AI Model2026
A GGUF build of Qwen3.6 (35B) post-processed with the Genesis numerical repair to reduce training noise and restore weight distributions; provides a more stable, uncensored multimodal (image+text) MoE model with long-context support for local use.
AI Model2026
27B multimodal reasoning model built on Qwen3.5-27B that preserves the base model's native multi-token-prediction head, full vision tower, and a 1,048,576-token YaRN context window. Designed for agentic tool use, long-context reasoning, and research deployments; released under Apache-2.0.
Uses pretrained multimodal LLMs as zero-shot, training-free reward models for text-to-image RL by scoring how well the original text prompt can be recovered from a generated image via image-conditioned prompt log-likelihood; includes a Self-SpectraReward closed-loop variant.
Continuously records egocentric visual and audio streams into a lightweight streaming memory that organizes experiences into current, short-term, and long-term tiers and retrieves multimodal evidence to answer queries about past events. Built for on-device use (smartphones/AI glasses) with dynamic retrieval routing.
AI Model2026
A GGUF-local variant of Qwen3.6-35B that applies a non-training 'Genesis' tensor-repair process and Hermes-agent fine-tuning to enable uncensored, multimodal (text+image) local inference. Highlights: MoE 35B spec, large native context, Hermes function-calling dataset transfer, and recommended quantization/runtime settings.
AI Model2026
Timestamp-aware realtime video→text model that processes incoming frames continuously, answers questions mid-stream or emits silence when evidence is insufficient, and can revise earlier outputs as new frames arrive. Built for timestamped multimodal interaction with a 256K context and an 11B-parameter backbone.
AI Video Papers2026
Comprehensive benchmark and automated evaluation framework for keyframe-conditioned video generation—decomposes keyframe execution into six metrics and assesses overall video quality with evidence-grounded MLLM judgments and specialized perception models.
AI Video Papers2026
Enables efficient, generalist video understanding by combining an Inflated 3D Vision Transformer and adaptive frame-resolution streaming with a scalable video data synthesis pipeline; ships as a fully open 4B-parameter MLLM that improves general, long-form, and streaming benchmarks.
Embodied AI2026
Analyzes adversarial weaknesses of World-Action Models (WAMs) via BadWAM, a framework that crafts visual perturbations to decouple a model’s imagined future from its executed actions. Introduces two attack modes—action-only (disruptive) and imagination-preserving (stealthy)—and shows large drops in closed-loop task success (e.g., 96.5%→43.1%).
AI Model2026
Policy-adaptive multimodal safety classifier that evaluates text and images against free-form natural-language policies and returns a continuous yes/no safety score. Produces a single-token verdict from a 3B-parameter model, supports multiple languages, and is designed for lightweight real-time moderation.
AI Agent Papers2026
A vision-language-action foundation model trained on 100k+ hours of real-world robot manipulation trajectories to follow natural-language instructions and adapt to downstream tasks with minimal fine-tuning. Uses a two-stage (pre-/post-) training recipe and a scalable auto-labeling pipeline; shows clear scaling benefits and state-of-the-art sim-to-real transfer on standard benchmarks.
AI Video Papers2026
Evaluates whether video models reason according to physical laws by treating generated videos as visible reasoning traces and using a three-stage Perception–Formulation–Deduction protocol. Includes Orchard (400 mechanics videos), chain-of-frames prompting on annotated first frames, and a hybrid MLLM-plus-objective scoring suite for stage-resolved diagnostics.