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š¤ Latest Submissions
Adaptive General-Purpose AI Agent
`Adaptive General-Purpose AI Agent is a Track 1 submission designed to solve hidden natural-language tasks efficiently across eight categories: factual question answering, mathematical reasoning, sentiment classification, summarization, named entity recognition, code debugging, logical reasoning, and code generation. The system is built as a containerized batch-processing agent that reads tasks from /input/tasks.json, classifies each prompt, selects an appropriate solving strategy, writes answers to /output/results.json, and exits automatically in the required competition format. The core idea is to avoid using a large external model for every task. Instead, the agent applies a hybrid routing architecture. Simple structured tasks such as arithmetic word problems, exact one-sentence summaries, basic sentiment classification, and selected logic puzzles are solved locally using deterministic logic. More open-ended or complex tasks such as factual knowledge questions, named entity extraction, code debugging, and code generation are escalated to an allowed Fireworks model. This approach improves token efficiency while preserving broad task coverage. The project also includes metrics tracking for route selection, Fireworks usage, latency, and token consumption. In a validated benchmark sample, the hybrid strategy reduced Fireworks calls from 6 to 4 and reduced total Fireworks token usage from 2011 to 1459, achieving a 27.4% token reduction. This demonstrates that selective escalation can meaningfully reduce external model usage without breaking the submission workflow. The system is modular, competition-aligned, and designed for further improvement through stronger local solvers, better routing confidence, and more efficient output control.`
13 Jul 2026
