technocracy

Endpoint protection systems increasingly rely on a combination of signature-based and behavioral detection mechanisms, yet their robustness under systematic code transformation remains insufficiently understood. This paper presents a multi-category evaluation of endpoint detection robustness under automated, semantic-preserving code transformations across diverse execution variants. We introduce TransForge, a generalized transformation framework designed to generate functionally equivalent execution variants for controlled robustness assessment across heterogeneous artifact categories and programming environments. Building on our prior work, ShellForge, which focused on a […]

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arXiv:2602.22401v1 Announce Type: new Abstract: AI agents — systems that execute multi-step reasoning workflows with persistent state, tool access, and specialist skills — represent a qualitative shift from prior automation technologies in social science. Unlike chatbots that respond to isolated queries, AI agents can now read files, run code, query databases, search the web, and invoke domain-specific skills to execute entire research pipelines autonomously. This paper introduces the concept of vibe researching — the AI-era parallel to “vibe […]

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arXiv:2501.02505v4 Announce Type: replace-cross Abstract: Ranking items based on pairwise comparisons is common, from using match outcomes to rank sports teams to using purchase or survey data to rank consumer products. Statistical inference-based methods such as the Bradley-Terry model, which extract rankings based on an underlying generative model, have emerged as flexible and powerful tools to tackle ranking in empirical data. In situations with limited and/or noisy comparisons, it is often challenging to confidently distinguish the performance of […]

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I enjoyed my conversations with Timnit when she was in the MSR-NYC lab, so her situation has been on my mind throughout NeurIPS. Piecing together what happened second-hand is always tricky, but Jeff Dean’s account and Timnit’s agree on a basic outline. Timnit and others wrote a paper for FAccT which was approved for submission by the normal internal review process, then later unapproved. Timnit threatened to leave unless various details about this unapproval were clarified. Google then […]

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arXiv:2602.21251v1 Announce Type: new Abstract: Legacy JavaScript systems lack type safety, making maintenance risky. While TypeScript can help, manually adding types is expensive. Previous automated typing research focuses on type inference but rarely addresses type checking setup, definition generation, bug identification, or behavioral correctness at repository scale. We present AgenticTyper, a Large Language Model (LLM)-based agentic system that addresses these gaps through iterative error correction and behavior preservation via transpilation comparison. Evaluation on two proprietary repositories (81K LOC) […]

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Reinforcement learning from verifiable rewards (RLVR) has improved the reasoning abilities of LLMs, yet a fundamental limitation remains: models cannot learn from problems that are too difficult to solve under their current policy, as these yield no meaningful reward signal. We propose a simple yet effective solution based on task reformulation. We transform challenging open-ended problems into cognitively simpler variants — such as multiple-choice and cloze formats — that preserve the original answer while reducing the effective search […]

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arXiv:2602.14020v1 Announce Type: new Abstract: We study operator-norm covariance estimation from heavy-tailed samples that may include a small fraction of arbitrary outliers. A simple and widely used safeguard is emph{Euclidean norm clipping}, but its accuracy depends critically on an unknown clipping level. We propose a cross-fitted clipped covariance estimator equipped with emph{fully computable} Bernstein-type deviation certificates, enabling principled data-driven tuning via a selector (emph{MinUpper}) that balances certified stochastic error and a robust hold-out proxy for clipping bias. The […]

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Learned optimizers are powerful alternatives to hand-designed update rules like Adam, yet they have seen limited practical adoption since they often fail to meta-generalize beyond their training distribution and incur high meta-training cost. For instance, prior work, VeLO, scaled meta-training to 4,000 TPU months ($sim$10$times$ GPT-3 compute) to meta-train a general-purpose optimizer but it failed to generalize beyond 600M parameters tasks. In this work, we present a surprising finding: by crafting a simple normalized optimizer architecture and augmenting […]

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Release: scan-for-secrets 0.3 New -r/–redact option which shows the list of matches, asks for confirmation and then replaces every match with REDACTED, taking escaping rules into account. New Python function redact_file(file_path: str | Path, secrets: list[str], replacement: str = “REDACTED”) -> int. Tags: projects

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arXiv:2602.06175v2 Announce Type: replace-cross Abstract: Expand-and-sparsify representations are a class of theoretical models that capture sparse representation phenomena observed in the sensory systems of many animals. At a high level, these representations map an input $x in mathbb{R}^d$ to a much higher dimension $m gg d$ via random linear projections before zeroing out all but the $k ll m$ largest entries. The result is a $k$-sparse vector in ${0,1}^m$. We study the suitability of this representation for two […]

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