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- Amazon releases ALoDLM-1.7B and 8B, adaptively looped diffusion language models built on Qwen3
Amazon published ALoDLM-1.7B and ALoDLM-8B on Hugging Face under CC BY-NC 4.0, diffusion language models initialized from Qwen3 that reuse shared transformer layers and let unresolved tokens keep refining their hidden states while confident tokens commit early, with a learned policy deciding when to stop looping. On the model card's table of eleven reasoning, knowledge, math, science and code benchmarks, ALoDLM-8B averages 80.3 versus 78.5 for Qwen3-8B, and the card plots single-stream throughput on one NVIDIA B200 against autoregressive and diffusion baselines. The weights are for noncommercial research and ship with an ALoDLM-aware nano-vLLM engine.
- AWS shows how to activate Amazon Bedrock private pricing offers across many accounts with License Manager entitlements
AWS has published a guide for organizations whose accounts must each accept AWS Marketplace Private Pricing Offers before using Amazon Bedrock models. It shows two ways to activate the offers' entitlements in AWS License Manager across dozens or hundreds of accounts: a lightweight script for a one-time fix, and CDK automation for ongoing event-driven processing.
- AWS shows BlockRun and Incarna using Bedrock AgentCore payments for pay-per-inference agents
An AWS Machine Learning blog post describes Amazon Bedrock AgentCore payments giving agents a managed way to pay for services on demand with infrastructure-enforced spend limits. It shows Incarna agents paying BlockRun for model inference one request at a time over x402, cutting the work of adding x402 support from months to days.
- AWS shows how to evaluate multi-agent systems with AgentCore Evaluations
An AWS blog post builds a Strands-based multi-agent supply chain decision system and evaluates tool selection, constraint handling and explainability with Amazon Bedrock AgentCore Evaluations.