Model Detail
ScaleDown: Extract
—ScaleDown: Extract is a multimodal model released by Scaledown. And supports text+image+file->text inputs.
ScaleDown: Extract is priced at $0.05/M input tokens and $0/M output tokens. At this input rate the model sits in the commodity tier and is suitable for high-volume workloads where per-call cost dominates the decision.
ScaleDown: Extract is published on Hugging Face but our pipeline has not yet captured architecture, license, or parameter-count metadata for this entry. The data is refreshed daily, so these fields typically populate within 24–48 hours of release.
ScaleDown: Extract is best fit for mixed text-and-image reasoning tasks such as document understanding, and high-volume batch jobs where per-call cost dominates the budget. 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.
Overlap, Unique and Conflict: Can LLMs Extract What They Can Recognize?
arXiv:2609.38799v1 Announce Type: new Abstract: Understanding multi-perspective alternative narratives requires identifying how their information agrees, conflicts, or differs across sources. Existing work on cross-text relations largely focuses on categorizing relations between predefined text pair
PACE: A Unified Condense-and-Extract Paradigm for Fast VLM Inference
arXiv:2608.27206v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) demonstrate exceptional visual reasoning capabilities, yet their inference costs escalate rapidly with the proliferation of visual tokens. Existing visual token pruning methods exhibit two fundamental limitations
Overthinking: Amplifying Reasoning Weights to Extract Learned Secrets
arXiv:2607.08173v2 Announce Type: replace Abstract: Black box auditing of language models is an essential pre-deployment tool, but it may miss subtle forms of misalignment and hidden information. To better elicit hidden information during an auditing process, we introduce \emph{overthinking}: the pr
Can LLMs Extract Architectural Design Decisions from Source Code Commits? - A Preliminary Exploratory Study
arXiv:2609.03721v1 Announce Type: cross Abstract: Context: Architectural Design Decisions (ADDs) capture the rationale behind the structure and evolution of software systems but are rarely documented explicitly, and are often hidden inside source code commits. Recovering them is important for Archit
