arXiv Machine Learning June 30, 2026 By Brian Keith-Norambuena

An Information-Geometric Justification for Composite Coherence in Event-Based Narrative Extraction

Read the original on arXiv Machine Learning →

arXiv:2606. 29118v1 Announce Type: cross Abstract: Graph-based narrative extraction relies on a coherence function to score transitions between events, but the coherence metrics in current use are defined operationally and lack an information-theoretic foundation.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

Hugging Face Trending Papers

Jun 28

Anisotropy Decides Cosine vs. Rank Metrics for Text Embeddings

The standard way to compare two text embeddings is cosine similarity. Scattered studies report that a different metric does better, but never pin down the geometric condition that decides when, or why.

llms rag fine-tuning

arXiv AI

Jun 3

Representational Capacity: Geometric Limits on Feature Representation in Transformer Language Models

arXiv:2606. 02765v1 Announce Type: cross Abstract: Model dimension ($d_{model}$) is a fundamental hyperparameter in transformer language models, yet its role in setting the geometric limits of feature representation remains under-explored.

By Alexander Guha

llms rag diffusion

arXiv Machine Learning

Aug 4

GeoFlowVLM: Geometry-Aware Joint Uncertainty for Frozen Vision-Language Embedding

arXiv:2605. 13352v2 Announce Type: replace Abstract: Standard dual-encoder vision-language models that map images and text to deterministic points on a shared unit hypersphere through $\ell_2$ normalization typically expose neither \emph{aleatoric} uncertainty (cross-modal ambiguity) nor \emph{epistemic} uncertainty (lack of training-distribution support).

By Mayank Nautiyal, Li Ju, Andreas Hellander, Ekta Vats, Prashant Singh

llms rag diffusion multimodal benchmarks

arXiv AI

Aug 7

Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations

arXiv:2608. 06305v1 Announce Type: new Abstract: Retrieval-augmented generation over long documents is dominated by one design: chunk the text, embed the chunks, and surface the top-k nearest neighbours of the query.

By Sagar Tamang, Ayush Vyas, Tabarakul Hazarika

rag agents

arXiv Machine Learning

Jul 16

The Hyperspherical Geometry of CLIP Latent Space: A Semantic Mixture Model

arXiv:2607. 13660v1 Announce Type: new Abstract: Contrastive Language-Image Pretraining (CLIP) representations form a semantic embedding space governed by cosine similarity, reflecting an intrinsic hyperspherical geometry.

By Zijie Yu, Gaowen Liu, Ramana Rao Kompella, Philip S. Yu, Yue Song

rag diffusion multimodal

arXiv Machine Learning

Jul 15

Watermark Forensics for Generative Models: An Information-Theoretic Perspective

arXiv:2607. 13003v1 Announce Type: cross Abstract: A watermark in a generative model's output is usually asked only whether a text is machine-made.

By Xiaoyu Li, Zheng Gao, Xiaoyan Feng, Jiaojiao Jiang, Yulei Sui, Jiankun Hu

llms safety

Related stories

Anisotropy Decides Cosine vs. Rank Metrics for Text Embeddings

Representational Capacity: Geometric Limits on Feature Representation in Transformer Language Models

GeoFlowVLM: Geometry-Aware Joint Uncertainty for Frozen Vision-Language Embedding

Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations

The Hyperspherical Geometry of CLIP Latent Space: A Semantic Mixture Model

Watermark Forensics for Generative Models: An Information-Theoretic Perspective