Salesforce's SFR-AutoR&D agents built SFR-Embedding v4 in about 80 hours
Salesforce introduced SFR-AutoR&D on November 11, 2026. The company says its agents produced SFR-Embedding v4 in about 80 hours, compared with the several weeks that human experimentation often requires.
Salesforce introduced SFR-AutoR&D on November 11, 2026, and says its agents produced SFR-Embedding v4 in about 80 hours, rather than the several weeks that human experimentation often requires, according to a report from Unite.AI.
What SFR-AutoR&D does
According to the report, SFR-AutoR&D is designed to run the research cycle autonomously. It works in two ways: by changing existing codebases, and by developing and testing new training methods for AI models.
Beyond a demonstration
The report describes the work as extending beyond a standalone research demonstration. SFR-AutoR&D had already improved SFR-Embedding V3, which Salesforce uses internally.
Key facts
- Announced: November 11, 2026
- Developer: Salesforce
- Output cited: SFR-Embedding v4
- Time claimed: about 80 hours, versus several weeks for typical human experimentation (Salesforce's claim)
- Prior result: improvements to SFR-Embedding V3
Benchmark scores, pricing, context window and parameter counts for SFR-Embedding v4 are not yet disclosed.
What this means
Salesforce's stated result is a reduction in research cycle time for an embedding model, from several weeks to about 80 hours. The 80-hour figure is the company's own claim. The system's earlier improvements to SFR-Embedding V3 suggest it is already part of Salesforce's model development work rather than only a research showcase.
Related Articles
Microsoft researchers backdoor LLM agents by reprogramming fewer than 10 dormant neurons, with no training
Microsoft researchers describe Lazarus, a method that reprograms fewer than 10 dormant neurons in an LLM to backdoor an agent built on it, with no training required. The modified model passes standard evals until a trigger appears. Microsoft says provenance and weight integrity are the key defenses.
Agentforce Memory policy update recalls 1.8x more stated facts and 2.2x more changed facts
Salesforce AI Research says an updated memory policy for Agentforce Memory recalls 1.8 times as many facts customers state outright and 2.2 times as many facts that change over time. The company says the underlying architecture is unchanged, and that the gains were measured on the live Agentforce system.
Meta FAIR's e-means reaches Lloyd's 25-epoch loss in 1.4 to 2.6 epochs, shipped in FAISS 1.16.0
Meta FAIR's e-means is a mini-batch k-means replacement shipped in FAISS 1.16.0. According to MarkTechPost, it reaches Lloyd's 25-epoch loss in 1.4 to 2.6 epochs, with 10-45x GPU speedups and no extra hyperparameters.
Amazon releases Mitra-v2, an open tabular foundation model that leads the single-model field on TabArena
Amazon has released Mitra-v2, a tabular foundation model for classification and regression that, according to Amazon, leads the single-model field on the TabArena benchmark of 51 datasets. Weights and code are available under Apache-2.0 on Hugging Face.
Comments
Loading...