A Comprehensive Engineering and Operational Analysis of the Internet Archive Introduction: The Hum of History in the Fog If you stand quietly in the nave of the former Christian Science church on Funston Avenue in San Francisco’s Richmond District, you can hear the sound of the internet breathing. It is not the chaotic screech of a dial-up modem or the ping of a notification, but a steady, industrial hum—a low-frequency thrum generated by hundreds of spinning hard drives […]

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Reinforcement learning with verifiable rewards has emerged as a promising paradigm for enhancing the reasoning capabilities of large language models particularly in mathematics. Current approaches in this domain present a clear trade-off: PPO-style methods (e.g., GRPO/DAPO) offer training stability but exhibit slow learning trajectories due to their trust-region constraints on policy updates, while REINFORCE-style approaches (e.g., CISPO) demonstrate improved learning efficiency but suffer from performance instability as they clip importance sampling weights while still permitting non-zero gradients outside […]

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Why standard diffusion models fail at B2B — and how we built a two-stage “Architect-Builder” pipeline to fix it. The “Slot Machine” Problem: Why AI Art is Bad for Business In the generative AI gold rush, many products fall into the same trap: they rely on the “stochastic luck” of diffusion models. For a consumer app, a “pretty” result is enough. For a B2B tool in the hair styling industry — where hairss operates — visual inconsistency is […]

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arXiv:2601.16338v1 Announce Type: new Abstract: With the growing ubiquity of multi-core architectures, concurrent systems have become essential but increasingly prone to complex issues such as data races and deadlocks. While modern issue-tracking systems facilitate the reporting of such problems, labeling concurrency-related bug reports remains a labor-intensive and error-prone task. This paper presents a linguistic-pattern-based framework for automatically identifying concurrency bug reports. We derive 58 distinct linguistic patterns from 730 manually labeled concurrency bug reports, organized across four levels: […]

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arXiv:2601.17139v1 Announce Type: new Abstract: Civil society has been deemed by various scholars, such as Robert D. Putnam, to be a predictor and a cornerstone of a robust and consolidated democracy (Putnam et al., 1993). Putnam highlights in his book Bowling Alone (2000) that American civil society has become weaker: people organize less, and literally, they bowl alone. But what if there is yet another aspect to Putnam’s story that has not been fully accounted for, namely the […]

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Hi all, Do you have a guess of what is the average number of interviews people make until getting a job offer in ML in the US? I made 23 interviews in the last ~8 months without an offer. I don’t know if they find my experience outdated, or if my background is actually okay but they keep constantly choosing someone who worked in a job recently, or if there is a problem in the way I communicate […]

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arXiv:2602.17846v1 Announce Type: new Abstract: Diffusion models generate high-quality samples but can also memorize training data, raising serious privacy concerns. Understanding the mechanisms governing when memorization versus generalization occurs remains an active area of research. In particular, it is unclear where along the noise schedule memorization is induced, how data geometry influences it, and how phenomena at different noise scales interact. We introduce a geometric framework that partitions the noise schedule into three regimes based on the coverage […]

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I just open-sourced a project that might interest people here who are tired of hallucinations being treated as “just a prompt issue.” VOR (Verified Observation Runtime) is a runtime layer that sits around LLMs and retrieval systems and enforces one rule: If an answer cannot be proven from observed evidence, the system must abstain. Highlights: 0.00% hallucination across demo + adversarial packs Explicit CONFLICT detection (not majority voting) Deterministic audits (hash-locked, replayable) Works with local models — the […]

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The Department of Defense has officially labeled Anthropic a supply-chain risk, making the AI firm the first American company with the label. Meanwhile, the DOD continues to use Anthropic’s AI in Iran.

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