Question Answering (QA) poses a challenging and critical problem, particularly in today's age of interactive dialogue systems such as ChatGPT, Perplexity, Microsoft Copilot, etc. where users demand both accuracy and transparency in the model's outputs. Since smaller language models (SLMs) are computationally more efficient but often under-perform compared to larger models, Knowledge Distillation (KD) methods allow for finetuning these smaller models to improve their final performance. Lately, the intermediate tokens or the so called `reasoning' traces produced by Chain-of-Thought (CoT) or by reasoning models such as DeepSeek R1 are used as a training signal for KD. However, these reasoning traces are often verbose and difficult to interpret or evaluate. In this work, we aim to address the challenge of evaluating the faithfulness of these reasoning traces and their correlation with the final performance. To this end, we employ a KD method leveraging rule-based problem decomposition. This approach allows us to break down complex queries into structured sub-problems, generating interpretable traces whose correctness can be readily evaluated, even at inference time. Specifically, we demonstrate this approach on Open Book QA, decomposing the problem into a Classification step and an Information Retrieval step, thereby simplifying trace evaluation. Our SFT experiments with correct and incorrect traces on the CoTemp QA, Microsoft Machine Reading Comprehension QA, and Facebook bAbI QA datasets reveal the striking finding that correct traces do not necessarily imply that the model outputs the correct final solution. Similarly, we find a low correlation between correct final solutions and intermediate trace correctness. These results challenge the implicit assumption behind utilizing reasoning traces for improving SLMs' final performance via KD.

Explosions in Armenia, broadcast on YouTube in 2020, revealed this new shape of war to the world. There, in a blue-tinted video, a radar dish spins underneath cyan crosshairs until it erupts into a cloud of smoke. The action repeats twice: a crosshair targets a vehicle mounted with a spinning dish sensor, its earthen barriers no defense against aerial attack, leaving an empty crater behind. The clip, released on YouTube on September 27, 2020, was one of many the Azerbaijan military published during the Second Nagorno-Karabakh War, which it launched against neighboring Armenia that same day. The video was recorded by the TB2.

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National e-Governance Division (NeGD), Ministry of Electronics and Information Technology (MeitY), Government of India (GOI), Intel, and the United Nations Development Programme (UNDP) recently held its seventh session, on'Demystifying Deep Learning', under the Digital India Dialogues Building Capacities. This session was attended by 95 government officials, from 10 ministries. The participation of the Intel experts, under the company's Digital Readiness portfolio, brings relevant industry experience and use case studies to these webinars aimed at policy makers. Intel's Digital Readiness for Leaders program aims to leverage AI-led expertise by engaging government officials and leaders in immersive workshops led by subject matter experts to help them grasp the practical application of emerging technologies. The session provided an insight into the workings of Deep Learning (DL) and how its application, and integration can play a key role in creating a framework to help improve services offered to the citizens.

My wife and I were recently driving in Virginia, amazed yet again that the GPS technology on our phones could guide us through a thicket of highways, around road accidents and toward our precise destination. The artificial intelligence (AI) behind the soothing voice telling us where to turn has replaced passenger-seat navigators, maps, even traffic updates on the radio. How on earth did we survive before this technology arrived in our lives? We survived, of course, but were quite literally lost some of the time. My reverie was interrupted by a toll booth. It was empty, as were all the other booths at this particular toll plaza.

My wife and I were recently driving in Virginia, amazed yet again that the GPS technology on our phones could guide us through a thicket of highways, around road accidents, and toward our precise destination. The artificial intelligence (AI) behind the soothing voice telling us where to turn has replaced passenger-seat navigators, maps, even traffic updates on the radio. How on earth did we survive before this technology arrived in our lives? We survived, of course, but were quite literally lost some of the time. My reverie was interrupted by a toll booth. It was empty, as were all the other booths at this particular toll plaza.

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My wife and I were recently driving in Virginia, amazed yet again that the GPS technology on our phones could guide us through a thicket of highways, around road accidents, and toward our precise destination. The artificial intelligence (AI) behind the soothing voice telling us where to turn has replaced passenger-seat navigators, maps, even traffic updates on the radio. How on earth did we survive before this technology arrived in our lives? We survived, of course, but were quite literally lost some of the time. My reverie was interrupted by a toll booth. It was empty, as were all the other booths at this particular toll plaza.

At the beginning of June, the Armenian Prime Minister, Tigran Avinyan, spoke about the artificial intelligence strategy that Armenia wishes to put in place. Several topics were discussed: fundamental research, applied research, infrastructure, public sector, private sector, training and financing. On the Yerevan side, we now wish to give priority to AI in order to join, in the long term, the countries already well advanced in the sector. In the state of play mentioned by the Prime Minister of Armenia, he wants to focus his strategy on basic research, which he believes would be the point requiring the least investment: a stable internet connection and supercomputers may be enough to conduct research or training on artificial neural networks. The government also wants to build on its strengths in mathematics and experimental and natural sciences.

The Azerbaijan defense ministry has released videos it claims to show drone attacks on the Armenian military in the Nagorno-Karabakh region earlier this month. The videos of the drone strikes have been posted on the Azerbaijan's defense ministry website and social media every day. Since September, Azerbaijan has deployed several different types of missile-firing drones in the conflict with Armenia. Missile-firing drones are now produced in many countries and have been used in battles including a U.S. drone strike that killed Iran's top general Qassem Soleimani at Baghdad airport last January. Following the September 11 terrorist attacks, unmanned combat weapons of various types have been increasingly used by the U.S. military in its war on terror.