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Simon Willison built a browser-based video compressor using the WebAssembly FFmpeg build, generated by Claude Fable 5.1 in Claude Code for web.
Willison recorded a demo video of his Equal Earth animation and wanted an optimized web-publishable version. He had Claude Fable 5.1 running in Claude Code for web build a compressor around the WebAssembly port of FFmpeg. The post is a short vibe-coding demonstration, not a model release or security event.
PhysStream autoregressive video model enables physics-grounded mid-generation motion control, cutting trajectory error 12% and FVMD 33% versus strongest baselines.
PhysStream is an autoregressive image-to-video model that incorporates structured scene memory—positional maps and object tracking maps derived online from previously generated frames—and supports fine-grained motion control via sparse velocity-increment signals encoding physical quantities. Training proceeds in two stages: a bidirectional model finetuned with motion-control conditioning, then a causal autoregressive model with scene memory. It reduces motion distribution distance (FVMD) by 33% and trajectory error by 12% over the strongest baselines, and human evaluators prefer it in over 85% of in-the-wild comparisons.
Ubuntu issued USN-8716-2 fixing FFmpeg VobSub, Vulkan HEVC, and NVDEC decoder flaws that could allow denial of service or code execution.
USN-8716-2 provides the Ubuntu 26.04 LTS counterpart to the FFmpeg fixes in USN-8716-1. Crafted media files could cause denial of service or arbitrary code execution through the VobSub subtitle demuxer (CVE-2026-64830), the Vulkan HEVC hardware decoder (CVE-2026-64831), and the NVDEC video decoder path.
Ubuntu's USN-8738-1 fixes multiple FFmpeg vulnerabilities allowing denial of service, arbitrary code execution, and sensitive information exposure.
Ubuntu Security Notice USN-8738-1 addresses several FFmpeg flaws. CVE-2026-66036 involves mishandled video frames in the hqdn3d filter, allowing denial of service or arbitrary code execution. CVE-2026-66038, found by Adrian Junge, leaks sensitive information via compressed video files, and CVE-2026-66039 allows denial of service or code execution through crafted audio files. Additional subtitle-file handling flaws were also fixed.
LACE introduces layer-wise compression for dynamic frame rate audio codecs, cutting sequence lengths and speeding TTS inference while preserving quality.
LACE (Layer-Adaptive Codec Encoding) applies an independent compression step at each quantization layer of a neural audio codec, enabling layer-specific segmentation boundaries instead of shared ones. Union alignment and boundary anchor mechanisms keep durations consistent for downstream text-to-speech. On LibriTTS, LACE achieves a better rate-quality tradeoff than prior dynamic frame rate codecs and improves TTS inference efficiency at competitive synthesis quality. Code is released in the ESPnet3 codec recipe.
Ubuntu USN-8750-1 fixes multiple FFmpeg memory-handling flaws that allow denial of service via crafted files.
Ubuntu Security Notice USN-8750-1 patches several FFmpeg vulnerabilities related to incorrect memory operations, reported by Seung Min Shin, Xinghang Lv, and others. If a user or automated system is tricked into opening a specially crafted file, an attacker could cause a denial of service. Affected CVEs include CVE-2026-12706, CVE-2026-30999, and CVE-2026-58049.
TempCloze benchmark tests Video-LLMs' temporal reasoning with 1,521 videos, finding temporal alignment is the primary failure mode across 31 models.
TempCloze is a video cloze benchmark in which models must identify the true missing middle clip given the beginning and ending clips, using 1,521 carefully filtered videos from seven sources, mostly long-take and egocentric footage. Distractors are constructed along three dimensions: Semantic, Alignment and Progression, with shared scenes and objects to reduce appearance cues. Evaluation of 10 proprietary and 21 open-source Video-LLMs found Alignment is the primary bottleneck, with models often recognizing plausible semantics and local event progression but struggling with temporal alignment.
Researcher NightmareEclipse published FalconFlank, a zero-day local privilege escalation in CrowdStrike Falcon Sensor abusing its Office malicious macro remediation feature.
A researcher known as Nightmare Eclipse published details of FalconFlank, a zero-day privilege escalation that abuses the Microsoft Office malicious macro removal feature in CrowdStrike Falcon Sensor, verified working on fully updated Windows 11 25H2 and Windows Server 2025 with Falcon Phase 3 Optimal Protection. CrowdStrike urged customers to disable the Office File Suspicious Macro Removal policy while it investigates, noting customers remain protected via Cloud Anti-malware for Microsoft Office Files; no CVE has been assigned. The same researcher previously released the Exploitarium dump of over 30 PoC exploits, and researcher Kevin Beaumont confirmed FalconFlank works.
MovieGrid arranges long videos on spatial grids during post-training, generating 6.05x more shots than temporal packing with state-of-the-art cross-shot consistency.
MovieGrid is a multi-grid post-training paradigm that decomposes long videos into temporally ordered chunks arranged on a spatial grid for joint modeling, enabling cross-chunk information exchange. The authors build the Multi-Grid Long Video (MGLV) dataset from 1,000 long-form videos, producing 54K grid videos paired with character-aware story prompts. Under the same token budget, MovieGrid generates 6.05x more shots than Temporal Packing in a 1,616-frame video. It achieves state-of-the-art intra-shot consistency of 0.9131 versus 0.8086 for HoloCine and inter-shot consistency of 0.5914 versus 0.5384 for StoryMem.
Trojanized 3CXDesktopApp installers for Windows and macOS loaded RC4-encrypted shellcode delivering a backdoor to hundreds of thousands of 3CX users.
Threat actors compromised the 3CXDesktopApp build process, adding malicious libraries (ffmpeg.dll, d3dcompiler_47.dll) to installers downloaded from the developer's website. The Windows MSI loader decrypts embedded shellcode with RC4, sleeps for a random 1-4 weeks, then beacons to C2 hidden in icon files on GitHub. Unit 42 fingerprinted 247,277 IPs across 199 countries using 3CX applications and blocked shellcode execution at 127 Cortex XDR customers between March 9-30, 2023.
Researchers propose Mask Forcing, a dual-noise masking rollout that mitigates mode collapse in autoregressive video diffusion distillation.
The paper targets over-saturation and over-smoothing in autoregressive video diffusion models distilled via Distribution Matching Distillation, attributed to reverse-KL mode-seeking behavior. Mask Forcing perturbs the student self-rollout with random masks along spatial and temporal axes, injecting cleaner tokens that act as denoising guidance for noisier tokens. Experiments show improved visual quality across multiple distillation methods without using real video data or extra post-training stages.
Viggle released Viggle-Animate, a 33.1B MiniMax-H3 finetune replacing video characters from one repainted frame, rendering 124 frames in 26 seconds on one GPU.
Viggle-Animate replaces the character in a video using only a driving video and one of its own repainted frames, with no pose estimator, segmentation mask, face tracker, or text encoder. It is a 33.1B full finetune of MiniMax-H3's ref2va transformer, jointly distilled with DMD across two teachers split by noise level, so rendering takes three forward passes per clip. On a B200 GPU it renders 124 frames in 26 seconds, 6.1x faster per clip than Wan2.2-Animate-14B in matched comparisons. The method assumes no person-specific representation, so it generalizes beyond humans; a demo, research write-up, and ComfyUI nodes are available.
Researchers introduce EditVid, a training-free video editing framework scoring 78.16 FiVE-Acc versus 58.95 for the strongest comparable baseline.
EditVid is a unified training-free framework for diverse instruction-guided and subject-guided video editing. It combines sparse causal memory for local coherence, correspondence-based post-attention token injection for long-range identity preservation, and soft latent blending for edit locality. On the FiVE benchmark it reaches 78.16 FiVE-Acc against 58.95 for the strongest evaluated training-free baseline, with competitive results on IVEBench. A user study showed 51.8% overall preference for EditVid over 7 competing methods.
A survey catalogs inference-efficiency techniques for video and audiovisual LLMs, mapping bottlenecks in sampling, encoding, token reduction, and LLM decoding.
This survey covers inference-efficiency mechanisms for visual and audiovisual video LLMs, reporting reductions in parameters, FLOPs, latency, memory, and token counts. It organizes methods by pipeline stage, covering frame sampling, modality encoding, connector-level token reduction, and LLM prefilling and decoding for systems built since late 2022. The authors compile accuracy-cost comparisons under shared host models and input protocols, identify gaps in audiovisual efficiency and standardized evaluation, and maintain a public repository.
Adobe adds in-timeline generative media to Premiere, letting editors generate video and audio clips using Firefly, Veo, Runway, Luma, and Kling models.
Adobe's new Generative Media tool lets Premiere editors highlight empty gaps in the timeline and generate context-aware, editable video, sound effects, music, and soundscapes without leaving the project. Editors can choose among underlying models including Adobe Firefly, Google Veo, Runway, Luma, and Kling. Beta AI audio tools can separate overlapping speakers and duck music under speech, and an AI Assistant is coming to After Effects for plain-language project commands.
Community fine-tune Minimax-h3_Singularity enhances MiniMax-H3 video generation with HDR quality, distant face restoration, and improved motion, trending #22 on Hugging Face.
Minimax-h3_Singularity is a community fusion fine-tune of the MiniMax-H3 multimodal video generation model, built from multiple checkpoints and refined with pruning and weight optimization. It supports Text-to-Video, Image-to-Video, Reference-to-Video, and Video-to-Video workflows in ComfyUI, and claims improvements in HDR clarity, distant face restoration, motion fluidity, and fantasy VFX. The authors recommend pairing it with the minimax_h3_ref2v_turbo_4step_v0.1 LoRA for four-step accelerated inference, and an online demo is available via RunningHub.
Community fine-tune Qwopus3.8-27B-Flash, built on Qwen3.8-27B, cuts agent reasoning latency with 12.8% faster decoding and 80.7% MTP acceptance.
Jackrong released Qwopus3.8-27B-Flash, a fine-tune of Qwen3.8-27B optimized for long-running agent workloads, reporting 12.8% faster decoding and 80.7% multi-token-prediction acceptance. Training used roughly 1.5 million teacher-scored SFT examples filtered to the top 10%, followed by reinforcement training with NVIDIA NeMo-RL and GSPO. The author notes an explicit trade-off: MMLU-Pro mixed-set scores are lower than the base model, and a known bug can produce incorrect Python indentation. Author-provided benchmarks have not been independently verified.
Microsoft released VibeVoice-ASR-Streaming-7B, an open streaming ASR model with speaker attribution, custom hotwords, and support for 10 languages under MIT license.
Microsoft Research released VibeVoice-ASR-Streaming-7B on Hugging Face, a unified streaming speech recognition model that continuously transcribes who said what as speech arrives. The 7B model supports customized hotwords for domain-specific terms and 10 languages including Chinese, English, French, German, Italian, Japanese, Korean, Portuguese, Russian, and Spanish. Code is available at github.com/microsoft/VibeVoice with a live demo, and a technical report is on arXiv (2609.02812). The model is licensed under MIT.
Mi-Ripple is a diagnosis-guided restoration workflow that removes digital ripple artifacts introduced by iterative AI image editing while preserving structure.
Iterative reference-conditioned image editing can introduce grid-like and granular textures known as digital ripple. Mi-Ripple separates periodic lattice artifacts from content-entangled granular texture, then applies selective spectral notching, structure-aware smoothing, and cleaned-reference regeneration. In fourteen notch-only executions, whole-image residual standard deviation was 0.08-0.44 in CIELAB lightness units, and reference cleaning reduced output debris density by 45% in a paired example.
ReMoMask-2 rebuilds retrieval in the generator's latent space for text-to-motion generation, achieving lowest FID on KIT-ML and SnapMoGen.
ReMoMask-2 is a retrieval-augmented text-to-motion framework that constructs its retrieval database directly in the generator's pre-quantization latent space and aligns text queries through a distilled lightweight projector, eliminating the representation gap. The framework combines Hierarchical Bidirectional Momentum contrastive learning, Semantic Spatial-Temporal Attention, and Topology Structured Masking to handle hierarchical motion structure. The retriever achieves state-of-the-art accuracy, and ReMoMask-2 attains the lowest FID on KIT-ML and SnapMoGen, with a single mask-transformer stage outperforming the previous two-stage pipeline while delivering the fastest inference.
ShallowStream builds streaming-video retrieval indexes from shallow MLLM layers, cutting per-frame prefill latency by up to 52.1x.
ShallowStream is a framework for streaming video understanding with multimodal LLMs that uses the model's shallow layers to simultaneously encode frames and maintain an always-on lightweight retrieval index via shallow-layer KV caches, avoiding full-depth prefill for every incoming frame. At query time, shallow-layer attention scores plus a diversity-aware selection strategy retrieve relevant context frames. It reports performance on par with the strongest existing streaming methods while reducing per-frame prefill latency by up to 52.1x and 10-second end-to-end latency by up to 11.9x, with code released on GitHub.
SimpleMemVLA passes full timestamped video history straight to a VLA backbone, setting state of the art on four memory benchmarks.
SimpleMemVLA is a vision-language-action model for long-horizon manipulation that removes the dedicated memory module entirely. It keeps sampled history intact and feeds it to the backbone as timestamped video, with the hidden states of a generated sub-task serving as the only channel into a standard flow-matching action head. Prefilling the shared history prefix during action execution keeps latency close to a single-frame VLA. The system sets a new state of the art on four memory benchmarks and outperforms retrieval, compression and recurrent-state mechanisms, with causal interventions confirming the policy genuinely reads its history.
A multi-signal pipeline detects LLM hallucinations, reaching F1 0.915 on HaluEval and cutting Qwen2.5-0.5B hallucination rates from 85.5% to 37.7% via DPO.
The paper presents a hallucination detection pipeline combining fine-tuned DeBERTa-v3 classification, Monte Carlo Dropout uncertainty, and temperature-scaled calibration. It achieves F1 0.915 and AUROC 0.977 on general-domain HaluEval tasks, with MC Dropout inference raising accuracy to 93.2%. Applying DPO to a Qwen2.5-0.5B generator reduces its hallucination rate from 85.5% to 37.7%, while cross-domain evaluation shows poor general-domain transfer to SciFact (F1 0.52) and PubMedBERT fine-tuning as the strongest adaptation (F1 0.63).
XPeng AI's X-AuT prunes speech LLM audio encoders, cutting Qwen3-ASR-0.6B error from 5.61% to 5.27% with fewer parameters.
X-AuT is a progressive compression framework for speech LLM audio encoders that selects layer combinations via short behavioral probes and restores pruned models using cross-scale distillation and LoRA finetuning while keeping the language-model backbone frozen. Compressing Qwen3-ASR-0.6B from 18 to 16 audio-encoder layers lowered macro-average error from 5.61% to 5.27% on ten Chinese-English benchmarks. A 14-layer model reached 5.75% error with 20.7% fewer audio-tower parameters, and progressive pruning outperformed direct pruning (5.75% vs 6.73%).
Researchers analyze the 'attention triangle' in audio-video diffusion models, showing bias-driven cross-attention routing causes semantic leakage and proposing inference-time interventions that improve grounding.
A study probes the three cross-attention edges linking text, audio, and video streams in audio-video diffusion models. It finds the audio-video edge is bidirectional and shaped by parameter-encoded biases, so prompts in tension with learned priors can be overridden, producing visually canonical but incorrect outputs. Attention-derived signals are used as diagnostics and to guide inference-time interventions that improve cross-modal semantic grounding while preserving generation quality.
DF26 benchmark shows humans and state-of-the-art deepfake detectors perform near chance on videos generated by seven modern text-to-video models.
Researchers introduce DF26, a benchmark of 271 real and 2,420 fully synthetic videos created by seven modern video generation models, all depicting single-person public-speaking scenarios such as direct-to-camera recordings, official statements, and studio interviews. Human viewers and state-of-the-art deepfake detectors scored close to random chance at distinguishing fakes from real footage. The authors argue current evaluation protocols are insufficient and call for benchmarks that explicitly measure robustness to modern generative model distribution shifts.
LynnReal-Omni unifies controllable video generation tasks in a 32B multimodal diffusion transformer, with a 27B Flash variant rendering 540p clips in 377 ms.
LynnReal-Omni is a native multimodal video generation framework built on a 32B shared multimodal diffusion transformer unifying text-to-video, image-conditioned generation, reference guidance, structural control, editing, restoration and long-video generation, accepting heterogeneous inputs like 3D renders and game recordings for agentic visual workflows. A dedicated 27B Flash model enables real-time rendering, producing a 22-frame 540p video in 377 ms on one H100 versus 843 ms for the full model. The work introduces a curated multi-shot audiovisual data pipeline and MSAVP, a 100-prompt, 20-metric evaluation design covering instruction following, plausibility, visual quality, temporal behavior and audio coordination.
FreeFlow is a bias-free hierarchical transformer achieving state-of-the-art optical flow results on Sintel, KITTI-2015, and Spring benchmarks.
FreeFlow replaces task-specific inductive biases like correlation volumes and iterative warping with a single feed-forward encoder-decoder combining window, shifted-window, and reduced-resolution global attention. It reaches 0.68/1.48 EPE on Sintel Clean/Final, 3.23 Fl-all on KITTI-2015, and 3.192 1px on Spring. The architecture scales consistently from small to large variants and remains memory efficient at 1080p inference.
SQD disaggregates LLM inference by quadratic versus subquadratic attention layers, improving energy efficiency up to 56% on heterogeneous systems versus GPU-only baselines.
SQD (SubQuadratic Disaggregation) splits decode not by operator type but by quadratic versus subquadratic attention, matching their distinct arithmetic intensity and memory footprints. For sparse attention LLMs it separates top-k selection (requiring full KV indexing) from top-k attention plus FFN; for linear and sliding-window models it separates dense attention layers from subquadratic layers plus FFN. On an adjusted 8xB200 heterogeneous proxy, tokens-per-joule improves 53% on GLM 5.2, 31% on Nemotron 3 Ultra, and 56% on Gemma 4 31B. A Rubin plus LPX analytical model shows 1.2x-1.5x tighter achievable latencies and up to 3.6x higher throughput versus attention-FFN disaggregation.
ActionSplice enables in-flight action editing in chunk-autoregressive video world models via a lightweight corrector, avoiding rollback or waiting for the next chunk.
ActionSplice is an inference framework that formulates in-flight action editing for chunk-autoregressive video world models as Counterfactual State Transport (CST), where a lightweight corrector transports the interrupted backbone-native representation toward the matched state induced by the revised action. The world model and sampler remain frozen, and sampling resumes without replaying completed evaluations. Across minWM-Wan Action2V and HY-WM1.5, the retargeting variant CST-R reduces rollback-relative LPIPS by 61.5% and 75.9% versus direct condition swapping, while the temporal-splicing variant CST-T reduces suffix LPIPS by 56.1% and 77.5% with 2.73x and 1.69x pixel-ready speedups over waiting.
TRACE, a training-free visual token pruning framework, cuts GUI agent inference latency and memory while keeping trajectory-wide visual evidence reusable.
TRACE is a training-free framework for trajectory-robust admission and coverage-aware evidence ordering that prunes high-resolution screenshot tokens accumulated in GUI agent trajectories. It ranks visual evidence using a query-independent layout-derived interaction prior combined with instruction relevance and feature novelty, and reserves part of the budget for native tokens distributed across the screen to repair spatial coverage. A monotone KV contraction incrementally compresses retired frames into compact session state, avoiding repeated visual encoding or pruning. Experiments across six GUI benchmarks and diverse models verify effectiveness under tight budgets, with source code to be released.
Kraken speech-to-speech translation model builds on Qwen3-8B with low-bitrate vector quantization and source-conditioned vocoding.
Kraken augments a pre-trained Qwen3-8B LLM with speech feature inputs and low-bitrate single-layer vector-quantized tokens trained to reconstruct SSL features. A separate token-to-waveform decoder, Autowave-X, is conditioned on source speech to improve non-linguistic transfer. Training used 150k hours of multilingual and multitask speech data. The model reportedly beats SeamlessM4T-Large v2 and Qwen2.5-Omni in translation quality and speaker/prosody transfer.
ReactVAU is a slow-fast streaming framework for real-time video anomaly understanding that reserves heavyweight MLLM reasoning for suspicious events, improving efficiency.
ReactVAU addresses causal streaming video anomaly understanding with three components: a lightweight Fast Detection Module using Spatial Grid Folding, Anomaly-Aware Persistent Memory that protects critical visual cues from temporal decay, and a Slow Reasoning Module activated only on suspicious events. This design minimizes heavyweight MLLM invocations during long normal intervals. Experiments show competitive anomaly detection and causal reasoning under strict streaming constraints with significantly enhanced computational efficiency.
AlayaVista is a camera-controllable streaming video world model that decouples panoramic scene evolution from perspective synthesis, trained on a 1,318-hour 4K dataset.
AlayaVista builds a 360-degree scene prior from a single perspective image, evolves it as a camera-conditioned panoramic latent state, and maps it to perspective video via a latent viewport renderer plus a perspective refiner. Chunk-autoregressive generation and few-step distillation enable efficient streaming. The authors introduce MUGEN, a real-world panoramic video dataset with 1,318 hours of at-least-4K video and rich semantic and geometric annotations.
Researchers introduced Brain2Semantics2Text, decoding sentence meaning from non-invasive MEG brain recordings via a semantic bottleneck, improving on prior Brain2Text methods.
The paper proposes Brain2Semantics2Text, a non-invasive speech decoding method that maps sentence-level magnetoencephalography (MEG) responses into a semantic embedding space and inverts those embeddings into natural language. Motivated by evidence that high-level semantic representations are distributed across cortex and evolve on slower timescales, the approach targets meaning rather than phonemes or words, avoiding the need for word-level alignment. The authors report improved sentence-level results compared to prior non-invasive Brain2Text methods despite the low signal-to-noise ratio of neural recordings.