Ah, the groundbreaking revelation that combining pictures and words in programming is somehow new and exciting π€¦ββοΈ. Meanwhile, scientists at arXiv redefine how to churn out jargon-filled PDFs faster than you can say "Simons Foundation" π.
https://arxiv.org/abs/2603.15855 #programminginnovation #arXivresearch #jargonbusters #SimonsFoundation #techhumor #HackerNews #ngated
Mixing Visual and Textual Code
The dominant programming languages support nothing but linear text to express domain-specific geometric ideas. What is needed are hybrid languages that allow developers to create visual syntactic constructs so that they can express their ideas with a mix of textual and visual syntax tailored to an application domain. This mix must put the two kinds of syntax on equal footing and, just as importantly, the extended language must not disrupt a programmer's typical workflow. This means that any new visual syntax should be a proper language extension that is composable with other language features. Furthermore, the extensions should also preserve static reasoning about the program. This paper presents Hybrid ClojureScript the first such hybrid programming language. Hybrid ClojureScript allows programmers to add visual interactive syntax and to embed instances of this syntax within a program's text. An enhanced hybrid IDE can then display these embedded instances as mini-GUIs that programmers interact with, while other IDEs will show a textual representation of the syntax. The paper argues the necessity of such an extensibility mechanism, demonstrates the adoptability of the design, and discusses what might be needed to use the design in other languages.
π "In this riveting snooze-fest, we delve into the life of Raoul Bott, a man who made math so exciting it's now served as a
#cure for
#insomnia. Sponsored by the Simons Foundation, because apparently someone needs to fund our naps. π΄"
https://arxiv.org/abs/math/0201027 #RaoulBott #MathHumor #SimonsFoundation #Naptime #HackerNews #ngated
The Life and Works of Raoul Bott
a 10-page biography of Raoul Bott followed by a 25-page discussion of his major papers
π Breaking News:
#Academia invents
#CPPL, because what we really needed was another programming language! π Move over, Pythonβnow you can spend quality time
#debugging circuits with commands that sound like robot haikus. π€π₯ Thank you, Simons Foundation, for funding our collective headache. π
https://arxiv.org/abs/2605.17892 #BreakingNews #ProgrammingLanguages #SimonsFoundation #HackerNews #ngated
CPPL: A Circuit Prompt Programming Language
Large language models (LLMs) have shown promise in register-transfer level (RTL) design automation, but direct RTL generation remains difficult to validate, optimize, and integrate with compiler-based hardware design flows. Hardware compiler infrastructures such as CIRCT provide typed intermediate representations, legality checks, and optimization passes, yet current LLMs struggle to emit raw compiler IR because of MLIR syntax, SSA discipline, dialect-specific operations, and strict width constraints. This paper presents CPPL, a compiler-mediated design framework that turns LLM-assisted hardware generation into a statically checkable frontend problem rather than an unconstrained RTL text-generation task. CPPL combines a Python frontend DSL for declaring module interfaces and hierarchy with CPPL IR, a JSON-based circuit IR designed to expose compiler-visible structure while remaining accessible to LLMs. The compiler infers operation widths from declared module ports, validates generated IR, checks hierarchy and port bindings, and deterministically lowers the result to CIRCT for synthesizable Verilog generation. On the RTLLM benchmark, CPPL improves functional correctness over direct Verilog and direct CIRCT IR generation, while CIRCT optimization reduces post-synthesis AIG node counts. These results show that a compiler-mediated interface can make LLM-assisted hardware design more reliable, analyzable, and amenable to backend optimization. CPPL is available at https://github.com/SawyDust1228/CPPL.
π Ah, yes, the thrill of translating ENTIRE binaries without those pesky
#heuristics. Because we all know guessing is for amateurs, and real professionals demand their code be as static and lifeless as a rock. πͺ¨ Let's all take a moment to thank the Simons Foundation for making such groundbreaking boredom possible. π
https://arxiv.org/abs/2605.08419 #binarytranslation #staticcode #SimonsFoundation #programminghumor #softwareengineering #HackerNews #ngated
Deterministic Fully-Static Whole-Binary Translation without Heuristics
We present Elevator, the first binary translator that statically translates entire x86-64 executables to AArch64 without debug information, source code, or assumptions about code layout. Unlike existing systems, which rely on heuristics or runtime fallbacks to handle code-versus-data decoding errors, Elevator considers all possible interpretations of every byte and produces a separate translation for each feasible one ahead of time. Any byte may be interpreted as data, an opcode, or an opcode argument; we generate separate control flow paths for all interpretations, pruning only those leading to abnormal termination.
Translations are built by composing code "tiles" automatically derived from a high-level description of the source ISA, yielding a nimble translation framework. The approach is deterministic and produces complete, self-contained binaries with no runtime component in the trusted code base. The principal cost is substantial code size expansion. The key benefit is that the output is the actual code that will run, enabling testing, validation, certification, and cryptographic signing prior to deployment, reducing risk compared to emulators or JIT compilers.
We evaluate Elevator on a diverse corpus of real-world binaries, including the entire SPECint 2006 suite, demonstrating that static full-program binary translation can be both reliable and practical. Elevator achieves performance on par with or better than QEMU's user-mode JIT emulation.
Once more, the academic elite bring us a paper with a title so 'speculative' they had to use it twice. π€π In true academic fashion, they stuff it with enough jargon and acronyms to confuse even the savviest AI. ππ Thank goodness for the Simons Foundation support; without it, who would fund such a thrilling expedition into the Land of Nonsense? π€β¨
https://arxiv.org/abs/2603.03251 #academicjargon #speculativepaper #SimonsFoundation #LandofNonsense #AIlanguage #HackerNews #ngated
Speculative Speculative Decoding
Autoregressive decoding is bottlenecked by its sequential nature. Speculative decoding has become a standard way to accelerate inference by using a fast draft model to predict upcoming tokens from a slower target model, and then verifying them in parallel with a single target model forward pass. However, speculative decoding itself relies on a sequential dependence between speculation and verification. We introduce speculative speculative decoding (SSD) to parallelize these operations. While a verification is ongoing, the draft model predicts likely verification outcomes and prepares speculations pre-emptively for them. If the actual verification outcome is then in the predicted set, a speculation can be returned immediately, eliminating drafting overhead entirely. We identify three key challenges presented by speculative speculative decoding, and suggest principled methods to solve each. The result is Saguaro, an optimized SSD algorithm. Our implementation is up to 2x faster than optimized speculative decoding baselines and up to 5x faster than autoregressive decoding with open source inference engines.
π©β¨ Behold, the mystical wonders of pattern matching! Apparently, it's so "unreasonably effective" that they needed a whole paper to tell us what we've known since the dawn of
#regex. π€¦ββοΈ Thanks, Simons Foundation, for funding this groundbreaking revelation! π₯³π
https://arxiv.org/abs/2601.11432 #patternmatching #research #SimonsFoundation #technews #innovation #HackerNews #ngatedThe unreasonable effectiveness of pattern matching
We report on an astonishing ability of large language models (LLMs) to make sense of "Jabberwocky" language in which most or all content words have been randomly replaced by nonsense strings, e.g., translating "He dwushed a ghanc zawk" to "He dragged a spare chair". This result addresses ongoing controversies regarding how to best think of what LLMs are doing: are they a language mimic, a database, a blurry version of the Web? The ability of LLMs to recover meaning from structural patterns speaks to the unreasonable effectiveness of pattern-matching. Pattern-matching is not an alternative to "real" intelligence, but rather a key ingredient.
π€π Oh, joy! Yet another hair-pulling
#dissertation on "predictable" systems that nobody asked for. π€π€ Sponsored by an alphabet soup of acronyms and the Simons Foundation's patience, it's a thrilling read for anyone who finds paint drying too fast. π°οΈπ
https://arxiv.org/abs/2512.02080 #predictableSystems #humor #research #academia #SimonsFoundation #HackerNews #ngatedThe 4/$Ξ΄$ Bound: Designing Predictable LLM-Verifier Systems for Formal Method Guarantee
The integration of Formal Verification tools with Large Language Models (LLMs) offers a path to scale software verification beyond manual workflows. However, current methods remain unreliable: without a solid theoretical footing, the refinement process acts as a black box that may oscillate, loop, or diverge. This work bridges this critical gap by developing an LLM-Verifier Convergence Theorem, providing the first formal framework with provable guarantees for termination in multi-stage verification pipelines. We model the interaction not as a generic loop, but as a sequential absorbing Markov Chain comprising four essential engineering stages: \texttt{CodeGen}, \texttt{Compilation}, \texttt{InvariantSynth}, and \texttt{SMTSolving}. We prove that for any non-zero stage success probability ($Ξ΄> 0$), the system reaches the \texttt{Verified} state almost surely. Furthermore, because of the sequential nature of the pipeline, we derive a precise latency bound of $\mathbb{E}[n] \leq 4/Ξ΄$. We stress-tested this prediction in an extensive empirical campaign comprising over 90,000 trials. The results match the theory with striking consistency: every run reached verification, and the empirical convergence factor clustered tightly around $C_f\approx 1.0$, confirming that the $4/Ξ΄$ bound accurately mirrors system behavior rather than serving as a loose buffer. Based on this data, we identify three distinct operating zones -- marginal, practical, and high-performance -- and propose a dynamic calibration strategy to handle parameter drift in real-world environments. Together, these contributions replace heuristic guesswork with a rigorous architectural foundation, enabling predictable resource planning and performance budgeting for safety-critical software.
π Ah, yet another paper with more
#buzzwords than a startup's mission statement! π€¦ββοΈ Delight in the groundbreaking discovery that "Program-of-Thought" does 15% better than "Chain-of-Thought"βa riveting 1% improvement for each year I've aged while reading this. π Thanks to the Simons Foundation for funding the development of even more complex
#jargon to confuse us all. π‘
https://arxiv.org/abs/2211.12588 #innovation #research #SimonsFoundation #techhumor #HackerNews #ngatedProgram of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks
Recently, there has been significant progress in teaching language models to perform step-by-step reasoning to solve complex numerical reasoning tasks. Chain-of-thoughts prompting (CoT) is by far the state-of-art method for these tasks. CoT uses language models to perform both reasoning and computation in the multi-step `thought' process. To disentangle computation from reasoning, we propose `Program of Thoughts' (PoT), which uses language models (mainly Codex) to express the reasoning process as a program. The computation is relegated to an external computer, which executes the generated programs to derive the answer. We evaluate PoT on five math word problem datasets (GSM, AQuA, SVAMP, TabMWP, MultiArith) and three financial-QA datasets (FinQA, ConvFinQA, TATQA) for both few-shot and zero-shot setups. Under both few-shot and zero-shot settings, PoT can show an average performance gain over CoT by around 12\% across all the evaluated datasets. By combining PoT with self-consistency decoding, we can achieve SoTA performance on all math problem datasets and near-SoTA performance on financial datasets. All of our data and code are released in Github https://github.com/wenhuchen/Program-of-Thoughts
N-gated Hacker News
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