Token pooling-aware training proposed for multi-vector embeddings
The Hugging Face post title describes a training method; no method, models or results are in the available text.
70 stories, newest first · one card per story
The Hugging Face post title describes a training method; no method, models or results are in the available text.
A curious user can recover original embeddings from a few hundred pairs and forge policy-compatible queries.
The score needs no hyperparameter tuning, scales linearly, and led 148 embedding benchmarks plus ImageNet tests.
Authors report 10-45x GPU speedups and a no-sweep rule for decay rate and batch size.
Belebele recall@1 rises 14.4 to 26.0 points, to 75.2%–77.7%, while English and Russian recall fall.
On AlphaEarth, trained SAEs leave eight times less variance unexplained than a frozen random decoder.
Across eight models, higher token-prediction scores predict higher retrieval ranks even when the relation is wrong.
At matched token budgets it lowered loss in 11 of 12 runs and raised average accuracy in 10 of 12.
Analysis shows between 225 million and 4 billion molecules in the Enamine REAL database have non-unique representations.

Enterprise Advance customers get it early next year; Nvidia expects faster queries and 90% fewer tokens.
The method leaves architecture, pooling, and contrastive loss unchanged and reports gains on retrieval and long-context benchmarks.
Untied gated checkpoints contain near-zero embeddings that amplify gradients 30+ orders of magnitude and trigger FP32 clipping, freezing rows in 20% of Telugu pretraining steps.
Early-exit probes keep over 96% of full-depth accuracy at half the compute without rebuilding the gallery.
Position paper traces convergence of embedding research across modalities toward joint embeddings for compositionality.
On 7,354 randomized trials, a two-stage embedding-plus-language-model filter recovers far more systematic-review evidence, with cost rising by search depth.
64-dimensional embeddings separate similar crops better than NDVI or harmonics, but transfer worse from the US to India.
The open model claims a new Pareto frontier while supporting quantization and selective encoder loading.
On SWE-bench Verified, structured tools added 17–21 points at 1.7–4B but only 4–5 points at 32B.
Adapted from Qwen3-Embedding-0.6B on more than one million pairs, it scores 0.5488 macro-average on eight FaMTEB tasks.
Softmax adapters and Total Variation similarity match or beat unconstrained retrieval on five benchmarks.
AllenAI releases OlmoEarth v1.3 with state-of-the-art embeddings covering the whole planet.
Authors report 97.8% of uncompressed performance at about 100× PLE compression, versus 66.3% for product quantization.
The post body was unavailable, so sizes, benchmarks, licenses, and release status are not stated.
Both models, distilled from an 18B teacher, share an embedding space so the 9B can index and the 0.6B can query.
Today 1.5K readers · 2.4K views · 753 reads · all time 189K views