PROOF-Gen: From Optimized Data to Better Distillation

by AI Generated Robotic ContentFAANGPosted on Comments are Disabled

Supervised fine-tuning on teacher-generated trajectories is the standard first stage for distilling tool-calling capabilities into deployable models. Post-training pipelines that drive shipped tool-calling agents re-run this stage on a daily or weekly cadence, paying the frontier-teacher cost each cycle, yet the mechanism is generate-and-filter (keep the teacher’s passing trajectories, discard the rest) and each cycle …

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Evaluate any agent framework with Amazon Bedrock AgentCore Evaluations

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AI teams building production agents face a frustrating asymmetry: the diversity of agent frameworks keeps growing, but evaluation tooling has not kept pace. Most evaluation systems assume you built your agent in a specific way: a specific SDK, a specific large language model (LLM) client, a specific tracing pattern. The moment you step outside that …

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FinOps for the AI era: New flexible billing and cost controls for agents

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Editor’s note: A product image was updated after initial publication. As AI takes on more complex work, business leaders face a new challenge: enabling rapid innovation using agents while protecting their margins and budgets. To get a real return on AI, financial operations (FinOps) and cost management must evolve alongside technology, giving you clear visibility, …

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Orchestration is the new challenge for CX in the age of AI agents

by AI Generated Robotic ContentAI/ML NewsPosted on Comments are Disabled

Presented by Tata Communications Enterprises are deploying AI agents, voice AI, and automation across messaging, voice, and digital channels faster than the architecture meant to support it. Most of that deployment has involved attaching conversational AI to legacy systems never built for it, says Gaurav Anand, global head of the Customer Interaction Suite at Tata …

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Simulated proving ground trains and assesses AI for actively controlling fluid dynamics

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A platform for training and comparing machine learning models to actively reduce drag, improve lift, cut noise and manage heat has been launched by an international team that includes researchers at the University of Washington, University of Michigan Engineering, RWTH Aachen University and the Technical University of Munich.

STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation

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Unified multimodal models that understand, reason over, and generate interleaved text–image sequences remain structurally fragmented: existing approaches either sacrifice visual fidelity through discrete tokenization, impose structural asymmetry by combining causal text generation with iterative diffusion-based denoising, or degrade pretrained understanding when adapting vision-language models for generation. We observe that autoregressive normalizing flows are autoregressive Transformers—sharing …

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Agentic observability with Amazon OpenSearch Service MCP Apps

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Observability agents are fast. They query alerts, correlate logs with traces, and produce a root cause hypothesis in minutes. The part that still takes time is verification. You read the agent’s text summary, open your observability tools in a browser, navigate to the trace waterfall, check the service map to scope impact, and cross-reference what …

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