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arxiv.orgpaper🧠 AI

Constraint Dependency Graph Retrieval for Compliance Detection on Long SKILL Documents

SkillCDG is a compliance detection framework that turns each long SKILL document (an enterprise SOP followed by LLM agents) into a constraint dependency graph. Atomic rules are nodes tied to source text spans, and mandatory prerequisites are the only edges. Policy-generated keywords seed rules, and their dependency closure fills the small auditor's context budget. Retrieval is trained with GRPO and selective on-policy distillation. It beats full-document and retrieval baselines on three enterprise datasets and two public benchmarks.

GRPOLLMRAG
SkillCDGarXiv

research, llm-apps, training· Added 3 Nov 2026, published 31 Oct 2026

arxiv.orgpaper🧠 AI

From Lookups to Lessons: Turning Retrieved Knowledge into Parametric Memory

ResMem is a method that distills retrieved knowledge into a parametric memory. The LLM reads retrieved text offline to produce teacher targets, and a memory sharing the same backbone learns an additive logit residual through self-distillation. On a Wikipedia subset it cuts teacher-construction storage by 94.7% versus a kNN pipeline. Across five QA datasets, mean token F1 rises from 32.83% to 34.48%, and it breaks 21% fewer correct answers than a kNN-trained memory.

RAGLLMkNN+1
ResMemMLP MemoryarXiv+1

research, llm-apps, training· Added 3 Nov 2026, published 30 Oct 2026

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

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