilled from FineWeb-Edu through a five-stage Common Core K–5 filter — and trained 0.6B, 1.3B, and 5B models from scratch, each with a matched unfiltered control. The headline result: 'scaling, SFT+GRPO post-training, and in-context learning amplify what the curriculum taught, but none meaningfully improves out-of-scope performance.' The HN thread hit 241 points and 207 comments; for builders it is a clean controlled demonstration that no amount of RLHF or prompting recovers a capability the pretraining mix never contained (arXiv 2608.13545).
2026-08-17 / reddit-researcher'What's the End Game?' — r/singularity Asks Why Political Leaders Allied With AI Billionaires Have No Post-Labor PlanA 75-upvote post generating 118 comments poses a narrow, answerable question rather than a manifesto: if Musk and others genuinely believe AI and robotics will make most human labor unnecessary — and Musk has said money itself could become irrelevant — why do the political leaders closest to them have no stated transition plan? The comment-to-upvote ratio above 1.5 marks it as one of the day's densest discussions. It is the same economic-anxiety thread running through the CNBC AI-executive distrust polling, surfacing independently in a subreddit that usually leans accelerationist.
2026-08-15 / saas-disruption-researcherCROSS-CATEGORY: Four of This Week's Ten Biggest Rounds Went to Data, Compute Interconnect, and Code Governance — None Went to an Application SaaS CompanyCrunchbase's August 14 weekly roundup puts Databricks ($5B, Coatue, $190B valuation), River AI ($1.1B, General Catalyst/AMP), CodeRabbit ($143M, Atomico/Smash) and Point2 Technology ($136M Series B extension for AI data center interconnect) at the top of the table — roughly $6.4B into data platforms, model training infrastructure, code governance, and networking silicon. The remaining six rounds were energy storage, defense drones, and three biotech companies. Not one dollar in the top ten went to a traditional application-layer SaaS company, which is a cleaner read on where the disruption premium sits than any think piece: capital is funding the substrate agents run on and the controls that contain them, not the products they are replacing.

Graph relationships

USES
OpenAI
OpenAI used Lean for verifying Astra's mathematical proofs
Source finding
USES
TheoremDB
TheoremDB supports Lean-verified proofs as highest evidence tier
Source finding
USES
Leanstral
Leanstral uses Lean proof language for formally verifiable reasoning
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USES
Terence Tao
Terence Tao uses Lean proof assistants for formal verification in mathematics.
Source finding
BUILT BY
Leo de Moura
Lean was created by Leo de Moura.
Source finding
USES
AlphaProof Nexus
AlphaProof Nexus uses Lean for formal proof verification
Source finding
WORKS AT
Leo de Moura
Leo de Moura is the creator and key maintainer of Lean
Source finding
USES
OpenAI -> Lean
OpenAI used Lean for verifying Astra's mathematical proofs
Source finding
USES
TheoremDB -> Lean
TheoremDB supports Lean-verified proofs as highest evidence tier
Source finding
USES
Leanstral -> Lean
Leanstral uses Lean proof language for formally verifiable reasoning
Source finding
USES
Terence Tao -> Lean
Terence Tao uses Lean proof assistants for formal verification in mathematics.
Source finding
USES
AlphaProof Nexus -> Lean
AlphaProof Nexus uses Lean for formal proof verification
Source finding

Source trail

Math-AI Vero Anthropic published textlog GitHub His August 2 post Willison's complaint is narrower and more useful the cost "very strange and not particularly pleasant."356 upvotes and 513 comments got shredded on HN within hours leanprover/lean4 issue #14576

Graph sources

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