Multi-agent debate often wastes compute by using a fixed adversarial stance, aggregating without deliberation, or stopping on heuristics. We introduce MACI, an active controller with two independent dials that decouple information from behavior: an information dial that gates evidence by quality, and a behavior dial that schedules contentiousness from exploration to consolidation. A moderator tracks disagreement, overlap, evidence quality, and argument quality, and halts when gains plateau. We provide theory-lite guarantees for nonincreasing dispersion and provable termination, with a budget-feasible scheduler. Across clinical diagnosis and news-bias tasks, MACI improves accuracy and calibration while reducing tokens, and converts residual uncertainty into precision RAG plans that specify what to retrieve next. We use a cross-family LLM judge (CRIT) as a conservative soft weight and stop signal, validated for order invariance and judge-swap stability; stability depends on using high-capability judges. MACI turns debate into a budget-aware, measurable, and provably terminating controller.

Misdiagnosis is a significant issue in healthcare, leading to harmful consequences for patients. The propagation of mislabeled data through machine learning models into clinical practice is unacceptable. This paper proposes EVINCE, a system designed to 1) improve diagnosis accuracy and 2) rectify misdiagnoses and minimize training data errors. EVINCE stands for Entropy Variation through Information Duality with Equal Competence, leveraging this novel theory to optimize the diagnostic process using multiple Large Language Models (LLMs) in a structured debate framework. Our empirical study verifies EVINCE to be effective in achieving its design goals.

Dengue fever, malaria, Zika, West Nile virus and other mosquito-borne diseases may have finally met their match in crowded cities across the tropics. An unmanned, subterranean, robotic probe dispatched into the sewers of Kaohsiung City, Taiwan has proven lethally effective at locating the hidden pools of stagnant water where mosquitos breed. The sewer robot searches, so Taiwan's exterminators can destroy it. Researchers with Taiwan's National Mosquito-Borne Diseases Control Research Center found that their robotic hunter helped dramatically curb the city's mosquito population -- dropping the number of blood-sucking bugs by nearly 70 percent. Researchers with Taiwan's National Mosquito-Borne Diseases Control Research Center found that their robotic hunter helped dramatically curb the city's mosquito population, dropping the number of blood-sucking bugs by nearly 70 percent, based on their'gravitrap index' Researchers designed an unmanned ground vehicle (top) to scour cracks and crevices deep in the sewers of Kaohsiung.

The overhead buzzing of unmanned aerial vehicles (UAVs) – aka drones – is an increasingly familiar sound in many parts of the world. Whether these helicopter-like devices are flown for fun, military purposes or commercial reasons, the global drone market is predicted to increase annually by nearly 14% between 2020 and 2025. Drones can give operators a birds-eye view of events – including natural disasters – as they unfold. And they can open up difficult-to-access places for emergency supplies to be delivered. This makes them well-suited to help in the response to humanitarian and environmental challenges.

In many parts of the world, mosquitoes are more than just a campsite nuisance -- they carry that cause an estimated 725,000 deaths per year. On Singapore, the effect isn't so terrible -- some mosquitoes carry dengue fever, but it affects less than a dozen people per year. But because it's a city and an island, Singapore is the perfect testing ground to see how easy it might be to get rid of the disease-carrying bugs, all sans gene editing. That's what Alphabet-owned healthcare company Verily hopes to do. The company, along with Singapore's environmental agency, plans to release male mosquitoes that carry Wolbachia, a naturally-occurring bacteria that reduces the bugs' ability to transmit disease and prevents their eggs from hatching.

Identification of the influential clinical symptoms and laboratory features that help in the diagnosis of dengue fever in early phase of the illness would aid in designing effective public health management and virological surveillance strategies. Keeping this as our main objective we develop in this paper, a new computational intelligence based methodology that predicts the diagnosis in real time, minimizing the number of false positives and false negatives. Our methodology consists of three major components (i) a novel missing value imputation procedure that can be applied on any data set consisting of categorical (nominal) and/or numeric (real or integer) (ii) a wrapper based features selection method with genetic search for extracting a subset of most influential symptoms that can diagnose the illness and (iii) an alternating decision tree method that employs boosting for generating highly accurate decision rules. The predictive models developed using our methodology are found to be more accurate than the state-of-the-art methodologies used in the diagnosis of the dengue fever.