Syria Government
US removes Syria from list of state sponsors of terrorism
The US has removed Syria from its list of state sponsors of terrorism after 47 years, lifting what it said was the final major barriers for private sector investment in the country and its reintegration into the global economy. US Secretary Of State Marco Rubio said the move was in recognition of the Syrian government's positive actions to fully distance itself from acts of international terrorism. The Trump administration has embraced Syria's new president, Ahmed al-Sharaa, a former militant who once had an American bounty on his head as the leader of an al-Qaeda off-shoot. His Islamist group, Hayat Tahrir al-Sham (HTS), was also removed from the US list of global terrorist organisations. HTS, formerly known as al-Nusra Front, was al-Qaeda's affiliate in Syria until Sharaa severed ties with the jihadist network in 2016.
Israeli drone strike on 'civilian vehicle' injures several in Syria
An Israeli drone strike in southwestern Syria has injured several people, the country's foreign ministry says, in the latest of near-daily Israeli incursions and attacks in the area. Syria's official Alikhbariya channel reported on Saturday that a man was wounded when an Israeli drone hit his truck near the town of Beit Jinn in the western Damascus countryside. The activity "posed an immediate threat to our forces", the military added. Syria's Ministry of Foreign Affairs and Expatriates said in a statement that Saturday's strike resulted in several civilian injuries. It said it "condemns in the strongest terms" the targeting of a "civilian vehicle" by an Israeli drone.
Trump Moves to Revoke Syria's Designation as State Sponsor of Terrorism
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Syrian army, Kurdish-led SDF accuse each other of ceasefire violations
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.
US to transfer Islamic State prisoners from Syria to Iraq
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.
Syrian army moves east of Aleppo after Kurdish forces withdraw
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.
LIVE: Deadly clashes erupt between Syrian army, SDF forces in Aleppo
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".
Ultra-Fast Language Generation via Discrete Diffusion Divergence Instruct
Zheng, Haoyang, Liu, Xinyang, Kong, Cindy Xiangrui, Jiang, Nan, Hu, Zheyuan, Luo, Weijian, Deng, Wei, Lin, Guang
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.
Syria's leader says his country has transformed from 'an exporter of crisis.'
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.
Hierarchical Level-Wise News Article Clustering via Multilingual Matryoshka Embeddings
Hanley, Hans W. A., Durumeric, Zakir
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.