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2,173 papers
The reasoning-effort term is studied through a registered paired contrast of Sonnet 5 with explicit high effort against the same model with effort omitted, using 30 AIME 2026 items and five calls per item.
A raido World-model-based Optimized Negotiation framework for Distributed UAV covERage (WONDER), which uses a Joint-Embedding Predictive Architecture (JEPA)-based radio world model to learn and predict the incremental radio effect of each candidate trajectory from deployment-available information and builds RadioDynamics, a comprehensive simulation environment that integrates UAV mobility, radio propagation, inter-UAV communication modeling, and digital-twin geometry.
It is proved that the randomized primal competitive ratio is in fact Theta(1) for arbitrary numbers of experts and the upper bound reduces reciprocal-max service costs to chasing positive bodies with covering row sparsity two.
Training variational quantum models requires choosing between parameter-shift gradients, which are exact but cost $O(P)$ forward evaluations, and simultaneous perturbation stochastic approximation (SPSA), which uses only two samples but produces high-variance estimates that can degrade optimisation on small supervised tasks. Whether the cheap gradient is usable depends on the variance that results from different choices of the SPSA perturbation scale, learning rate, and gain-decay schedule. We varied those quantities across a broad grid on a 6-qubit, 60-parameter QNLI classifier and compared the best configurations to parameter-shift AdamW and BuresQNG. AdamW-style SPSA with $c_0=0.01$, $\eta=0.10$, $\gamma=0.10$ reached $55\% \pm 11\%$ test accuracy, improving over the default configuration ($49\% \pm 6\%$) but remaining 16-19 percentage points below the parameter-shift baselines because the two-sample SPSA gradient estimate has too much variance for reliable optimisation of 60 parameters in 40 epochs. Classical-gain SPSA and Bures-preconditioned SPSA performed worse, at $51\%$ and $46\%$ respectively. Bures-preconditioning a noisy two-sample SPSA gradient amplifies perturbation noise.
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This work proposes Ricci-Diffusion, a curvature-guided graph diffusion method inspired by Ricci flow, which exhibits a Ricci-flow-like evolution, in which relative edge-level curvature modulates local transport in the diffusion kernel and guides edge-weight updates toward a more regular graph geometry.
This work proposes MITRE-SAGE, a multi-agent retrieval-augmented generation framework that integrates semantic and structural cybersecurity knowledge to improve the reliability and interpretability of LLM-based QA systems and proposes MITRE-QA, a comprehensive benchmark for evaluating LLMs across diverse cybersecurity knowledge tasks.
Results demonstrate that simple digital markers can power a practical early-warning system by the fifth week of the semester, and confirm that weighted academic momentum is the strongest predictor, followed by its interaction with LMS engagement.
The results show that ComNetX can preserve the quality of strong modularity-based solvers while reducing update time on large graphs: in paired runs on the largest real graph, Local Leiden keeps final modularity within 0.006 of full-snapshot recomputation while achieving a 41.9 +/- 0.2x speedup.
Recommendations are provided that focus on improving transparency in reporting, advocating for the broader adoption of multi-level fairness techniques, and ensuring that health equity is explicitly prioritized in future research efforts.
It is argued that the inductive bias of locality means that the machinery of effective field theory from physics can be usefully applied to describe the denoising dynamics of Brownian motion.
Intent-Driven Dynamic Chunking (IDC) is introduced, a novel approach that uses predicted user queries to guide document segmentation and aligning document structure with anticipated information needs significantly boosts retrieval performance, particularly for long and heterogeneous documents.
A two-player zero-sum repeated game between a learner and nature whose value identity generates Bayesian updating and an exact accounting of exponential-weights regret at once is given, and supplies the comparator-class variational form that a wide class of concentration phenomena share.
General Science
Climate & Sustainability
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
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