Russian forces have been using drones to hunt and attack civilians in Ukraine and continue to do so, according to Human Rights Watch (HRW). In a report released on Tuesday, HRW stated that the Russian military has repeatedly deployed unmanned drones to attack civilian targets in its more than three-year war with Ukraine. The NGO said that dozens of civilians have been killed and hundreds injured in violation of the laws of war. Referencing video from Russian drones and witnesses and survivors, the rights watchdog alleges that Russia has "deliberately or recklessly" hunted civilians and civilian objects, particularly in the southern city of Kherson, using "commercially available quadcopter drones" made domestically and in China. "Russian drone operators are able to track their targets, with high-resolution video feeds, leaving little doubt that the intent is to kill, maim, and terrify civilians," Belkis Wille, a director on arms and conflict at HRW, said in a statement.

Ukraine's surprise Sunday attack on Russian offensive weapons caches may be a good time for the U.S. to reflect on its own weaknesses, should one of its adversaries attempt a similar strike. Col. Seth Krummrich, a retired Army Special Forces commander and vice president at the Virginia-based security firm Global Guardian, warned that the U.S. remains vulnerable to drone attacks. "Interestingly, it is not a technological gap, it is a policy/authority process to engage and deny drone attacks," Krummrich said. "I assess it will take a major drone strike in the U.S. to change policy." Even civilian operations have a tough time getting approval for drone-interception-authority protections, the NFL excepted, he said.

What message does Ukraine's Operation Spider's Web send to Russia and US? Ukraine carries out large-scale drone strikes on multiple Russian airbases.Read more Eighteen months in the making, Ukraine's Operation Spider's Web saw hundreds of AI-trained drones target military aircraft deep inside Russia's borders. Ukrainian President Volodymyr Zelenskyy says Sunday's attacks will go down in history. He followed them up with a proposal for an unconditional ceasefire as the two sides met in Istanbul. The European Union is preparing its 18th package of sanctions on Russia, while US President Donald Trump has threatened to use "devastating" measures against Russia if he feels the time is right. So, is the time right now?

Situation awareness is essential for understanding and reasoning about 3D scenes in embodied AI agents. However, existing datasets and benchmarks for situated understanding are limited in data modality, diversity, scale, and task scope. To address these limitations, we propose Multi-modal Situated Question Answering (MSQA), a large-scale multi-modal situated reasoning dataset, scalably collected leveraging 3D scene graphs and vision-language models (VLMs) across a diverse range of real-world 3D scenes. MSQA includes 251K situated question-answering pairs across 9 distinct question categories, covering complex scenarios within 3D scenes. We introduce a novel interleaved multi-modal input setting in our benchmark to provide text, image, and point cloud for situation and question description, resolving ambiguity in previous single-modality convention (e.g., text). Additionally, we devise the Multi-modal Situated Next-step Navigation (MSNN) benchmark to evaluate models' situated reasoning for navigation. Comprehensive evaluations on MSQA and MSNN highlight the limitations of existing vision-language models and underscore the importance of handling multi-modal interleaved inputs and situation modeling. Experiments on data scaling and cross-domain transfer further demonstrate the efficacy of leveraging MSQA as a pre-training dataset for developing more powerful situated reasoning models.

Ukrainian drones struck multiple military airbases deep inside Russia on Sunday in a major operation a day before the neighbours held peace talks in Istanbul. The Russian Defence Ministry said Ukraine had launched drone strikes targeting Russian military airfields across five regions, causing several aircraft to catch fire. The attacks occurred in the Murmansk, Irkutsk, Ivanovo, Ryazan, and Amur regions. Air defences repelled the assaults in all but two regions – Murmansk and Irkutsk, the ministry said. "In the Murmansk and Irkutsk regions, the launch of FPV drones from an area in close proximity to airfields resulted in several aircraft catching fire," the Defence Ministry said.

Former U.S. ambassador to Ukraine John Herbst explains the impact of the drone strike on Russian air bases. Russian and Ukrainian delegations have begun talks in Istanbul, Turkey, on Monday, less than 24 hours after a massive Ukrainian drone attack struck Russian airfields. The two delegations entered Ciragan Palace in Istanbul alongside a group of senior Turkish officials. It is the second round of peace talks to take place in the three years since Russia invaded Ukraine. Images from the event show many of the Ukrainian delegation wearing military uniforms, while the Russian group exclusively wore suits.

Kyiv, Ukraine – Any description of Ukraine's attacks on Russia's fleet of strategic bombers could leave one scrambling for superlatives. Forty-one planes – including supersonic Tu-22M long-range bombers, Tu-95 flying fortresses and A-50 early warning warplanes – were hit and damaged on Sunday on four airfields, including ones in the Arctic and Siberia, Ukrainian authorities and intelligence said. Moscow did not comment on the damage to the planes but confirmed that the airfields were hit by "Ukrainian terrorist attacks". Videos posted by the Ukrainian Security Service (SBU), which planned and carried out the operation, which was called The Spiderweb, showed only a handful of planes being hit. The strategic bombers have been used to launch ballistic and cruise missiles from Russian airspace to hit targets across Ukraine, causing wide scale damage and casualties.

Deep Neural Networks (DNNs) are known to be vulnerable to adversarial attacks, i.e., an imperceptible perturbation to the input can mislead DNNs trained on clean images into making erroneous predictions. To tackle this, adversarial training is currently the most effective defense method, by augmenting the training set with adversarial samples generated on the fly. Interestingly, we discover for the first time that there exist subnetworks with inborn robustness, matching or surpassing the robust accuracy of the adversarially trained networks with comparable model sizes, within randomly initialized networks without any model training, indicating that adversarial training on model weights is not indispensable towards adversarial robustness. We name such subnetworks Robust Scratch Tickets (RSTs), which are also by nature efficient. Distinct from the popular lottery ticket hypothesis, neither the original dense networks nor the identified RSTs need to be trained.

Although pre-trained transformers and reprogrammed text-based LLMs have shown strong performance on time series tasks, the best-performing architectures vary widely across tasks, with most models narrowly focused on specific areas, such as time series forecasting. Unifying predictive and generative time series tasks within a single model remains challenging.

In many reinforcement learning (RL) applications, incorporating heuristic rewards alongside the task reward is crucial for achieving desirable performance. Heuristics encode prior human knowledge about how a task should be done, providing valuable hints for RL algorithms. However, such hints may not be optimal, limiting the performance of learned policies. The currently established way of using heuristics is to modify the heuristic reward in a manner that ensures that the optimal policy learned with it remains the same as the optimal policy for the task reward (i.e., optimal policy invariance). However, these methods often fail in practical scenarios with limited training data.