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.
Already linked to a challenge workflow.
Sign in to keep private prompt variations.
Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Set up a simple simulated IoT environment that generates sensor data and injects a specified anomaly. Run your neuro-symbolic agent within this simulation. Capture the agent's detection, explanation, and the generated sequence of control actions. Verify that the actions are appropriate and comply with safety rules.
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 rubric, target behavior, and pass-fail criteria as the baseline for evaluation.
Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.
Make sure the prompt catches regressions instead of just mirroring the happy-path examples.
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.
Build a Neuro-Symbolic Agent for IoT Anomaly Response
Addressing the challenge of reasoning under perceptual uncertainty in real-world applications like IoT device operation, this challenge focuses on developing a neuro-symbolic agent. The agent will integrate the natural language understanding and high-level planning capabilities of an agent with a symbolic reasoning system. Its primary function will be to detect anomalies in simulated IoT sensor data streams and generate adaptive, context-aware responses to maintain system stability, akin to optimizing power distribution system restoration. Participants will need to design how continuous, often noisy, sensor data is translated into discrete symbolic facts, which then inform a symbolic planner. this will be crucial for enforcing schema-driven validation and ensuring the logical consistency and safety of the LLM's generated plans and actions. The goal is a robust system that can handle complex scenarios where a purely neural or symbolic approach might fall short, bridging the gap between continuous perception and discrete symbolic planning to enable intelligent, adaptive control.
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.