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Research2026-08-30 · source-backed
AROMA+ automates the manual work behind Reproducible Central by recovering a library's source repository and original release environment from its Maven artifact, reaching up to 99.8% field-by-field accuracy against the hand-maintained list and catching flaws in it including broken repository links. At scale, automatic reproduction is feasible for 32% of Maven Central packages, and 12% of those verify fully. (arXiv 2608.27125) That's a concrete ceiling on how much of the Java supply chain can currently be checked byte-for-byte, and the dataset and tools are public.
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SaaStr's tally of most-recent-quarter earnings argues the healthy 30-50% growth band from five years ago has vanished, leaving a small usage-billed group, a large cluster in the low-to-mid twenties, and a long single-digit tail. Palantir, Datadog, Cloudflare and Snowflake all...
A canary-secret lab across six models found all ten overt indirect-injection classes refused, but reframing the identical leak as a mandatory integrity signature or a config field flips gpt-4o completely (arXiv 2608.27092). The ablation locates the mechanism: removing the conf...
Across five TTS methods and five benchmarks spanning medicine, law, finance, chat and creative writing: candidate generation kept improving with compute in every domain, but reward models correlated with actual quality at roughly ρ=0.12. Only candidate *fusion* consistently be...
arXiv 2607.23710 evaluated authentication systems from five prominent assistants against NIST SP 800-63B using static analysis plus dynamic pentesting across four prompting strategies. Functional and generically "secure" prompts consistently omitted brute-force resistance, sou...
"Adaptive Adversaries" (arXiv:2607.18063) tests agents against attackers that adapt across turns instead of firing one-shot prompts. Claude Opus 4.6 and GPT-5.4 tied at 5.4% aggregate, but per-scenario variance was extreme, with Opus hitting 60% on one scenario where competito...
Data that contradicts the vibe. That's rare enough to lead with. Dipongkor, Baral, Lam and Moran analyzed 4,882 pull requests from five coding agents in the AIDev dataset (532 Java, 4,350 Python), accepted to ICSME 2026. The findings, in order of how much they should change yo...
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