Weekly Recap

Ion-specific effects on water mobility extend to a nanometer-scale hydration layer around polymers, following a universal mobility gradient.

Diverse LLMs, when strategically routed and collaborating, can collectively achieve significantly fairer outputs than any single model acting alone.

A combinatorial atlas of monolayer stacking configurations explains observed nonlinear optical phenomena and predicts novel metastable structures.

Polyzwitterions unexpectedly expand with added salt, a behavior opposite to typical polyelectrolytes and analogous to protein denaturation.

A combinatorial atlas of monolayer stacking configurations explains observed nonlinear optical phenomena and predicts novel metastable structures.

Polyzwitterions unexpectedly expand with added salt, a behavior opposite to typical polyelectrolytes and analogous to protein denaturation.

Ferroelectrics and indoor photovoltaics unlock the potential for truly autonomous, low-power edge AI, moving beyond the energy constraints of centralized data centers.

A novel Ewald-based hybrid method accurately models precipitation by decoupling particle-scale quasistatic diffusion from mesoscale transient solute evolution, revealing how localized precipitation can suppress subsequent nucleation.

3D nanoprinting with electron and ion beams unlocks emergent quantum phenomena in magnetism and superconductivity by enabling precise geometric tuning beyond bulk material limits.

Extended Hubbard parameters ($U$ and $V$) precisely tune DFT band-edge electronic structure for 2D TMDs, unlocking quantitative accuracy for optical and spintronic properties.

Superconducting Nb-Ge films with a critical field of 0.8 T form at just 390 $^\circ$C, enabling integration with Ge/SiGe heterostructures.

Plasticity in layered semiconductors is an illusion of experimental design, with brittle fracture dominating under common micro-scale tests but significant plastic deformation achievable via controlled eccentric loading.

Massive database search sees 1,000x speedup and 10,000x energy efficiency gains by performing hyperdimensional computations directly within multi-bit ferroelectric NAND memory.

The current understanding of METTL3 biology in HCC is summarized, focusing on its structure, assembly within the MTC, regulation of coding and non-coding RNAs, and its roles in driving key oncogenic signaling cascades, such as JAK/STAT3, PI3K/AKT/mTOR, Wnt/β-catenin, Hippo/YAP, Notch, Hedgehog, and epithelial–mesenchymal transition.

Rovibrational spectra can now be computed with unprecedented efficiency by pruning the vibrational basis using approximate wavefunctions, outperforming traditional polyad schemes.

Litz wire insulation breaks down 20% lower at 30 kHz than at 1 kHz, revealing a critical frequency-dependent degradation mechanism for high-power converters.

Ion-specific effects on water mobility extend to a nanometer-scale hydration layer around polymers, following a universal mobility gradient.

Bulk TbMn6Sn6 exhibits anisotropic mesoscale magnetic textures across its spin-reorientation transition, challenging the assumption of a long-range skyrmion lattice.

Chiral bubble formation in active fluids is driven by rare nucleation events from a metastable homogeneous state, not just instabilities, with a critical radius and work barrier governing growth.

Hidden oxygen divacancy configurations in HfO$_2$ exhibit mixed polymorph character, fundamentally altering phase transformation pathways and favoring polar orthorhombic over monoclinic phases.

LLMs can prospectively formulate novel materials phenomena, moving beyond executing predefined objectives to independently identifying unique guest-host interactions and their underlying mechanisms.

High-order FDTD schemes for nonlinear dispersive optics achieve unconditional stability and asymptotic-preserving behavior, accurately simulating soliton propagation and harmonic generation.

Floquet dressing is now achievable in bulk metals, enabling optical control over itinerant electronic states without needing to open energy gaps.

AI agents can autonomously perform complex scientific analysis and manuscript drafting in under a week, flagging novel scientific anomalies for human investigation.

A single forward pass through an atomistic foundation model can predict material stability with 30x less computation than a full relaxation, while providing calibrated probabilities and a clear rule for when to "think slowly."

A machine learning framework efficiently uncovers interpretable parameter regions for multistationarity in complex chemical reaction networks, outperforming brute-force sampling.

A unified source-conditioned transport framework accurately predicts single-cell population transitions across diverse perturbations without requiring paired observations.

A 1.46M parameter EEG foundation model matches larger counterparts in cross-disorder transfer, unlocking efficient clinical AI.

Recurrent neural network dynamics, not just local features, explain the temporal progression of perceptual grouping in complex natural scenes.

Simple SIR models can reproduce complex multi-wave epidemic patterns with surprisingly low dynamic complexity, but incidence data alone cannot reliably recover underlying transmission dynamics.

Unmodified Nine-Hole Peg Test now reveals detailed visuomotor coordination deficits by synchronizing gaze, hand, and object dynamics.

Near-perfect CKD classification on a small dataset is achievable, but highly sensitive to leakage and driven by specific lab features, not generalizable clinical insight.

Novel antimicrobial peptides with potent activity against a critical ESKAPE pathogen are discovered within US retail milk, driven by genetic and supply-chain factors.

A single digital twin framework can now unify lifelong health data, from genetics to environment, to accurately predict disease trajectories and risks across a person's life.

LLMs' apparent behavioral fidelity in sequential tasks can mask fundamentally incorrect generative mechanisms, failing to recover latent structure even with extended context.

Unknown traffic objects are detected with 2.6% higher recall than RT-DETR by fusing semantic and visual prompts symmetrically, enabling real-time open-world detection without NMS.

GPU kernel optimization agents can regress by up to 19% when transferring distilled knowledge across DSLs, revealing that source attempt quality, not just success, dictates transfer efficacy.

Folding multi-branch redundancy slashes speculative decoding costs in diffusion LLMs, unlocking up to 2x faster generation.

A single verification prompt is insufficient; learning a reliability score from multiple, complementary perspectives unlocks robust self-verification for VLMs.

A focused narrative review of studies of treatment-exposed solid tumours, distinguishing paired human tissue measurements from unpaired associations, functional perturbations, post-withdrawal experiments and clinical studies identified one that combined a stroma-directed intervention, CAF-resolved pharmacodynamics and a controlled treatment-effect estimate.

Artificial intelligence should be regarded as a complementary decision-support tool whose practical contribution depends on combining computational methods with biological knowledge and real-world agricultural observations.

The Biomod2 ensemble model was employed to compare the differences in species distribution predictions derived from two distinct data sources, to evaluate the respective influence of each environmental variable, and to characterize the future shifts in suitable habitats of Anoplophora glabripennis across climatic zones in China under climate change scenarios.

Higher income and older age were associated with a stronger sense of community, better perceived community safety, and improved health, and policymakers should consider the impact of community factors on well-being during health crises.

While the study was closed prematurely prior to completion, the sequence of regorafenib prior to anti-EGFR therapy suggested a possible survival benefit.

This study explored community perspectives on the influence of climate change on anxiety, trauma, depression, emotional wellbeing, and psychosocial development among young children in Malawi.

Unsupervisedly discovering genetic subgroups and performing layered meta-analysis unlocks 74 previously hidden association loci in a heterogeneous pig cohort and substantially reduces genomic inflation in the UK Biobank.

Brain control isn't a hierarchy, but a dynamic spectrum of influence shaped by context and practice, challenging decades of neuroscience dogma.

Activity-efficient RNNs exhibit a surprising dissociation between high-variance neural activity and causal output, and noise robustness actively hinders activity efficiency.

Directed transport can drive traveling wave solutions in viral spread models, even outperforming diffusion.

Model-generated DNA can now be traced with perfect accuracy, even after single-base edits, using a watermark detectable without knowing its precise location or strand.

Three generations of computational approaches to plant GRN inference are reviewed and measure them against a five-level ladder of evidence, including statistical association, conditional dependence, physical binding, functional regulation and causal circuits.

This study proposes an Artificial Intelligence (AI)-based model, namely Integrating Feature Reduction Models for Heart Disease Prediction (IFRM-HDP), which combines a hybrid feature selection strategy incorporating Boruta and minimum Redundancy Maximum Relevance with a Temporal Convolutional Network to capture complex dependencies among clinical attributes and improve classification performance.

Single-photon cameras transition from ambiguous to near-deterministic scene recovery as photon budget increases, a phenomenon precisely modeled by a novel Hypergeometric generative framework.

This mini-review highlights recent advances in AI-driven LIB recycling, focusing on the integration of machine learning, deep learning, and computer vision across the battery recycling value chain.

A cognitive formulation of breastfeeding trauma is presented, drawing from an existing formulation model for moral injury in PTSD, using cognitive therapy to support cognitive behavioural therapists to conceptualise breastfeeding trauma through the lens of moral injury where appropriate.

A training-light approach boosts fisheye object detection by 80% by strategically addressing input distortion, feature misalignment, and decision-level fusion with minimal parameter updates.

World models need to forget selectively, discarding outdated instance facts while rigidly retaining fundamental invariants like physics, a nuance standard forgetting metrics miss.

Output-layer gradients alone are sufficient for effective post-training data selection, slashing feature-extraction costs by up to 10x.

Success conditioning, a staple in RL, is formally proven to converge to optimal policies across broad MDP classes, with explicit convergence rates established.

Discovered options from high-dimensional observations unlock state-of-the-art Atari performance, drastically accelerating learning in complex, exploration-heavy games.

Most GNN explainers fail to detect indirect influences or alternative implementations of the same logic, even on seemingly robust benchmarks.

Learning conservative and periodic population dynamics is now 4-14x faster without needing numerical solvers.

A single AI pipeline can now detect marine environmental anomalies directly on satellites, drastically reducing data transmission needs and enabling real-time alerts.

Forgetting is not always beneficial in non-stationary bandits; evidence, not just change, should trigger adaptation.

Reward hacking is mitigated and sample quality preserved in few-step discrete diffusion models by replacing intractable KL penalties with a novel inverse-distillation regularization.

Reconstructing private client data from federated learning updates is significantly improved by treating model updates as path integrals, outperforming single-point gradient approximations.

Forward KL divergence in distillation inflates student entropy, while reverse KL can deflate it, offering a new lens on implicit regularization.

Predicting the predictable in latent space unlocks S2S weather forecasting skill beyond current AI model limits, matching and exceeding physics-based ensembles at critical 5-6 week horizons.

Structured variational inference unlocks richer epistemic uncertainty estimation in GPLVMs, outperforming mean-field approximations on manifold reconstruction.

Security benchmarks for LLM agents are fundamentally flawed: changing a tool's name can drastically alter reported attack success rates, invalidating cross-model comparisons.

Unlabeled physiological signals unlock multimodal EEG foundation model adaptation, matching supervised performance with zero-shot generation.

Ambiguity-aware NL2SQL generation framework produces realistic, complex queries that boost model performance on challenging real-world benchmarks, outperforming existing synthetic data.

Most "long-range" GNN benchmarks fail to actually test long-range interactions, but a new framework provides provably long-ranged tasks and a per-range error oracle.

Real-world robots can now learn faster and require drastically less human help by adaptively deciding *when* and *how* to be assisted, rather than relying on fixed rules.

Autonomous robotic navigation achieved 70% success in physically accessing cerebral veins, a critical step towards less invasive brain-computer interfaces.

Recurrently folding older time series context onto a most-recent anchor dramatically improves forecasting accuracy by naturally prioritizing recent evidence.

LLMs for math translate statements towards generality, widening quantification domains in over 60% of cases, yet fail to explicitly state implicit hypotheses, suggesting a shallow understanding of theoretical relationships.

A single 30-hour fMRI dataset unlocks whole-brain decoding of naturalistic speech, revealing continuous performance gains with more data.

Deep tabular generators fail to consistently outperform trivial baselines, even on datasets orders of magnitude larger than typical clinical data.

Diverse LLMs, when strategically routed and collaborating, can collectively achieve significantly fairer outputs than any single model acting alone.

Automated crack identification in fusion materials now scales across diverse conditions without manual tuning, enabling standardized inputs for predictive modeling.

Mechanistic edits can now offer provable guarantees of skill removal and preservation across entire input regions, not just tested subsets.

Mixture-of-Experts for order execution masks training failures rather than improving performance, with annealed exploration proving more critical for stability.

Few-step sampling in flow matching models achieves significantly higher fidelity by analytically integrating the dominant affine component of the dynamics, leaving only a smaller neural residual to be integrated.

Neural embeddings of conversation reveal individual identity, not just conversational role or trait differences, suggesting a fundamental organization of brain activity around the person, not the interaction.

Time series models can achieve state-of-the-art long-term forecasting by dynamically blending recent dynamics with retrieved historical trajectories, rather than relying solely on a single look-back window.

Deep generative priors unlock robust multistatic ultrasound recovery in rank-deficient regimes, outperforming linear methods and providing crucial uncertainty quantification.

Most visual computation in VLMs is redundant, allowing for a 55% FLOP reduction with minimal accuracy loss by simply bypassing token-specific projections in early and late layers.

Long-horizon agents achieve state-of-the-art performance by decoupling planning credit from execution guidance via structured subtask trees.

Metropolis-Hastings sampling for LLM policy composition consistently outperforms SIR, offering a provably better approximation of desired reward trade-offs with the same computational budget.

Drug-target affinity model performance rankings flip dramatically when evaluated on unseen protein targets, revealing a critical flaw in standard benchmarking.

Most graph EDL methods fail to reduce epistemic uncertainty with more data, but bootstrap ensembles show promise for consistent uncertainty estimation.

Operator-informed initialization for Fourier Feature PINNs directly combats spectral bias by tailoring weight distributions to the PDE's differential operator, leading to balanced frequency convergence and improved accuracy.

K-fold cross-validation's statistical evidence diminishes with increasing folds when data heterogeneity creates strong inter-fold dependence, a phenomenon quantified by an effective number of folds.

Pretrained generative models can directly sample exact Bayesian posteriors for data assimilation by conditioning their interpolant paths on observations, bypassing costly retraining.