Moebius beats FLUX at 0.22B, FID swings 3.2× on retraining, Ant blames E2M1

Sources

Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance huggingface.co

A lightweight image inpainting framework achieves high-fidelity results with significantly reduced parameters and inference time through novel local-global interaction blocks and adaptive distillation strategies.

The FID Lottery: Quantifying Hidden Randomness in Generative-Model Evaluation huggingface.co

Analysis of FID variance across different training and sampling seeds reveals significant reproducibility issues in image generation evaluation, with retraining causing larger fluctuations than resampling, and recommends updated evaluation protocols with error bars and optimal guidance tuning.

Rethinking Shrinkage Bias in LLM FP4 Pretraining: Geometric Origin, Systemic Impact, and UFP4 Recipe huggingface.co

Uniform 4-bit training with RHT-based quantization outperforms E2M1-based methods by eliminating shrinkage bias and improving training stability across large language model architectures.

Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States huggingface.co

LOCUS assembles a comprehensive corpus of U.S. municipal and county ordinance codes with an access layer built for legal AI research. The pipeline pairs OCR with ModernBERT-based classifiers and scorers, filling a long-standing gap in authoritative local legal text at scale.

Current World Models Lack a Persistent State Core huggingface.co

Today’s video world models drift on hidden physical state once the camera looks away, prioritizing appearance fidelity over worldline consistency. The WRBench evaluation argues AGI-aimed systems need a persistent state core rather than better pixels, with code released for reproducible testing.

Beyond Static Leaderboards: Predictive Validity for the Evaluation of LLM Agents huggingface.co

Aggregate scores on agent benchmarks mask rank instability and miss deployment-relevant dimensions like cost and reliability. The paper, tied to IBM’s AssetOpsBench, calls for falsifiable out-of-distribution criteria and HELM-style successors built around predictive validity instead of single-number rankings.

Taylor-Calibrate: Principled Initialization for Hybrid Linear Attention Distillation huggingface.co

Converting pretrained Transformers into hybrid linear attention models like Gated DeltaNet usually loses quality at init. Taylor-Calibrate uses teacher attention statistics to align write gates, output gates and projections before distillation, cutting the token budget needed to recover full softmax performance.

FAPO: Fully Autonomous Prompt Optimization of Multi-Step LLM Pipelines huggingface.co

Cisco’s FAPO goes beyond prompt-only optimizers like GEPA by also rewriting the chain structure of multi-step LLM pipelines. Using a prompt-first search driven by Claude Code, it beats baselines on CTIBench-RCM and a CVE-to-CWE security mapping task.

Thinking with Visual Grounding huggingface.co

Interleaving natural-language reasoning with explicit point and box grounding helps vision-language models stay tied to image evidence. A SAM3-based agent synthesizes training traces at scale, and grounding-aware reinforcement learning improves results on counting and spatial reasoning benchmarks.

Context-Aware RL for Agentic and Multimodal LLMs huggingface.co

ContextRL trains agentic and multimodal LLMs with an indirect auxiliary reward for selecting context that supports the correct answer. Built on contrastive context data, it beats data-augmentation baselines on long-horizon reasoning and visual question answering across diverse benchmarks.

HumanScale: Egocentric Human Video Can Outperform Real-Robot Data for Embodied Pretraining huggingface.co

Egocentric human video can effectively replace teleoperated robot trajectories for embodied model pretraining, achieving better performance with reduced data collection costs.

Duration Aware Scheduling for ASR Serving Under Workload Drift huggingface.co

Duration-aware scheduling policies improve ASR serving latency by leveraging audio length as a predictor for processing time, with SJF and HRRN algorithms showing significant median latency reductions while maintaining throughput.

Understanding the Behaviors of Environment-aware Information Retrieval huggingface.co

Large language models can be trained via reinforcement learning to adapt query formulation strategies for different retrievers, with distinct optimal query styles and improved performance through retriever-specific guidance and model scaling.

ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing? huggingface.co

ImageWAM demonstrates that pretrained image editing models can effectively replace video generation in world action models for robot control, achieving better performance with reduced computational costs.

ReSyn: A Generalized Recursive Regular Expression Synthesis Framework huggingface.co

A divide-and-conquer framework named ReSyn enhances regex synthesis accuracy by decomposing complex problems, combined with a parameter-efficient synthesizer called Set2Regex that handles example permutation invariance.

Playful Agentic Robot Learning huggingface.co

Embodied robots learn reusable skills through self-directed play and exploration, then apply these skills to improve performance on downstream tasks without additional training.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World huggingface.co

ENPIRE framework enables autonomous robotics research through a closed-loop system that automates policy improvement via environment feedback, policy refinement, and evolutionary code optimization.

DragMesh-2: Physically Plausible Dexterous Hand-Object Interaction with Articulated Objects huggingface.co

DragMesh-2 enables dexterous hand-object interaction through contact-driven manipulation, with PICA enhancing robustness under varying contact loads without tactile feedback.

Configurable Clinical Information Extraction with Agentic RAG: What Works, What Breaks, and Why huggingface.co

ACIE, an agentic RAG system deployed in a clinical setting, demonstrates high accuracy in extracting medical information from complex patient contexts, achieving 96.5% acceptance rate by nuclear-medicine physicians across 7,326 judgments.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI huggingface.co

LegalHalluLens audits AI systems in legal workflows by identifying specific error patterns and directional biases in hallucinations across different claim types, enabling more reliable deployment through targeted diagnostic and mitigation approaches.

S-Agent: Spatial Tool-Use Elicits Reasoning for Spatial Intelligence huggingface.co

S-Agent is a spatial reasoning framework that enhances visual language models with temporal memory and hierarchical spatial tools to enable continuous 3D world understanding from multi-view imagery.

Adaptive Volumetric Mechanical Property Fields Invariant to Resolution huggingface.co

AdaVoMP predicts dense spatially-varying mechanical properties for 3D objects using a sparse adaptive voxel structure and transformer encoder-decoder model, enabling realistic deformable simulations with improved accuracy and efficiency.

No Resource, No Benchmarks, No Problem? Evaluating and Improving LLMs for Code Generation in No-Resource Languages huggingface.co

Research addresses code generation challenges for no-resource programming languages by developing benchmarks and proposing a method that combines further pre-training with weight difference transfer to create specialized instruction-following models at reduced computational cost.

JAMER: Project-Level Code Framework Dataset and Benchmark on Professional Game Engines huggingface.co

Game development frameworks and benchmarks were created using data from game jam competitions to evaluate code generation and project-level programming capabilities.

LooseControlVideo: Directorial Video Control using Spatial Blocking huggingface.co

LooseControlVideo enables intuitive 3D spatial control in text-to-video generation using sparse oriented 3D boxes as proxies, achieving superior trajectory accuracy and occlusion handling compared to existing methods.

FlowBender: Feedback-Aware Training for Self-Correcting Conditional Flows huggingface.co

FlowBender is a closed-loop framework that addresses constraint satisfaction in diffusion and flow models by training networks to correct alignment errors using inference-time feedback, outperforming traditional supervised and guidance-based approaches across multiple tasks.

The Data Manifold under the Microscope huggingface.co

A benchmarking framework is introduced to study data-manifold geometry by extending dSprites and COIL-20 datasets with additional transformation dimensions and dense sampling, enabling accurate estimation of curvature, reach, and volume for theoretical analysis and validation.

Holo-World: Unified Camera, Object and Weather Control for Video World Model huggingface.co

A unified controllable video world model generates videos from a single image while preserving scene structure and transferring to target weather states through specialized parameterization and conditioning techniques.

FreeStyle: Free Control of Style-Content Dual-Reference Generation from Community LoRA Mining huggingface.co

FreeStyle is a scalable dual-reference generation framework that uses community LoRA mining to create large-scale style-content triplets while addressing content leakage through disentanglement mechanisms and a comprehensive benchmark.

DF3DV-1K: A Large-Scale Dataset and Benchmark for Distractor-Free Novel View Synthesis huggingface.co

A large-scale real-world dataset called DF3DV-1K is introduced to address the lack of clean and cluttered image sets for distractor-free radiance field research, containing 1,048 scenes with 89,924 images across 128 distractor types and 161 scene themes, along with a curated subset DF3DV-41 for robustness evaluation, and demonstrates improved performance when used to fine-tune a diffusion-based 2D enhancer for radiance field methods.

JanusMesh: Fast and Zero-Shot 3D Visual Illusion Generation via Cross-Space Denoising huggingface.co

A fast, training-free framework generates text-driven 3D visual illusions by decoupling generation into cross-space dual-branch denoising and view-conditioned texture synthesis for seamless geometric fusion and semantic coherence.

LedgerAgent: Structured State for Policy-Adherent Tool-Calling Agents huggingface.co

LEDGERAGENT is a method for customer service agents that maintains task states in a separate ledger to improve policy adherence and state management during tool calling.

Selective Synergistic Learning for Video Object-Centric Learning huggingface.co

Selective Synergistic Learning (SSync) addresses limitations in video object-centric learning by selectively distilling reliable cues through pseudo-labeling and transitive merging to improve object decomposition quality and robustness.

Multi-LCB: Extending LiveCodeBench to Multiple Programming Languages huggingface.co

Multi-LCB addresses the limitation of LiveCodeBench by providing a multi-language benchmark for evaluating LLMs across twelve programming languages while maintaining contamination controls and evaluation protocols.

References

Simon Willison — ‘Porting Moebius’ simonwillison.net

Porting the 0.2B Moebius inpainting model to run entirely in the browser using ONNX Runtime Web and WebGPU — about 1.3GB of weights, but inference runs locally with no server.

HUSTVL — PixelHacker project page hustvl.github.io

PixelHacker (0.86B) introduces Latent Categories Guidance (LCG) with 116 foreground and 21 background categories, achieving FID 8.59 on Places2 at 512 — and serves as the teacher model distilled into Moebius.

SANA paper (arxiv 2410.10629) arxiv.org

SANA replaces vanilla attention with ReLU-based linear attention, removes positional embeddings, and uses Mix-FFNs that integrate depth-wise convolutions — the same architectural primitives Moebius’s LλMI block adopts.

Diffusion2GAN / E-LatentLPIPS project page mingukkang.github.io

E-LatentLPIPS operates directly on latents, delivering ~9.7× speedup and reducing per-iteration VRAM from 15.0GB to 0.6GB on an A100 versus decoding to pixels for standard LPIPS.

Simon Willison Substack — porting writeup simonw.substack.com

I had to bypass the higher-level Transformers.js library in favor of raw ONNX Runtime Web to properly manage the WebGPU backends — Claude Code did most of the translation from the PyTorch reference.

GitHub hustvl/Moebius repo github.com

Experiments primarily conducted at 512×512; users report VAE checkpoint must be placed in ./weight/vae and the project depends on flash-linear-attention plus a specific diffusers version — no open issues yet and no ComfyUI integration.

Clean-FID project page (Parmar et al., CMU) cs.cmu.edu

Most popular deep learning libraries (PyTorch, TensorFlow, OpenCV) implement image resizing incorrectly… resulting in vastly different FID scores for the same model.

Rethinking FID (arXiv 2510.00815) arxiv.org

FID — even in its ‘clean’ form — assumes a Gaussian distribution of features that may not exist in modern, complex text-to-image datasets, and correlates poorly with human judgment on diffusion outputs.

r/MachineLearning thread on ‘Exposing Flaws of Generative Model Evaluation Metrics’ reddit.com

DINOv2-ViT-L/14 consistently outperforms FID and CLIP-based scores in large-scale human psychophysics experiments on realism judgments.

Scalable Interpolant Transformers (SiT), ECCV 2024 ecva.net

SiT achieves FID 2.06 on ImageNet 256×256, surpassing DiT’s 2.27 without increasing parameter counts or GFLOPs.

Kyutai papers page kyutai.org

Companion error-bar calculator and GS-FID code are released so practitioners can convert any reported FID into a 95% confidence interval based on the measured seed-noise floor.

willisma/sit GitHub repository github.com

Reference SiT training code used as the experimental substrate; published checkpoints report single-seed FIDs without variance over training restarts.

Spheron Network — NVFP4 vs MXFP4 guide spheron.network

an 8-billion-parameter model… using MXFP4 required 36% more training tokens to reach the same loss level as the NVFP4-trained model

36Kr — AMD/Penn State FP4 training writeup eu.36kr.com

deterministic Hadamard rotations provide better convergence, reducing overhead from 27% to roughly 9% compared to randomized versions

EmergentMind — Micro-Rotated-GPTQ (MR-GPTQ) emergentmind.com

For NVIDIA’s NVFP4 format, the extremely small quantization group size (G=16) can ‘neutralize’ traditional outlier mitigation because a single outlier still dominates its tiny 16-element block

NVIDIA Research — NVFP4 QAD Report research.nvidia.com

NVFP4 utilizes an E2M1 structure… supplemented by a two-level scaling hierarchy: per-block FP8 scales and per-tensor FP32 scales… QAD employs a high-precision teacher model to guide a quantized student model through KL divergence loss

CVPR 2026 Workshop — ‘Not All NVFP4 QAT Recipes Are Equal’ (Du et al.) openaccess.thecvf.com

FAAR (Format-Aware Adaptive Rounding) uses learnable parameters to optimize rounding decisions specifically for the non-uniform E2M1 grid

APXML — LLM Compression course notes on Blackwell INT4 apxml.com

Blackwell’s Tensor Cores are ‘all-in’ on floating-point; running INT4 models often requires software emulation, which negates the architecture’s performance gains