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A bstract In the framework of the MSSM, we examine several simplified models where only a few superpartners are light. This allows us to study WIMP-nucleus scattering in terms of a handful of MSSM parameters and thereby scrutinize their impact on dark matter direct-detection experiments. Focusing on spin-independent WIMP-nucleon scattering, we derive simplified, analytic expressions for the Wilson coefficients associated with Higgs and squark exchange. We utilize these results to study the complementarity of constraints due to direct-detection, flavor, and collider experiments. We also identify parameter configurations that produce (almost) vanishing cross sections. In the proximity of these so-called blind spots, we find that the amount of isospin violation may be much larger than typically expected in the MSSM. This feature is a generic property of parameter regions where cross sections are suppressed, and highlights the importance of a careful analysis of the nucleon matrix elements and the associated hadronic uncertainties. This becomes especially relevant once the increased sensitivity of future direct-detection experiments corners the MSSM into these regions of parameter space.

A bstract Natural realisations of supersymmetry require light stops t ˜ 1 , making them a prime target of LHC searches for physics beyond the Standard Model. Depending on the kinematic region, the main search channels are t ˜ 1 → t χ ˜ 1 0 , t ˜ 1 → W b χ ˜ 1 0 and t ˜ 1 → c χ ˜ 1 0 . We first examine the interplay of these decay modes with c ˜ 1 → c χ ˜ 1 0 in a model-independent fashion, revealing that a large parameter space region with stop mass values m t ˜ 1 up to 530 GeV is excluded for any t ˜ 1 → c χ ˜ 1 0 branching ratio by LHC Run I data. The impact of c ˜ 1 → c χ ˜ 1 0 decays is further illustrated for scenarios with stop-scharm mixing in the right-handed sector, where it has previously been observed that the stop mass limits can be significantly weakened for large mixing. Our analysis shows that once the c ˜ 1 → c χ ˜ 1 0 bounds are taken into account, non-zero stop-scharm mixing can lead to an increase in the allowed parameter space by at most 35%, with large areas excluded for arbitrary mixing.

We analyse the rare kaon decays [Formula omitted] and [Formula omitted] [Formula omitted] in a dispersive framework in which the weak Hamiltonian carries momentum. Our analysis extends predictions from lowest order [Formula omitted] chiral perturbation theory ( [Formula omitted]PT [Formula omitted]) to fully account for effects from final-state interactions, and is free from ambiguities associated with extrapolating the kaon off-shell. Given input from [Formula omitted] and [Formula omitted], we solve the once-subtracted dispersion relations numerically to predict the rates for [Formula omitted] and [Formula omitted]. In the leptonic modes, we find sizeable corrections to the [Formula omitted]PT [Formula omitted] predictions for the integrated rates.

We calculate the decay rates for KS → γγ and KS → γl + l − (l = e or μ)within a dispersive framework in which the weak Hamiltonian carries momentum. Given input from KS → ππ and γγ (*)→ππ, we solve the once-subtracted dispersion relations numerically and find that final-state ππ interactions generate sizeable corrections to the predictions from 3-flavour chiral perturbation theory. Our analysis predicts BR(KS → γγ) = (2.34 ± 0.31) × 10−6, BR(KS → γe + e −) = (4.38 ± 0.57) × 10−8, and BR(KS → γμ + μ −) = (1.45 ± 0.27) × 10−9.

Recent results from the LHC's first run have revealed intriguing departures from lepton flavour universality in the semi-leptonic decays of B-mesons. We discuss the complementary role that rare kaon decays can provide in testing new physics explanations of these flavour anomalies. In the framework of minimal flavour violation, we relate the chiral low-energy constants involved in K → π ℓ ℓ ′ and K → ℓ ℓ ′   ( ℓ = μ   or   e ) with the new physics Wilson coefficients of the b → s effective Hamiltonian. We comment on the determination of these low-energy constants at NA62 and future kaon experiments, as well as the required improvements in sensitivity necessary to test the B-physics anomalies in the kaon sector.

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform. Federated learning can be used to train medical AI models on sensitive personal data while preserving important privacy properties; however, the sensitive nature of the data makes it difficult to evaluate approaches reproducibly on real data. The MedPerf project presented by Karargyris et al. provides the tools and infrastructure to distribute models to healthcare facilities, such that they can be trained and evaluated in realistic settings.

We analyse the rare kaon decays K S → γ γ and K S → γ ℓ + ℓ - ( ℓ = e or μ ) in a dispersive framework in which the weak Hamiltonian carries momentum. Our analysis extends predictions from lowest order S U ( 3 ) L × S U ( 3 ) R chiral perturbation theory ( χ PT 3 ) to fully account for effects from final-state interactions, and is free from ambiguities associated with extrapolating the kaon off-shell. Given input from K S → π π and γ γ ( ∗ ) → π π , we solve the once-subtracted dispersion relations numerically to predict the rates for K S → γ γ and K S → γ ℓ + ℓ - . In the leptonic modes, we find sizeable corrections to the χ PT 3 predictions for the integrated rates.

Proprietary AI systems have recently demonstrated impressive capabilities on complex proof-based problems, with gold-level performance reported at the 2025 International Mathematical Olympiad (IMO). However, the training pipelines behind these systems remain largely undisclosed, and their reliance on large \"internal\" models and scaffolds makes them expensive to run, difficult to reproduce, and hard to study or improve upon. This raises a central question: can small, open models also be trained to achieve competitive reasoning performance on difficult Olympiad-level math? In this paper, we answer this question by building QED-Nano, a 4B model post-trained for Olympiad-level proofs. Our training recipe has three stages: (1) supervised fine-tuning to imbue good proof-writing styles by distilling from DeepSeek-Math-V2, (2) reinforcement learning (RL) with rubric-based rewards, and (3) expanding RL with a reasoning cache, which decomposes long proofs into iterative summarize-and-refine cycles and enables stronger test-time reasoning. QED-Nano surpasses the proof-generation performance of much larger open models, including Nomos-1 and GPT-OSS-120B, and approaches the performance of proprietary models like Gemini 3 Pro, at a fraction of the inference cost. To support further research on open mathematical reasoning, we release the full QED-Nano pipeline, including the QED-Nano and QED-Nano-SFT models, the FineProofs-SFT and FineProofs-RL datasets, and the training and evaluation code.