services could presage an era of peace and plenty. The capacity to accelerate scientific research could result in cures for disease and solutions for climate change and resource shortages. As Demis Hassabis AI gets used more, total infrastructure and compute demand rises – dragging costs up again. The result is a flywheel of growth that puts pressure on cloud providers, chipmakers, and enterprise IT budgets different domains. Some are optimized for reasoning, others for speed or code generation. The result is a move away from vendor lock-in. Instead of consolidating under a single provider who can gate

token will lie between ?? and ??? and matrix multiplication does not obey a commutative law. As a result, we must recompute the keys for all the prefix tokens during inference, which will significantly utilization. (2) Secondly, we leverage vLLM (Kwon et al., 2023) with large batch sizes as our inference backend to accelerate the inference speed. (3) Thirdly, we carefully design a scheduling strategy for offloading remain a liquid D. become a solid Answer: Table 13 | An example of ARC. 35 PROMPT Evaluate the result of a random Boolean expression. Q: not ( ( not not True ) ) is A: Let’s think step by step. Remember

Instruction Description Move Moves data from one register to another. Ret Returns the object in register result to caller’s register. Invoke Invokes a function at in index. InvokeClosure Invokes a Relay closure dispatch_global_func(mod, "main", {input_name: input_shape}, tvm.relay.vm.exp_dispatcher) vmc = relay.backend.vm.VMCompiler() with tvm.autotvm.apply_graph_best("resnet50_v1_graph_opt.log"): vm = vmc.compile(mod

NN/ACL/CMSIS-NN and TVM ○ Integrate optimized ACL/CMSIS-NN kernels into TVM? ○ Implement Arm NN generic backend in TVM for more flexibility with the runtime plugins? ○ Integrate TVM codegen into Arm NN? ● CI

graphs) Direct access from other languages QQ octoML HTVM Overview *。 Plug directly into TVYM as a backend *，Target C to emit code for microcontrollers that is device- agnostic AuroTYM QQ octoML AutoTVM

accept subgraphs and build binary/library/engine for runtime dispatching ● Codegen path: src/relay/backend/contrib//codegen.cc ● Flow overview data weight1 weight3 weight2 output Build()

updates regularly, getting feedback, and improving performance and safety at every step. The result: users access new advancements in AI early and often—and your feedback shapes future products and source code. Security, guardrails, and routing logic are all built in. 18 AI in the EnterpriseAs a result, AI app development has accelerated dramatically, helping Mercado Libre employees do amazing things systems of record on behalf of users, without technical instructions 
 or API connections. The result: end-to-end automation, freeing teams from repetitive tasks and boosting efficiency across the enterprise

put: result = Runner.run(churn_detection_agent, , context=ctx.context) GuardrailFunctionOutput( output_info=result.final_output, tripwire_triggered=result.final_output

be diverse, of high quality, and well written. One small mistake can confuse the model and will result in undesired output. If you are trying to generate output that is robust to a variety of inputs, run(prompt) Snippet 1. Creating a ReAct Agent with LangChain and VertexAI Code Snippet 2 shows the result. Notice that ReAct makes a chain of five searches. In fact, the LLM is scraping Google search results table, it’s also helpful to track the version of the prompt (iteration), a field to capture if the result was OK/NOT OK/SOMETIMES OK, and a field to capture feedback. If you’re lucky enough to be using

操作路径多元、开放， 且对结果没有明确要求 DeepSeek 两种模型对比（5R） 维度 V3模型 R1模型 Regulation （规范性） 强规范约束 （操作路径明确） 弱规范约束 （操作路径开放） Result （结果导向） 目标确定性高 （结果可预期） 目标开放性高 （结果多样性） Route （路径灵活性） 线性路径 （流程标准化） 网状路径 （多路径探索） Responsiveness （响应模式）