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Trump is silent so far on Iran strategy as U.S. death toll hits 17 U.S. President Donald Trump speaks to reporters in the Oval Office on July 14. | Doug Mills / The New York Times If U.S. President Donald Trump knows where he wants to take the war in Iran, he's giving little sign of it publicly. The conflict continued to spiral over the weekend, with both sides trading attacks in the aftermath of an Iranian strike that killed two U.S. service-members in Jordan and injured others. Both Iran and the U.S. have largely abandoned the tentative peace deal the president had touted. The week of escalating missile and drone strikes underscored the risk that the conflict will reignite in full again even as the man who started it stayed silent, only acknowledging the U.S. troop fatalities in a brief conversation with a media outlet. U.S. Central Command announced Sunday that a third service member died the day before during an controlled detonation of an unexploded Iranian drone in northern Iraq. And the U.S. said unidentified remains of another victim in Friday's Jordan strike were being examined.

Social animals have anticipatory tactics ahead of conflict--including killing their rivals' young. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . In the animal kingdom, life can sometimes look more like than .

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'Digging with a needle': Generals stall peace as Sudan's el-Obeid burns As drone attacks rain down on el-Obeid and the Rapid Support Forces (RSF) tighten their months-long siege, the capital of North Kordofan has emerged as the latest flashpoint in Sudan's grinding war of attrition. Despite mounting international alarm and renewed US diplomatic pressure aimed at securing a nationwide truce, Sudan's warring generals remain deeply entrenched. Both the Sudanese Armed Forces (SAF) and the RSF appear locked in a pursuit of outright military victory, largely sustained by a continuous flow of foreign weapons. Through the lens of the escalating crisis in el-Obeid, a grim reality is unfolding: Civilian suffering is increasingly weaponised amid polarised domestic narratives, while geopolitical manoeuvring repeatedly stalls any viable path to peace. El-Obeid holds immense strategic value.

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Existing methods like data mixture strategies face limitations, including heavy reliance on expert knowledge and conflicting optimization signals. While model merging offers parameter-level conflict-resolution strategies through integrating specialized models' parameters, its potential for 3H optimization remains underexplored. This paper systematically compares the effectiveness of model merging and data mixture methods in constructing 3H-aligned LLMs for the first time, revealing previously overlooked collaborative and conflict relationships among the 3H dimensions and discussing the advantages and drawbacks of Mdata mixture (data-level) and model merging (parameter-level) methods in mitiodgating the conflict for balanced 3H optimization.

Characterizing a large language model's (LLM's) knowledge of a given question is challenging. As a result, prior work has primarily examined LLM behavior under knowledge conflicts, where the model's internal parametric memory contradicts information in the external context. However, this does not fully reflect how well the model knows the answer to the question. In this paper, we first introduce a taxonomy of five knowledge statuses based on the consistency and correctness of LLM knowledge modes. We then propose KScope, a hierarchical framework of statistical tests that progressively refines hypotheses about knowledge modes and characterizes LLM knowledge into one of these five statuses. We apply KScope to nine LLMs across four datasets and systematically establish: (1) Supporting context narrows knowledge gaps across models.

Multi-task learning (MTL) is a widely explored paradigm that enables the simultaneous learning of multiple tasks using a single model. Despite numerous solutions, the key issues of optimization conflict and task imbalance remain under-addressed, limiting performance. Unlike existing optimization-based approaches that typically reweight task losses or gradients to mitigate conflicts or promote progress, we propose a novel approach based on Continual Optimization with Symmetry Teleportation (COST). During MTL optimization, when an optimization conflict arises, we seek an alternative loss-equivalent point on the loss landscape to reduce conflict. Specifically, we utilize a low-rank adapter (LoRA) to facilitate this practical teleportation by designing convergent, loss-invariant objectives. Additionally, we introduce a historical trajectory reuse strategy to continually leverage the benefits of advanced optimizers. Extensive experiments on multiple mainstream datasets demonstrate the effectiveness of our approach. COSTis a plug-and-play solution that enhances a wide range of existing MTL methods. When integrated with state-of-the-art methods, COSTachieves superior performance.

Learning instability is a long-standing problem across machine learning, but it is especially acute in the overparameterized regime that defines modern deep learning: large models fine-tuned or trained on limited data traverse flat loss landscapes with many nearly-equivalent minima, and stochastic factors (initialization, data order, dropout, hardware non-determinism) can route optimization to very different solutions. The rise of large pretrained models (LPMs) makes the problem more urgent: training cost is high, downstream data is often small, and repeated runs for variance reduction are prohibitive. We introduce \textbf{GRAIN} (\textbf{G}roup \textbf{A}ggregation via m\textbf{IN}-norm objective), a lightweight training algorithm that replaces the mean aggregation used in mini-batch optimization (both across mini-batches and within a mini-batch) with a min-norm convex combination of group-wise gradients. \mName guarantees a non-negative inner product between the aggregated update and every group gradient, resolving intra- and inner-batch gradient conflict, and retains an $\mathcal{O}(1/T)$ convergence rate comparable to SGD. Under mild smoothness and absolute-continuity assumptions, the min-norm solution differs almost surely from the arithmetic mean, which yields a uniform-stability bound for \mName strictly tighter than the standard bound for SGD. Empirically across generation, classification, and regression at LPM scale, \mName delivers consistent improvements in mean performance and reductions in run-to-run variance over a broad suite of tasks, with no extra training-time or storage cost beyond a single backward pass.