We present FloatLib, a verified arbitrary-precision floating-point arithmetic library in Lean 4 that combines broad format coverage, machine-checked correctness, and efficient certified execution. To our knowledge, FloatLib is the first Lean library to unify IEEE binary and decimal arithmetic, arbitrary-width posits, P3109, and user-defined formats and rounding rules behind interchangeable certified software backends. Every certified backend is proved equal to a complete encoded specification, preserving signed zeros and exceptional values, while numerical theorems connect execution to real rounding, error bounds, and exactness. FloatLib combines exhaustive certified tables for small formats with verified word and limb kernels based on guard-and-sticky invariants and independently checked quotient candidates. Its posit development additionally proves standard rounding thresholds and exact quire accumulation within capacity for arbitrary widths. Across matched workloads, FloatLib achieves speedups of up to 1.46x over FLoPS and 116x over Universal, while remaining slower in some regimes such as binary arithmetic against MPFR. Independent conformance testing includes more than 102 million TestFloat evaluations with zero differences under the tested relation. We release the library, proofs, benchmarks, evaluation data, and guide as open source.
A metamorphic testing approach that uses semantics-preserving rewrite rules, focusing on floating-point special values and peep-hole optimizations, to uncover meaningful performance issues in SMT solvers' handling of floating-point formulas is proposed.
LLMs are making code modernization, translation, and rewriting easier and more scalable, enabling software to be transpiled across programming languages and compilers, and optimized for specific hardware platforms. For numerical software relying on finite-precision arithmetic, however, these transformations may introdu...
Laura Titolo· Proceedings of the 11th ACM...· 0 citations
Results confirm that permutation-based MBA obfuscation offers a practical, composite, and resilient defense against symbolic execution, balancing strong protection with lightweight performance overhead.
Mo-Xuan Wang, Hai-Yan Hu, Hao-Hang Qin et al.· Journal of computing and sec...· 0 citations
An optimized 64-bit fused multiply-add architecture that combines multiplication and addition into one operation, which cuts down on rounding errors and overall error and is a strong and flexible solution for high-performance computing applications is proposed.
S. T. Devi, D. T. Devi, B. Bhavana et al.· International Journal of Com...· 0 citations
Interval arithmetic provides guaranteed error bounds for finite-precision numerical computations, but obtaining these error bounds is much slower than the original computation. Even on highly memory bound workloads like small 1D stencils, the existing approaches fail to hide the overhead of changing from scalar to inte...
Xing-Jian Qi, R. Strzodka· Proceedings of the Internati...· 0 citations
Modern isogeny-based cryptosystems spend much of their running time in finite-field, elliptic-curve, and higher-dimensional isogeny arithmetic. Exploiting SIMD parallelism is nontrivial: routines such as Montgomery ladders contain loop-carried dependencies, while point, pairing, and theta-coordinate formulas expose onl...
Weize Wang, Chutong Wang, Yu Wu et al.· 0 citations
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
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