In the last Turkish town on the road to Aleppo, jihadis and spies mixed with reporters trying to prove themselves. At the Hotel Istanbul, I was to meet a Syrian activist who went by the code name Harvester. I was twenty-two and wanted to learn how journalists work in conflict zones. In the spring of 2013--when I was twenty-two and had little sense of the direction my life might take--I travelled to the Turkish-Syrian border, to learn how journalists work in war. During the previous two years, the security forces of the Syrian President, Bashar al-Assad, had carried out a brutal crackdown on peaceful protesters, and the situation had spiralled into armed conflict, with thousands of civilians dead and hundreds of thousands of refugees spilling into neighboring countries. Rebels had captured half of Syria's largest city, Aleppo, and most of the territory north and west of it, including the official crossings into southern Turkey. In practice, that meant any freelancer, humanitarian, or ...

Assad regime planned US journalist's kidnap for weeks, BBC finds US journalist Austin Tice, who disappeared in Syria in 2012, was kidnapped in a calculated operation which was more than a month in the planning, the BBC can reveal. A member of the group which detained Tice has also claimed the abduction was sanctioned by Syria's leader at the time, President Bashar al-Assad. Tice was seen getting into a car with a driver he knew and trusted. For years, many assumed they had stumbled upon a government checkpoint, and that the abduction of the journalist had been opportunistic. But we have uncovered evidence that, in fact, his disappearance was anything but chance.

'Trump will be gone in three years': Top Democrats try to reassure Europe US Secretary of State Marco Rubio was the centre of attention at the Munich Security Summit, as European leaders wondered apprehensively what tone he would strike in his remarks on Saturday. While his speech did not fully allay their concerns, it has been viewed as a reassurance to allies that while US relations may have frayed under Donald Trump, they will not break. Rubio's was not the only American political voice at the security summit, however. And even if the secretary of state's remarks had not been so well-received - if he had sharply criticised Europeans the way Vice-President JD Vance did at the conference last year - there were other American politicians doing their best impression of the Persian poet, counselling: This too shall pass. If there's nothing else I can communicate today, California Governor Gavin Newsom said at a conference event on Friday, Donald Trump is temporary.

Why is the US Fed chair criminal probe causing alarm? Venezuela's defence minister has accused the United States of using the country as a "weapons laboratory" during the abduction of President Nicolas Maduro and his wife, Cilia Flores, on January 3. Vladimir Padrino Lopez said last week that the US had used Venezuela as a testing ground for "advanced military technologies" that rely on artificial intelligence and weaponry never used before, according to the Venezuelan newspaper El Universal. On Sunday, US President Donald Trump told the New York Post that US forces had indeed used a weapon he referred to as "the discombobulator". "I'm not allowed to talk about it," he said, adding that the weapon "made equipment not work" during the operation. Details of the US military mission to abduct Maduro have not been made public, but the US has been known to use weapons to disorient soldiers and guards or disable equipment and infrastructure in the past.

To utilize the raw inputs and symbolic knowledge simultaneously, some recent neuro-symbolic learning methods use abduction, i.e., abductive reasoning, to integrate sub-symbolic perception and logical inference. While the perception model, e.g., a neural network, outputs some facts that are inconsistent with the symbolic background knowledge base, abduction can help revise the incorrect perceived facts by minimizing the inconsistency between them and the background knowledge. However, to enable effective abduction, previous approaches need an initialized perception model that discriminates the input raw instances. This limits the application of these methods, as the discrimination ability is usually acquired from a thorough pre-training when the raw inputs are difficult to classify. In this paper, we propose a novel abduction strategy, which leverages the similarity between samples, rather than the output information by the perceptual neural network, to guide the search in abduction. Based on this principle, we further present ABductive Learning with Similarity (ABLSim) and apply it to some difficult neuro-symbolic learning tasks. Experiments show that the efficiency of ABLSim is significantly higher than the state-of-the-art neuro-symbolic methods, allowing it to achieve better performance with less labeled data and weaker domain knowledge.

This article looks at how reasoning works in current Large Language Models (LLMs) that function using the token-completion method. It examines their stochastic nature and their similarity to human abductive reasoning. The argument is that these LLMs create text based on learned patterns rather than performing actual abductive reasoning. When their output seems abductive, this is largely because they are trained on human-generated texts that include reasoning structures. Examples are used to show how LLMs can produce plausible ideas, mimic commonsense reasoning, and give explanatory answers without being grounded in truth, semantics, verification, or understanding, and without performing any real abductive reasoning. This dual nature, where the models have a stochastic base but appear abductive in use, has important consequences for how LLMs are evaluated and applied. They can assist with generating ideas and supporting human thinking, but their outputs must be critically assessed because they cannot identify truth or verify their explanations. The article concludes by addressing five objections to these points, noting some limitations in the analysis, and offering an overall evaluation.

The deployment of pre-trained perception models in novel environments often leads to performance degradation due to distributional shifts. Although recent artificial intelligence approaches for metacognition use logical rules to characterize and filter model errors, improving precision often comes at the cost of reduced recall. This paper addresses the hypothesis that leveraging multiple pre-trained models can mitigate this recall reduction. We formulate the challenge of identifying and managing conflicting predictions from various models as a consistency-based abduction problem, building on the idea of abductive learning (ABL) but applying it to test-time instead of training. The input predictions and the learned error detection rules derived from each model are encoded in a logic program. We then seek an abductive explanation--a subset of model predictions--that maximizes prediction coverage while ensuring the rate of logical inconsistencies (derived from domain constraints) remains below a specified threshold. We propose two algorithms for this knowledge representation task: an exact method based on Integer Programming (IP) and an efficient Heuristic Search (HS). Through extensive experiments on a simulated aerial imagery dataset featuring controlled, complex distributional shifts, we demonstrate that our abduction-based framework outperforms individual models and standard ensemble baselines, achieving, for instance, average relative improvements of approximately 13.6\% in F1-score and 16.6\% in accuracy across 15 diverse test datasets when compared to the best individual model. Our results validate the use of consistency-based abduction as an effective mechanism to robustly integrate knowledge from multiple imperfect models in challenging, novel scenarios.