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Cerebras summarizes five orchestration patterns for multi-agent workflows

Cerebras published a blog post summarizing five patterns for building multi-agent workflows: parallel generation followed by human selection, swarm parallelism, staged parallelism, sequential handoff, and separation of building from verification. Using Codex with Figma MCP to replicate a website as an example, the article states that a single agent averaged 36.5 minutes per run, 12 human interventions, and a 100% failure rate, while the multi-agent setup took 5.2 minutes, required 2 interventions, and succeeded on the first attempt; in internal testing, the sequential loop reduced human interventions by 84.3%. The author argues that multi-agent setups expand the effective context window from about 200K to over 25M, and notes that Codex Spark's speed of about 1,200 tokens/second makes parallel and verification steps feasible.

Stanford's HomeBody connects GPT-6 Astra to the Unitree G1 to tidy up the kitchen

The Stanford team launched the HomeBody project, connecting GPT-6 Astra to the Unitree G1 to enter a kitchen and execute language instructions; it can gather coffee bags, discard designated cardboard boxes, and, when medication is not in view, find the drawer from memory and retrieve the medicine to hand to a person. The system first has Astra guide the G1 to explore an unfamiliar kitchen and save camera images, LiDAR, and joint poses as spatial memory, then reconstructs a digital kitchen in Isaac Sim, after which Astra calls reusable skills such as navigation, grasping, placing, and opening drawers, with the skill modules handling specific motion planning and feeding back results. The team says deploying to a new kitchen requires no additional training data collection or training of specialized motion policies, but perception, planning, and skill execution run on a single RTX 4090 laptop, Astra makes decisions remotely, the initial reconstruction involves preparation time and API costs, and overheating of the finger servos is also one of the current limitations.

Fireworks Evaluates DeepSeek V4 Pro Against Fable 5 and Kimi K3

Fireworks tested DeepSeek V4 Pro 0813 on four benchmarks: SWE-Bench Verified, LiveCodeBench v6, Aider Polyglot, and Terminal-Bench 2.1. It scored 95.2% on SWE-Bench Verified, higher than Kimi K3's 92.6% and Fable 5's 85.4%, and 92.0% on LiveCodeBench v6. On cost, DeepSeek V4 Pro spent $0.309 per task solved on SWE-bench, versus $0.808 for Fable 5; on LiveCodeBench the figures were $0.040 and $0.225, respectively. Its weakness is the multilingual breadth of Aider Polyglot, where it scored only 48.9% on Java, versus 74.5% for Fable 5. After oracle routing on a combined set of 409 tasks, accuracy was 92.4%, higher than the 86.6% achieved by Fable 5 alone, at a cost of $0.279 versus $4.510 per task, with 86.3% of tasks routed to V4 Pro.

Kimi K3 Complete Developer Guide: 2.8 Trillion Parameters, 1M Context, and Together AI Usage

Together AI has released the Kimi K3 developer guide, introducing Moonshot AI's 2.8-trillion-parameter open-weight model, which uses KDA hybrid linear attention, Attention Residuals, and Stable LatentMoE (896 experts with 16 activated), and supports a 1M token context and native visual input. The guide provides usage details for the OpenAI-compatible API, including reasoning_effort (low/high/max, default max), streaming reasoning_content, structured outputs, tool calling, and dynamic tool loading, and notes that sampling parameters are fixed at temperature 1.0 and top_p 0.95. Pricing is $0.30 per 1M tokens for cached input, $3.00 per 1M tokens for uncached input, and $15.00 per 1M tokens for output, with thinking tokens billed as output.