Model Detail
SHADOW-250M
—SHADOW-250M is a large language model released by NODEMIND. The model is registered under the text-generation pipeline tag on Hugging Face, distributed under the permissive mit license.
The mit license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
SHADOW-250M is best fit for general-purpose chat and instruction-following 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.
SPIN: Shadow Predictive Indexer for Sparse Attention
arXiv:2610.09025v2 Announce Type: replace Abstract: Indexer-based sparse attention reduces the cost of core attention by passing only a fixed, small number of important tokens to it. However, the indexer must still score the entire KV cache at every decoding step. This scoring overhead becomes a maj
After a Decade: Bringing Shadow Removal into the Real World with Agentic Training Data
arXiv:2609.38607v1 Announce Type: cross Abstract: Shadow removal looks nearly solved on established benchmarks, yet remains brittle in the real world. Models have advanced; the paired training data they rely on have barely changed in nearly a decade. The reason is simple: obtaining a shadow-free tar
DiffusionShadow: Diffusion-based Shadow Caching for Neural Volume Rendering
arXiv:2609.30658v1 Announce Type: cross Abstract: Implicit neural representations (INRs) have gained momentum in scientific visualization due to their compactness and scalability to large datasets, making them well suited for integration with direct volume rendering (DVR). However, real-time volume
Safeguarding LLM Agents against Long-Horizon Threats via Shadow Memory
arXiv:2605.03228v2 Announce Type: replace-cross Abstract: As large language model (LLM)-powered agents are increasingly deployed to perform complex, real-world tasks, they face a growing class of attacks that exploit extended user-agent-environment interactions to pursue malicious objectives improba
How to Compress KV Cache in RL Post-Training? Shadow Mask Distillation for Memory-Efficient Alignment
arXiv:2605.06850v2 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has emerged as a crucial paradigm for unlocking the advanced reasoning capabilities of Large Language Models (LLMs), encompassing frameworks like RLHF and RLAIF. Regardless of the specific optimization algorithm (e
