Bitwise
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Bitcoin is cratering, but a new Wall Street crypto hype is on the rise
In one very small, and at least to date obscure, corner of the crypto market, investors are rushing in rather than heading for the exits. So-called HYPE exchange-traded funds are taking in new assets from investors at a time when the leading crypto bets, including bitcoin and ether, are tanking. In May, Bitwise and 21shares launched spot ETFs tracking indexes for HYPE, a decentralized crypto asset that operates on its own blockchain, hyperliquid.
HP re-releases classic computer science calculator: The HP-16C
| Best used for: | Programming; Computer science; Logic design; Engineering | | Entry-system logic: | RPN (Reverse Polish Notation) | | Keyboard: | Numeric, with dedicated base mode keys (HEX, DEC, OCT, BIN) | | Advanced functions: | Integer arithmetic, bitwise operations, logical tests, base conversions (HEX/DEC/OCT/BIN), word-size control (1–64 bits), floating-point math, keystroke programming, conditional branching, subroutines, flags | | Memory registers: | 99 | | Program memory: | 203 B...
Fault tolerance estimation in digital circuits with visualised generative networks
Announce Type: replace Abstract: We propose a new numerical method to estimate the fault tolerance of failure modes in digital circuit structures with a generative network sampling technique. From a random input of generated bitwise configurations of ideally digitalised analog currents in the digital circuit design with classical logical gates, expected output currents are compared to the realistic signals of a numerical experiment at the discriminator part of the Generative Adversarial...
Bit-Exact AI Inference Verification Without Performance Tradeoffs
Announce Type: replace Abstract: Verifying claims about AI workloads is a prerequisite for credible AI governance of covert adversaries (who comply with monitoring only when detection likelihood is high), yet the apparent non-determinism of GPU floating-point arithmetic forces auditors to accept approximate output matches. Covert adversaries can exploit unverifiable degrees of freedom in monitored computation.
Scaling Neural Network Verification with Tensor Parallelism and Fully Sharded Data Parallelism
arXiv:2606.09377v1 Announce Type: new Abstract: Formal neural network verification -- proving that a network satisfies safety properties for \emph{all} inputs in a specified domain -- is bounded in practice by GPU memory: standard implementations of bound-propagation algorithms (IBP, CROWN, $\alpha$-CROWN) require weight and relaxation-coefficient matrices to reside entirely on one accelerator. We adapt two parallelism techniques originally developed for large-scale model training to the...
TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks
arXiv:2510.16028v4 Announce Type: replace Abstract: Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces). Yet ML-as-a-Service reveals little about what actually ran or whether returned outputs faithfully reflect the intended inputs. Users lack recourse against service downgrades (model swaps, quantization, graph rewrites, or discrepancies like altered ad embeddings).