Xingyu Zheng, Xianglong Liu +5State Key Laboratory of Complex & Critical Software Environment, Beihang University, School of Computer Science and Engineering, Beihang University +6
Accelerates text-to-image diffusion for pretrained flow-matching models using a staged low-to-high-resolution pipeline: fast low-res sampling, pixel-space GAN super-resolution, light latent noising, and short high-res refinement — >10× end-to-end speedups without retraining.
Compiles natural-language function specifications into compact, locally-executable neural programs (PAW) that run on a small frozen interpreter; a 4B compiler emits LoRA adapters for a 0.6B runtime to provide offline, low-memory fuzzy text functions.
AI Model2026
Depth-conditioned LoRA for Krea‑2 that extracts a depth map from any input image and generates new images preserving the original 3D structure and composition while changing content and style. Single 862MB LoRA, works with Krea‑2‑Raw and Krea‑2‑Turbo.
AI Model2026
Instruction-tuned compact conversational model (Qwen3-4B-based) that generates short, chat-style replies and is optimized to run on a single mid-range GPU. Uses ChatML prompts, bfloat16 safetensors and is released under Apache-2.0; the model card notes a joke/placeholder disclaimer.
AI Model2026
Runs a full 27B-class language model using end-to-end binary (1.125-bit) weights, cutting FP16 size to ~3.9 GB. Key features: 262k-token context, custom 1-bit kernels for Apple MLX and CUDA, and an optional DSpark drafter for faster decoding. Best when memory footprint matters; trades some FP16 accuracy for on-device feasibility.
AI Model2026
Provides a 27B-class Qwen3.6-derived language model in GGUF with end-to-end ternary weights (Q2_0_g128), reducing deployed footprint to ~7.2 GB while retaining ~95% of FP16 reasoning ability and enabling on-device 262K-token context inference.
AI Model2026
Runs a full 27B-class Qwen3.6-derived language model in a ~3.9 GB 1-bit GGUF pack for on-device inference with a 262K-token context; true 1.125 bits/weight binary representation, DSpark speculative drafter, and llama.cpp (CUDA/Metal/CPU) support.
AI Model2026
Runs a full 27B-class Qwen3.6-derived LLM in a ~7.2 GB ternary/2‑bit format for on-device or single‑GPU text generation, retaining ~95% of FP16 performance and supporting a 262K‑token context. Designed for laptop/GPU deployment; exceeds typical phone memory limits.
AI Agent Papers2026
Trains cross-platform GUI agents by combining a Uni-GUI cross-platform dataset with platform-conditioned multi-teacher on-policy distillation, enabling a shared policy to adapt to new platforms while retaining platform-specific behaviors; suitable for research on continual GUI agent learning and cross-platform adaptation.
AI Model2026
Fine-tuned variant of Qwen3.6-27B that cuts internal reasoning (‘thinking’) token usage by roughly 46% on average while preserving benchmark accuracy and safety behavior. Targets lower latency and inference cost; ships on Hugging Face with GGUF quantizations for local use.
Transfers RL-induced policy shifts from a smaller 'weak' teacher to a stronger target by using the teacher's post-/pre-RL log-ratio as a dense implicit reward applied on the student's on-policy states. Enables reuse of RL supervision without running RL rollouts on the target, improving sample/time efficiency.
AI Video Papers2026
Provides a reflexive agentic framework for long-horizon video understanding that replaces costly iterative reasoning with dual contextual states: a consolidated global multimodal script and parametric latent states for fast retrieval and response, improving speed and memory efficiency.