@Nikobar

Your agents don't finish — and it's not the model, it's the harness. Beontheloop is the runnable system that makes a long agent run (Claude Code, Codex, Cursor) actually finish, verified — not vibes. Read the free Deck; join the waitlist to lock the founding price on the Harness Starter Kit.

Abstract This manuscript develops a unified algebraic, computational, and physical framework for rep-resenting, certifying, and exploiting structured partitions of high- dimensional spaces. Serving as the operational initialization for the categorical classification of non-associative gauge vacua, this work translates determinantal sheaf theory and functorial doubling into exact, machine-verifiable algorithms. The text begins with fully certified octonion (k = 3) and sedenion (k = 4) case studies,followed by applied treatments of determinantal geometry, canonicalization via Luna slices, spectralmass gap diagnostics, and exact topological transport. It concludes with the formal specification of a minimal reproducible artifact bundle, establishing the exact computational standards required for independent verification. "The Moduli Degeneracy: The Real Post-Quantum Cryptography .arr Standard": Table of ContentsIntroductionPart I: The Initialization StagesChapter 1: Octonions as Initialization Stage: Functorial Origin, Determinantal Sheaf, and Certified Structure * 1.1 Introduction and overview * 1.2 Functorial origin of O * 1.3 Left multiplication family and determinantal loci * 1.4 Determinantal sheaf W * 1.5 Orbit reduction and canonicalization via Luna slices * 1.6 Certification mandate: Positivstellensatz and Thom encodings * 1.7 Main structural theorem (octonion determinantal classification) * 1.8 Worked canonical example (sheaf language and full certificates) * 1.9 Stabilizer computation and gauge projection * 1.10 Spectral gap, mass gap certification, and Davenport-Mahler * 1.11 Concluding remarks Chapter 2: Sedenions: Doubling, Zero Divisors, Determinantal Pathologies, and Certified Examples * 2.1 Introduction and overview * 2.2 Functorial doubling: S = D_beta(O) * 2.3 Algebraic pathologies: zero divisors, alternator, and cohomology * 2.4 Left multiplication, determinantal loci, and the sheaf W * 2.5 Primary decomposition and embedded components * 2.6 Main structural statements for S * 2.7 Automorphisms, derivations, and the induced G_2 action * 2.8 Stabilizer computation under the induced action * 2.9 Certification mandate for sedenion examples * 2.10 Canonical seeds and worked sedenion examples * 2.11 Topological and spectral pathologies * 2.12 Algorithmic complexity and practical heuristics * 2.13 Figures, incidence tables, and data artifacts * 2.14 Concluding remarks Part II: Operational Geometry and CanonicalizationChapter 3: Degeneracy Planes and Determinantal Geometry (Applied) * 3.1 Overview and objectives * 3.2 Determinantal ideals and schemes * 3.3 The determinantal sheaf W * 3.4 Local normal forms near transversal points * 3.5 Degeneracy planes and hyperplane arrangements * 3.6 Algorithmic detection and certification * 3.7 Local normal forms: computational extraction of slice equations * 3.8 Examples revisited: octonion and sedenion loci * 3.9 Incidence data, Hasse diagrams, and repository manifest * 3.10 Concluding remarks Chapter 4: Canonicalization and Stabilizers (Operational) * 4.1 Overview and objectives * 4.2 Preliminaries and notation * 4.3 Stabilizer extraction and representation computation * 4.4 Deterministic canonicalization with field extension handling * 4.5 Certificate invariants and mandatory verification checklist * 4.6 Numeric witness to exact reconstruction refined * 4.7 Complexity, heuristics, and mitigations * 4.8 Worked operational sketch with representation data * 4.9 Concluding remarks Part III: Physical Translation and Stability**Chapter 5: Algebraic Charge and Spectral Readings (Applied Technical) * 5.1 Overview and objectives * 5.2 Algebraic charge and the physical vacuum dictionary * 5.3 Transverse spectral extraction: isolating the physical mass gap * 5.4 Signature diagnostics, Sturm counts, and multiplicity handling * 5.5 Topological responses: holonomy, Chern evaluations, and loop safety * 5.6 Compatibility checks: representation vs spectral projectors * 5.7 Exact certificate format and verification checklist (revised) * 5.8 Complexity, heuristics, and practical safeguards * 5.9 Concluding remarks Chapter 6: Perturbations, Topology, and Minimal Artifact Bundle * 6.1 Overview and objectives * 6.2 Notation and standing hypotheses * 6.3 Algebraic stability of determinantal fibers * 6.4 Transverse spectral continuity and certified mass gaps * 6.5 Certified projector transport and holonomy * 6.6 Lyapunov-Schmidt reduction adapted to the determinantal sheaf * 6.7 Minimal artifact bundle (final specification) * 6.8 Verification recipes and automation notes * 6.9 Concluding remarks Part IV: Appendices: The Open StandardAppendix D: Artifact Bundle Specification * D.1 Scope and intent * D.2 High level bundle layout * D.3 File format specifications * D.4 Cryptographic and integrity conventions * D.5 Verification scripts and deterministic checks * D.6 Numeric precision and interval arithmetic policy * D.7 Reproducibility checklist * D.8 Provenance and authorship metadata * D.9 Notes on archival and decentralized reproducibility * D.10 Reproducibility checklist (duplicated section in source) * D.11 Concluding remarks Appendix E: Computational Benchmarks, Reproducibility Notes, and Extended Proofs * E.1 Overview * E.2 Benchmark summary * E.3 Verification log format and canonical examples * E.4 Container recipe and reproducibility notes * E.5 Extended technical lemmas and proofs * E.6 Practical troubleshooting and FAQ * E.7 Concluding remarks

A U.S.-based technology company, Vercel Inc., agreed today to a enter a Stipulated Dismissal with the United States to resolve civil contempt proceedings related to Vercel’s failure to comply with a federal search warrant issued under the Electronic Communications Privacy Act.

Global oil inventories are being rapidly depleted to offset lost supplies, raising fears that the world could soon reach operational limits that trigger severe shortages and much higher oil prices.

A separate zero-day also disclosed by Nightmare Eclipse appears to be patched as well.

Just a machine that made you stop and think: "wow...somebody put a ridiculous amount of engineering into this". Could be anything.sometimes the most impressive machines are the ones that make incredibly difficult things look effortless. submitted by /u/hannimalki [link] [Kommentare]

Hey All, I am currently working on ASR models, and I have gathered some recent literature. From my literature search, it seems like the ASR models are getting more and more powerful due to two main things. Because pseudo-labelled data is growing, supervised models are rising rapidly. Whisper-large-v3 has been trained on 5M hours of weakly supervised data, and Nvidia Parakeet v3 has been trained on 660k hours of labelled data (open-sourced). Funny enough, Nvidia Parakeet v3 actually beats Whisper-large-v3 on almost every benchmark, even though it has a smaller model size and smaller data scale. So clearly, scale is not everything. New architectures are on the rise; We used to have self-supervised + CTC to solve the ASR task, but now it seems like Transducer, and Token-Duration-Transducers are taking off. As well as attention encoder-decoder architectures (Qwen) that are all trained in a supervised manner. Now, given that the labelled data is very huge, and the new architectures are coming up, are we saying bye to the self-supervised learning approaches like Data2Vec2.0, WavLM, etc., for ASR, and will we only use them for general-purpose speech tasks? This is actually not similar to how computer vision operates now. Dinov3 is a self-supervised approach that is extremely performant in segmentation, classification, depth estimation etc but I do not see this in the speech domain now. ASR is dominated by these huge supervised architectures (which is a dense-prediction task), as well as emotion recognition, diarization, and speech seperation are also all dominated by the supervised approaches. Do you think we will have our Dino moment with a new self-supervised architecture? Or supervised learning is the way to go? How would these methods actually perform if we trained a self-supervised model on these huge datasets? submitted by /u/ComprehensiveTop3297 [link] [Kommentare]

I would love feedback on the data quality and the 3D renderings specifically, because the renderings were the hardest part about getting this to work. Basically, Chaveta is a agentic dataset curation tool that allows you to submit a prompt and instantly receive a dataset for: - World models - Robotics (JSON Trajectories) - LLM Fine Tuning - Geological - Synthetic Tool Calling / LLM flows - Time series For the robotics path, you can also download to MCAP or simple JSON and we have a render tab that allows you to edit joints visually + we provide copy/paste scripts for importing the dataset into things like Transformers. Let me know what you think. submitted by /u/ComradePampers [link] [Kommentare]