arXiv Machine Learning
Jun 15
Temporal Straightening for Latent Planning
arXiv:2603. 12231v2 Announce Type: replace Abstract: Learning good representations is essential for latent planning with world models.
Retrieval-augmented generation
Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.
arXiv Machine Learning
Jun 15
Adaptive Identification and Modeling of Clinical Pathways with Process Mining
arXiv:2512. 03787v2 Announce Type: replace Abstract: Clinical pathways are specialized healthcare plans that model patient treatment procedures.
arXiv Machine Learning
Jun 15
PepALD: Macrocyclic Peptide Generation via Autoregressive Latent Diffusion
arXiv:2606. 14510v1 Announce Type: new Abstract: Macrocyclic peptides are promising therapeutic candidates for intracellular targets, but their design requires simultaneous control over non-natural monomer chemistry, ring topology, membrane permeability, and target binding.
arXiv AI
Jun 15
FactoryLLM: A Safe and Open-Source AI Playground for Evaluating LLMs in Smart Factories
arXiv:2606. 14119v1 Announce Type: new Abstract: Fault diagnostics and recovery in smart factories is challenging because critical information is dispersed across manuals of multiple machines which are interconnected through the manufacturing process.
arXiv AI
Jun 15
Hyperdimensional computing for structured querying on tabular data embeddings
arXiv:2606. 13871v1 Announce Type: new Abstract: Tabular data embeddings have become a cornerstone of data profiling and data integration pipelines, enabling tasks such as entity annotation and resolution; schema matching; column type detection; and table search, among others.
arXiv AI
Jun 15
MirrorCheck: Efficient Adversarial Defense for Vision-Language Models
arXiv:2406. 09250v5 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly susceptible to sophisticated adversarial attacks, including adaptive strategies specifically designed to bypass existing defenses.
arXiv AI
Jun 15
Verbatim Chunks Beat Extracted Artifacts: A Controlled Ablation of Memory Representations for Long LLM Conversations
arXiv:2601. 00821v3 Announce Type: replace Abstract: A growing class of conversational-memory systems compresses dialogue history into structured artifacts -- extracted facts, decisions, or events -- on the premise that distilled structure retrieves better than raw text.
arXiv Machine Learning
Jun 15
PCR-CA: Parallel Codebook Representations with Contrastive Alignment for Multiple-Category App Recommendation
arXiv:2508. 18166v5 Announce Type: replace-cross Abstract: Modern app store recommender systems struggle with multiple-category apps, as traditional taxonomies fail to capture overlapping semantics, leading to suboptimal personalization.
arXiv Machine Learning
Jun 15
IntSeqBERT: Learning Arithmetic Structure in OEIS via Modulo-Spectrum Embeddings
arXiv:2603. 05556v2 Announce Type: replace Abstract: Integer sequences in the OEIS span values from single-digit constants to astronomical factorials and exponentials, making prediction challenging for standard tokenised models that cannot handle out-of-vocabulary values or exploit periodic arithmetic structure.
arXiv Machine Learning
Jun 15
Multi-fidelity aerodynamic data fusion by autoencoder transfer learning
arXiv:2512. 13069v2 Announce Type: replace Abstract: Accurate aerodynamic prediction often relies on high-fidelity simulations; however, their prohibitive computational costs severely limit their applicability in data-driven modeling.
Towards Data Science
Jun 14
Vision LLMs are PDF Parsers Too: Reading Charts and Diagrams for RAG
Enterprise Document Intelligence [Vol. 1 #5quater] - The other parsers read the words on a page.
Hugging Face Trending Papers
Jun 14
Bayesian Networks with Latent Time Embedding for Stage-Aware Causal Modeling of Alzheimer's Disease Progression
Alzheimer's disease (AD) progression is often described through the amyloid-tau-neurodegeneration, or AT(N), cascade. However, most longitudinal models represent this cascade either as a fixed sequence of biomarkers or as a black-box forecasting task.
Hugging Face Trending Papers
Jun 14
Mitigating Visual Hallucinations in Multimodal Systems through Retrieval-Augmented Reliability-Aware Inference
Multimodal large language models (MLLMs) have demonstrated strong capabilities in vision-language understanding and natural-language response generation. However, these systems can still produce overconfident predictions and hallucination-like outputs, particularly when the visual evidence is weak, ambiguous, or semantically inconsistent.
Hugging Face Trending Papers
Jun 13
Defending against Adaptive Prompt Injection Attacks via Reasoning-enabled Task Alignment
Indirect prompt injection attacks hijack LLM-based agents by embedding malicious instructions in third-party data that the agent retrieves during task execution. Existing defenses report near-zero attack success rate on static benchmarks, yet recent adaptive evaluations show that these results collapse once the attacker is allowed to optimize against the deployed defense.
Towards Data Science
Jun 13
Larger Context Windows Don’t Fix RAG — So I Built a System That Does
Increasing context size in RAG systems doesn’t improve accuracy for aggregation tasks—it makes errors harder to detect. In this article, I benchmark retrieval-based pipelines against a deterministic full-scan engine across 100,000 rows and show why computation queries must be routed away from RAG entirely.
Towards Data Science
Jun 13
Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload
Enterprise Document Intelligence [Vol. 1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine.
Towards Data Science
Jun 12
When PyMuPDF Can’t See the Table: Parse PDFs for RAG with Azure Layout
Enterprise Document Intelligence [Vol. 1 #5bis] - The same relational tables.
arXiv AI
Jun 12
When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answering
arXiv:2601. 19827v4 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) extends large language models (LLMs) beyond parametric knowledge, yet it is unclear when iterative retrieval-reasoning loops meaningfully outperform static RAG, particularly in scientific domains with multi-hop reasoning, sparse domain knowledge, and heterogeneous evidence.
arXiv AI
Jun 12
HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers
arXiv:2606. 13289v1 Announce Type: cross Abstract: Holistic visual tokenizers are fundamental to unified multimodal models (UMMs) as they map diverse visual inputs into a unified representation space.
arXiv AI
Jun 12
Towards More General Control of Diffusion Models Using Jeffrey Guidance
arXiv:2606. 13240v1 Announce Type: cross Abstract: A key strength of diffusion models lies in their flexibility, since their outputs can be controlled at sampling time through guidance.