Model Detail
locate-anything.cpp-gguf
—locate-anything.cpp-gguf is an image generation model with 3B parameters released by mudler. The model is registered under the object-detection pipeline tag on Hugging Face, distributed under a other license.
locate-anything.cpp-gguf ships with 3B parameters, distributed as a quantized weight variant for lower-VRAM inference. Distribution is governed by the other license — review the exact terms before commercial deployment.
locate-anything.cpp-gguf is best fit for text-to-image generation and creative iteration. It is a less obvious choice for production photography pipelines that need exact reproducibility. 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.
Locate, Judge, Read: Role-Specialized Tool Agents over a Knowledge-Point Index for Open-Domain Multimodal Document QA
arXiv:2610.13128v1 Announce Type: new Abstract: Question answering over large heterogeneous document collections requires first finding the right passages and then reading them, yet current retrieval-augmented agents give one model both kinds of tools and let it schedule itself. We present KnowForge
Locate-then-Sparsify: Attribution Guided Sparse Strategy for Visual Hallucination Mitigation
arXiv:2603.16284v2 Announce Type: replace-cross Abstract: Despite the significant advancements in Large Vision-Language Models (LVLMs), their tendency to generate hallucinations undermines reliability and restricts broader practical deployment. Among the hallucination mitigation methods, feature ste
The Position Curse: LLMs Struggle to Locate the Last Few Items in a List
arXiv:2605.07127v1 Announce Type: cross Abstract: Modern large language models (LLMs) can find a needle in a haystack (locating a single relevant fact buried among hundreds of thousands of irrelevant tokens) with near-saturated accuracy, yet fail to retrieve the last few items in a short list. We ca
Can We Locate and Prevent Stereotypes in LLMs?
arXiv:2604.19764v1 Announce Type: cross Abstract: Stereotypes in large language models (LLMs) can perpetuate harmful societal biases. Despite the widespread use of models, little is known about where these biases reside in the neural network. This study investigates the internal mechanisms of GPT 2
Locate-Then-Examine: Grounded Region Reasoning Improves Detection of AI-Generated Images
arXiv:2510.04225v2 Announce Type: replace-cross Abstract: The rapid growth of AI-generated imagery has blurred the boundary between real and synthetic content, raising practical concerns for digital integrity. Vision-language models (VLMs) can provide natural language explanations, but standard one-
Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models
arXiv:2601.14004v4 Announce Type: replace Abstract: Mechanistic Interpretability (MI) has emerged as a vital approach to demystify the opaque decision-making of Large Language Models (LLMs). However, existing reviews primarily treat MI as an observational science, summarizing analytical insights whi
