arXiv Machine Learning
Jun 5
Anchor PCA
arXiv:2606. 06233v1 Announce Type: cross Abstract: Principal component analysis (PCA) is one of the most widely used unsupervised dimension reduction techniques.
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 5
The ASE-LSE Disagreement Landscape: An End-to-End Characterisation of Extremes and Structural Drivers
arXiv:2605. 22346v2 Announce Type: replace-cross Abstract: Two of the most widely used methods for analysing graph data, Adjacency Spectral Embedding and Laplacian Spectral Embedding, often produce different results when applied to the same graph.
arXiv Machine Learning
Jun 5
MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry
arXiv:2606. 05693v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise for molecular property prediction, but their ability to reason over chemical structures remains limited, as molecular representations such as SMILES differ substantially from the natural language on which LLMs are primarily trained.
arXiv Machine Learning
Jun 5
Executable Schema Contracts: From Automatic Ingestion to Multi-Source Retrieval
arXiv:2606. 05415v1 Announce Type: cross Abstract: Real-world data spans tables, documents, and semi-structured files with implicit semantics.
arXiv Machine Learning
Jun 5
PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data
arXiv:2606. 06328v1 Announce Type: new Abstract: In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire respiratory channel is unavailable during overnight monitoring.
arXiv Machine Learning
Jun 5
$p$-adic Bi-Filtrations for Topological Machine Learning on Genomic Sequences
arXiv:2606. 06117v1 Announce Type: cross Abstract: We introduce pVR, a topological machine learning framework for alignment-free genomic sequence classification that combines $p$-adic numbers with topological data analysis.
arXiv Machine Learning
Jun 5
Detectability in Diversity: Improved Canary Crafting for Privacy Auditing in One Run
arXiv:2605. 27292v2 Announce Type: replace Abstract: Privacy auditing aims to empirically assess privacy leakage in machine learning models using membership inference attacks (MIAs), and to derive lower bounds on differential privacy (DP) parameters.
arXiv Machine Learning
Jun 5
HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data
arXiv:2506. 11152v4 Announce Type: replace-cross Abstract: Single-cell transcriptomics and proteomics have become a great source for data-driven insights into biology, enabling the use of advanced deep learning methods to understand cellular heterogeneity and gene expression at the single-cell level.
arXiv Machine Learning
Jun 5
Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series
arXiv:2506. 00188v2 Announce Type: replace Abstract: Early and accurate detection of anomalies in time-series data is critical due to the substantial risks associated with false or missed detections.
arXiv Machine Learning
Jun 5
Algebraic Diversity: Group-Theoretic Spectral Estimation from Single Observations
arXiv:2604. 03634v5 Announce Type: replace Abstract: We establish that temporal averaging over multiple observations is the degenerate case of algebraic group action with the trivial group $G=\{e\}$.
arXiv Machine Learning
Jun 5
NIV: Neural Axis Variations for Variable Font Generation
arXiv:2606. 05261v1 Announce Type: cross Abstract: Variable fonts enable continuous variation of glyph geometry along semantic design axes such as weight, width, slant, and optical size.
Hugging Face Trending Papers
Jun 4
$p$-adic Bi-Filtrations for Topological Machine Learning on Genomic Sequences
We introduce pVR, a topological machine learning framework for alignment-free genomic sequence classification that combines $p$-adic numbers with topological data analysis. Each DNA sequence is encoded along two complementary axes: a $p$-adic distance on $k$-mer prefixes, which captures hierarchical positional structure, and a compositional $L_1$ distance on $k$-mer frequencies, which captures local sequence content.
Hugging Face Trending Papers
Jun 4
Learning of Robot Safety Policies via Adversarial Synthetic Scenarios
In this work, we propose an agentic gamification framework for hazard-informed learning of robot safety policies through synthetic scenarios. We model scenario generation as an adversarial game between two agents: a Red Team that explores the space of potential failures by constructing hazardous situations, and a Blue Team that incrementally refines safety policies to prevent them.
Hugging Face Trending Papers
Jun 4
QCFuse: Query-Aware Cache Fusion via Compressed View for Efficient RAG Serving
Retrieval-augmented generation (RAG) improves large language model (LLM) answer quality by grounding generation in external evidence, but processing retrieved contexts makes the prefill stage a dominant serving cost. RAG cache fusion reduces this cost by reusing precomputed key-value (KV) caches for retrieved chunks and selectively recomputing tokens under the current prompt.
arXiv Machine Learning
Jun 4
KODA: Contrastive Representation Comparison and Alignment for Vision-Language Foundation Models
arXiv:2606. 04180v1 Announce Type: new Abstract: Vision-language foundation models such as CLIP and SigLIP provide widely used representations for multimodal learning systems.
arXiv AI
Jun 4
Cascading Hallucination in Agentic RAG: The CHARM Framework for Detection and Mitigation
arXiv:2606. 04435v1 Announce Type: new Abstract: Multi-step agentic retrieval-augmented generation (RAG) pipelines have demonstrated significant capability for complex reasoning tasks, yet remain vulnerable to a class of failure that existing hallucination detection mechanisms systematically miss: cascading hallucination, where errors introduced at early pipeline stages propagate and amplify across successive reasoning steps, producing confident but factually incorrect final outputs.
arXiv AI
Jun 4
No More K-means: Single-Stage Sparse Coding for Efficient Multi-Vector Retrieval
arXiv:2605. 30120v3 Announce Type: replace-cross Abstract: Multi-vector retrieval (MVR) models, exemplified by ColBERT, have established new benchmarks in retrieval accuracy by preserving fine-grained token-level interactions.
arXiv AI
Jun 4
Extending Fair Null-Space Projections for Continuous Attributes to Kernel Methods
arXiv:2511. 03304v2 Announce Type: replace-cross Abstract: With the on-going integration of machine learning systems into the everyday social life of millions the notion of fairness becomes an ever increasing priority in their development.
arXiv AI
Jun 4
L$^3$: Large Lookup Layers
arXiv:2601. 21461v3 Announce Type: replace-cross Abstract: Modern sparse language models typically achieve sparsity through Mixture-of-Experts (MoE) layers, which dynamically route tokens to dense MLP "experts.
arXiv Machine Learning
Jun 4
Test-Time Compute Scaling for ASR with Depth-Conditioned Looped Transformers
arXiv:2606. 04678v1 Announce Type: new Abstract: End-to-end ASR systems typically use fixed-depth acoustic encoders at inference, making it difficult to trade additional test-time computation for improved recognition without training a larger model.