hybrid-model

ResNLS is a hybrid neural network model combining ResNet and LSTM that improves stock price forecasting by emphasizing dependencies between stock prices, achieving at least 20% improvement over baselines and demonstrating practical trading applications.

This study proposes a hybrid TCN-Transformer model to predict satellite collision probability early using conjunction data messages, improving risk assessment for low Earth orbit operations.

SymbolicLight V2 is a hybrid neuromorphic language architecture that combines sparse event computation with continuous-state processing to achieve low-energy inference, with FPGA and ARM implementations demonstrating significant energy savings and throughput improvements.

Tydra is a hybrid Transformer-SSM architecture for tabular data that reduces inference time by 30% compared to TabPFN while maintaining similar predictive performance and outperforming Hydra.

SGLang has updated its deployment recipes for the Qwen3.8-27B model on RTX 5090 and RTX Pro 6000 hardware, adding variants for different configurations with tuning options.

Release of Ling-3.0-tiny, a hybrid reasoning model with 7.9B total parameters and only 1.3B active per token, free for a week.

The builder of LOLM announces a hybrid Transformer-SSM language model and agent system from Qira, featuring a controller for live decisions, run receipts, a CLI, coding sandbox, MCP support, and lower-cost hosted access.

Liquid AI released LFM2.5-2.6B, a 2.6B-parameter hybrid model optimized for on-device deployment with 128K context, agentic post-training, and fast inference (220 tok/s on Apple M5 Max) under 2.5GB memory.

This paper presents a hybrid probabilistic forecasting system that integrates time series decomposition (Prophet) with NLP techniques applied to Bolivian news coverage to predict roadblocks, achieving improved AUC-ROC and Brier Score over purely statistical models.

This paper presents ClickGuard, a browser extension that detects and spoils clickbait news using a hybrid machine learning model combining transformer embeddings and linguistic features, achieving 91% F1-score on a combined dataset.

Cactus Hybrid is a post-trained Gemma 4 model that outputs confidence scores, allowing on-device inference with routing to larger models when confidence is low, achieving performance comparable to Gemini 3.1 Flash-Lite with minimal calls to the larger model.

This paper presents a hybrid approach for detecting online polarization in English and Hausa using DeBERTa for English and AfroXLMR-Social for Hausa and fine-grained subtasks, with LoRA and data augmentation to address computational and data constraints.

MiniCPM-SALA is a 9B-parameter hybrid attention model that interleaves sparse and linear attention to overcome the quadratic compute and large KV cache bottlenecks of long-context Transformers. It achieves 3.5x faster inference than Qwen3-8B at 256K tokens and supports up to 1M tokens on consumer GPUs, with a cost-effective continual training approach that reduces training costs by ~75%.

Graham Neubig shares a sidekick architecture for reducing LLM costs by delegating simple tasks to a smaller agent, with a 200-line example using the OpenHands SDK. This approach is also used in Cognition's Devin Fusion hybrid-model harness.

Proposes HybridCodec, a novel framework combining temporally compressed discrete tokens with continuous residuals to improve speaker characteristic retention in speech language models, reducing autoregressive steps while maintaining quality.

Liquid AI released LFM2.5-230M, a compact 230M-parameter hybrid model optimized for on-device deployment with fast edge inference speeds (213 tok/s on Galaxy S25 Ultra) and built for agentic tasks via reinforcement learning.

The NPU on AMD Strix Halo devices is now usable for AI inference, enabling hybrid mode that combines NPU and iGPU for faster prompt processing. Tools like Lemonade and AMD's ROCm software make this possible.

This paper presents a Hybrid NARX-LLM framework for predicting Greenland iceberg discharge, using a Physics-Informed Prompt method to guide an LLM for residual correction, improving accuracy over traditional NARX models.

D2H-AD is a novel anomaly detection framework using Hyperdimensional Computing (HDC) that combines distance-based and density-aware encoding. It outperforms five baselines across multiple benchmarks, offering lightweight, interpretable, and efficient performance for edge AI and IoT.

This paper proposes a hybrid classical-quantum variational autoencoder for neural topic modeling, embedding parameterized quantum circuits in the inference network. Experiments on the AgNews dataset demonstrate improved topic coherence and diversity compared to state-of-the-art classical models, showing viability on NISQ-era quantum devices.