Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Design a Langroid multi-agent architecture for linguistic forensics. Define at least three agent roles (e.g., Semantic Analyzer, Style Critic, Synthesizer), their expertise, and how they would communicate via A2A protocol. How would Gemini 2.5 Pro Deep Think be specifically invoked for complex cases, and how would OpenAI o3 handle simpler ones? Detail the adaptive thinking budget mechanism.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Deconstructing AI Prose Quirks
The subtle 'quirks' in AI-generated prose, often a result of 'overfitting,' are increasingly being mimicked by human writers, blurring the lines of textual authenticity. This challenge tasks you with building a sophisticated multi-agent system using Gemini 3 Pro and Langroid to perform linguistic forensics, accurately identifying AI-generated content, human-mimicking-AI content, and purely human writing. Your system will employ A2A protocol for collaborative analysis, leveraging Gemini 3 Pro's Deep Think mode for profound semantic and stylistic insights. Agents will adapt their thinking budgets based on the complexity of the text, providing detailed reports on characteristic AI linguistic patterns and suggesting 'de-AI-ification' strategies to restore human-like prose, effectively becoming a 'prose purity' guardian.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.