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AlphaGenome Atlas: A predictive map of every possible DNA letter change in the human genome

Google DeepMind released AlphaGenome Atlas, a free 1-petabyte platform predicting the molecular effects of all ~9 billion possible single-letter DNA variants.

Google DeepMind introduced AlphaGenome Atlas, containing precomputed predictions for the effects of roughly 9 billion single-nucleotide variants across the human genome, spanning hundreds of human and mouse cell types. The 1-petabyte dataset is more than 30 times larger than the AlphaFold Database and includes an AlphaGenome Variant Impact (AVI) score combining AlphaGenome and AlphaMissense predictions for both coding and non-coding regions. External collaborators have already used it to identify and experimentally verify variants in unsolved rare disease research. It is available via a free web portal, the AlphaGenome API, and as a skill in Google Antigravity.

Google DeepMind·Sep 8, 14:00 UTC · 8d agoAI research 2 sources

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Google's AI genome system evaluates every possible one-base change

Google's AlphaGenome AI system predicts functional effects of non-coding DNA variants in humans and mice.

Google's AlphaGenome AI system evaluates genomic sequences to predict gene expression, transcription factor binding, chromatin accessibility, splice site usage, and related genomic features. The system is currently limited to human and mouse sequences and a limited set of well-studied cell types, but its predictions generally match or exceed specialized software tools. Researchers can use it to assess whether non-coding variants are likely significant and generate hypotheses about their function.

Ars Technica · AI·Sep 9, 16:34 UTC · 6d agoAI research

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The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100

The 2026 PNPL competition releases LibriBrain100, a MEG speech dataset with 32 extra subjects, targeting word classification and cross-subject BCI generalization.

The 2025 PNPL competition on non-invasive speech decoding from MEG achieved F1-macro scores of 95.6% for speech detection and 73.6% for phoneme classification, built on LibriBrain's ~50 hours of single-subject data. The 2026 edition extends this with LibriBrain100, adding 32 subjects (~40 minutes each) plus ~80 hours of within-subject data. Two tracks target within-subject word classification at scale and cross-subject generalization with subject-specific fine-tuning shrinking from ~40 to ~20 to ~10 minutes, aiming at clinically feasible non-invasive BCIs for people with profound paralysis.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI research

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How Lossless Is Lossless Speculative Decoding? The Role of Numerical Precision in Orthrus

Reproduction study finds Orthrus speculative-decoding trajectories match the reference model in only ~45% of cases under BF16, but 100% under FP32.

Researchers independently reproduced Orthrus, a hybrid autoregressive-diffusion architecture claiming lossless speculative decoding via intra-model consensus, testing exact trajectory matching on 1,190 prompts across 12 domains. Under BF16, exact matching occurred in only 45% of cases for the authors' checkpoint and 43% for an independently trained model, with matching probability strongly tied to reference-model response-conditional perplexity. Despite trajectory divergence, downstream lm-eval-harness benchmarks showed no systematic degradation, while FP32 evaluation yielded exact matching on all prompts.

Hugging Face daily papers·Sep 13, 20:00 UTC · 2d agoAI research1

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Attackers conceal phishing lures using invisible Unicode characters

Threat actors use invisible Unicode characters (ASCII smuggling) to hide phishing lures and evade email security filters.

Threat actors have adopted the ASCII smuggling technique in phishing campaigns, embedding invisible Unicode characters in emails to conceal malicious lures. The approach is designed to evade email security filters that scan for visible phishing indicators. The report gives no victim counts or named campaigns.

BleepingComputer·Sep 6, 14:23 UTC · 10d agoPhishing & fraud in the wild

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CVE-2026-86219: Authen::SASL::Perl::DIGEST_MD5 versions before 2.2100 for Perl accept replayed authentication responses via unverified nonce in server_step

Authen::SASL::Perl::DIGEST_MD5 before 2.2100 for Perl accepts replayed DIGEST-MD5 authentication responses via unverified nonce handling (CVE-2026-86219).

CVE-2026-86219 affects Authen::SASL::Perl::DIGEST_MD5 versions before 2.2100 for Perl. The server_step function does not verify the nonce, allowing replayed authentication responses to be accepted in DIGEST-MD5 SASL exchanges. The fix is available in version 2.2100 of the perl-authen-sasl distribution.

oss-security·Sep 6, 18:55 UTC · 9d agoVulnerabilityCVE-2026-862191

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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·Sep 4, 17:32 UTC · 11d agoAI research1

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Fly Language Model (FLM) Wires the Full Fruit Fly Connectome Into a Frozen 1.2B LLM, and Its Own Controls Show the Wiring Does Not Help

Researchers wire the full fruit fly connectome (166,700 nodes) into a frozen LiquidAI LFM2.5-1.2B LLM, but controls show no fly-specific benefit.

The Fly Language Model (FLM) couples the complete MaleCNS v1.0 fruit fly connectome (166,700 nodes, 25,582,938 edges) to a frozen LiquidAI LFM2.5-1.2B-Instruct backbone, training only a 278,528-parameter readout (~0.0238% of backbone parameters). The fly readout improved NLL by 0.0222 nats/token (perplexity 3.98 to 3.90) on 32 SmolTalk dialogues, but a direct-input control without the graph beat it in all three seeds. Relabeling node identities removes the gain and the recurrence contracts state differences by 0.6 per token, so the connectome adds no long-range memory. The MIT-licensed code runs locally on Python 3.12, but study artifacts remain private, limiting independent reproducibility.

MarkTechPost·Sep 12, 18:51 UTC · 3d agoAI research1

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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·Sep 10, 17:57 UTC · 5d agoAI research1

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Breaking the Seal: Static Deobfuscation of JSCeal’s Compiled V8 Bytecode

Check Point's hasherezade details static deobfuscation of JSCeal, a V8-bytecode stealer targeting cryptocurrency applications since March 2024.

JSCeal is an infostealer distributed as compiled V8 bytecode (.jsc) executed by a bundled Node.js runtime, aimed at cryptocurrency applications. Other vendors track the same family under the names WEEVILPROXY or MeadowLocust. Check Point Research has tracked the campaign since early 2024, with activity dating back to March 2024. The write-up presents a static approach to unpacking the bytecode without executing it.

Check Point Research·Aug 31, 13:38 UTC · 16d agoMalware in the wild1

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H Company Releases NeoMME: A Family of 260M and 800M Single-Tower Multimodal Encoders That Drop the Vision Tower and Causal Decoder

H Company released NeoMME, 260M/800M single-tower multimodal encoders matching 3.75B ColQwen2.5 on ViDoRe v3 while being 14.4x smaller, under Apache 2.0.

H Company released NeoMME, a family of 262,937,906- and 793,715,032-parameter bidirectional encoders that process text and raw 32x32 image patches in a single tower, pretrained via masked diffusion and released under Apache 2.0 with day-zero Hugging Face Transformers support. NeoMME-Retriever-260M reaches 0.523 nDCG@10 on ViDoRe v3, matching 3.75B-parameter ColQwen2.5 while being 14.4x smaller; the 800M model scores 0.556. Hierarchical token pooling with int8 and binary quantization shrinks late-interaction indexes from roughly 1.5 MB to 6 kB per page while retaining 95.19% of nDCG@10; text-only BEIR retrieval remains a weak spot.

MarkTechPost·Sep 6, 21:06 UTC · 9d agoAI research

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How Far Can Synthetic Data Take Thai OCR?

Synthetic-only training adapts PaddleOCR-VL into Wayu-Paxa-OCR-Zero, cutting Thai printed-page CER from 6.64% to 1.24% without real labels.

The study disentangles which factors of synthetic OCR data transfer to real Thai documents, finding typeface diversity, 2D structure, and real handwriting glyphs matter most. Using 45,723 synthetic pages, the authors adapt the 0.9B-parameter PaddleOCR-VL-1.6 into Wayu-Paxa-OCR-Zero, reducing median CER from 6.64% to 1.24% on printed pages and from 74.87% to 20.55% on handwriting. The model outperforms Typhoon OCR v1 7B on all five evaluation sets.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI research

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ZDI-26-564: NVIDIA Transformers4Rec load_model_trainer_states_from_checkpoint Deserialization of Untrusted Data Remote Code Execution Vulnerability

ZDI discloses CVE-2026-24232, a CVSS 7.8 deserialization RCE in NVIDIA Transformers4Rec's load_model_trainer_states_from_checkpoint function.

The Zero Day Initiative published ZDI-26-564, a deserialization of untrusted data vulnerability in NVIDIA Transformers4Rec. Remote code execution is possible, but exploitation requires user interaction such as visiting a malicious page or opening a malicious file. The flaw is tracked as CVE-2026-24232 with a CVSS score of 7.8.

ZDI Published Advisories·Aug 13, 05:00 UTC · Aug 13, 2026AdvisoryCVE-2026-24232

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Privacy Failure in Split-LLM Training, The Returned Gradient Nullifies the Decoys

Researchers show split-LLM training leaks privacy via zero-valued gradients on decoy rows, exposing which activations are real despite passing forward-channel checks.

A systems-security case study of a two-node split-LLM training setup found that the returned output gradient from an Untrusted Cloud Node is exactly zero for decoy rows, revealing which rows are real. Across nine seeds, zero patterns identified real rows in 4,096 of 4,096 frames per run, and an attack on frame contents recovered 0.65 to 1.50 percentage points of extra tokens over a baseline. Both datasets passed forward-channel privacy and quality checks but failed once the returned gradient was included. Row-wise gradient clipping and noise closed the leak for roughly 0.01 nats of held-out cross-entropy, though five unmeasured attack classes remain.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI safety & security

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The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding

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.

Hugging Face daily papers·Sep 8, 20:00 UTC · 7d agoAI research2

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ROSETTA: Efficient and Accurate Privacy-Preserving LLM Decoding via Hybrid CKKS/TFHE Evaluation

ROSETTA is a hybrid CKKS/TFHE homomorphic encryption framework for privacy-preserving LLM decoding, achieving up to 4.8x Softmax and 2.1x end-to-end speedups.

The paper proposes ROSETTA, a hybrid CKKS/TFHE fully homomorphic encryption framework for private inference on generative LLMs, targeting the nonlinear operations that dominate autoregressive decoding cost. It introduces an adaptive segmented lookup-table protocol based on TFHE and a scheme-aware operator-selection framework that assigns each nonlinear operator to CKKS or TFHE to minimize latency. Experiments show up to 4.8x Softmax speedup and 1.5-2.1x end-to-end decoding speedup over the state-of-the-art CacheMir framework.

arXiv cs.CR·Sep 15, 09:51 UTC · 1d agoResearch

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Canonical Color as a Lens into Concept Decodability in Vision Encoders and VLMs

Probing study shows vision encoders make canonical color linearly decodable from grayscale images and tie it to object identity.

Researchers use canonical color as a controlled testbed for measuring conceptual (not just visible) information in vision encoder representations. A dataset of objects with canonical colors was built, and probes on both color and grayscale images show canonical color remains decodable even when color is removed from the input, linked to predicted object identity. Extending to full VLMs, they find post-training has a surprisingly large effect on color decodability in the vision encoder.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:50 UTC · 7d agoAI research1

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New AI Attack Hides Malicious Instructions in Normal-Looking Text to Evade Safety Filters

Check Point researchers show crafted prose hides policy-violating instructions that bypass all tested LLM gatekeepers, including GPT-4o mini and Llama Guard 3.

A new prompt-crafting technique embeds malicious payloads inside grammatical, natural-looking text without Base64, invisible Unicode, or obvious encodings, defeating lightweight pre-screening gatekeepers. In testing, all four evaluated gatekeeper models—gpt-4o-mini-2024-07-18, gpt-oss-safeguard:20b, claude-3-haiku-20240307, and llama-guard3:8b—classified the crafted wrappers as safe at a 100% bypass rate across 23 obfuscated prompts. GPT-5 Thinking in high-reasoning mode recovered and acted on the hidden instruction in 17 of 18 tests (~94.4%), often spending over a minute and multiple Python executions. Researchers recommend paraphrasing untrusted input, hardening gatekeeper policies, and applying defense-in-depth controls for agentic deployments.

GBHackers·Sep 11, 05:53 UTC · 5d agoAI safety & security 2 sources

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Google DeepMind Releases AlphaGenome Atlas

Google DeepMind released AlphaGenome Atlas, a 1-petabyte database pre-computing effects of all 9 billion single-nucleotide variants in the human genome, with a unified AVI score.

Google DeepMind launched AlphaGenome Atlas, a database that predicts the regulatory impact of every possible single nucleotide variant across the roughly 3 billion base pairs of the human genome, yielding a 1-petabyte dataset. It introduces the AlphaGenome Variant Impact (AVI) score, combining coding and non-coding predictions for rapid variant prioritization. Broad Institute researchers used it to support solving an unsolved rare disease case via a predicted DNM1 splice variant, and analysis of 54,000+ UK Biobank participants uncovered 22% more non-coding genetic associations, including 19 regions linked to BMI. The Atlas is available through a no-code web portal.

Hacker News · AI·Sep 8, 14:55 UTC · 7d agoAI researchHN 44↑ · 4 comments

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Testing race conditions with memory access tracing and stack-based delay injection

Google Project Zero released MAccConc, Linux kernel tooling that traces memory accesses to explore and test race condition interleavings.

A Google Project Zero researcher published MAccConc (Memory Access Concurrency), tooling for exploring possible interleavings of multithreaded test cases in the Linux kernel, available on GitHub. The tools use KCOV with ASAN outline-mode instrumentation to record per-access memory traces, enabling automatic testing of all A-B-A interleavings plus terminal and GUI explorers for manual analysis. The work targets confirming race condition candidates, building reliable regression tests, and enabling concurrency fuzzing, drawing on ideas from SKI and Ned Williamson's sockfuzzer.

Google Project Zero·Sep 8, 07:00 UTC · 8d agoResearch1

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HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses

HypoEvolve couples a generational genetic algorithm with specialized LLM agents to generate drug-repurposing hypotheses, beating six baselines on DepMap selectivity (0.171 vs 0.115).

HypoEvolve coordinates specialized LLM agents through a generational genetic algorithm in which scientific judgments and new proposals reshape a hypothesis population. Evaluation centers on drug repurposing, linking mechanistic explanations to target-level biological claims assessed via external measures adapted from DepMap and Open Targets. Across 34 cancer types, HypoEvolve scores highest against six baselines on both measures, with DepMap selectivity of 0.171 versus 0.115 for the strongest baseline, and gains generalize to held-out cancer types.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:44 UTC · 1d agoAI research1

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VU#308749: Remote Code Execution and Arbitrary File Read Vulnerabilities in Kaltura Servers

Insecure deserialization in Kaltura's legacy HTML5 Player V2 (mwEmbed) allows arbitrary file read and remote code execution; Player V7 is unaffected.

CERT/CC issued VU#308749 for two flaws in Kaltura's HTML5 Player V2 library (mwEmbed/html5lib), both caused by the same insecure deserialization bug. The flaws enable arbitrary file read and remote code execution via the exposed mwEmbedLoader.php endpoint. Affected versions include html5lib v2.45, v2.103 and earlier, and other v2.x releases; the currently supported Player V7 is unaffected. No evidence of exploitation was cited.

CERT/CC Vulnerability Notes·Aug 25, 16:11 UTC · 21d agoVulnerability

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Google DeepMind Releases AlphaGenome Atlas With Precomputed Molecular Effect Predictions and AVI Scores for 9 Billion Human DNA Variants

Google DeepMind launched AlphaGenome Atlas, precomputing molecular effect predictions and AVI impact scores for ~9 billion human single-nucleotide variants in a 1-petabyte catalogue.

Google DeepMind released AlphaGenome Atlas, a 1-petabyte catalogue of precomputed molecular effect predictions for roughly 9 billion possible single-nucleotide variants in the human genome. It introduces the AlphaGenome Variant Impact (AVI) score, combining AlphaGenome regulatory predictions with AlphaMissense, plus per-variant feature attributions and over 2,500 recurrent DNA sequence motifs. DeepMind reports best-in-class AVI performance on variant pathogenicity and rare disease benchmarks. Early users at the Broad Institute, University of Exeter, and Stowers Institute demonstrated rare-disease variant reprioritization and 22% more non-coding associations across 54,000+ UK Biobank genomes.

MarkTechPost·Sep 8, 19:00 UTC · 7d agoAI research2

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Deepmind's AlphaGenome Atlas maps every possible DNA change in the human genome

DeepMind's AlphaGenome Atlas precomputes impact predictions for ~9 billion human DNA variants in a 1-petabyte dataset; its AVI score beats CADD in benchmarks

Google DeepMind released the AlphaGenome Atlas, precomputing functional-effect predictions for roughly 9 billion human genome variants (about 27,000 prediction values per variant) in a one-petabyte dataset more than 30 times the size of the AlphaFold database. The accompanying AlphaGenome Variant Impact Score (AVI), a small neural network combining AlphaGenome, AlphaMissense and evolutionary conservation features (18 inputs versus CADD's 150+), outperformed existing tools on clinically classified variants, ranking causal variants in the top 50 candidates for 29.5% of solved GREGoR cases versus 12.5% for CADD. A GREGoR epilepsy case illustrates the impact: AVI elevated a previously unclear DNM1 splice variant that lab experiments confirmed as likely disease-causing. The atlas is available for noncommercial use via web portal, API and a Google Antigravity skill, with a commercial version planned through Google Cloud.

The Decoder·Sep 9, 13:40 UTC · 7d agoAI research

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CVE-2026-34486: Apache Tomcat EncryptInterceptor Fail-Open Bypass

Technical analysis shows CVE-2026-34486 is a one-line fail-open regression in Tomcat Tribes enabling unauthenticated RCE via Java deserialization.

CVE-2026-34486 is a fail-open regression in Tomcat's Tribes EncryptInterceptor, introduced while fixing the CVE-2026-29146 padding-oracle flaw; Apache rated it Important and NVD scores it 7.5 High. Decryption failures are now forwarded to an unfiltered ObjectInputStream, and public PoC repositories demonstrate unauthenticated RCE on default port 4000 when clustering, the EncryptInterceptor, a reachable receiver and deserialization gadgets are all present. Affected releases 9.0.116, 10.1.53 and 11.0.20 are fixed in 9.0.117, 10.1.54 and 11.0.21; Tomcat 8.5 is unaffected.

Web discovery (articles for new exploits & KEV entries)·Sep 8, 20:09 UTC · 7d agoExploit / PoCCVE-2026-34486CVE-2026-291461

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NeoMME: an efficient Multimodal-native and Multilingual Encoder

H Company released NeoMME, an efficient multimodal-native and multilingual encoder, via a post on the Hugging Face blog.

H Company published a Hugging Face blog post introducing NeoMME, described as an efficient multimodal-native and multilingual encoder. The article body was unavailable in the feed, so architecture, benchmarks, and licensing details could not be verified. The post suggests a new encoder release relevant to multilingual multimodal model development.

Hugging Face Blog·Sep 3, 13:13 UTC · 13d agoModel release

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Hardware Fingerprinting FTQC via Quantum Decoder Timing

Quantum decoder timing on IBM Heron processors forms a side channel enabling device fingerprinting with 89% accuracy and workload inference.

The work demonstrates that wall-clock syndrome-decoding times on fault-tolerant quantum computers constitute a novel hardware side channel. Using per-shot decoder timings from three IBM Heron processors collected over 68 days, a passive observer can reconstruct detector-firing distributions, estimate logical error rate, infer code distance, and fingerprint the specific device with up to 89% accuracy versus 33% for random guessing. Noisy simulation based on Google's 105-qubit Willow processor distinguishes nine surface-code patches at 81% accuracy, showing the channel persists across vendors and code families.

arXiv cs.CR·Sep 10, 19:24 UTC · 5d agoResearch

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ZDI-26-543: Microsoft Windows ICC File Parsing Out-Of-Bounds Write Remote Code Execution Vulnerability

ZDI discloses an out-of-bounds write in Windows ICC file parsing via Mscms.dll enabling remote code execution (CVE-2026-54984, CVSS 7.8).

ZDI advisory ZDI-26-543 describes an out-of-bounds write in Microsoft Windows ICC file parsing that allows remote attackers to execute arbitrary code. Exploitation requires interaction with the Mscms.dll color management library, though attack vectors may vary by implementation. The flaw has a CVSS rating of 7.8 and is assigned CVE-2026-54984.

ZDI Published Advisories·Aug 11, 05:00 UTC · Aug 11, 2026VulnerabilityCVE-2026-549841

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Decomposition Buys Integrity, Not Yield

Study of 600 production deep-research traces finds agent-tree decomposition loses findings at rate N^(1-δ); flat architectures maximize yield.

The paper models multi-agent decomposition as a tree where an agent holding b items retains each with probability r(b); with r(b)=1/b every tree delivers exactly one finding regardless of shape. Analysis of 600 production deep-research traces estimates delta=0.34 retention decay, and 1,012 annotated traces show one brief in sixteen goes off-target per tier, giving an alignment penalty of 0.536. Depth still cuts root context exposure from N to N^(1/k) and is cheaper at scale, with a hazard model over 743,819 production tool calls showing delegation is an opening move rather than a response to filling context.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:04 UTC · 21h agoAI research

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