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“Datasets”

arxiv.orgpaper🧠 AI

Glance: The Generator Is Its Own Reranker

Glance is a RAG method in which the generator also reranks. It adds a lightweight decision adapter and pointer head that score candidate passages from one shared prefill, so a single resident 9B backbone does generation and reranking. After 39 minutes of MS MARCO fine-tuning it reaches nDCG@10 of 0.741 on SciFact and 0.345 on NFCorpus, against 0.721 and 0.326 for bge-reranker-v2-m3. On 300 HotpotQA questions it improves answer F1 by 0.045 over cross-encoder-selected context. Evaluation covers two retrieval datasets, one QA dataset and one backbone family.

RAGLLMbge-reranker-v2-m3

MS MARCOSciFactNFCorpus+1

llm-apps, research· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

M-PFT: Angular Margin for Calibrated Prompt-Tuning of Fine-Tuned Vision-Language Models

M-PFT is an angular-margin method for prompt tuning of vision-language models that fixes base-to-novel dual miscalibration (underconfidence on base classes, overconfidence on novel classes). It adds two losses: minimizing the maximum cosine similarity between tunable class embeddings, and minimizing angular distance to each zero-shot embedding. Tests on 9 prompt-tuning methods and 15 datasets show lower novel-class calibration error at comparable accuracy and negligible overhead.

vision-language modelprompt tuning

arXiv

research, multimodal, training· Added 31 Oct 2026, published 29 Oct 2026

endorlabs.comarticle🧠 AI

What is an AIBOM? AI bill of materials explained for security teams | Endor Labs

An AIBOM is a machine-readable inventory of models, datasets, dependencies, AI services, agents and MCP servers, with evidence of origin, license, integrity and evaluation. The Endor Labs explainer walks through a worked CycloneDX example for a fine-tuned support assistant. It compares CycloneDX ML-BOM, SPDX 3.x, the OWASP AIBOM project and the May 2026 CISA/G7 guidance, and lists what an AIBOM cannot show: runtime behavior and agent actions.

CycloneDXSPDXSBOM+5

Endor LabsOWASPCISA+3

ai-policy, ai-infra· Published and added 29 Oct 2026

endorlabs.comarticle🛡️ Security

What is an AIBOM? AI bill of materials explained for security teams | Endor Labs

AIBOM explainer from Endor Labs: an AI bill of materials is a machine-readable inventory of models, datasets, dependencies, AI services, agents and MCP servers, with origin, license and integrity evidence. It walks through a CycloneDX entry for a fine-tuned model, compares CycloneDX ML-BOM, SPDX 3.x, the OWASP AIBOM Generator and the May 2026 CISA/G7 minimum-elements guidance, and lists a six-step build process. It also states what an AIBOM cannot show: model safety, runtime behavior and coding-agent actions.

CycloneDXSPDXSBOM+2

EU AI ActISO/IEC 42001CISA+5

Supply Chain, AI Security, GRC· Published and added 29 Oct 2026

huggingface.coarticle🧠 AI

Announcing Project ProtoStar: Making Publicly Funded Science AI-Ready

Project ProtoStar is an initiative by Carnegie Mellon, Purdue, Georgia Tech, Arizona State, George Washington University, Astera Institute, ORCA and Hugging Face to release publicly funded science datasets, models and code in AI-ready formats. Hugging Face reports over 17,000 public science datasets and tripled sharing in 2026. The first challenge, run by CMU from 2 to 13 November 2026, asks teams to forecast climate indicators for 210 US cities.

Hugging Face

Project ProtoStarCarnegie Mellon UniversityPurdue University+15

ai-policy, training, evals· Published and added 29 Oct 2026

arxiv.orgpaper🧠 AI

Data Scaling Laws in Stereo Matching: From In-domain Fitting to Cross-domain Generalization

This paper studies data scaling laws in stereo matching and releases XStereo, a realistic synthetic dataset of 200 thousand samples. In-domain training on five large datasets shows two regimes: a standard power law, then saturation, with a transition point roughly fixed for a given model. Cross-domain performance depends only weakly on source data scale, and distribution and coverage diversity reduce domain gaps more.

XStereoarXiv

research, training, multimodal· Added 30 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🧠 AI

Bayes-Adaptive MDPs for Rollout Allocation to Enhance GRPO Training

AdaRoll-GRPO is a method that allocates GRPO rollout budgets per question by difficulty instead of uniformly. It frames allocation as a sequential decision problem and learns an allocation policy with a Bayes-adaptive MDP, computed offline so training adds no online optimization cost. Experiments on several reasoning datasets and LLM architectures show gains over standard GRPO at the same total rollout budget.

GRPOLLM

AdaRoll-GRPOarXiv

training, research· Added 28 Oct 2026, published 27 Oct 2026

eprint.iacr.orgpaper🛡️ Security

OblivDGL: Oblivious Deep Graph Learning Made Practical

OblivDGL is a secure computation framework for GNN training and inference, modelled on Deep Graph Library. It uses an oblivious message-passing paradigm and an oblivious graph representation built on function secret sharing (FSS). It leaks only total node and edge counts. On four benchmark graph datasets it cuts training communication up to 1024x and runtime up to 14.3x against prior secure GNN systems.

Deep Graph LibraryGraph Neural Network

NDSS 2027

AI Security· Added 28 Oct 2026, published 27 Oct 2026

arxiv.orgpaper🧠 AI

DRILS: Dependency-based Requirement Change Impact Analysis Using LLMs

DRILS is an LLM-based agentic framework for requirements change impact analysis. It uses multiple agents to generate candidate impact sets, with an LLM-as-a-judge promoting diversity in selection. It adds RE-RAG, a tuned retrieval technique, and a requirement dependency graph as a knowledge source. On three curated datasets it reaches 87.1% recall, 76.3% precision and 81% F1 at lower cost than baselines. The authors release the datasets.

LLMRAG

arXivDRILSRE-RAG

research, agents, llm-apps· Added 27 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

Learning What to Share: Expert Specialization for Time-Series Forecasting

Persistent Closed-Form Routing (PCR) is a plug-and-play method for multivariate time-series forecasting. It alternates exact conditional estimation of channel-specific mixture weights with gradient training of low-rank residual experts around a frozen pretrained forecaster. Across eleven datasets and three backbones, PCR-K4 cuts dataset-balanced MSE by 3.86-14.25% and beats competing specialization methods with fewer trainable parameters. The paper also introduces GalaxyTS, a synthetic dataset with exact shared and channel-specific references.

Mixture of ExpertsTransformer

GalaxyTSPCRPCR-K4+1

research· Added 27 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

Unlocking the Specialization Capability of PPG Foundation Models for Wearable Signals through Structured Pruning and Adaptive Fine-Tuning

SPAFT is a structured prune-then-finetune framework for PPG (photoplethysmography) foundation models used on wearable signals. It finds that pretrained PPG models have inherent sparsity and redundancy, then restricts fine-tuning to a compact parameter subspace chosen by parameter importance and structural redundancy. Across four PPG foundation models, five datasets and ten tasks, it wins 82.8% of regression and 87.5% of classification comparisons.

SPAFTarXiv

research, training· Added 27 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

TriageRAG: Failure-Aware Self-Training for Adapting Retrieval-Augmented Language Models to Specialized Domains

TriageRAG is a failure-aware self-training method for adapting retrieval-augmented LLMs to specialized domains using only an unlabeled corpus. It probes the student on self-generated questions with and without retrieved context, sorts them into known, solved, context-failure and retrieval-failure, and routes each to a matching training signal. With Qwen3-4B on 11 datasets it beats budget-matched SimRAG by 1.2-6.4 points and recovers closed-book accuracy where retrieval hurts.

RAGLLMQwen3

TriageRAGSimRAGCS-Bench+1

research, llm-apps, training· Added 27 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🛡️ Security

On Task, Off Track: Benchmarking LLM and Agent Robustness to Near-Target Prompt Injection

NTBench (Near-Target Benchmark) tests LLM and agent robustness to near-target prompt injection, where injected instructions steer a response toward a plausible but incorrect or harmful outcome instead of abandoning the task. It builds reference-guided target-instruction pairs on existing datasets, with human validation, and evaluates seven LLMs on ten language datasets and two agent benchmarks. Attack success rose 9.3 to 35.8 points (incorrect targets) and 1.7 to 17.3 points (harmful targets) over fixed-target baselines.

LLM

NTBenchnear-target prompt injectionprompt injection

AI Security· Added 26 Oct 2026, published 23 Oct 2026

eprint.iacr.orgpaper🛡️ Security

For Thirty Pieces of Silver: Overcoming Non-IID via ZKP Sample-Level Auditing and Forcing Attackers into Traitors in Federated Learning

Preprint (IACR ePrint 2026/2611) proposing a federated learning defense that replaces gradient detection with zero-knowledge-proof sample-level audits of client datasets. A staking mechanism forces attackers to train on discovered poisoned samples with flipped labels, producing counter-gradients that remove the backdoor without knowing the trigger. ZKP commitments lock the training data, which blocks adaptive attacks and gradient tampering. On CIFAR-10 under Dir(0.1) non-IID, it holds attack success rate below 6% against distributed backdoor and edge-case attacks.

distributed backdoor attackedge-case attackadaptive attack+3

AI Security· Added 24 Oct 2026, published 23 Oct 2026

arxiv.orgpaper🧠 AI

CellTrack-Bench: Microscopic Cell Tracking Benchmark for Vision-Language Models

CellTrack-Bench is a benchmark that tests vision-language models on microscopic cell tracking and compares them with specialised trackers. It has 1,537 problems covering 9,041 cell-linking decisions, drawn from 15 cell-tracking datasets across four imaging modalities, with a hard subset built from failures shared by distinct algorithmic trackers. The authors compare environment harnesses, benchmark 9 closed-source and 13 open-weight models, and analyse failure modes.

vision-language modelLLM

CellTrack-BencharXiv

evals, multimodal, research· Added 24 Oct 2026, published 22 Oct 2026

arxiv.orgpaper🧠 AI

Symmetry-Based Parameter Efficient Low-Rank Fine-Tuning for High-Throughput Calculation of Lattice Thermal Conductivity

SBFS-FT (symmetry-based few-shot fine-tuning) adapts the MatterSim 1M universal machine-learned interatomic potential with low-rank adaptation on a small set of compounds sharing one crystal symmetry. Tested on half-Heusler (space group 216), space group 225 and spinel (227) datasets, it cut lattice thermal conductivity error 2-3 times versus the base model. It matched per-compound fine-tuning while taking 5-7 times less compute for 100 compounds.

MatterSimLoRA

MatterSim 1Mmoment tensor potentialspolyMLPs

research, training· Added 23 Oct 2026, published 21 Oct 2026

arxiv.orgpaper🛡️ Security

Sign Here: Attributing a Source for a More Trustworthy Software Supply Chain

Sign Here is an arXiv paper (October 2026) testing whether building packages from source, instead of pulling from registries, prevents malware. It aggregates 47,066 malicious npm and PyPI packages from five datasets and indexes 3.9 million npm and 764 thousand PyPI packages. Checking for an attributed source blocks 87.7 to 99.9 percent of malware depending on vetting level, but only 47.4 percent of benign packages attribute a buildable source.

npmPyPI

Shai-HuludAxios

Supply Chain· Added 24 Oct 2026, published 20 Oct 2026

arxiv.orgpaper🛡️ Security

SoK: PIAtlas: Decomposing the Unbounded Prompt Injection Attack Space

PIAtlas is a taxonomy of prompt injection attacks organized by how the injected instruction subverts the model, with six mechanism classes as roots of a technique and sub-technique hierarchy. The authors release PICorpus, a license-clean collection of public prompt-injection datasets, and map thousands of records to PIAtlas under a human-verified protocol. Results show attacks combine several techniques from few mechanisms, agent benchmarks cover only part of the space, and adaptive attackers favor mechanisms that curated benchmarks underrepresent.

LLM

PIAtlasPICorpusprompt injection

AI Security· Added 21 Oct 2026, published 18 Oct 2026

arxiv.orgpaper🧠 AI

FedRAPP: Federated Test-Time Prompt Tuning for Vision-Language Models with Reliability-Aware Prompt Personalization

FedRAPP is a personalized aggregation framework for Source-Free Federated Test-Time Prompt Tuning (SF-FTTPT), where clients adapt CLIP soft prompts on unlabeled, non-IID test streams without source data. It combines reliability-aware client assessment, a server-side exponential moving-average prompt memory and similarity-guided peer aggregation. Across nine CLIP benchmark datasets and ImageNet variants it beats standard federated aggregation baselines while keeping prompt-only efficiency.

CLIPvision-language model

FedRAPPImageNetarXiv

research, multimodal· Added 20 Oct 2026, published 16 Oct 2026

arxiv.orgpaper🧠 AI

Enhancing RAG-based Medical VQA with Gated Multimodal Representations

This paper presents a multimodal retrieval-augmented generation framework for Medical Visual Question Answering. A Gated Multimodal Unit learns a joint query representation from the medical image and the clinical question, so evidence retrieval uses both modalities while the Med-LVLM stays frozen. On benchmark datasets it consistently beats a reference Med-LVLM and stays competitive with state-of-the-art methods. The page gives no dataset names or numbers.

RAGvision-language model

arXiv

research, llm-apps, multimodal· Added 20 Oct 2026, published 16 Oct 2026

arxiv.orgpaper🛡️ Security

Stealthy Poisoning Attacks via Reused KV Cache of Reasoning

R^2ECAP (Reused Reasoning-Cache Poisoning) is a black-box attack that plants poisoned reasoning traces through a chat interface, exploiting LLM systems that reuse KV caches of reasoning. A preceding trace steers the model, and the retained trace keeps residual adversarial influence in its cache after the preceding trace is discarded. On three open-weight LLMs and three datasets it reaches up to 79% ASR, beats the white-box HijackKV in 8 of 9 settings, and reaches 46% ASR in a reasoning-trace retrieval system. Only 1% of retained responses are flagged by a popular prompt injection detector.

LLM

R^2ECAPHijackKVprompt injection

AI Security· Added 20 Oct 2026, published 16 Oct 2026

arxiv.orgpaper🧠 AI

Perceive, Predict, and Plan: a Unified World Action Model for Egocentric Dexterous Manipulation

P^3-WAM is a unified world action model for egocentric dexterous manipulation with three modes: Perceive (reconstruct articulated hand motion and camera trajectories from video), Predict (generate visual futures under prescribed actions) and Plan (jointly generate action trajectories and their anticipated visual outcomes from an initial image, a language instruction and an optional visual goal). It adds a compact structured 3D action representation to a pretrained video diffusion transformer through a mixture-of-transformers design with shared base weights, modality-specific adaptation and bidirectional visual-action attention. Randomized modality visibility lets video and 3D actions act as either context or target. Unified training beats single-mode training and outperforms specialized baselines on 3D hand reconstruction and action-conditioned video prediction across four datasets, and gives better instruction adherence than video-generation baselines.

diffusion modelTransformervision-language model

P^3-WAMarXiv

research, multimodal· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🧠 AI

Private Retrieval Augmented Generation via Random Projection

Proposes protecting the retrieval datastore in RAG systems by applying the same random projection to datastore embeddings and query embeddings. This provably preserves query-item semantic similarity while substantially mitigating data extraction attacks. Across multiple RAG architectures and datasets it beats prior differential-privacy-based methods on retrieval and generation quality, with comparable empirical privacy under strong attacks, making it a practical baseline for privacy-preserving RAG.

RAGLLM

arXiv

research, llm-apps· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🛡️ Security

Private Retrieval Augmented Generation via Random Projection

Proposes applying the same random projection to both datastore embeddings and query embeddings in RAG systems. The authors claim this provably preserves query-to-item semantic similarity while substantially mitigating datastore data-extraction attacks. Across several RAG architectures and datasets it reportedly beats prior differential-privacy-based defenses on retrieval and generation quality, with comparable empirical privacy under strong attack models. It is presented as a lightweight baseline for privacy-preserving RAG.

LLM

differential privacydata extraction attacks

AI Security· Added 17 Oct 2026, published 15 Oct 2026

huggingface.coarticle🧠 AI

Apertus 1.5 Release Notes

Release notes for Apertus 1.5, a fully open (weights, data, training details) continued pretraining of the 8B and 70B Apertus models on 4T and 2T additional tokens. It adds early-fusion image and audio understanding via discrete tokenizers (Emu3.5, WavTokenizer) without architectural change, a thinking mode, 262k context, and better tool use. It documents a four-stage pretraining and five-stage post-training pipeline (SFT, long-context SFT, RLVR, DPO, charter-alignment SDPO), plus evaluation via the MLLM-eval-suite and Every Eval Ever (EEE) Eval Cards, released checkpoints and datasets.

Hugging Facelm-evaluation-harnesslmms-eval+2

ApertusApertus 1.5Emu3.5+17

models, training, multimodal, evals· Published and added 15 Oct 2026

securitylabs.datadoghq.comwriteup🛡️ Security

Jev as a judge: How counterfeit policy steers Jev

Datadog Security Labs tests TypeSafe AI's Jev decision model as a prompt-injection detector and as the judge behind LangChain's tool-risk gating. On four public datasets Jev had 99.66% precision and 82.90% recall. A prepended "counterfeit policy" that redefines the attack categories cut recall by over 79 points. In a LangChain scenario, a user-editable profile field lowered the mean is_risky score from 0.893 to 0.290, below the 0.5 block threshold. Plain prompt injection did not move the score.

JevLangChain

TypeSafe AIcounterfeit policyprompt injection+3

AI Security· Added 29 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🧠 AI

Hide and Seek: HIDE Concepts to SEEK explanations for black box models

Hide and Seek (HnS) is a concept-level attribution method for black-box vision models. It needs no model weights and no concept-labeled datasets. Given a concept and an image set, it uses a vision-language model to retrieve concept-aligned images and localize the concept, masks the localized region, and takes attribution from the change in the black-box model's output. It agreed better with ground-truth attributions than existing methods on CelebA and COCO-Stuff, and it works on tasks existing concept attribution methods cannot handle, such as image captioning and satellite image segmentation.

vision-language model

Hide and SeekHnSCelebA+1

research, multimodal· Added 12 Oct 2026, published 9 Oct 2026

x.comtool🧠 AI

Gurasees (@gurasees0) on X

Robo dir is a directory that aggregates robotics and physical-AI datasets in one place. The launch tweet claims 3,494 datasets, 86,424 Hugging Face uploads, 236B+ frames and 2.2M+ hours of data. It also exposes the index through a single MCP server with no sign-in, so agents can search for robot training data programmatically.

Model Context ProtocolHugging Face

Robo dir

training, mcp, multimodal· Published and added 9 Oct 2026

arxiv.orgpaper🧠 AI

SWE-Critix: Post-Training Generalist Verifiers for Assessing Coding-Agent Trajectories

SWE-Critix is a post-training pipeline that trains generalist verifiers for coding-agent trajectories. Given a task and its (often hundreds-of-steps) trajectory, the verifier predicts a completion label and writes a natural-language analysis explaining the judgment. Starting from Qwen3-30B-A3B reasoning, classification accuracy on a held-out test set rises from 57% to 66% after SFT and 78% after RL, using trajectories from CodeForge-Preview. Models, datasets and training code are released.

Qwen3Mixture of Experts

SWE-CritixCodeForge-PreviewQwen3-30B-A3B+1

research, coding-agents, training, evals· Added 12 Oct 2026, published 9 Oct 2026

arxiv.orgpaper🧠 AI

Compile the Table: Query-Calibrated Operator Compression for Tabular In-Context Learning

Proposes QCOC (Query-Calibrated Operator Compression) for tabular in-context learning. It compiles the full KV cache of the in-context examples once into compact shared memory. The cache is built from clustered joint-KV prototypes that keep per-cluster multiplicities and the original example count. Prototype values are calibrated against attention query vectors with an anchored closed-form solution. On 64 held-out OpenML-CC18 datasets it has the best mean accuracy among compared compression and retrieval methods. On seven long tables it averages 0.23 points below full context. Compressing 8,192 examples to 512 slots gives 10.5x cache compression, and single-core CPU serving over 1,000 queries is up to 508x faster than dynamic retrieval and 1.98x faster than full context.

Transformer

QCOCOpenML-CC18

research, inference· Added 9 Oct 2026, published 8 Oct 2026

huggingface.coarticle🧠 AI

The model that didn't exist, so you made it yourself

A Hugging Face blog post describing hands-on use of "ML Intern," an agent in HuggingChat that plans a training job, asks for a spending budget, runs a smoke test, then fine-tunes, evaluates and publishes a model to the Hub. The author walks through six concrete projects — a citrus-disease VLM fine-tune of Qwen3.5-2B, a Huggy character LoRA on FLUX.2 klein, a camera-angle-rotation LoRA and a "doodle-in" inpainting LoRA for Qwen-Image 2.1, and two distilled text-to-image/text models (a 0.8B prompt-rewriter distilled from a 9B teacher, and a 4-step distilled Agate Preview 002) — with datasets built, zero-shot baselines, step counts, hardware (A10G/A100), and total compute costs (roughly $2–$37 per project). It also details a prompt-writing methodology (verified-facts sections, baseline requirement, smoke-test-before-full-run, cost caps) and links a public repo of the exact prompts used.

Hugging FaceLoRAGGUF+5

ML InternQwen-Image 2.1Qwen3.5-2B+12

agents, training, prompting· Published and added 8 Oct 2026

arxiv.orgpaper🛡️ Security

Detecting Adversarial Images through Response Profiles of Vision-Language Models

This paper introduces a detector for adversarial images targeting frozen vision-language models (VLMs) by building a 'response profile' from how an image interacts with a bank of general semantic prompts — using category-level statistics, inter-prompt relationships, deviation from clean reference distributions, and stability under weak image transforms, then classifying that compact profile with a lightweight model. Evaluated across multiple public datasets, several CLIP-style visual backbones, and gradient-based, optimization-based, automated, and spatial attacks, the method outperforms embedding-geometry baselines, generalizes partially to unseen attacks, and is tested against detector-aware adaptive attacks.

CLIP

AI Security· Added 9 Oct 2026, published 7 Oct 2026

huggingface.coarticle🧠 AI

One Model Family, Two Gold-Level Results: Fine-Tuning Nemotron for IOI and IMO

NVIDIA describes how it specialized Nemotron 3 base models into domain experts for competitive programming (IOI) and olympiad mathematics (IMO) using a four-step recipe: strong base model, curated domain data with reasoning traces, SFT/RL post-training, and an inference loop (GenCorrect for IOI, generate-verify-refine for IMO) that searches and refines candidate answers. Nemotron-3-Nano-CC and Nemotron-3-Ultra-CC reached gold-level IOI 2026 scores (535.4/600), while SFT and RL checkpoints of Nemotron 3 Ultra combined to score 30/42 on IMO 2026, exceeding the gold threshold with no formal prover or external tools. The post releases training datasets, checkpoints, the Nemotron-IMO-Bench benchmark, and the NeMo-Skills inference pipelines on Hugging Face.

Hugging FaceMixture of Experts

NemotronNemotron 3Nemotron-3-Nano-CC+11

training, evals, models· Published and added 7 Oct 2026

arxiv.orgpaper🧠 AI

Covariate-dependent Nonparametric $g$-modeling for regression via infinite Mixture-of-Expertizing class

This arXiv methodology paper extends empirical Bayes g-modeling, which estimates a latent prior over regression coefficients from data, to the case where the entire prior distribution depends on covariates. The authors show the unrestricted nonparametric MLE (NPMLE) of this covariate-dependent prior is ill-posed, and resolve it by introducing an "infinite Mixture-of-Expertizing" class of conditional priors, proving that the resulting NPMLE is exactly a softmax-gated Mixture of Experts whose number of experts is determined by the data rather than fixed in advance. They give two exemplar-based estimation algorithms with automatic expert selection, a post-hoc aggregation step for interpretability, and theoretical results (Lipschitz continuity of conditional priors, universal approximation of continuous gating functions by aggregated softmax gates), validated on synthetic and real datasets.

Mixture of Experts

research· Added 7 Oct 2026, published 6 Oct 2026

arxiv.orgpaper🧠 AI

How to scale your HEP ML models: A recipe for robust architecture comparisons at scale

This paper proposes a systematic procedure for deriving robust scaling laws and comparing architecture design choices at scale, validated first on toy problems and then applied to multi-task transformers trained on the ~11 billion-jet ATLAS JetSet2 dataset. It covers both compute-constrained and data-constrained regimes, jointly predicting optimal model size, training horizon, learning rate and batch size under early stopping, and finds a near-equal sqrt(C) scaling of model and dataset size along with benefits from auxiliary training objectives. The work argues that large, high-quality simulation datasets are a prerequisite for reliable scaling studies and future foundation models in high-energy physics.

Transformer

ATLAS JetSet2

research, training· Added 6 Oct 2026, published 5 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Glimpsing the Leakage Flow for Deep Learning-Based Side-Channel Analysis using Stochastic Leakage Model

GlimpseDNN is a model-agnostic framework that uses a stochastic leakage model to visualize, layer-by-layer, what physical leakage information a deep neural network extracts during profiled side-channel analysis (SCA) to recover secret keys. The authors (Trevor Yap and Shivam Bhasin, NTU) validate it on simulated traces and public datasets (ASCADf, ASCADr, ASCADf_desync50, ASCADf_desync100), showing it can reveal leakages even in deeper network layers when they are not visible in raw traces, with applications to designing more side-channel-resistant hardware.

ASCADfASCADrASCADf_desync50+2

Hardware & RF· Added 8 Oct 2026, published 5 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Output Policies for Anomaly Detection with Homomorphic Encryption: Threshold Resolution, Decision Preservation, and Numerical Stability

This IACR eprint paper studies output policies for CKKS homomorphic-encryption-based anomaly detection, evaluating whether limiting released scores and decisions can hide a private detection threshold without breaking existing decisions. Across 45 plaintext and 45 CKKS experiments on three intrusion-detection datasets and autoencoder scoring models, the authors show thresholds remain estimable, quantization preserves about 99.86 percent of decisions, and a surrogate model trained on only 128 decision-only responses achieves 0.817 mean balanced accuracy at predicting decisions on new inputs, a decision-prediction model-extraction-style attack whose success depends heavily on data-splitting strategy. It argues numerical threshold leakage and decision-prediction leakage are distinct protection goals that output policies for encrypted intrusion detection systems must address separately.

CKKSHomomorphic Encryption

CIC-IDS2017

AI Security· Added 8 Oct 2026, published 5 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Randomness-Anchored Runtime Discovery of Cryptographic Operations on Linux Using eBPF

This IACR ePrint paper presents a Linux runtime cryptographic discovery system built on eBPF user-space probes that detects both classical and post-quantum (ML-KEM) cryptographic operations as they execute, complementing static binary/SBOM analysis with dynamic observation. It uses "randomness anchoring" — correlating observed key-generation/encapsulation randomness with subsequent crypto API calls in the same process — to raise detection confidence without adding false positives, reporting perfect precision/recall on classical and ML-KEM workloads, median detection latency of 28 μs, and 14–18% overhead on key-generation-bound workloads. A nine-application case study (drawn from/paired with QED datasets) compares static vs. runtime views and shows how eBPF probe coverage limits what can be observed, relevant to building a runtime cryptographic bill of materials for PQC migration compliance.

eBPFLinuxML-KEM+1

QED datasets

AppSec, GRC· Added 8 Oct 2026, published 5 Oct 2026

arxiv.orgpaper🛡️ Security

VulValidate: Auditing Function-Level Vulnerability Labels with Executable Evidence

VulValidate is a framework where LLM agents coordinate dynamic-analysis tools to reconstruct vulnerable/fixed code revisions and build triggering experiments that verify whether a function labeled "vulnerable" in a commit-derived dataset actually exhibits the vulnerability. Applied to all 35,849 vulnerable-labeled instances in BigVul, PrimeVul, and DiverseVul, it confirms 57.2%, relabels 19.0% as mislabeled, and leaves the rest undecided or unmeasured, producing a corrected 15,890-function release; retraining five detectors on corrected labels improves F1 for four of five on each dataset. The work exposes how commit-based labeling noise inflates or depresses vulnerability-detector benchmarks and ships a reusable auditing skill and corrected datasets.

LLM

BigVulPrimeVulDiverseVul

AppSec· Added 6 Oct 2026, published 4 Oct 2026

arxiv.orgpaper🧠 AI

VulValidate: Auditing Function-Level Vulnerability Labels with Executable Evidence

VulValidate is a framework where LLM agents coordinate dynamic-analysis tools to reconstruct vulnerable/fixed code revisions, craft triggering inputs, and compare runtime behavior, producing executable evidence for whether a function-level vulnerability label is actually correct. Applied to all 35,849 "vulnerable" instances in the BigVul, PrimeVul, and DiverseVul datasets, it confirms 57.2%, corrects 19.0% of labels, and leaves 22.3% undecided, yielding a corrected release of 15,890 distinct vulnerable function bodies validated against expert review. Retraining five detectors on the corrected labels changes measured F1 on BigVul and DiverseVul, showing that commit-derived vulnerability labels used across the field are substantially noisy and that agentic, execution-grounded auditing is a viable correction method.

VulValidateBigVulPrimeVul+1

evals, agents· Added 6 Oct 2026, published 4 Oct 2026

arxiv.orgpaper🛡️ Security

CorrectGuard: Eyes-Off Correctness Estimation for Black-Box Security Guardrails

CorrectGuard is a framework for estimating whether black-box AI security guardrails made correct decisions on inputs that are inaccessible to humans (production privacy) or to the guardrail-auditing model itself, using only labeled "eyes-on" data and no internal access to the guardrail. The authors test in-context-learning, embedding, and finetuning-based correctness classifiers across 13 safety/security datasets (harmful content, jailbreaks, prompt injection, extraction) and multiple open-weight guardrails, showing up to 25-point macro-accuracy gains in error identification and improved risk-based abstention/ranking of guardrail decisions.

LLM

AI Security· Added 5 Oct 2026, published 2 Oct 2026

arxiv.orgpaper🧠 AI

CorrectGuard: Eyes-Off Correctness Estimation for Black-Box Security Guardrails

CorrectGuard is a framework for estimating how accurately black-box security guardrails (jailbreak, harmful-content, prompt-injection and extraction detectors) perform on inputs that cannot be inspected by humans or by the evaluating model itself, using an independent correctness model trained only on labeled "eyes-on" data. The authors test in-context-learning, embedding-based, and fine-tuned correctness classifiers under leave-one-dataset-out evaluation across 13 safety/security datasets and multiple open-weight guardrails, showing up to 25-point macro-accuracy gains and improved risk-coverage tradeoffs (AURC reduced from 0.33–0.44 to 0.17–0.22) for guardrail decision ranking and abstention. The work matters for anyone building auditing or eval pipelines for opaque safety classifiers without privileged access to their internals.

CorrectGuard

evals, research· Added 5 Oct 2026, published 2 Oct 2026

arxiv.orgpaper🧠 AI

Trust the Direction, Search the Step: Zero-and-First-Order Methods for LLM Fine-Tuning

This paper introduces ZFO (Zero-and-First-Order optimization), a lightweight fine-tuning framework that decouples search direction from step-size selection: a trusted first-order optimizer picks the direction, while two extra zeroth-order function evaluations along that single dimension build a local curvature-aware model to choose the step size, avoiding a full line search. The authors give theoretical guarantees on finite-difference curvature estimates, near-optimal step selection, and convergence to a stationary-point neighborhood, and show empirically that ZFO improves optimization and final performance over fixed-step first-order baselines across several language models and datasets. Code is released publicly, making it a usable technique for practitioners fine-tuning LLMs who want cheaper adaptive step-size control.

PyTorch

training, research· Added 2 Oct 2026, published 1 Oct 2026

x.comthread🧠 AI

Rahul Sain (@RahulSain714) on X

Innodata open-sourced a toolkit and 14 accompanying datasets for benchmarking factuality and hallucination rates in LLMs, tested against models including Llama2 and Mistral. The toolkit is aimed at identifying input conditions that trigger problematic or hallucinated outputs, giving practitioners a reproducible way to probe model reliability.

Llama2Mistral

Innodata

evals· Published and added 1 Oct 2026

thedefiant.ioarticle🧠 AI

Blockworks Opens Its Full Dataset To AI Agents

Blockworks, a crypto research and data provider, launched a hosted Model Context Protocol server that gives AI agents like ChatGPT, Claude and Cursor read access to the datasets underlying its research and its Intel app. The MCP server is included free with every existing API plan and uses the same API key and permission scope as the regular API, rather than requiring separate onboarding.

Model Context Protocol

BlockworksChatGPTClaude+2

mcp· Published and added 1 Oct 2026

arxiv.orgpaper🛡️ Security

SoK: A Large-Scale Empirical Study of Emulation-Based Dynamic Analysis Research for ARM Cortex-M Firmware (Extended Version)

This SoK paper empirically evaluates 24 published emulation-based dynamic analysis tools for ARM Cortex-M firmware re-hosting, using large-scale datasets from OTACAP and FirmLine totaling 4,571 deduplicated ARM firmware samples. It finds that only 34.5% of samples can be successfully fuzzed by any existing tool, with average code coverage of just 10% and frequent false crashes/hangs, exposing systemic gaps across the emulation, fuzzing, and bug-diagnosis pipeline stages.

ARM Cortex-M

OTACAPFirmLine

Hardware & RF· Added 2 Oct 2026, published 30 Sep 2026

arxiv.orgpaper🛡️ Security

SparLeak: Privacy Leakage from Sparse Attention in LLM Inference on Shared GPUs

SparLeak is a side-channel attack exploiting a newly identified GPU micro-architectural leak, Sparsity-Induced Memory Access (SIMA), caused by input-dependent key-value cache access patterns in sparse attention mechanisms used for long-context LLM inference on shared GPUs. The attack recovers prefill-phase query attributes and reconstructs decoding-phase LLM responses by profiling token-level sparsity patterns from page-level memory observations, achieving 90.9% attribute-inference and 87.3% response-reconstruction success across three LLM architectures, three sparse-attention mechanisms, and three privacy-sensitive datasets. The authors release anonymized SIMA traces, trained attack models, and evaluation scripts/artifacts.

LLM

SparLeakSparsity-Induced Memory Access

AI Security, Hardware & RF· Added 2 Oct 2026, published 30 Sep 2026

arxiv.orgpaper🧠 AI

A Function-level Dataset of Vulnerable and Fixed Source Code in JavaScript and TypeScript

JsVul is a function-level dataset of vulnerable and fixed JavaScript and TypeScript source code, built from seven sources using a language-specific pipeline that strips noise such as minified code and cosmetic edits, then applies multi-stage deduplication and heuristic labeling. Delivered as time-ordered JSONL pairs of pre-fix and post-fix code, it is designed to train and evaluate ML-based vulnerability-detection models, and the paper argues language-aware preprocessing improves data quality over generic multi-language datasets.

JavaScriptTypeScript

JsVul

training, research· Added 30 Sep 2026, published 29 Sep 2026

arxiv.orgpaper🛡️ Security

A Function-level Dataset of Vulnerable and Fixed Source Code in JavaScript and TypeScript

JsVul is a function-level dataset of vulnerable and fixed JavaScript/TypeScript code pairs, aggregated from seven sources and built with a language-specific pipeline that strips noise (minified code, cosmetic edits), deduplicates across commits, and applies heuristic labeling of security-relevant changes. It is released in time-ordered JSONL format to support training and evaluating automated vulnerability detection models specifically for the JS/TS ecosystem, where prior datasets were largely generic and multi-language.

JavaScriptTypeScript

AppSec· Added 30 Sep 2026, published 29 Sep 2026

arxiv.orgpaper🧠 AI

FurE: Efficient Instance-Specific 3D Fur Reconstruction without Animal-Fur Datasets

FurE is a strand-based method for reconstructing editable 3D animal fur from multi-view images without requiring animal-fur training data. It optimizes a root-conditioned latent field decoded into strand geometry via a PCA-based decoder trained on human-hair strand data, combined with a defurred animal body recovered using a surface-constrained Gaussian Frosting representation and part-based priors, achieving a 10x speedup over dense per-strand optimization baselines while retaining strand fidelity on synthetic and real-world sequences.

Gaussian FrostingPCA

Prakhar Kaushik

multimodal, research· Added 29 Sep 2026, published 28 Sep 2026

arxiv.orgpaper🧠 AI

Copy the Same, Distill the Difference: Initializing Linear Vision Transformers

This paper studies how to initialize efficient linear-attention Vision Transformers from pretrained Softmax ViT weights instead of training from scratch. It finds that attention weights are operator-specific and don't transfer well by direct copying (distillation with a tailored loss is needed to recover token-routing behavior), while MLP weights are operator-agnostic and can simply be copied, and combining copied MLPs with distilled attention lets linear ViTs match or surpass Softmax ViT performance across model sizes and datasets.

Transformervision-language model

Vision Transformer

research, multimodal· Added 29 Sep 2026, published 28 Sep 2026

arxiv.orgpaper🧠 AI

InfiniHand: Streaming World-Space Hand Motion Estimation from Egocentric Video

InfiniHand is an end-to-end streaming feed-forward model that jointly estimates MANO hand-pose parameters, camera trajectory, and world-space hand location from uncalibrated egocentric video, replacing the usual cascade of separate hand-pose and SLAM systems. It uses persistent spatiotemporal memory coupled with hand-centered visual features, trained in two stages on ~5,000 hours of aggregated egocentric datasets, and reports a 21.4% PA-p reduction on ARCTIC over ViDiHand plus reduced world-space drift while running at 11.19 FPS (2x the throughput of HaWoR).

MANO

ARCTICHaWoRViDiHand

research, multimodal· Added 29 Sep 2026, published 28 Sep 2026

arxiv.orgpaper🧠 AI

EvE: An Alternate Optimizer to Adam

EvE (Evolutionary Explorer) is a steady-state, population-of-four differential evolution optimizer with a targeted Adam fallback, proposed as an alternative to Adam for cheap, early ranking of configurations in hyperparameter and architecture search. Under a fixed evaluation-cost-matched budget it wins or ties Adam on 76% of 70 (problem, dimension) cells across seven scalable benchmarks up to one million variables, and on real tasks (MLP on MNIST, LoRA fine-tuning of a 1.5B-parameter LLM on two datasets) it finishes 1.7-3.9x faster at a modest quality cost. Inside successive halving on UCI Adult it completes hyperparameter/architecture searches 3.1-3.5x faster while ranking configurations nearly as consistently as Adam does with itself across seeds (Kendall's tau 0.66-0.69).

LoRA

MNISTGSM8KUCI Adult

training, research· Added 29 Sep 2026, published 28 Sep 2026

arxiv.orgpaper🧠 AI

A Large-Scale Measurement of AI Bill of Materials Completeness in Hugging Face Models

This paper empirically measures the completeness of AI Bills of Materials (AIBOMs) generated for roughly 97.5K Hugging Face model repositories, examining structural/metadata fields, model identity, license and external-reference data, and model-card documentation such as datasets, limitations, safety-risk and environmental information. It finds that generated AIBOMs satisfy required structural fields but are weak on AI-specific documentation like responsible-use, environmental impact, and meaningful descriptions, and argues for better model-card practices and automated AIBOM validation.

Hugging Face

ai-policy, research· Added 26 Sep 2026

arxiv.orgpaper🧠 AI

Online Safety Monitoring for LLMs

This arXiv preprint studies online safety monitoring for deployed LLMs: a real-time monitor converts a verifier signal from an external model into an alarm decision via thresholding, with the threshold calibrated using risk control methods. Tested on mathematical reasoning and red-teaming datasets, the simple thresholded design performs competitively against more complex sequential-hypothesis-testing monitors, suggesting simple calibrated alarms are a viable practical approach to catching unsafe generations post-alignment.

ai-safety, research· Added 26 Sep 2026

arxiv.orgpaper🛡️ Security

A Large-Scale Measurement of AI Bill of Materials Completeness in Hugging Face Models

This paper empirically studies AI Bills of Materials (AIBOMs) generated for ~97.5K public Hugging Face model repositories, measuring how completely they capture provenance, licenses, datasets, model-card details, safety-risk assessments, and environmental information. It finds that generated AIBOMs satisfy required structural/metadata schema fields but are weak or missing on AI-specific documentation such as model cards, responsible-use notes, limitations, and dataset declarations, exposing transparency and governance gaps in the ML model supply chain. The authors argue for better model-card practices, repository-level traceability, and automated AIBOM validation.

Hugging FaceSBOM

AIBOM

Supply Chain, GRC· Added 26 Sep 2026

arxiv.orgpaper🛡️ Security

Online Safety Monitoring for LLMs

This paper proposes an online safety monitor for deployed LLMs that converts a verifier model's signal into a real-time alarm via a threshold calibrated using risk control, flagging when the model's outputs can no longer be trusted as safe. Tested on mathematical reasoning and red-teaming datasets, the simple thresholded verifier approach matched more complex monitors built on sequential hypothesis testing.

LLM

AI Security· Added 26 Sep 2026

arxiv.orgpaper🧠 AI

Quantum Feature Selection for Biomedical Data Analysis

This paper formulates metabolomic feature selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem, balancing feature relevance against redundancy to prepare high-dimensional data for machine learning models like computational biomarkers. The authors test Bias-Field Digitized Counterdiabatic Quantum Optimization (BF-DCQO) and QAOA on gate-based quantum hardware, comparing against classical filter, wrapper, and embedded feature selection methods (including exhaustive search and Iterative Tabu Search) on three Autism Spectrum Disorder metabolomic datasets. Results show reduced runtime versus exhaustive/Tabu search and competitive classifier performance, though the authors explicitly do not claim quantum advantage, framing the work as a NISQ hardware feasibility demonstration.

Autism Spectrum Disorder

research· Added 26 Sep 2026, published 24 Sep 2026

arxiv.orgpaper🛡️ Security

JevOut: Natural Context Can Flip Decision Models

This paper studies "decision models" like Jev, which map unstructured language directly to probability distributions over a fixed set of choices to drive routing, tool selection, and action triggering. The authors use an optimizer guided by the model's own option probabilities to craft short, fluent context additions that preserve the original question and gold answer yet flip the model's decision: 61.4% of initially correct decisions (312/508) are flipped for Jev, with 229 reaching ≥0.7 confidence in the wrong option, and three other decision systems show flip rates of 64.9%-73.2% across seven datasets. The results argue that treating these models' probability outputs as reliable decision interfaces is dangerous, since ordinary contextual phrasing — not adversarial-looking text — is enough to hijack them.

LLM

Jev

AI Security· Added 26 Sep 2026, published 24 Sep 2026

arxiv.orgpaper🧠 AI

JevOut: Natural Context Can Flip Decision Models

This arXiv paper shows that dedicated decision models (e.g., Jev) which map language to probability distributions over finite choices can be "flipped" from a correct answer to a high-confidence wrong one by adding short, fluent, natural-seeming context — without altering the source, question, choices, or gold answer. Using an optimizer that refines context additions based on the model's option probabilities, the authors flip 61.4% of initially correct decisions in Jev within 64 accepted evaluations, and 64.9%-73.2% across three other decision systems on seven datasets. The findings raise concerns about relying on these models' probability outputs as trustworthy decision interfaces for routing, tool selection, or triggering actions.

Jev

research, evals· Added 26 Sep 2026, published 24 Sep 2026

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