Agent Capabilities and Harness Engineering — Why Agents Are Systems, Not Just Models Highlights: In Graham Neubig’s segment of CMU 11-768: AI Agents (taught with Daniel Fried), the argument is that assembling an agent is easy while making one work reliably is not. We walk through the capabilities that separate the two — tool calling, long-context coherence, customizability, task management, environment understanding, and safety — and the two levers available for each: harness engineering and…
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What Are AI Agents and How Do They Work? — From Tool Calls to the ReAct Loop Highlights: The opening lecture of CMU’s 11-768: AI Agents (Fall 2026), taught by Daniel Fried and Graham Neubig, sets out what an agent actually is and where current systems break down. We walk through the anatomy of an agent — environment, observations, actions, reward — and follow the path from plain next-token prediction to tool calls, chat templates,…
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Fame and Uniqueness: Measuring What Makes a Cover Memorable Highlights: Romaniuk’s Distinctive Brand Asset framework (2018) splits brand memorability into two axes — Fame (exposure history) and Uniqueness (visual distinctiveness) — but only one of them is recoverable from a picture. We build a full measurement pipeline on top of Gemini 2.5 Pro: genuine pairwise visual-distinctiveness judgments aggregated with the Bradley–Terry model (not a single holistic LLM call), a 13-family literature-grounded absolute-scoring taxonomy, and real…
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