Search: “isotope-mapping”

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TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription

TART pipeline transcribes guitar audio to tablature with fingering and technique labels, outperforming prior baselines by up to 8.5 Tab F1 points.

TART is a modular four-stage pipeline for audio-to-tablature guitar transcription, combining an audio-to-MIDI transcription model, an expressive technique classifier, an audio-conditioned T5 encoder-decoder for string-fret assignment, and an automated tablature generator. In zero-shot evaluation on GuitarSet, EGDB, and two noisy augmented benchmarks, it averages 81.35% audio-to-MIDI F50 (+6.67 over the best baseline), 71.8% string-fret Tab F1 (+8.5), and 54.08% end-to-end Tab F1. The authors state it is the first framework to generate tablature with both fingering and expressive technique annotations directly from guitar audio.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:55 UTC · 6d agoAI research2

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Grouped Value Attention: Efficient KV Caching via On-Demand Key Reconstruction

Grouped Value Attention stores grouped values and reconstructs content keys via a learned linear map, cutting KV-cache size about 45-47% versus GQA.

GVA stores only grouped values and reconstructs content keys with a learned linear map absorbed into the query at decode time, while a small shared decoupled RoPE channel preserves positional information via a separately cached positional key. At 350M parameters trained on 30B FineWeb-Edu tokens, the 16-dimensional positional variant scores 44.18 average accuracy across five tasks versus 44.36 for GQA and 43.88 for MLA. Custom decoding kernels are in development with an open-source release planned.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoAI research

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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 · 12d agoAI research1

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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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MicroHasTEE: Bare-Metal Haskell for Type-Level Peripheral Ownership on Armv8-M

MicroHasTEE expresses TrustZone secure and non-secure firmware as one typed Haskell program, catching peripheral-ownership inconsistencies at compile time.

MicroHasTEE is a multiparty Haskell framework that models both TrustZone firmware images as participants in a single typed program, using type-level capability ledgers to track peripheral acquisition, configuration, transfer, and finalization. MicroHs compiles the shared program twice to produce separate bare-metal Secure and Non-secure firmware images. The framework rejects inconsistent resource use, post-configuration attribution changes, wrong-domain callbacks, and calls to unregistered Secure services. A door-lock case study on an STM32U5 Nucleo board produced images of 232.7 KiB and 228.4 KiB of flash with roughly 220 KiB of SRAM per domain.

arXiv cs.CR·Sep 11, 08:34 UTC · 5d agoResearch1

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Atlas: Efficient Verifiable Semantic Search

Atlas delivers zero-knowledge proofs for HNSW semantic search, verifying RAG retrieval in under a second on SIFT1M and 2.0 seconds at 100M vectors.

Atlas lets a search provider prove that a query was answered correctly against a committed HNSW index without revealing the index, addressing provider deviations like truncation or bias. It combines offline preprocessing, a fixed-size-state restructuring of HNSW with a correctness proof, and timestep-tagged batching of per-step arguments. The system proves queries in under a second on SIFT1M and 2.0 seconds at 100 million vectors while preserving plaintext HNSW recall, and proven retrieval maintains end-to-end RAG answer quality at lower cost than prior verifiable retrieval systems.

arXiv cs.CR·Sep 10, 17:30 UTC · 6d agoResearch1

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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·Sep 4, 16:02 UTC · 12d agoAI research

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What Breaks Local Watermarks? A Robustness Benchmark for Local Invisible Image Watermarking

First systematic robustness benchmark of five local invisible image watermarking methods across 55 transformations finds all are vulnerable, with inpainting and geometric misalignment completely breaking payload…

The paper presents the first systematic robustness benchmark for local invisible image watermarks, covering 55 image transformations across signal distortions, coordinate alignment changes, indirect local edits, and direct watermark edits. It evaluates five methods: MaskWM, WAM, OmniGuard, TrustMark, and PixelSeal, all supporting localization natively or with minimal adaptation. Results show every method is vulnerable to some transformation; MaskWM offers the strongest payload recovery and localization but the lowest clean-image quality, and synchronization further improves its recovery under geometric transformations. Geometric misalignment and generative local edits such as inpainting and outpainting can completely impair payload recovery, while signal distortions are often tolerated.

arXiv cs.CR·Sep 15, 08:30 UTC · 1d agoResearch

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Quenched Ensemble Sampling

Quenched Ensemble Sampling generalizes nested sampling's hard energy constraint to repulsive potentials, traversing first-order phase transitions where tempering fails.

Quenched Ensemble Sampling generalizes nested sampling's hard energy constraint into a family of repulsive potentials at the energy boundary, preserving monotone energy descent while making the constrained target amenable to scalable gradient-based kernels. On synthetic phase-transition models it estimates marginal likelihood and draws posterior samples across first-order transitions where popular alternatives such as tempering fail. Applications include marginal likelihood estimation for Bayesian neural network architecture comparison and partition function estimation in a high-dimensional continuous lattice field theory.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:16 UTC · 2d agoAI research1

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Embedded Graph Flows for Categorical Graph Generation

Researchers propose Embedded Graph Flows, a generative model with learned categorical embeddings that beats DiGress and GruM on molecular graph benchmarks.

Embedded Graph Flows (EGF) learns continuous embeddings for node and unordered-edge categories and transports Gaussian noise toward these endpoints using a permutation-equivariant graph transformer. On QM9 it achieves the best result on all four reported metrics, with a Fréchet ChemNet Distance of 0.150 versus 0.717 for DiGress and 0.812 for GruM. On ZINC250k it retains the lowest NSPDK MMD, indicating close agreement with local substructures of reference molecules. Code is released on GitHub.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:26 UTC · 12d agoAI research1

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m-a-p/YuE2-3B — new model trending #30 on Hugging Face

M-A-P released YuE2-3B, an open music generation model that outperforms Suno v5 on WildSongBench and runs locally on a 24GB GPU.

The M-A-P (multimodal-art-projection) team released YuE2-3B, an open-weights music generation model that turns lyrics and a style prompt into full songs with vocals and accompaniment. It uses an AR-NAR Mixture-of-Transformers backbone with symbolic planning and flow matching through a VAE, and supports editable scores (melody and chords, including ABC notation) plus agentic editing workflows. On 192 WildSongBench prompts it reports a SongBench average of 6.9632 (best-of-8) versus 6.8721 for Suno v5, claimed as state of the art among evaluated open and proprietary models. It runs 48 kHz stereo inference locally on a single 24GB NVIDIA GPU without quantization, with companion releases including YuE2-Vae, MERT-v2 encoders, the WildSongBench dataset, and SheetSage2.

Hugging Face trending models·Sep 9, 18:30 UTC · 7d agoModel release1

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CodeTD: Topology of Attention Detects Hallucinations in Code LLMs

CodeTD detects hallucinations in code LLMs before execution by analyzing topological patterns of attention maps, outperforming recent baselines.

CodeTD applies topological data analysis (TDA) to code LLM attention maps to quantify prompt-generation mismatch as a pre-execution correctness signal. Experiments cover HumanEval, MBPP, BigCodeBench, and MultiPL-E across 5 programming languages and 10 code LLMs up to 34B parameters. The method outperforms recent baselines and transfers between coding benchmarks, helping catch code that fails the task or embeds security vulnerabilities.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:26 UTC · 9d agoAI research1

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ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF — new model trending #3 on Hugging Face

ISTA-DASLab releases GSQ-RCO non-uniform GGUF quantizations of Qwen3.8-27B down to 2.5 bpw, with task-lossless IQ3_S matching BF16 benchmark scores.

ISTA-DASLab released GGUF quantizations of Qwen3.8-27B produced with GSQ (Gumbel-Softmax Quantization) and RCO (Riemannian Constrained Optimization), non-uniform methods that allocate per-tensor precision via gradient-based search under a total size budget. Four checkpoints range from 2.50 bpw (8.4 GB) to 3.50 bpw (11.8 GB), plus a BF16 vision projector (mmproj) enabling multimodal use. The recommended IQ3_S build is task-lossless, matching the BF16 base exactly on AIME25 (100.00) and LiveCodeBench v6 (85.71) at roughly one fifth of the BF16 size. Optional -mtp variants add a Multi-Token Prediction head for speculative decoding in llama.cpp.

Hugging Face trending models·Aug 28, 18:38 UTC · 19d agoModel release1

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Forging Tree-Ring: Reproducing and Instrumenting Black-Box Semantic Watermark Forgery

Reprompt watermark forgery reproduces on Stable Diffusion XL using free-tier T4 GPUs, with forged images accepted by the genuine detector 5 of 6 times.

The authors reproduce the Reprompt forgery attack of Müller et al. against Tree-Ring watermarking on Stable Diffusion XL using the released code on free-tier dual T4 GPUs with 14.6 GB usable memory, versus the A40 hardware of the original study. Over six trials, the genuine detector flagged genuine images 6/6, clean images 0/6, and forged images 5/6, at 325-332 seconds per attack. They also recovered the detector's discarded non-central chi-square statistic and built two natural scores separating forged images from the clean null at AUC 0.861 and 0.972. The notebook, pinned fork, and all measurement artifacts are released with the paper.

arXiv cs.CR·Sep 11, 14:35 UTC · 5d agoResearch

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Show HN: MultiMatte, a Promptable Image Background Removal Model

Feyn releases MultiMatte, a promptable background-removal model fine-tuned from Meta's SAM 3 via LoRA, outputting alpha mattes that beat SAM 3 on segmentation benchmarks.

Feyn introduced MultiMatte, a promptable image background-removal model built on Meta's SAM 3 (860M parameters). It modifies only 19.49M parameters (2.27%) using a rank-16 LoRA adapter and replaces binary masks with alpha mattes to handle fuzzy boundaries like hair. On the DIS-VD benchmark it scores 0.901 S-measure versus SAM 3's 0.667, and it improves on SAM 3 across all twelve evaluated splits. Training used 19,953 images for 14,000 steps with focal and Dice loss, and the merged weights are available via the nobg library and a web demo.

Hacker News · security·Sep 10, 15:50 UTC · 6d agoModel releaseHN 21↑ · 4 comments1

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A Graph-Based Approach for Mapping Kernel-Level Telemetry to MITRE ATT&CK

Trace2ATT&CK maps eBPF kernel telemetry to MITRE ATT&CK via provenance graphs and RAG with local open-weights LLMs, validated on 347 Atomic Red Team tests.

Trace2ATT&CK collects kernel-level events via eBPF, correlates attacker commands into a provenance graph, and derives compact graph representations suitable for LLM-based reasoning, mapping behavior to MITRE ATT&CK techniques with ranked candidates and rationales. Mapping uses both pure LLM prompting and retrieval-augmented generation grounded in the ATT&CK knowledge base. It was evaluated on 347 Linux Atomic Red Team tests using locally deployed open-weights LLMs. RAG consistently improved ATT&CK mapping over pure prompting, and provenance graphs substantially outperformed raw telemetry, without compromising data confidentiality.

arXiv cs.CR·Sep 11, 13:35 UTC · 5d agoResearch

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Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation

Marigold V2 adapts diffusion transformers for monocular depth estimation, improving AbsRel 16-26% over the previous best on KITTI and ETH3D.

Huawei's Bayer lab revisits the Marigold approach to repurpose image generation and editing models built on the diffusion transformer (DiT) architecture into monocular depth estimators. The recipes target single-step inference from pretrained multi-step flow-matching models, with remedies including alignment to ground-truth semantic features and a two-stage fine-tuning protocol using a Sinkhorn-based loss. The resulting model produces crisper depth maps that generalize out-of-distribution and also achieves state-of-the-art results on surface normals estimation and intrinsic image decomposition.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoAI research

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OracleZoom: On-Policy Self-Distillation Inspired Reference-Constrained Recursive Image Super Resolution

OracleZoom enables recursive extreme-scale image super-resolution via reference-constrained on-policy distillation, reducing hallucinations at deep zoom scales.

OracleZoom tackles recursive super-resolution, where repeated feeding of predictions back into the same model leaves deeper-scale outputs unsupervised as required source resolution grows geometrically. The framework trains on its own trajectory while carrying the last ground-truth evidence beyond the supervision boundary, combining direct and cross-scale supervision, a no-reference quality objective, a KL-constrained pretrained latent prior, and EMA consistency. Across seven datasets it achieves state-of-the-art SR quality across zoom scales, averaging 0.713 CLIPIQA with larger gains at deeper scales and significantly reduced hallucinations. Code, data, and models are publicly released.

Hugging Face daily papers·Sep 5, 20:00 UTC · 11d agoAI research

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Observational Indistinguishability and Integrity Blind Regions in Hybrid Quantum-Classical Workflows

Framework formalizes integrity blind regions in hybrid quantum-classical workflows, validated across 3,600 label interventions with conformal detection rules.

The paper presents a claim-relative evidence and reference framework for integrity of hybrid quantum-classical workflows, distinguishing structural blind regions caused by observational indistinguishability from finite-batch statistical misses. Experiments over 3,600 label interventions show exact label-path invariance for feature and prediction views. The geometry-aligned construction detects 343 of 2,700 conclusion-changing interventions using the conformal rule and 1,183 of 2,700 with the uncorrected union, with executed conformal clean false-action rates of 0.048-0.059.

arXiv cs.CR·Sep 15, 13:16 UTC · 1d agoResearch

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Subgroup Packing for Batched PASTA Transciphering

Interleaved subgroup packing cuts PASTA homomorphic transciphering server cost by a median 1.60x at the expense of remaining noise capacity.

The paper studies how record word layout affects the cost of PASTA transciphering, where a server converts symmetrically encrypted records into homomorphic ciphertexts. A subgroup layout interleaves records so cyclic shifts preserve positions, reducing required displacements from 255 to 128 versus contiguous packing. In HElib across twelve paired corpora under six homomorphic keys, the median direct-to-subgroup server cost ratio is 1.60 including fresh generation, conversion, and two queries, but with less remaining noise capacity.

arXiv cs.CR·Sep 11, 09:24 UTC · 5d agoResearch

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Scaling Verification of Cryptographic Software with Aeneas, Rust, and Lean

Microsoft SymCrypt implementations of SHA-3 and ML-KEM verified in Lean via Aeneas-extracted Rust models, with AI agents writing proofs.

The paper develops a methodology for verifying production Rust cryptographic code by using Aeneas to extract pure models into Lean, avoiding low-level pointer and aliasing reasoning. Applied to Microsoft's SymCrypt, it verifies SHA-3 and ML-KEM implementations ported from C to Rust and extends SymCrypt with FrodoKEM, ML-DSA, and HPKE. A 237 KLOC Lean development establishes safety, panic-freedom, and functional correctness of 16.7 KLOC of Rust supporting post-quantum cipher suites on x86-64 and ARM. AI agents autonomously write formal proofs verified by the Lean kernel, and evaluation shows verified Rust meets SymCrypt's performance and portability requirements.

arXiv cs.CR·Sep 14, 14:32 UTC · 2d agoResearch1

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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·Sep 9, 20:00 UTC · 7d agoAI research

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HyQuant: Hybrid-Precision Quantization for LLM Attention

HyQuant keeps most LLM attention states low-bit while preserving vertical-line tokens and local windows in high precision, maintaining near-lossless accuracy.

HyQuant is a hybrid-precision quantization framework for LLM attention that quantizes most attention states to low bits while keeping accuracy-critical vertical-line tokens and local-window states in full precision, selected via lightweight attention-pattern signals. In the prefill stage it uses a hybrid-precision attention operator, and in the decode stage it applies the same principle to KV-cache compression with fused dequantization and attention computation. Across diverse tasks, models, and datasets it maintains nearly lossless accuracy; code is available on GitHub.

Hugging Face daily papers·Aug 27, 20:00 UTC · 20d agoAI tools & infra1

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Building AI to accelerate science and improve lives

Google highlights AI-for-science advances: AlphaGenome Atlas mapping 9 billion genetic variants, WeatherNext 3 weather model, and global health AI tools.

Google detailed AI advances across science and health, including AlphaGenome Atlas, which mapped all 9 billion possible single-letter genetic changes in the human genome and was made openly available. WeatherNext 3 delivers 50% more accurate precipitation forecasts a day or more ahead and is already in products. AlphaFold is used by 4 million researchers in 190 countries, TB chest X-ray screening has processed 25,000+ scans across six nations, and the diabetic retinopathy model has supported 1.15 million screenings. Google also released its AI & Economy ATLAS global usage insights.

Google · AI·Sep 15, 16:00 UTC · 1d agoAI industry

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Kaininja: Extending Native 3D Generators to the Part Level

KaiNinja extends TRELLIS.2 native 3D generation to part-level assets via a dual-volume O-Voxel representation, cutting whole-object Chamfer distance by 40%.

KaiNinja extends the TRELLIS.2 native 3D generator to produce part-level assets instead of one fused mesh, enabling downstream editing, rigging, and simulation. A dual-volume form of the O-Voxel representation solves the problem that a single volume cannot represent interfaces where two parts touch. The model needs no segmentation network, is partly trained on LLM-agent-authored part data, lowers whole-object Chamfer distance by 40%, and raises strict part F-score by 16% versus other part-generation pipelines.

Hugging Face daily papers·Sep 13, 20:00 UTC · 3d agoAI research

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Searching for New Physics with Reinforcement Learning

Researchers apply reinforcement learning to identify SMEFT operators explaining particle physics anomalies, reproducing and improving known CDF W-mass results.

The paper introduces a reinforcement learning method to search the large Standard Model Effective Field Theory (SMEFT) operator space for explanations of measurement anomalies. It was validated on the CDF W-mass anomaly, reproducing and improving known results, then applied to a harder multi-anomaly scenario. RL efficiently navigates complex loop-level operator correlations that bias human-driven phenomenological analysis.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:07 UTC · 7d agoAI research

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CoRA-NAS: Coarse Ranking and Anchor-Residual Refinement for Neural Architecture Search

CoRA-NAS combines zero-cost proxy ranking with low-cost learning-curve refinement, achieving the best worst-space Spearman correlation across NAS benchmarks.

The paper proposes CoRA-NAS, a two-stage neural architecture search framework pairing a static ranking prior (CoRA-Rank) with learning-curve refinement (CoRA-Refine) that extrapolates early validation curves for sampled anchors and propagates residual corrections with an ExtraTrees model at about 1% of full training cost. Across NAS-Bench-201, NAS-Bench-101, TransNAS-Bench-101, and NATS-SSS it achieves mean Spearman correlations of 0.946, 0.715, 0.786, and 0.894 respectively, with the best worst-space correlation of 0.715 among compared methods. On NAS-Bench-201/CIFAR-100 its selected architecture reaches 73.32% accuracy versus a 73.37% ground-truth best.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:49 UTC · 6d agoAI research

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N4D Mesh Controller: New infrastructure, a UPX-packed agent labeled "go-titan," and how to hunt for it

Datadog researchers detonated a new N4D Mesh Controller sample in microVMs, revealing rotated infrastructure, a UPX-packed go-titan agent, and persistence behavior.

Datadog Security Research executed a newer N4D Mesh Controller sample in isolated microVMs, uncovering rotated command infrastructure and a UPX-packed agent labeled go-titan. Runtime evidence showed MCP tool abuse, multi-service scanning, and persistence mechanisms. The report provides hunting guidance for defenders tracking this infrastructure.

Datadog Security Labs·Aug 20, 00:00 UTC · 27d agoMalware

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A Ranking Approach for Measuring Calibration

Researchers propose rankECE, a ranking-based calibration error measure with theoretical guarantees that outperforms binned ECE approximations.

The paper introduces rankECE, an alternative to Expected Calibration Error (ECE) that measures miscalibration by comparing points with neighboring predicted-probability values. It addresses the impossibility of estimating ECE with guaranteed accuracy in assumption-free settings. Theoretical guarantees and empirical results establish rankECE as a better proxy for ECE than the binned approximations most commonly used in practice.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:34 UTC · 5d agoAI research1

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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·Sep 4, 16:40 UTC · 12d agoAI research

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