arXiv:2607.14587v1 Announce Type: new
Abstract: Qubes OS is a revealing case for security measurement because its architecture makes component boundaries security-relevant. We present a protocol-driven longitudinal analysis of 109 public Qubes Security Bulletins (QSBs, 2011--2025), the official Qubes-maintained Xen Security Advisory (XSA) tracker, and a secondary vulnerability-event sensitivity series. The study measures the public advisory record rather than latent vulnerability incidence or realized compromise. The methodology combines audited deterministic component attribution, change-point analysis, overdispersion checks, severity-proxy weighting, censoring sensitivity, documentary latency lower bounds, and baseline-aware evaluation of vulnerability discovery models (VDMs).
The results show persistent upstream dependence in that public record. On the official tracker, 113 of 464 XSAs affect Qubes; under primary labeling, 87 of 109 QSBs (79.8\%) are attributable to Xen, CPU/microarchitectural, or other upstream components rather than Qubes-core logic, with similar results under weighted views. Change-point analyses identify 2015Q1 as the dominant break in the quarterly advisory series, while post-2018 annual disclosure rates are statistically flat. Poisson inferences are stable under dispersion diagnostics and negative-binomial sensitivity checks. The attribution codebook performs well in a stratified 30-QSB audit, and S-shaped VDMs fit descriptively but do not significantly outperform a rolling-mean baseline in short-horizon forecasts.
Overall, the Qubes public advisory record appears stable, but not quiet: disclosure activity plateaus at a higher level than in the earliest years, while the observed burden remains concentrated in upstream trust anchors.
Science Journals
arXiv:2607.12839v3 Announce Type: replace
Abstract: Modern edge system-on-chips (SoCs) combine CPUs, integrated GPUs (iGPUs), and neural processing units (NPUs), yet existing LLM runtimes typically make coarse device-level decisions or optimize operators in isolation. As a result, they underutilize heterogeneous resources, particularly on unified-memory platforms where performance depends on both device placement and task-graph coordination. We present HeteroMosaic, a heterogeneity-first scheduling framework for edge LLM inference. HeteroMosaic first uses a heterogeneous roofline model to identify when combining iGPU and NPU execution is beneficial. It then decomposes inference into dependency-preserving micro-batches that expose cross-accelerator overlap and applies trace-guided co-optimization of scheduling and device allocation under practical effects such as memory contention, DVFS, device variation, and NPU runtime overheads. We implement HeteroMosaic in PyTorch C++ and evaluate it on three AMD Ryzen AI platforms spanning NPU-heavy, balanced, and iGPU-heavy designs. On the balanced platform, HeteroMosaic achieves up to 1.73X speedup over an iGPU baseline, 1.78X over an NPU baseline, and 2.05X over frameworks such as llama dot cpp, while reducing energy by up to 45.3%. It also improves performance over prior heterogeneous edge AI solutions by up to 2.35X.
A system-level approach to generalized feedback Nash equilibrium seeking in partially observed games
arXiv:2503.24159v2 Announce Type: replace-cross
Abstract: This work proposes an algorithm for seeking generalized feedback Nash equilibria (GFNE) in noncooperative dynamic games. The focus is on cyber-physical systems with dynamics which are linear, stochastic, potentially unstable, and partially observed. We employ System Level Synthesis (SLS) to reformulate the problem as the search for an equilibrium profile of closed-loop responses to noise, which can then be used to reconstruct a stabilizing output-feedback policy. Under this setup, we leverage monotone operator theory to design a GFNE-seeking algorithm capable to enforce closed-loop stability, operational constraints, and communication constraints onto the control policies. This algorithm is amenable to numerical implementation and we provide conditions for its convergence. We demonstrate our approach in a simulated experiment on the noncooperative stabilization of a decentralized power grid.
arXiv:2607.14614v1 Announce Type: new
Abstract: Reinforcement learning with verifiable rewards (RLVR) commonly uses entropy for advantage shaping. However, entropy cannot distinguish useful uncertainty from detrimental confusion, limiting its effectiveness as a correctness signal. We propose Contrastive Policy Optimization (CPO), which uses token-level contrastive disagreement between reference-guided and vanilla generation distributions for correctness-aware advantage shaping. Both theoretical and empirical results show that this disagreement reliably indicates token-level correctness. We further show that On-policy Distillation is a special case of CPO, where the posterior distribution is instantiated by an external teacher model. CPO also resolves the zero-advantage problem. Experiments on in-domain and out-of-domain benchmarks demonstrate that CPO substantially outperforms entropy-based RLVR methods while maintaining strong generalization. Further analysis shows that correct and incorrect responses naturally support exploration and exploitation respectively, and balancing both leads to the best performance.
arXiv:2607.14588v1 Announce Type: new
Abstract: Combining refreshable tactile displays (RTDs) with conversational AI offers a promising approach to accessible data visualization for people who are blind or have low vision (BLV). However, it remains an open question how these modalities should be integrated to support accessible data experiences. We address this through a co-design process with three BLV co-designers. Building on our prior Wizard-of-Oz study, we created a conversational tactile data interface (CTDI) that combines an RTD with an LLM-powered conversational agent, refined through four workshops over eight months. In addition to the resulting system, Graphy, we contribute design knowledge and recommendations for CTDIs. Co-designers used touch as the primary sensemaking channel for spatial understanding of the data's shape, trends, and relationships, reserved the agent for what touch could not resolve (e.g., calculation and analysis), and used the chart on the RTD to verify the agent's responses. Key findings include: a layered presentation that scaffolds chart exploration through progressive, interactive layers; a feedback grammar that distinguishes user- and agent-initiated tactile feedback; and a sequential interaction pattern -- select, confirm, ask, verify -- where each step grounds the last.
arXiv:2607.14520v1 Announce Type: cross
Abstract: Nonlinear thermodynamic computers based on Langevin dynamics exploit thermal fluctuations as a physical substrate for computation. Recent work has shown that quartic-confined fluctuating degrees of freedom can act as thermodynamic neurons capable of nonlinear function approximation at finite observation times. Here we extend this paradigm from mean-only readout to moment-resolved readout. Instead of representing each driven reservoir solely by its first moment, we construct a response vector from the elementwise raw polynomial moments
\(\mathbb{E}[\bm{x}]\),
\(\mathbb{E}[\bm{x}^{\odot 2}]\), and
\(\mathbb{E}[\bm{x}^{\odot 4}]\).
These observables combine displacement and central-shape contributions and are naturally aligned with the linear, quadratic, and quartic terms of the local driven dynamics.
We further introduce a heterogeneous multi-reservoir architecture in which three reservoirs with distinct initialization and training histories form a joint \(2304\)-dimensional response representation. Under the fixed MNIST \(60000/10000\) reproduction protocol, feature-level fusion achieves the best observed accuracy of \(9695/10000=96.95\%\), compared with \(9682/10000=96.82\%\) for the strongest single-reservoir model and \(9684/10000=96.84\%\) for equal-weight logit averaging. An exact paired McNemar test does not establish a statistically significant improvement over the strongest single reservoir, but the ablation and wrong-set overlap results provide suggestive evidence of complementary classification errors. These results motivate higher-order polynomial-moment readout and reservoir heterogeneity as candidate design principles for finite-time Langevin computing.
arXiv:2607.14566v1 Announce Type: new
Abstract: This paper presents a fully automated end-to-end framework for adversary emulation from MITRE ATT&CK-aligned CTI reports using LLMs. Unlike prior work, which either executes prewritten playbooks or partially automates playbook generation, our framework unifies playbook generation, execution, and failure recovery in a single workflow. In particular, although AURORA, the most recent prior study, generates playbooks from CTI reports, it still requires partial manual intervention and does not revise playbooks based on execution failures. Our framework generates Caldera playbooks from CTI reports, executes them automatically, and revises failed Abilities through a failure-type-aware recovery mechanism. Evaluated on 11 CTI reports with Claude Sonnet 4.5, GPT-4o, Gemini 2.5 Pro, and Grok 4 Fast, the framework achieved its best results with Claude Sonnet 4.5: 27.3 Abilities per playbook, 84.22% execution success after revision, and CTI Precision, Recall, and F1 of 73.95%, 52.48%, and 60.50%, respectively. The failure recovery mechanism consistently improved execution success across all evaluated LLM models by 14.59%p to 17.23%p. On the 10 CTI reports selected from AURORA's dataset, this mechanism further increased the final execution success rate, surpassing that of AURORA, which represents the state-of-the-art adversary emulation system.
arXiv:2607.14349v1 Announce Type: new
Abstract: While Large Language Models (LLMs) excel in many general NLP tasks, their formal reasoning capabilities are often compromised by content effects, demonstrating a measurable bias towards real-world plausibility. In this paper, we present our system for SemEval-2026 Task 11, which evaluates the ability of models to disentangle formal logic from content across 12 languages with and without distractor premises. We address this challenge using mDeBERTa-v3 networks fine-tuned on a synthetic, rule-based dataset of syllogistic schemes to avoid the semantic noise of LLM-augmented data. To explicitly decouple plausibility from logical structure, our training pipeline employs a multi-objective loss function combining Adaptive Group Distributionally Robust Optimization (DRO), a scheduled differentiable bias penalty, and KL-Divergence consistency regularization. Our system achieved #1 ranks and perfect Ranking Scores (100.0) with 0.00% bias and 100.0% accuracy on Subtask 1 (English), Subtask 2 (Noisy English), and Subtask 3 (Multilingual). On the highly complex Subtask 4 (Noisy Multilingual), the system achieved the 6th rank with 89.06% Accuracy and F1-score, alongside a limited 2.89% Bias and a 37.78 Ranking Score. Our dataset generation engine and codebase are publicly available to facilitate future work on robust logical reasoning.
arXiv:2607.14615v1 Announce Type: new
Abstract: A spline dimensional decomposition (SDD) surrogate effectively represents high-dimensional engineering responses with localized features and complex nonlinearities in uncertainty quantification (UQ). However, limited training data can make coefficient estimation from function values severely ill-conditioned. We propose gradient-enhanced SDD (GE-SDD), which trains the surrogate using function values and partial derivatives. A diagonal row-weight matrix balances the function and derivative blocks by their Frobenius norms. We solve the balanced system through ridge regression in probability-weighted Sobolev coordinates and select the regularization parameter using grouped K-fold cross-validation to prevent information leakage. Mapping the solution back to the L2-orthonormal SDD basis preserves closed-form mean and variance estimates. We evaluate the proposed GE-SDD on a two-dimensional continuous exponential function, a linear dynamical system with three uncertain parameters, and a 30-dimensional 25-bar truss. GE-SDD is more accurate than standard SDD and uses gradients more robustly than gradient-enhanced Kriging. GE-SDD achieves a median NRMSE of 1.022% on the nonsmooth benchmark, compared with 8.731% for Kriging. For the truss, GE-SDD yields lower NRMSE and more accurate standard-deviation estimates than Kriging at moderate training sizes and above. Overall, the benefits of gradient augmentation depend on input dimension, basis resolution, training size, and the target UQ quantity.
One-Shot Generative Design for Disordered Metamaterials via Self-Organizing Neural Cellular Automata
arXiv:2607.14475v1 Announce Type: new
Abstract: Disordered metamaterials feature microstructures with inherent randomness and irregularity, enabling them to achieve broader property coverage and superior performance unavailable in their regular counterparts. Despite their promise, designing disordered microstructures is substantially harder than designing regular ones. Their design remains trapped between manual parameterizations with limited expressiveness, and generative AI that is data-hungry and struggles to generalize. To address these limitations, we propose a generative design framework based on Neural Cellular Automata that dynamically grows complex microstructures through learned local interaction rules, inspired by the self-organizing processes in natural materials. This framework requires only a single training template, yet accommodates diverse disordered microstructures and adapts to irregular domains and arbitrary discretizations. By manipulating the learned local rules, we can steer the growth process to generate microstructures unseen during training, providing control over orientation, anisotropy, and directional thickness without retraining. As a dynamic, local growth process, it naturally produces spatially varying microstructures that transition smoothly to enable location-specific mechanical properties. We demonstrate this in a multiscale mechanical cloaking design, where microstructures vary across the space to meet an optimized heterogeneous property distribution. Our design enables excellent cloaking performance without complicated post-processing and incompatible assembly common in existing methods. This data-efficient, generalizable approach opens access to previously intractable disordered materials for biomedical implants and soft robotics.
arXiv:2506.03549v2 Announce Type: replace-cross
Abstract: Quantum position verification (QPV) enables multiple verifiers to certify a prover's location using quantum communication and physical assumptions. With experimental demonstrations of QPV becoming increasingly feasible, enhancing the practicality and security of QPV protocols is more important than ever. In this work, we make three key contributions toward this goal. First, we introduce a robust QPV protocol in which the verifier's state preparation is independent of channel loss, improving reliability in real-world conditions. Second, we refine existing security analysis techniques to bolster protocol resilience against experimental imperfections. Third, we identify and address some implicit assumptions present in existing security analyses, providing a framework to eliminate such assumptions. Additionally, as an example of QPV application beyond location verification, we illustrate how QPV can be leveraged for authenticating classical communication in quantum key distribution.
arXiv:2604.23743v2 Announce Type: replace-cross
Abstract: Variational quantum circuits train poorly on chaotic forecasting, usually blamed on barren plateaus (exponentially vanishing gradients). Using an exactly simulable four-qubit variational quantum physics-informed circuit fit to Lorenz-63, we show the barren-plateau explanation fails: the failure is an architectural capacity ceiling fixed by the circuit time-encoding, not its trainable depth. Four measurements support this. (i) A McClean-comparable gradient-variance estimator sits at the local-cost Haar/2-design scale 2^(-2n)=3.9e-3 at n=4; on structurally live parameters it decays about ninefold with depth then saturates there, large enough to train, not an exponential collapse. (ii) At a common budget of 200 optimiser iterations (600, in three stages, for layer-wise), gradient descent, layer-wise, and SPSA reach the same order of magnitude of loss, so no optimiser unlocks a better basin. (iii) The output-Jacobian rank saturates at 33 from five layers on, so depth buys no new output directions. (iv) A Fourier analysis explains why: the qubit-1 phase encoding acts on the initial |0> and is inert, so the maximum accessible frequency is 2.5/t_max=0.83 Hz, identical at every depth and about 4.4x below the narrowest Lorenz component bandwidth. The corrected band has dimension 1+2x5=11 per observable, and 3x11=33 equals the measured rank ceiling exactly, unifying the two diagnostics. A trained depth sweep agrees: mean loss improves with depth then flattens once the rank saturates. We correct our earlier preprint diagnosis, which compared unnormalised gradient norms to the McClean threshold, and place the advantage of fixed reservoirs and classical echo-state networks in architecture, not quantum mechanics.
arXiv:2607.14455v1 Announce Type: new
Abstract: Single-Robot Motion Planning (SRMP) in highly non-convex constrained environments, where robots must satisfy collision-free guarantees, dynamic feasibility, and task-related constraints, is challenging under complex constraints and computational limits. Recent Model-Based Diffusion (MBD) approaches recast the SRMP as trajectory optimization that samples from a posterior over trajectories, using known dynamics, and analytically estimates the score function from rollout samples to guide diffusion denoising toward a low-cost, clean trajectory without demonstration learning. While existing works further adapt MBD to constrained environments and showcase promising performance, they are still limited by (1) enforcing safety either via soft feasibility diffusion priors or hard projection operators, but lack a unified framework to integrate both, and (2) fixing safety enforcement to neglect the changing of diffusion scheduling. Therefore, we introduce Model-Based Diffusion via Constraint Optimization and Adaptive Scheduling (MD-COAS) for SRMP that unifies the inexact Augmented Lagrangian Method (iALM) soft diffusion prior with a Convex Feasible Set (CFS)-based hard projection operator, and adaptively schedules and co-optimizes safety enforcement, along with diffusion scheduling. Experiments demonstrate that our method achieves higher safety \& success rates, faster convergence, and lower final costs than baseline planners on randomly generated highly non-convex 2D benchmarks and a 7-DoF robot arm avoidance task.
arXiv:2607.14141v1 Announce Type: new
Abstract: Bayesian Belief Networks (BBNs) are powerful tools for decision-making under uncertainty. However, building their structures and estimating parameters are difficult. Currently, researchers must choose between relying on expert judgement or using large datasets to learn the structure and parameters of the network. We propose a new methodology using Large Language Models to bridge the gap between expert opinion and data-driven learning. This approach uses a panel of AI agents to estimate probabilities based on specific personas and context. We then apply a trimmed-mean rule to remove noise from these responses. We develop a six step BBN framework and illustrate it to model customer intention to consult a doctor in an alternative healthcare system. The model reveals that while self efficacy appears to be a major factor, its actual causal impact is small. In contrast, subjective norms have a much stronger effect in modelling customers' intention. The most effective strategy is to improve both confidence and community norms simultaneously.
arXiv:2607.14354v1 Announce Type: new
Abstract: Aiming for high injection efficiency in three-dimensional spiral injection, the underlying physical principles governing beam formation and matching should be systematically organized within a unified canonical framework. However, a general theoretical framework explaining why particular beam distributions become naturally matched has not yet been established. In this work, a canonical description of three-dimensional spiral injection is developed based on the eigensystem of the symplectic covariance matrix J{\Sigma}. Canonical modal families are introduced to represent the underlying beam structure, and finite-emittance beam distributions are synthesized by statistical broadening around the corresponding modal skeletons while preserving their canonical topology. Unlike conventional beam-matching methods based on Twiss parameters or eigen-emittance analysis, the proposed framework employs canonical symplectic modes as design variables for beam-family synthesis. The proposed framework provides a unified description of beam geometry, eigen-emittance, and canonical angular momentum, and enables arbitrary beam distributions to be interpreted in terms of dominant canonical modes. Beyond providing a canonical design representation of three-dimensional spiral injection, the proposed framework establishes a direct connection between canonical beam dynamics and experimentally realizable injection beams, thereby providing a theoretical basis for beam synthesis and high-efficiency injection design. This framework enables the systematic representation, synthesis, and evaluation of finite-emittance spiral injection beams in canonical modal space.
arXiv:2607.14589v1 Announce Type: new
Abstract: This paper proposes a quasi-BP decoding scheme for BCH codes that preserves the parallelizable structure of belief propagation while exploiting code automorphisms and optimized redundant parity-check matrices. To eliminate the computationally expensive $\tanh$ and $\tanh^{-1}$ functions in check node updates, we further introduce a neural-network-based variant that replaces them with a lightweight convolutional neural network trained under a triple-constraint loss function enforcing non-negativity and order consistency. Simulation results for three BCH codes demonstrate that quasi-BP decoding achieves competitive frame error rate performance, with a gap within 0.25 decibels compared with belief propagation decoding of an LDPC code of similar blocklength. The neural-network-based variant incurs negligible performance loss while enabling stable deployment with arithmetic operations on hardware accelerators. Concatenation with an ordered statistics decoding variant further bridges the gap to the maximum-likelihood bound. Hence, the proposed schemes offer a viable path toward high-throughput, low-latency decoding of BCH codes in next-generation communication systems.
arXiv:2607.14275v1 Announce Type: new
Abstract: Context engineering has become central to building reliable AI agents, yet it remains largely unmeasured. Agents do not fail in isolation: their behavior is shaped by the instructions, tools, memory, retrieved knowledge, guardrails, and untrusted inputs accumulated in their context. When this context is weak, agents drift, hallucinate, misuse tools, ignore constraints, become vulnerable to injection, and waste tokens. This paper validates context-engineering quality as an independent leading indicator of agent reliability. We implement the measurement in ProofAgent-Harness, an open-source infrastructure for AI agent evaluation that uses multi-juror, consensus-based scoring. The harness assesses context across seven criteria: role clarity, guardrail coverage, instruction consistency, tool schema quality, grounding sufficiency, injection hardening, and token efficiency. Crucially, the context score is isolated from behavioral metrics and release decisions, enabling a non-circular validation. Through a controlled context-quality study across regulated agent domains, holding frontier LLM agents fixed and varying only their operating context, we show that context-quality criteria consistently predict their corresponding behavioral outcomes. Grounding sufficiency predicts hallucination resistance, guardrail coverage predicts manipulation resistance, instruction consistency predicts instruction following, and tool-schema quality predicts tool use. These findings establish context measurement as a validated preflight signal for agent reliability and position context engineering as an auditable layer of agent evaluation and governance.
arXiv:2607.14357v1 Announce Type: new
Abstract: Advertisers delegate bidding to autobidders; users delegate tasks to language-model agents. A person describes what they want to an automated proxy that acts in a mechanism on their behalf. This is the revelation principle in production, and it forces a question classical theory assumes away: when is it optimal to describe yourself honestly to your own proxy?
We show the answer turns on one quantity, the proxy's within-range regret. The most a principal can gain by misreporting equals the regret of the proxy's honest-report action against those the principal could have steered it to take. Honest self-description is optimal exactly when the proxy already plays the best action it can reach, that is, when it is loyal (Theorem 1). The identity unifies auction-specific autobidding results and pins down when the faithful-communication assumption behind language-model elicitation proxies (Huang et al.) holds.
The identity constrains guardrails placed on proxies, from bid caps to a model's alignment layer. No guardrail can be at once binding (it displaces the truthful action from the proxy's best reachable outcome), truthful (honest reporting stays optimal), and capability-preserving (that outcome stays reachable through some report); any two preclude the third (Theorem 2). A safety constraint that alters what a model does while leaving its best output reachable makes honest description of intent suboptimal, so a sharper report can gain. This is the incentive behind prompt-engineering and jailbreaking.
Because within-range regret is #P-hard to compute exactly, we estimate it from samples and maintain it as a model is updated, at a cost set by how far the model drifts, not how often it changes. Running it on production language models from five providers under an alignment-style cap, we find honest reporting leaves surplus unclaimed on every model, recovered by inflating the report.
arXiv:2607.15078v1 Announce Type: new
Abstract: Accurate modeling of compressible premixed flames, encompassing both deflagrations and detonations, remains a significant challenge for predictive Large Eddy Simulation (LES) due to the strong coupling between the thermochemical state and the local thermodynamic state. This work presents a manifold-based turbulent combustion model that ensures a fully consistent thermodynamic state between model and flow solver through an iterative procedure. The framework reproduces critical quantities including temperature, radical species, and source term profiles, addressing limitations of existing approaches that rely on low-Mach perturbations or tabulated ZND detonations without thermodynamic consistency. Validation is performed against one-dimensional and high-fidelity RDE-like data, demonstrating that the thermodynamically consistent model consistently outperforms existing approaches across a broad range of compressible flame regimes - including both deflagration and detonation. The results highlight the importance of fully accounting for the thermodynamic state to achieve accurate predictions. By capturing both deflagrative and detonative behavior within a single framework, the model provides a unified, versatile tool for LES of high-speed reacting flows and offers a foundation for future studies of compressible reacting flows, including applications to rotating detonation engines and other supersonic combustion systems.
arXiv:2607.15268v1 Announce Type: new
Abstract: Myocardial infarction (MI) remains a leading cause of mortality worldwide. Echocardiography (Echo) is a widely available modality for MI assessment, where regional wall motion abnormality is a key indicator. Prior learning based methods for myocardial motion analysis often use handcrafted descriptors or densely supervised estimation, but the need for extensive annotation limits applicability. Foundation models have recently improved vision-based Echo analysis; however, most methods operate on single views and segment-level localization remains unreliable under view-dependent ambiguity, especially in apical views. To address this, we propose MCF-Net, a novel motion-guided multi-view fusion framework that fuses myocardial motion cues with foundation model representations to localize infarction. Visual features are extracted using EchoPrime, a pretrained Echo foundation model shared across dual views. Cardiac motion is modeled with extremely sparse supervision: a single annotated template frame is transferred across videos to initialize point tracking, avoiding dense labels. Motion-derived segment-aware soft masks provide coarse spatial priors that selectively enhance features for challenging myocardial segments. A motion-conditioned fusion mechanism then integrates motion and vision across views, refining predictions without overriding strong appearance cues. On segment-level MI localization, MCF-Net achieves 72.4\% F1 and 84.9\% accuracy, outperforming state-of-the-art motion-only, vision-only, and fusion baselines.
arXiv:2607.14476v1 Announce Type: new
Abstract: Hydrated electrons of opposite spins pair to form dielectrons at sufficiently high concentrations that can be achieved by dissolution of alkali metals in water. While experimental investigations of these systems are challenging due to their vigorous, even explosive, reactivity, simulations open the possibility to characterize the structure and reactivity of hydrated dielectrons and the effects of alkali cations thereon. Here, we present ab initio molecular dynamics simulations of a hydrated dielectron without or with explicit Li$^+$ or Cs$^+$ counterions. While the overall solvation structure is preserved in all these systems, the presence of cations has a distinct effect of increasing the dielectron gyration radius by about 10% and forming cation-specific cation-dielectron arrangements. Moreover, analysis of water bond lengths reveals in all studied systems a substantial elongation of first-shell O-H bonds oriented toward the dielectron, providing a structural explanation for vibrational red-shifts observed in resonance Raman measurements. Finally, at the 10 ps simulation timescale, rare reactive events were observed, albeit only for the system without metal cations, where hydride intermediates stable on picosecond timescales were identified. These observations also suggest that on the investigated timescales, metal cations may suppress hydrated dielectron reactivity.
arXiv:2607.14367v1 Announce Type: new
Abstract: Federated fine-tuning of large pre-trained models increasingly relies on Low-Rank Adaptation (LoRA) to reduce communication and computation, but heterogeneous clients can make adapter aggregation unstable. We identify the data-parameter interference as a geometric source of this instability. This interference is controlled by the alignment between LoRA update subspaces and client activations, suggesting that federated LoRA aggregation should be viewed not only as parameter averaging but also as subspace allocation. We propose Dynamic Subspace Boosting (Dysco), a plug-in method that allocates client-specific LoRA subspaces in a federated and dynamic manner. In each round, clients compute activation-insensitive subspaces from local representations and transmit only the resulting bases; the server then constructs client-specific merged subspaces through a closed-form solution that maximizes compatibility with other clients' insensitive directions. To handle representation drift, Dysco performs multi-round subspace boosting to preserve past update directions while adapting to future representations. We provide a convergence analysis that embeds the data-parameter interference as an aggregation-error term in a standard federated optimization bound, and prove that Dysco's server-fixed merged subspaces yield a tighter upper bound on this error. Experiments on controlled synthetic federated tasks and on MIMIC-IV clinical-note classification with Llama-3.2-1B show that Dysco substantially reduces interference, reduces the final-round synthetic training loss by up to 9 times relative to baselines under the orthogonal-subspace partition the theory identifies, improves all five tested FL algorithms by up to 4.3% on MIMIC, outperforms recent federated LoRA methods, and adds only 0.9% wall-clock overhead. Our code is available at https://github.com/illidanlab/Dysco.
arXiv:2507.03209v2 Announce Type: replace-cross
Abstract: The discovery of new ionizable lipids for efficient lipid nanoparticle (LNP)-mediated RNA delivery remains a major bottleneck in RNA therapeutics development. Recent advances demonstrate the potential of machine learning (ML) models to predict transfection efficiency directly from lipid structure, enabling high-throughput virtual screening and accelerating lead identification. However, as new models for LNP transfection prediction continue to emerge, the lack of rigorous and standardized benchmarking poses a significant risk and may undermine confidence in their reliability for discovery. Here, we present a robust ML benchmarking framework for evaluating transfection prediction models based on ionizable lipid structures. This framework systematically benchmarks diverse molecular representations paired with a broad range of ML architectures spanning traditional models, feedforward neural networks, and state-of-the-art graph-based methods. In addition, the presented framework supports assessment of model generalization and evaluates prediction reliability beyond standard regression metrics. Using a curated dataset of 1,100 unique ionizable lipid structures derived from the HeLa transfection dataset originally reported by Xu et al., we show that within this framework, models leveraging explicit molecular substructure encoding consistently achieve the highest predictive accuracy and should serve as essential baselines for the development of new, more sophisticated models. In contrast, some current graph-based models, including AGILE, Chemprop, and KPGT, tend to show comparatively lower accuracy. The presented framework provides a standardized, transparent, and comprehensive benchmarking resource that enables meaningful comparison of emerging architectures and establishes strong baselines for future development of predictive models in lipid-based RNA delivery.
arXiv:2510.16657v3 Announce Type: replace-cross
Abstract: Synthetic data has been increasingly used to train frontier generative models. However, recent studies raise key concerns that iteratively retraining a generative model on its self-generated synthetic data may keep deteriorating model performance, a phenomenon often coined model collapse. In this paper, we investigate ways to modify the synthetic retraining process to avoid model collapse, and even possibly help reverse the trend from collapse to improvement. Our key finding is that by injecting information through an external synthetic data verifier, whether a human or a better model, synthetic retraining will not cause model collapse. Specifically, we situate our theoretical analysis in the fundamental linear regression setting, showing that verifier-guided retraining can yield near-term improvements, but ultimately drives the parameter estimate to the verifier's "knowledge center" in the long run. Our theory further predicts that, unless the verifier is perfectly reliable, these early gains will plateau and may even reverse. Indeed, our experiments across linear regression, Variational Autoencoders (VAEs) trained on MNIST, and fining-tuning SmolLM2-135M on the XSUM task confirm these theoretical insights.
arXiv:2607.12766v2 Announce Type: replace-cross
Abstract: A fundamental question in receptor-mediated endocytosis remains unanswered: what initial driving force brings ligands and receptors into close proximity? While previous models assume pre-existing contact and overlook this initiation problem, we propose that entropic forces from nanoscale biomolecules in crowded cellular environments provide the essential driving mechanism. We develop a unified continuum model rooted in the Onsager variational principle, where engulfment depth serves as the generalized coordinate and the driving force derives from a free energy landscape of entropic, binding, membrane, and cytoskeleton contributions. The framework naturally incorporates: (i) entropy-driven adhesion as initiation; (ii) ligand-receptor binding as the sustaining force; (iii) membrane deformation via the Helfrich-Canham Hamiltonian; and (iv) cytoskeleton viscoelasticity through the elastic-viscoelastic correspondence principle. The kinetic phase diagram predicts a critical biomolecule concentration for initiation, a lower bound of ligand density for complete engulfment, a finite size window for engulfable particles, and an optimal virus radius of 30--60 nm that decreases with increasing binding energy. The Onsager solubility condition naturally yields the phase boundaries. The model exhibits asymptotic consistency with the classic Asakura-Oosawa result in the large-particle flat-surface limit. Stiffer cells lead to longer engulfment times and narrower size windows. Strikingly, the optimal size matches HIV-1 dimensions under physiologically realistic parameters. This work provides a variational foundation for cellular uptake with implications for virology, nanotechnology, and drug delivery.