Tagged “embeddings”

6 posts.

All Model Architecture19 Training & Alignment22 Inference & Serving18 Agents & Orchestration11 Reasoning & Evaluation26 Safety, Security & Governance7 Platforms & Practice20

Reasoning & Evaluation 23 min

Clustering Has No Ground Truth: Impossibility, Validation, and What a Cluster Can Promise

In 2002 Jon Kleinberg proved that no clustering function can satisfy three properties almost everyone would ask for. Every algorithm is therefore a definition of what a cluster is, and every validation index is another definition…

clustering unsupervised-learning evaluation metrics ∑ ◫

Reasoning & Evaluation 23 min

From Features to Circuits: What Attribution Graphs Explain, and the Fraction They Do Not

Swap the Texas features for British Columbia and Claude answers Victoria instead of Austin. That single intervention is the strongest evidence yet that a language model performs genuine multi-step reasoning inside one forward pas…

interpretability mech-interp safety alignment ∑ ◫

Platforms & Practice 25 min

Learning Without Labels: The Collapse Problem at the Heart of Self-Supervised Learning

Every joint-embedding method has the same trivial solution available to it: map every input to the same vector. The history of self-supervised learning is the history of preventing that, and the methods that look most different f…

self-supervised-learning contrastive-learning representation-learning embeddings ∑ ◫

Platforms & Practice 10 min

Retrieval Is a Ranking Problem: Why Your RAG System Doesn't Need a Better Embedding Model

Teams tune the embedding model and the vector database, then wonder why answers are still wrong. Both are the least important parts of the stack. Retrieval is a two-stage ranking problem, and information retrieval solved the shap…

rag retrieval reranking chunking ∑ ◫

Reasoning & Evaluation 24 min

The Curse of Dimensionality: Why Distances Stop Meaning Anything, and Why Learning Works Anyway

Scatter 1,000 random points in a 1,000-dimensional cube and the farthest one from a query is only about 12% farther away than the nearest. By that arithmetic nearest-neighbour search should be meaningless, and yet every vector da…

supervised-classical learning-theory embeddings vector-search ∑ ◫

Reasoning & Evaluation 24 min

The Leaderboard Is Not Your Corpus: Why Top-Ranked Embedding Models Disappoint in Production

Embedding models are chosen from a leaderboard more often than from an experiment, and the leaderboard now publishes training splits for its own test sets. Between contamination, task-family averaging and geometry no benchmark me…

embeddings retrieval-rag evaluation benchmarks ∑ ◫