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AI Is Learning to Write Genetic Code

AI models generated viable bacteriophage genomes, with 16 designs successfully replicating and some outperforming the original virus at attacking E. coli.

Two AI models generated complete genome designs for bacteriophages modeled on ΦX174, a virus known to infect E. coli. Researchers produced about 700,000 candidate designs, selected 285 promising ones, and synthesized DNA inserted into E. coli, yielding 16 viable viruses. Some of the newly generated viruses proved more effective at attacking E. coli than the original bacteriophage.

Schneier on Security · 26d agoAI research

PhysStream: Streaming Physics-Grounded Video Generation with Structured Scene Memory and Fine-Grained Motion Control

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.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research 2 sources1

Google’s Atlas of the human genome could pave the way for new treatments

Google DeepMind launches AlphaGenome Atlas, a catalog of predicted molecular effects for roughly nine billion single-letter DNA changes across the human genome.

Google DeepMind unveiled AlphaGenome Atlas, a roughly 1-petabyte dataset predicting how each of about nine billion possible single-nucleotide variants affects molecular biology, including non-coding regions that regulate gene behavior. It builds on the AlphaGenome model released in 2025 and adds a Variant Impact Score (AVI) to help researchers rank variants. The catalog is available for noncommercial research via a web portal, the Antigravity platform, and the AlphaGenome interface, with commercial access on Google Cloud planned.

The Verge · AI · 8d agoAI industry 2 sources

NVIDIA Details BioNeMo Inference Runtime (BioIR): 2.90x Higher Boltz-2 Folding Throughput and 58.5K Residues per GPU-Hour on 8xH100

NVIDIA released BioNeMo Inference Runtime (BioIR), an open-source PyTorch-compatible library delivering 2.90x higher Boltz-2 protein-folding throughput on 8xH100 GPUs.

NVIDIA detailed BioIR, a Python library that accelerates Boltz-2, OpenFold2, and OpenFold3 structure-prediction inference on NVIDIA GPUs while preserving standard PyTorch workflows. On a matched benchmark of 1,000 human dimers on 8xH100 80GB GPUs, BioIR delivered 58.5K folded residues per GPU-hour versus 20.2K for a torch.compile baseline, a 2.90x throughput gain. BioIR already powered the AlphaFold Database expansion, generating about 31 million candidate complexes across 4,777 proteomes, with 1.81 million released as high-confidence predictions. Extrapolated to 1 million targets, estimated folding energy drops from 35 MWh to 11 MWh at 8-GPU TDP equivalents.

MarkTechPost · 6d agoAI tools & infra1

Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research1

Can Edge-Deployable Vision-Language Models Identify Species?

Evaluation of 2-8B VLMs (Qwen3-VL, Gemma3) against BioCLIP on camera-trap species ID shows all models degrade sharply on field imagery.

The study tests whether edge-deployable 2-8B vision-language models carry genuine taxonomic knowledge, comparing Qwen3-VL 2B/4B/8B and Gemma3 4B against the 300M specialist BioCLIP on a 96-species task across clean iNaturalist photos and six LILA.science camera-trap collections. All models degrade 9.6-26.6 percentage points on field imagery, and BioCLIP outperforms every VLM by 33.2-59.2 points on an expanded 200-image sample, suggesting specialized data rather than scale drives the gap. Under open-set prompting, 5.9-9.6% of responses are syntactically valid but taxonomically nonexistent species names, with fabrication rankings replicating across evaluation sets.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research1

How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules

OpenAI profiles César de la Fuente's lab using ChatGPT and Codex alongside deep-learning models to accelerate antimicrobial molecule discovery.

OpenAI published a case study on bioengineer César de la Fuente's lab, which uses ChatGPT and Codex for hypothesis brainstorming, code writing, dataset processing, and bridging knowledge gaps across biology, chemistry, and computer science. The lab's deep-learning models scan genome and protein databases for antimicrobial peptide candidates, potentially cutting initial searches from years to hours. Bacterial antimicrobial resistance was associated with about five million deaths in 2021, a toll projected to roughly double by 2050.

OpenAI News · 6d agoAI industry1

Top 10 Best Ransomware Protection Solutions in 2026

A 2026 buyer's guide ranks ten ransomware protection tools by kill-chain role as extortion shifts from encryption to data theft.

The roundup organizes defenses across the ransomware kill chain: prevention-grade EPP/EDR platforms, containment layers, rollback specialists, and immutable recovery. Recommended products include CrowdStrike, Microsoft Defender, Sophos, SentinelOne, Bitdefender, Trend Micro, Halcyon, Huntress, and Malwarebytes. It stresses that many crews now extort on stolen data without encrypting, making exfiltration detection and response speed as important as rollback.

Cyber Security News · 6d agoIndustry1

Thousands of Hacked WordPress Sites, One Operation: Unmasking StopAndProtect

Check Point uncovers the StopAndProtect ransomware operation infecting thousands of WordPress sites via ClickFix social engineering and PowerShell downloaders.

Check Point Research first observed the StopAndProtect ransomware family in mid-May 2026 and linked its infrastructure to thousands of hacked WordPress sites. The infection chain begins with ClickFix social engineering that prompts victims to run a PowerShell command, followed by two stages of additional downloaders. The research maps the shared infrastructure unifying the operation.

Check Point Research · 29d agoRansomware in the wild

Threats Making WAVs - Incident Response to a Cryptomining Attack

Guardicore researchers dissect a cryptomining attack that hid a cryptominer inside WAV files, mapping the full infection chain and response steps.

Guardicore security researchers present a full analysis of a cryptomining attack that concealed a cryptominer inside WAV audio files. The report documents the complete attack chain from detection through infection, network propagation, and malware analysis. It also includes recommendations for optimizing incident response processes in data centers.

Akamai Blog · 8d agoMalware in the wild

Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens

Researchers release AssayBench-Loop, a 1,389-screen CRISPR benchmark, and AssayLoop, a framework that learns adaptive hit discovery policies.

The paper introduces AssayBench-Loop, a large-scale benchmark of 1,389 CRISPR screens across five phenotype categories for adaptive hit discovery under budget constraints. It also introduces AssayLoop, which combines AssayFormer, a transformer-based amortized acquisition policy trained across historical screens, with LLM-derived biological priors via an adaptive handoff. On temporally held-out screens, AssayLoop achieves 5.67-fold enrichment over random selection and recovers 27.7% of hits after assaying roughly 5% of the candidate library, outperforming existing adaptive-design methods and standalone LLMs.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research1

12 Best Ransomware Protection Solutions Compared (2026): Features & Pricing

GBHackers compares 12 ransomware protection solutions for 2026, recommending layered stacks of EDR prevention, managed detection, containment, and guaranteed recovery.

The editorial comparison argues no single product stops ransomware, recommending a layered strategy across prevention, early detection, blast-radius containment, and clean recovery. CrowdStrike and SentinelOne are named best-in-class EDR, Huntress and Sophos MDR for managed 24/7 coverage, ColorTokens for microsegmentation containment, and Rubrik and Acronis for guaranteed recovery. The guide notes ransomware is now professionalized with double extortion and encryption sprints measured in minutes.

GBHackersupdated · 13h agofirst · 5d agoIndustry 14 sources1

TempCloze: Can Video-LLMs Identify the Missing Middle?

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.

Hugging Face daily papers · 16d agoAI research

VidaForge: Open Research Infrastructure for Video Pretraining Data Recipes

VidaForge releases open infrastructure and VIDAFORGE-3M (3.14M clips, 6,475 hours) linking video pretraining data recipes to downstream model performance.

VidaForge is an open research infrastructure that represents a video pretraining data recipe as an executable five-stage workflow from raw videos to training datasets. The team compares data recipes with different coverage and quality during early from-scratch pretraining of Wan 2.1 and V-JEPA 2.1, finding that broader-coverage recipes achieve the highest downstream benchmark scores while loss-based evaluation favors different recipes. They also release VIDAFORGE-3M, containing 3.14 million scene-level clips totaling 6,475 hours with fine-grained annotations and curation signals for video data-recipe research.

Hugging Face daily papers · 11d agoAI research

Lightweight Vision Transformer Compression for On-Device Plant Disease Detection in Resource-Constrained Agricultural Field Conditions

A unified ViT compression pipeline (H-BAC pruning, quantization, distillation) cuts plant-disease models 54.5x to 6.01 MB while keeping 95.13% accuracy.

Researchers combined Hessian-Balanced Adaptive Block Pruning (H-BAC), guided by second-order sensitivity estimation, with quantization and attention-based knowledge distillation to compress Vision Transformers for on-device chilli plant disease detection in India. On a 3-class cross-village, cross-device out-of-distribution dataset, the integrated pipeline reduced model size from 327.42 MB to 6.01 MB (54.5x) at 95.13 +/- 2.32% accuracy, matching the 95.13% FP32 baseline. Ablations also show a directly trained 6.01 MB INT8 student reaches 94.87% accuracy, indicating where pruning and distillation add limited value.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research

New Python-Based Payload MechaFlounder Used by Chafer

Unit 42 identified MechaFlounder, a new Python backdoor used by the Chafer group to target a Turkish government entity via HTTP-based C2.

Unit 42 reports that in November 2018 the Chafer group targeted a Turkish government entity, reusing the win10-update[.]com infrastructure reported earlier in 2018 by ClearSky. The new secondary payload, tracked as MechaFlounder, is Python compiled with PyInstaller and acts as a backdoor supporting file upload/download and command execution. It beacons over HTTP using anomalous GET requests containing the username and hostname, suggesting a custom C2 server rather than a standard web server. Code overlap with OilRig's Clayside VBScript was noted, but Chafer and OilRig remain tracked as separate groups.

Palo Alto Unit 42 · Aug 17, 2026Threat actor

Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled pure-autoregressive testbed shows task-specific validation losses rank image tokenizers differently, with I2T loss the most consistent signal.

Researchers built a controlled pure-autoregressive testbed and tracked task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They find losses should be analyzed per task because they exhibit distinct scaling behavior and rank tokenizers differently, and that the loss-performance relationship depends on the predicted token space. I2T loss, computed over a shared text vocabulary, correlates consistently with both generation and visual understanding performance after supervised finetuning. Case studies revisit the discriminator, semantic supervision, and vocabulary size as tokenizer design axes.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research1

Multi-Grid Post-Training for Long-Form Multi-Shot Video Generation

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.

Hugging Face daily papers · 11d agoAI research

BambooToken malware controls Windows and Linux systems via MQTT

Lumen Black Lotus Labs exposes BambooToken, a China-aligned malware framework using MQTT C2 to backdoor Windows and Linux systems at roughly a dozen enterprises.

Lumen's Black Lotus Labs documented BambooToken, a previously unknown malware framework active since at least 2023 that adopted MQTT for command-and-control in 2024-2025 variants targeting Windows and Linux. Infection occurs via DLL side-loading through digitally signed Tendyron OnKey USB-token software or an impersonated Kingsoft Office installer; dead code suggests keylogging, clipboard theft, audio/webcam capture, and screenshot modules. Telemetry identified roughly a dozen compromised entities, mostly in Asia and South America, including a Hong Kong GitLab server and possibly users of the SpeedCN VPN service. Targeting patterns are consistent with China-aligned operations, though no attribution to a known cluster was made.

BleepingComputerupdated · 1d agofirst · 1d agoMalware in the wild 2 sources1

How Does mHC Use Its Residual Streams? Selective Routing and Near-Identity Mixing

Analysis of DeepSeek-V4-Flash shows four-stream mHC residual blocks use only about two streams effectively, with late-layer mixing providing little benefit.

The study examines the four-stream residual pathway of DeepSeek-V4-Flash, finding typical attention or FFN sites effectively use about two streams and that residual mixing is modest, occurring primarily in early layers. Replacing late mixers with identity increases C4 perplexity by only 1.9% while replacing early mixers raises it by 41%. Retaining the three largest routing weights per token increases perplexity by at most 2.7%, showing the model uses only part of the flexibility afforded by the four-stream design.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research

World in World: Explore the World with World Models

World in World is a training-free inference-time interface enabling camera-controlled rerendering, long-horizon revisiting, and motion transfer with frozen causal video world models.

The framework converts heterogeneous control evidence into camera- and time-labelled clean visual states that are read through the native self-attention of a frozen causal video model. Evidence includes source-video observations, target-view scene projections, geometry renderings for completing newly exposed regions, and retrieved generated states beyond the rolling cache. A correspondence router combines persistent point identities with geometry for token correspondences, while evidence-wise attention CFG independently regulates each auxiliary channel. The shared interface supports camera-controlled rerendering, long-horizon revisiting, and human-motion transfer without additional training.

Hugging Face daily papers · 7d agoAI research

Repeat-After-Me: Black-Box Adaptive Visual Prompt Injection

Researchers unveil Repeat-After-Me, a black-box visual prompt injection achieving over 80% success on Qwen3.6-27B and 47% on GPT-5.5.

Researchers present Repeat-After-Me, a black-box adaptive visual prompt injection that induces frontier VLMs to reveal PII or make malicious tool calls via injected images. It exceeds 80% attack success rate on Qwen3.6-27B and 47% on GPT-5.5 even when the benign user prompt is unrelated and does not authorize the injected task. In a real-world OpenClaw Discord deployment, a minimally injected image can overwrite TOOLS.md, enabling later remote code execution and secret exfiltration.

arXiv cs.CR · 13d agoAI safety & security

SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models

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.

Hugging Face daily papers · 15d agoAI research

C2Looper: A New Backdoor Likely Tied To Ransomware With GitHub C2

Zscaler ThreatLabz identified C2Looper, a new Rust-based backdoor likely tied to ransomware actors and delivered via ClickFix chains, using GitHub for C2.

In July 2026, Zscaler ThreatLabz identified C2Looper, a new Rust-based backdoor family. The malware supports arbitrary command execution, reconnaissance, and deployment of second-stage payloads, and uses GitHub as its command-and-control channel. ThreatLabz assesses with low-to-medium confidence that it is delivered through multi-stage ClickFix infection chains and is likely leveraged by a ransomware-related threat actor. The analysis covers multiple C2Looper variants, their network protocols, and capabilities.

Zscaler ThreatLabz · Aug 17, 2026Malware in the wild1

WarmBloodAban/Minimax-h3_Singularity — new model trending #22 on Hugging Face

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.

Hugging Face trending models · 11d agoModel release7· 1 read

UniH^3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration

UniH3 unifies hierarchical homogeneity and heterogeneity modeling for all-in-one medical image restoration across modalities and degradation types.

UniH3 introduces a Hierarchical Homogeneity Memory module that distills shared anatomical priors from high-quality images, injected via a Homogeneity-Guided Attention mechanism. A Hierarchical Heterogeneity Balancer mitigates inter- and intra-task conflicts during multi-task optimization. It achieves state-of-the-art on MedIR-2D-500K and MedIR-3D-3D benchmarks for both all-in-one and single-task restoration, with code released on GitHub.

Hugging Face daily papers · 7d agoAI research

Why Is Video Still So Expensive? A Survey of Inference-Efficiency Mechanisms in Video and Audiovisual LLMs

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.

Hugging Face daily papers · 8d agoAI research

Augustinian BabyLM: What Ostensive Definition Can and Cannot Teach a Small Language Model

Study shows visually grounded token embeddings in a small masked LM persist through training and improve object-property knowledge, but escape standard BabyLM benchmarks.

The paper implements ostensive definition for a small DeBERTa masked language model trained on 10M words, seeding visually grounded tokens with embeddings derived from labeled image regions before training. Visual initialization leaves a persistent, seed-replicated advantage on object-property knowledge (COMPS) and a corpus-tailored Visual-Property Swap benchmark covering color, material, size, and shape, but has no effect on most BabyLM grammar benchmarks. Synthetic grounding of previously unseeded words causally transfers the advantage to exactly those words.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research