#adversarialrobustness
Live, measured metrics for the hashtag #adversarialrobustness from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #adversarialrobustness
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Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-10-16 02:24 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · fosstodon.org (Mastodon public search API) · fetched 2026-10-16 02:24 UTCNo related tags with measured usage found for #adversarialrobustness.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-10-16 02:24 UTCEverything below is measured over the latest 2 public posts (spanning ~4524 hours).
Posting hours (UTC)
Languages: English (2)
Avg boosts / post: 0
Top of the latest posts
fly51fly (@fly51fly) CMU 연구진이 LLM 감독(oversight) 실패와 적대적 악용을 줄이기 위한 샤딩(sharding) 접근을 제안한 논문입니다. 모델·감독 작업을 분할하는 설계가 안전성 평가 및 공격 내성에 어떤 영향을 주는지 탐구하는 연구로, 에이전트 및 LLM 안전성 시스템 설계에 참고할 만합니다. https://x.com/fly51fly/status/2086933422925349335 #llmsa
fly51fly (@fly51fly) 대규모 언어모델(LLM)의 Best-of-N 샘플링 상황에서 발생하는 적대적 리스크(adversarial risk)를 통계적으로 추정하는 방법을 제안한 연구가 arXiv에 공개되었습니다. Microsoft Research 연구진이 제출한 논문으로, 샘플링 기반 생성 과정에서의 취약성 평가와 리스크 추정 기법을 소개하고 실험으로 유효성을 보였습니다. https://x.com/fly51fly/
#adversarialrobustness across platforms
every network with a public tag surfaceFollow #adversarialrobustness straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/adversarialrobustness