#Data & training

Factory introduces Agent Effectiveness for integrated usage and delivery outcomes

Factory's Agent Effectiveness feature is now live, linking data with delivery cycles, work intentions, and delivered outcomes to help engineering leaders understand delivery acceleration points, robot work content, and how usage maps to actual output. This feature provides an operational view of AI investment return by connecting Droid usage with delivery results, addressing questions about delivery speed changes, profit concentration points, and whether new capabilities are used for priority work.

ABBEL framework: Achieving efficient long-range interaction through belief updates

The ABBEL framework replaces the complete interaction history with natural language belief states as the working context for an agent and utilizes a belief scoring mechanism to supervise the content of each belief state, achieving model performance close to that of a complete context model in collaborative code generation tasks while reducing the peak context token length by about 75% and training steps by 50%.

Prime Intellect Integrates 365,000+ Software Engineering, Terminal, and Search Environments for Agent Reinforcement Learning

Prime Intellect integrates 23 agent task sets for software engineering, terminal, and search, providing a unified API, runtime/sandbox hooks, including approximately 198,000 software engineering tasks, 28,600 terminal tasks, and 137,600 search tasks. It decomposes the environment into three layers of task sets, sandbox, and runtime through verifiers v1, standardizes the scoring logic for different task sets, and provides 135,000 pre-built open-source task images.

GPT-4-class capabilities cost less than $1 per month, how data systems serve agents, and whether they belong to agents or are built by agents.

GPT-4-class capabilities cost has dropped to less than $1 per month, with reasoning prices decreasing by over 50 times annually. Agents will dominate data system workloads, creating three new challenges: designing data systems for agents that support agentic speculation and optimize repetitive queries; building agent-specific data systems capable of managing agent cluster states and coordinating consensus; enabling agents to synthesize trustworthy custom data systems.

Meta AI releases Brain2Qwerty v2, a non-invasive brain-computer interface that decodes sentences in real-time

Meta AI releases Brain2Qwerty v2, a non-invasive brain-computer interface that decodes sentences in real-time with a word accuracy rate of 61%; v1 training code and dataset are open-sourced, with the v1 dataset released by collaborator BCBL.

prime-rl v0.6.0 released: efficient training of multi-expert models at 1T parameter scale

prime-rl v0.6.0 released, supporting efficient training of multi-expert models at 1T parameter scale, GLM-5 can support 131k sequence length, 256 rolling batch size, and single-step training within 5 minutes, requiring only 28 H200 nodes to complete.

Microsoft releases seven new MAI large models and introduces cutting-edge fine-tuning technology

Microsoft releases seven new MAI large models, available on the Foundry platform and optimized for its own products, while opening up to developers through OpenRouter, Fireworks, and Baseten, for the first time supporting developers in fine-tuning model weights themselves

Prime Intellect releases FrontierSWE on Environments Hub

Prime Intellect takes FrontierSWE to the Environments Hub, an ultra-long temporal open-source coding benchmark developed by Proximal, which includes challenges such as optimizing compilers or training SOTA protein prediction models, with an average task duration of 11 hours and almost unsolvable.

SWE 1.6 Released: Enhancing the software engineering agent model experience.

SWE 1.6 is the latest model built specifically for software engineering agents, providing general support on Windsurf with a speed of 950 tok/s and free for the next 3 months. Compared to SWE-1.5, SWE 1.6 improves by over 10% on SWE-Bench Pro by jointly optimizing user experience and reasoning fluency, significantly reducing behaviors like overthinking and redundant tool calls, making model interactions more efficient.

Collaborate with Browserbase to train browser and computer using agents

We collaborate with Browserbase to provide the best training experience for browser and computer using agents. Browserbase provides scalable browser infrastructure, supporting Microsoft's Fara-7B and Google's Gemini 2.5 CUA.

How do SPEX and ProxySPEX algorithms scale to identify LLM interactions

The SPEX algorithm identifies key interactions of LLMs through signal processing and coding theory, analyzing the impact of features, data, and model components with only a small amount of disturbance, achieving high fidelity in sentiment analysis tasks even with thousands of features; The ProxySPEX algorithm leverages model hierarchical structure characteristics, reducing the number of disturbances by 10 times while maintaining performance.

Anthropic has released a new metric observed exposure to evaluate AI replacement risk.

Anthropic introduces a new metric, observed exposure, to quantify AI replacement risk, combining LLM theoretical capabilities with actual usage data, focusing on automated work scenarios; the study finds that current AI coverage is only a fraction of the theoretical value, high-risk occupations are mostly highly educated, high-income women, and unemployment rates have not significantly increased since the end of 2022, but hiring for exposed occupations among young workers has slowed.

How scientists use Claude to accelerate research and discovery

Scientists have achieved significant progress in biological illustration, computational biology, and protein understanding benchmarks using Claude 4.5, a model now available to top researchers worldwide via API for free, and has built custom systems that go beyond literature reviews or coding assistance. In the lab, Claude serves as a collaborative partner throughout the research process, compressing projects that usually take months into hours using various tools, and discovering large-scale data patterns that humans might overlook, eliminating bottlenecks in knowledge-intensive tasks, and even enabling entirely new research approaches that traditional methods cannot adopt.

INTELLECT-3: A 100B+ parameter expert mixed model trained using large-scale reinforcement learning

INTELLECT-3 is a 106B parameter expert mixed model, trained on the GLM 4.5 Air base model using SFT and RL, achieving optimal performance at its scale on math, code, science, and reasoning benchmarks, surpassing many larger front-line models.

Prime Intellect releases SYNTHETIC-2: a dataset of four million collaboratively generated reasoning trajectories.

Prime Intellect releases the SYNTHETIC-2 dataset, containing four million verified reasoning trajectories, covering the most comprehensive complex reinforcement learning tasks and validators to date. The dataset is generated through a pipeline parallel distributed reasoning collaboration involving global computing contributors, with 1,253 GPUs (including 49 8xH200 nodes, 43 8xH100 nodes, and a large number of consumer-grade 3090 and 4090 GPUs) participating in its creation over 3 days.

DeepSeek releases SYNTHETIC-2 open-source reasoning dataset and planetary-scale pipeline parallel reasoning system.

DeepSeek releases SYNTHETIC-2, which includes 20 complex reinforcement learning tasks and verification reasoning trajectories, supporting heterogeneous computing from consumer-grade GPUs to super-large-scale clusters, using the DeepSeek-R1-0528 model to generate data and label difficulty.

PCCL - Prime Collective Communications Library Released: A fault-tolerant communication library designed for global distributed training.

PCCL released, designed for global distributed training, with fault tolerance, supports operation on the public internet, bandwidth up to 45 Gbit/s between each pair of data centers, training bandwidth up to 25 Gbit/s between North America and Western Europe, supports dynamic member joining and rejoining, verified its high fault tolerance through long-term stress tests on major sockets (Linux, macOS, Windows WSA).

INTELLECT-2 Released: The first 32B parameter model trained through global distributed reinforcement learning

INTELLECT-2 is the first 32B parameter model trained using global distributed reinforcement learning, employing fully asynchronous RL across dynamic heterogeneous computing contributor groups to train reasoning language models. It has open-sourced the training framework PRIME-RL and components such as TOPLOC and SHARDCAST, while improving the GRPO training recipe and data filtering techniques, enhancing QwQ-32B performance on mathematical and coding tasks.

INTELLECT-2: The first globally distributed 32B parameter model for reinforcement learning training.

INTELLECT-2 Released, First to Achieve Global Distributed 32B Parameter Model Reinforcement Learning Training, Anyone Can Contribute Heterogeneous Computing Power Without Permission, Eliminates Communication Bottlenecks Through Asynchronous Reinforcement Learning Paradigm, Supports Heterogeneous Inference Nodes, Inference Nodes Only Need 4×RTX 3090 GPUs to Participate in Training.

Enhance Dify RAG with InfraNodus: Expand Your LLM Context

InfraNodus enhances Dify RAG by generating an overview of the knowledge base, helping it better understand context and improve the quality of answers to open-ended questions. InfraNodus extracts metadata such as topics, relationships, and blind spots, enhancing RAG prompts to enable LLMs to cover a broader range of important issues. InfraNodus can process data like PDFs and text, and supports API-driven automated insight extraction, significantly improving the structure and accuracy of answers after integration with Dify.

DeepSeek releases SYNTHETIC-1: a large-scale distributed synthetic reasoning dataset

MetaGene-1: A 7B parameter metagenomic base model for epidemic monitoring.

MetaGene-1 is a 7B parameter metagenomic base model pre-trained on 1.5T DNA/RNA sequencing data for epidemic monitoring and pathogen detection. It achieves SOTA on pathogen detection and metagenomic embedding benchmarks, with a 512-token context length suitable for short sequence anomaly detection, but limited for complex task applications. The model was developed in collaboration by USC, Prime Intellect, and the Nucleic Acid Observatory, and is open-sourced.

INTELLECT-1 Released: The First Global Jointly Trained 10B Parameter Model

INTELLECT-1 is released, the first 10B parameter language model trained through global collaboration, based on the Llama-3 architecture, completing 1T token training on 112 H100 GPUs across 5 countries and 3 continents, with the PRIME framework achieving a 400x reduction in communication bandwidth, 96% computational utilization, supporting sequence lengths of 8192 and a vocabulary of 128256, with the model code and data open-sourced.

How Dify.AI empowers this global consumer electronics leader to integrate generative AI

The global consumer electronics leader serving over 100 million users has integrated generative AI into its operations through Dify.AI, bridging the gap between business and technology. Dify.AI's low-code platform allows novice users to quickly utilize mainstream models, expert users to independently customize solutions, and developers to enhance efficiency through component-based workflows. More than 200 AI applications have been created within a month, with 60 in use, reducing user voice analysis time from 8 hours to 3 hours.

INTELLECT–1 globally distributed training the first 10B parameter model

INTELLECT-1 launches the world's first 10B parameter model distributed training, inviting the public to contribute computing power to advance the open-source AGI process. The training is based on the OpenDiLoCo open-source implementation, extending the 1B parameter scale of DeepMind DiLoCo's method by 10 times, significantly reducing communication frequency (up to 500 times), and lowering bandwidth requirements by 2000 times through quantized pseudo-gradients (int8) and 500-step outer optimizer synchronization.

OpenDiLoCo: An open-source global distributed low-communication training framework

OpenDiLoCo successfully replicated and extended DeepMind's DiLoCo experiment to a 10B parameter model scale, achieving cross-continent, three-country distributed training through Hivemind and PyTorch FSDP, maintaining 90-95% computational resource utilization. The framework is based on the open-source implementation of Hivemind, supporting dynamic resource joining/leaving and fault-tolerant training, significantly reducing communication requirements by 500x while maintaining performance on 1.5B parameter models.