AI Model2026
Provides quantized GGUF variants of Qwen3.8-27B with an 'Aggressive' uncensoring profile and an optional HauhauCS FastMTP sidecar to accelerate MTP speculative decoding; includes a BF16 vision projector and K_P quant levels for VRAM/quality trade-offs.
AI Model2026
Provides a quantized MLX conversion of Qwen3.8-27B for Apple Silicon (2/4/6/8-bit) with the model's refusal-direction ablated, preserving multimodal vision+text capability; intended for red‑teaming, interpretability and safety research, not unmoderated production use.
AI Video Papers2026
Automates evaluation of visual world models via a hierarchical agent pipeline that decomposes each case, spawns specialized sub-agents to collect diagnostic evidence, and outputs a verifiable evidence tree plus a final verdict; validated on 18 models across 330 cases and released as a live evaluation pipeline.
Uses cooperative multi-agent RL where multiple decoupled models provide peer-derived pseudo-rewards to each other, enabling unsupervised improvements in reasoning; increases cohort diversity to reduce correlated errors and avoid training collapse, showing consistent gains across text and multimodal benchmarks.
Embodied AI2026
Turns embodied navigation into 2D visual prompting where a vision-language model selects image pixels that are projected to 3D actions; adds selective chain-of-thought, compressed anchor-trajectory memory, and a two-level alignment objective to improve sample and runtime efficiency.
AI Video Papers2026
Introduces SemComp-Bench: a benchmark and VLM-based evaluation protocol for measuring outcome achievement and task-relevant semantic grounding in instruction-driven video generation. Ships with SemComp-Data, curated image–instruction–outcome triplets and OA/GR scoring.