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j1s4nn / jev-adaptive-granularity-WM

Official code, locked protocols, and audit artifacts for "Jev-Routed Temporal Granularity with Recovery and Hysteresis for Causal Video Generation": per-segment fine/coarse/hold routing for Causal-rCM (Wan2.1-1.3B) via the Jev oracle, with two-GPU (T4/A100) feasibility audits.

▲ 33/7d ★ 33 Python 5d ago
NandhaKishorM / laya

Non-autoregressive System 1 decision engine. Typed choice, score and yes/no decisions over any text in a single forward pass, in 100+ languages, with a router that picks the right checkpoint per request.

★ 34k Python 1d ago
EleutherAI / gpt-neox

An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries

★ 7.5k Python 9d ago
deepopen-com / deepopen

非自回归System 1决策引擎,专为结构化类型决策场景设计 DeepOpen Multilingual, non-autoregressive System 1 decision engine.

★ 2.0k Python 22d ago
nv-tlabs / ardy

Official implementation of ARDY: Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation (SIGGRAPH 2026).

★ 1.0k Python 5d ago
thu-ml / Causal-Forcing

[ICML 2026] Official codebase for "Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation" & Causal Forcing++

★ 1.0k Python 17d ago
MeiGen-AI / OPSD-V

On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators

★ 697 Python 10d ago
google-deepmind / simply

Minimal and scalable research codebase in JAX, designed for rapid iteration on frontier research in LLM and other autoregressive models.

★ 585 Python today
NVIDIA / flashdreams

high-performance inference and serving library for interactive autoregressive video and world models

★ 518 Python today
Heman10x-NGU / openJev-verdict-2.0

Calibrated 151M Non-Autoregressive Decision Engine beating TypeSafe Jev & Laya on LocalLLaMA/typed-decisions (77.10% acc, 0.0636 Brier, 0.0144 ECE)

★ 297 Python 1mo ago
Zihan-Su / Self_Gradient_Forcing_Plus

Autoregressive video generation with decoupled context-writing and denoising parameters, enabling up to 24-hour generation from 5s training windows.

★ 211 Python today
Heman10x-NGU / Verdict-open-jev

Non-autoregressive decision engine on ModernBERT (151M) with calibrated uncertainty (RLCD), TypeSafe AI Jev benchmark audit, and in-browser WebGPU playground

★ 111 Python 23d ago
OPPO-Mente-Lab / CuteTTS

CuteTTS: a lightweight continuous autoregressive TTS model that runs efficiently on GPUs, CPUs, and Apple silicon.

★ 83 Python 1mo ago
groundingpi / GroundAnything

A 4B diffusion grounding model for fast, precise visual evidence extraction. Unifies diverse grounding tasks through parallel decoding, with optional self-speculative decoding delivering 4.51x autoregressive throughput on COCO.

★ 79 Python 6d ago
delaprada / Mask-Forcing

Official Implementation for Mask Forcing: Improving Autoregressive Video Diffusion Distillation via Dual-Noise Masking Rollout

★ 75 Python 1mo ago
nv-tlabs / cmd

Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation

★ 70 Python 1mo ago
ASLP-lab / SemBridge

SemBridge: Semantic Token Anchoring for Continuous-Latent Autoregressive Speech Generation

★ 63 Python 2d ago
Ch3nOff / dual-loop-controller

Hardware-aligned latent deliberation framework decoupling System 2 continuous reasoning from System 1 autoregressive decoding.

★ 63 Python 8d ago
SAGAR-TAMANG / sarvam-jev

Generation-free typed decisions on Indic LLMs. An open Jev-style inference engine on sarvam-1: constrained logit readout instead of autoregressive JSON. Runs client-side in the browser.

★ 60 Python 1mo ago
mosaichunk / MosaiChunk

MosaiChunk: Compositing Spatio-Temporal Memory for Autoregressive Video Generation

★ 59 Python 8d ago
yanghaha0908 / GROW

Official code for "GROW: Group-Relative Advantage-Weighted On-Policy Reinforcement Learning of Autoregressive-Diffusion Text-to-Speech Model"

★ 46 Python 2mo ago
intikhab49 / open-jev-typed-decision-engine

Open reproduction of TypeSafe Jev: a 150M typed decision engine (noul/choice/score in one non-autoregressive pass, calibrated confidence). 0.697 vs Jev's 0.727, 2.5x better calibrated, 4x faster, free. Trains on a Colab T4 in 30 min.

★ 46 Python 14d ago