How many Syrians have returned? A ceasefire between the Syrian army and the Kurdish-led Syrian Democratic Forces (SDF) appears to be largely holding, even as the two sides have accused each other of violating its terms. The army on Sunday said the SDF launched multiple drone attacks in the Aleppo countryside, while the United States-trained Kurdish forces on Monday accused the army of targeting a Kurdish-majority city near the Turkish border. An initial four-day ceasefire between the Syrian army and the SDF was extended by 15 days soon after it expired on Saturday night. The official Syrian Arab News Agency (SANA) reported that the SDF launched more than 25 explosive drones on the army positions in the Aleppo countryside on Sunday, breaching the newly extended ceasefire.

The US military has launched a mission to transfer up to 7,000 Islamic State (IS) group fighters from prisons in north-eastern Syria to Iraq, as Syrian government forces take control of areas long controlled by Kurdish-led forces. US Central Command said it had already moved 150 IS fighters from Hassakeh province to a secure location in Iraq. The move aimed to prevent a breakout that would pose a direct threat to the United States and regional security, it added. On Tuesday night, Syria's government announced a new ceasefire with the Kurdish-led Syrian Democratic Forces (SDF), after the militia alliance withdrew from al-Hol camp, which holds thousands of relatives of IS fighters. Separately on Wednesday, Syria's defence ministry said seven soldiers were killed in a drone attack by Kurdish forces in the Kurdish-dominated province of Hasakah.

The Syrian army is moving into areas east of Aleppo city, after Kurdish forces started a withdrawal. Syrian troops have been spotted entering Deir Hafer, a town about 50km (30 miles) from Aleppo. On Friday, the Kurdish Syrian Democratic Forces (SDF) militia announced it would redeploy east of the Euphrates river. This follows talks with US officials, and a pledge from Syrian President Ahmed al-Sharaa to make Kurdish a national language. After deadly clashes last week, the US urged both sides to avoid a confrontation.

At least three civilians and a Syrian soldier have been killed after clashes erupted between the Syrian army and the Kurdish-led and US-backed Syrian Democratic Forces (SDF) in Aleppo, according to the state news agency SANA. Earlier, Syria's defence ministry said three soldiers were injured after SDF fired drones at a military checkpoint near Deir Hafer, east of northern province. Heavy machine gunfire and fighting have been reported in the areas of Sheikh Maqsoud and Ashrafiyah. The ministry says it will respond to the "aggression in an appropriate manner".

Fast and high-quality language generation is the holy grail that people pursue in the age of AI. In this work, we introduce Discrete Diffusion Divergence Instruct (DiDi-Instruct), a training-based method that initializes from a pre-trained (masked) discrete diffusion language model (dLLM) and distills a few-step student for fast generation. The resulting DiDi-Instruct model achieves comparable or superior performance to its dLLM teacher and the GPT-2 baseline while enabling up to 64$\times$ acceleration. The theoretical foundation of DiDi-Instruct is a novel framework based on integral KL-divergence minimization, which yields a practical training algorithm. We further introduce grouped reward normalization, intermediate-state matching, and the reward-guided ancestral sampler that significantly improve training stability, model coverage, and inference quality. On OpenWebText, DiDi-Instruct achieves perplexity from 62.2 (8 NFEs) to 18.4 (128 NFEs), which outperforms prior accelerated dLLMs and GPT-2 baseline. These gains come with a negligible entropy loss (around $1\%$) and reduce additional training wall-clock time by more than $20\times$ compared to competing dLLM distillation methods. We further validate the robustness and effectiveness of DiDi-Instruct through extensive ablation studies, model scaling, and the generation of discrete protein sequences. In conclusion, DiDi-Instruct is an efficient yet effective distillation method, enabling language generation in the blink of an eye. We will release both code and models at github.com/haoyangzheng-ai/didi-instruct.

On Wednesday, officials and diplomats sounded the alarm on A.I.'s ability to undermine the integrity of information and fabricate fake voice and video tapes. They also warned that it posed a threat to cybersecurity and would enable the rise of autonomous weapons. Still, some argued that, if used responsibly and with guardrails, A.I. potentially could also help foster peace and stability. Secretary General António Guterres, who for the past year has championed efforts to regulate A.I., said that the Council had a responsibility to ensure the military use of artificial intelligence complies with international law and the U.N. Charter. "From design to deployment to decommissioning, A.I. systems must always comply with international law; military uses must be clearly regulated," Mr. Guterres said, before ending his speech with a warning and a call to action.

Contextual large language model embeddings are increasingly utilized for topic modeling and clustering. However, current methods often scale poorly, rely on opaque similarity metrics, and struggle in multilingual settings. In this work, we present a novel, scalable, interpretable, hierarchical, and multilingual approach to clustering news articles and social media data. To do this, we first train multilingual Matryoshka embeddings that can determine story similarity at varying levels of granularity based on which subset of the dimensions of the embeddings is examined. This embedding model achieves state-of-the-art performance on the SemEval 2022 Task 8 test dataset (Pearson $ρ$ = 0.816). Once trained, we develop an efficient hierarchical clustering algorithm that leverages the hierarchical nature of Matryoshka embeddings to identify unique news stories, narratives, and themes. We conclude by illustrating how our approach can identify and cluster stories, narratives, and overarching themes within real-world news datasets.

Exclusive drone footage reveals the extensive destruction in Zabadani, a city near Damascus, which endured heavy shelling from Bashar al-Assad's forces during Syria's war.

With the collapse of the Assad regime and Russia's declining influence in Syria, some are saying that an opportunity for a rapprochement between Israel and Ukraine now exists, where it hadn't before. "Israel needs to be more involved in supporting Ukraine," Yuli Edelstein, the chair of Israel's Foreign Affairs and Defense Community and a member of the ruling Likud party, told Fox News Digital. "The situation has changed, it's time for Israel to step up." Edelstein, a leading voice in Israel's defense and foreign policy discussions, said "the enemy of my enemy is my friend," adding, "We see a strategic alliance between the Russians and the Iranians. If before it was the great Russia adopting Iran, now it's important to recognize that the balance of power has changed."

It is beyond dispute that the potential benefits of artificial intelligence (AI) in military intelligence are considerable. Nevertheless, it remains uncertain precisely how AI can enhance the analysis of military data. The aim of this study is to address this issue. To this end, the AI demonstrator deepCOM was developed in collaboration with the start-up Aleph Alpha. The AI functions include text search, automatic text summarization and Named Entity Recognition (NER). These are evaluated for their added value in military analysis. It is demonstrated that under time pressure, the utilization of AI functions results in assessments clearly superior to that of the control group. Nevertheless, despite the demonstrably superior analysis outcome in the experimental group, no increase in confidence in the accuracy of their own analyses was observed. Finally, the paper identifies the limitations of employing AI in military intelligence, particularly in the context of analyzing ambiguous and contradictory information.