Large language models (LLMs) have demonstrated strong reasoning capabilities. Nevertheless, they still suffer from factual errors when tackling knowledge-intensive tasks. Retrieval-augmented reasoning represents a promising approach. However, significant challenges still persist, including inaccurate and insufficient retrieval for complex questions, as well as difficulty in integrating multi-source knowledge. To address this, we propose Beam Aggregation Reasoning, BeamAggR, a reasoning framework for knowledge-intensive multi-hop QA. BeamAggR explores and prioritizes promising answers at each hop of question. Concretely, we parse the complex questions into trees, which include atom and composite questions, followed by bottom-up reasoning. For atomic questions, the LLM conducts reasoning on multi-source knowledge to get answer candidates. For composite questions, the LLM combines beam candidates, explores multiple reasoning paths through probabilistic aggregation, and prioritizes the most promising trajectory. Extensive experiments on four open-domain multi-hop reasoning datasets show that our method significantly outperforms SOTA methods by 8.5%. Furthermore, our analysis reveals that BeamAggR elicits better knowledge collaboration and answer aggregation.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Under heavy enemy fire, a dismounted squad of soldiers encounters incoming sniper fire from a building and rapid movements behind certain windows, when a nearby drone is instantly and automatically tasked with quickly surveilling the area before aggregating data and propagating live-saving, combat-sensitive information directly back to commanders and soldiers. How could it be possible, in a nearly instantaneous fashion, to circumvent or avoid otherwise time-consuming communications channels and procedural impediments to the immediate tasking of an attack drone? One answer, now being explored in cutting edge research by scientists with the Army Research Laboratory, is to measure, process, analyze and transmit electrochemical signals from the human brain.

File photo - U.S. soldiers from the 3rd Cavalry Regiment watch as CH-47 Chinook helicopter from the 82nd Combat Aviation Brigade lands after an advising mission at the Afghan National Army headquarters for the 203rd Corps in the Paktia province of Afghanistan December 21, 2014. Warrior Maven: What is the primary purpose of the Army's AI Task Force? Matty: The Army AI Task Force was established with a Secretary of the Army directive in October of 2018. There are four thrusts or top initiatives from the Secretary's directive. One component is we are leveraging AI to help our talent management in human resources.

The drones fell out of the sky over China Lake, California, like a colony of bats fleeing a cave in the night. Over 100 of them dropped from the bellies of three Boeing F/A-18 Super Hornet fighter jets, their sharp angles cutting across the clear blue sky. As they encircled their target, the mechanical whir of their flight sounded like screaming. This was the world's largest micro-drone swarm test. Conducted in October 2016 by the Department of Defense's Strategic Capabilities Office and the Navy's Air Systems Command, the test was the latest step in what could be termed a swarm-drone arms race.