Model Detail
Anima
▼ 1.7%Anima is an AI model with 2B parameters released by circlestone-labs. Distributed under a other license.
Anima ships with 2B parameters. Distribution is governed by the other license — review the exact terms before commercial deployment.
Downloads of Anima have moved -1.7% over the past 24 hours, -4.5% over the trailing thirty days. That is a slight downtrend, consistent with normal cooling as newer models compete for the same workloads. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.
Anima is best fit for general-purpose AI workloads. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Animating Petascale Time-varying Data on Commodity Hardware with LLM-assisted Scripting
arXiv:2603.07053v3 Announce Type: replace Abstract: Scientists face significant visualization challenges as time-varying datasets grow in speed and volume, often requiring specialized infrastructure and expertise to handle massive datasets. Petascale climate models generated in NASA laboratories req
Assert, don't describe: Linguistic features that shift LLM reasoning about animal welfare
arXiv:2606.26104v3 Announce Type: replace-cross Abstract: Animal-welfare advocates produce a lot of writing, and increasingly that writing trains the language models that millions of people then ask about animal welfare. Using vocabulary-matched stance-contrast probes on a held-out animal-welfare be
Parameter-Efficient Vision-Language Adaptation with Continuous Metadata Conditioning for Animal Re-Identification
arXiv:2607.09443v1 Announce Type: cross Abstract: Long-term animal re-identification (ReID) must remain robust to gradual morphological evolution and seasonal appearance shifts. Although recent vision-language models provide strong pretrained visual representations, adapting them to longitudinal eco
ManimAgent: Self-Evolving Multimodal Agents for Visual Education
arXiv:2606.30296v2 Announce Type: replace Abstract: Multi-round reflection lets agents built on large language models recover from failures within a single task, but each task remains an isolated episode: lessons learned across many reflection rounds on one task are discarded before the next begins.
LUNA: Learning Universal 3D Human Animation Beyond Skinning
arXiv:2606.31981v1 Announce Type: cross Abstract: Creating photorealistic, animatable 3D human avatars from monocular images still largely depends on Linear Blend Skinning (LBS) and parametric body models, which constrain expressivity and often introduce artifacts due to imperfect fitting. We propos
Animation2Code: Evaluating Temporal Visual Reasoning in Video-to-Code Generation
arXiv:2606.28593v1 Announce Type: cross Abstract: While recent vision-language models (VLMs) have achieved significant improvements on static visual-to-code tasks such as generating code for webpages, charts, or SVGs, it remains unclear whether they can recover temporal dynamics when motion is prese
