Papers

A paper's overview figure is often the first thing readers see: it must explain the method correctly and look polished. We present Sivia, an overview-figure system that reads the paper, plans its relations, adapts visual organization from recent top-conference papers, and composes a design for image generation. Sivia improves with use through human-guided recursive self-improvement (RSI) at two levels: learning to draw revises its drawing skill from feedback on accuracy and presentation, and learning to learn revises the updater that proposes those revisions. A personal profile adapts presentation to its user's aesthetic preferences while the scientific content stays fixed. A diagnostic study of 1,964 overviews for 445 papers (17,973 relation judgments) shows that most errors are omissions and that source-derived relation plans raise required-relation accuracy (RRA) by up to 19.85 points. On 48 test papers, learning both levels reaches 93.54% RRA, 15.31, 9.38, and 8.33 points above the initial skill, the Fixed-updater, and the GEPA baselines, while readability and visual quality rise from 3.95 and 4.00 to 4.70 and 4.78 on five-point scales. Learned presentation profiles raise liking from 4.02 to 4.73 and adoption from 34 to 45 of 48 tasks. Code and released artifacts are available at https://github.com/exsinger-hub/Sivia

Semi-autoregressive diffusion large language models (dLLMs) improve decoding parallelism through iterative block-wise denoising, but scaling them with mixture-of-experts (MoE) layers introduces a large expert parameter footprint that exceeds memory-constrained GPU capacity. Expert offloading is a natural remedy, yet existing MoE serving systems target autoregressive decoding and rely on intra-iteration layer-wise prefetching: while computing one layer, they predict and load experts for subsequent layers. Under dLLM inference, block-wise routing expands the active expert working set within each iteration, making such prefetches difficult to complete in time and costly when mispredicted. Consequently, existing prefetch-based solutions often degenerate into on-demand expert loading with high decoding latency. We propose OLED-MoE, an expert offloading system that shifts the optimization target from intra-iteration prefetching to inter-iteration expert retention. Its key insight is that adjacent denoising iterations exhibit strong expert routing overlap, and token confidence indicates which experts are likely to be reused. OLED-MoE uses confidence-guided inter-iteration prediction to retain high-value experts in GPU memory without introducing extra prefetch traffic. It further compensates unavoidable cache misses through CPU-GPU cooperative execution, jointly considering dynamic expert computation load and predicted future reuse. Across diverse dLLM workloads, OLED-MoE reduces time per output token (TPOT) by 1.23x-7.93x and improves expert cache utilization by 1.44x-4.23x over state-of-the-art offloading systems. Notably, OLED-MoE approaches full-residency performance while using only 40% of the expert GPU memory, incurring merely 23% higher TPOT despite a 60% reduction in expert memory footprint. OLED-MoE's source code is publicly available at https://github.com/flashserve/OLED-MoE.

Quantization errors in video diffusion transformers can be amplified or attenuated by subsequent denoising updates, making local reconstruction error an incomplete predictor of final impact. We introduce PulseQuant, a 4-bit post-training quantization method that combines trajectory sensitivity with activation geometry to guide offline calibration. Isolated block–step interventions estimate propagation risk, which prioritizes sensitive trajectory states during row-radius selection. With these radii fixed, response-subspace correction uses neighboring-code edits to reduce residual components along dominant activation directions. Both stages preserve the original 4-bit weight representation. Controlled interventions show that short-horizon propagated error predicts final latent error more reliably than immediate block-output error, supporting calibration beyond local reconstruction objectives. Evaluations on Wan models, Self Forcing, and MiniMax-H3 demonstrate improvements in key consistency and dense-reference metrics while remaining competitive on other attributes across model scales and generation paradigms.

The local linear minimization oracle (Ferris and Zavriev, 1996; arXiv:2605.08850), or Local LMO, solves constrained convex problems without having to compute a projection: it minimizes a linear model over the intersection of the feasible set with a ball around the current iterate. We show that, whenever the ball radius does not exceed the Polyak radius, the Local LMO step is the Euclidean projection of the current iterate onto the intersection of the feasible set with a half-space that separates the iterate from the solution set; this projection is nonetheless computable by a linear oracle alone. We demonstrate that Local LMO belongs to a broader family of projection methods which may be indexed by the depth of the localizing half-space. For objectives with $\vartheta$-Hölder continuous gradient, every method from this family whose half-space lies sufficiently deep drives the best of its first $K$ iterates to optimality at the universal rate $\mathcal{O}(K^{-(1+\vartheta)/2})$, matching the non-accelerated universal gradient method of Nesterov (2015). Run at the Polyak radius, Local LMO attains the same rate when the constrained optima are also unconstrained ($\|\nabla f(x_\star)\| = 0$), and the rate $\mathcal{O}(K^{-1/(2-\vartheta)})$ otherwise.

Coding-agent evaluation has progressed from resolving individual issues to carrying out long-horizon development, yet task completion is still largely assessed within a single codebase. In software ecosystems, many features and bug fixes require coordinated changes across multiple repositories. We introduce WideSWE to evaluate coding agents on such cross-repository tasks. Mining and reviewing changes across 103 software ecosystems yields 120 real-world tasks, balanced between 60 bug fixes and 60 features. We derive prompts from related issues and pull requests. We systematically review and adapt hidden tests to support diverse correct implementations while preserving required behavior and regression checks. Across seven agent configurations, full task success ranges from 10.83% to 42.50%, with the configuration pairing Codex CLI with GPT-5.6-sol achieving the highest rate. Trajectories show agents failing to identify necessary changes, recognizing changes but leaving them unfinished, or modifying the required repositories without fully satisfying the request. To examine whether working on one repository at a time can alleviate these difficulties, we compare it with joint execution under identical prompts. Independent execution mainly recovers omitted work and is less effective at correcting previously attempted but unsuccessful implementations. Joint execution can use information from related repositories to guide implementation and verification. Code is available at https://github.com/ZJU-ACES-ISE/WideSWE.

We consider the focusing, $L^2$-supercritical and $\dot{H}^2$-subcritical nonlinear fourth-order Schrödinger equation. The scattering of radially symmetric solutions below the ground state was proved by Guo [Comm. Partial Differential Equations (2016)] and Dinh [Nonlinearity (2021)]. In this paper, we extend the scattering results to group-invariant solutions. In Komada–Masaki [Nonlinearity (2024)], the scattering of group-invariant solutions below the ground state was proved under a certain hypothesis. To remove the hypothesis, we establish the non-optimal scattering result for general solutions, where the threshold of action is less than certain fraction of the action of the ground state. This result is analogous to that in Pausader–Shao [J. Hyperbolic Differ. Equ. (2010)] for the $L^2$-critical nonlinear fourth-order Schrödinger equation.

Quadratically nonlinear systems driven by additive noise arise throughout science and engineering, both as stochastic dynamical systems in their own right and as reduced-order models (ROMs) of turbulent flows. We propose stochastic physics-constrained operator inference (SPOpInf), a non-intrusive method that learns a stochastic differential equation, i.e., a quadratic–linear drift and a diffusion amplitude, from data. Unlike operator inference (OpInf), which fits a deterministic drift by least squares, SPOpInf maximizes an Euler–Maruyama increment likelihood in which the residual covariance is itself an unknown, so the deterministic and stochastic components are calibrated jointly; it enforces the energy-preserving quadratic structure exactly, through linear equality constraints imposed inside the optimization; and it works in an upper-triangular parametrization that removes the structural non-identifiability of the full quadratic tensor. The resulting optimization is solved by an expectation–maximization (EM)-based iterative learning algorithm that updates the drift coefficients and the noise levels in turn: each iteration requires only an inexpensive weighted least-squares solve for the drift and a residual-variance update for the noise. With five stochastic benchmark systems that show intermittency and chaos, SPOpInf recovers the deterministic operators more accurately than OpInf, satisfies the imposed constraints to machine precision, accurately estimates the noise amplitudes, and reproduces long-time statistics. Applied to a stochastic Burgers equation and a quasi-geostrophic double-gyre flow, the same estimator yields non-intrusive stochastic ROMs that remain stable over long times where unconstrained alternatives blow up.

Large language model agents increasingly rely on compound programs for retrieval, tool use, reasoning, and verification, yet their failures often arise from local procedural decisions. Existing reinforcement-learning and prompt-optimization approaches typically rely on scalar rewards or repeatedly modify entire prompts, making it difficult to capture and reuse procedural improvements while preserving a frozen agent. To address this problem, We propose Natural-Language Policy Gradients (NLPG), an external policy-memory method for improving a fixed agent without changing its model parameters or program structure. NLPG diagnoses execution traces, propagates downstream feedback backward through the module graph, and converts recurring failures into route-local natural-language corrections that are aggregated into bounded policy updates for subsequent executions. Across six benchmarks covering memory, reasoning, instruction following, and evidence verification, NLPG also outperforms the strongest listed baseline for each benchmark by 8.71 percentage points on average. These results provide evidence that evaluated procedural experience can be transformed into local and interpretable policy updates, enabling continual improvement of frozen agents.

A central goal in robotics is to enable manipulation across changing tasks and environments. Vision-language-action (VLA) models provide broad manipulation capabilities but can struggle when execution requires diagnosing failures and adapting behavior. Strong agents can discover effective interventions through interaction with these policies. We propose Recursive Harness Distillation to accumulate this experience as reusable guidance across agents. A strong agent distills its experience into a playbook for a light agent, then recursively refines the playbook using the light agent's execution feedback. The resulting playbook enables agents to reuse accumulated intervention knowledge in new task instances without updating model parameters. In real-world manipulation, the harness improves success from 37.3% to 64.0%. On SimplerEnv Bridge, the light agent with the playbook achieves 66.7% success, compared with 41.7% for the GR00T-only baseline, and outperforms the strong agent without a playbook. The same playbook also benefits the strong agent, which reaches 79.2% success. These results demonstrate the feasibility of harness distillation for robotics: intervention experience can be accumulated, refined through execution, and reused across agents to improve manipulation.

Deterministic neural networks and neural operators provide point predictions with no intrinsic measure of reliability. Yet, predictive uncertainty may stem from irreducible outcome variability, finite data, or limitations of the chosen model class. Monte Carlo dropout offers a computationally convenient way to construct a predictive distribution through stochastic feature masking, without training multiple independent networks or explicitly inferring a posterior over model parameters. However, its perturbation law is largely prescribed a priori and typically factorised across latent coordinates. We introduce Optimal Transport Dropout (OTD), which instead learns the predictive mapping and the law of its latent perturbations jointly. Starting from a simple independent reference distribution, OTD transports latent perturbations through a learnable flow and propagates them through the predictive neural network, thereby inducing a structured predictive law. Training uses the strictly proper Energy Score, while a kinetic-action term geometrically regularises the transport. Synthetic benchmarks show that OTD captures multimodal predictive distributions, generates meaningful dispersion when the model is misspecified, and exhibits contracting dispersion as more training data or greater model capacity are provided. For a field-valued partial differential equation surrogate, predictive dispersion strongly aligns with the spatial pattern of prediction errors. On this task, compared with Monte Carlo dropout, OTD yields more accurate predictions and better-calibrated, substantially narrower intervals. On real-world regression benchmarks, it further shows competitive accuracy and better probabilistic predictions compared to established baselines. OTD therefore offers a way to learn structured predictive uncertainty without explicit posterior inference or ensembles of independently trained predictors.

Fraud detection is an important area of research in the insurance business due to its financial implications. The primary aim of a fraud detection model is to identify fraud and non-fraud cases with high accuracy along with other important metrics such as Sensitivity, Specificity, Precision, F1-score, False Positive Rate, False Discovery Rate, AUC etc. To achieve this, we need to explore a suitable classification model to identify fraud and non-fraud cases. In this work, we have used auto insurance data set and explored classification models such as Decision Tree (DT), Random Forest (RF), XGBoost, LightGBM and Gradient Boosting Machine (GBM). To overcome the problem of data imbalance, we have employed MWMOTE and TGAN techniques. We have used Topological Node Feature(TNF) based spectral embedding for low dimensional data representation along with some popular embedding methods like MDS, Isomaps and t-SNE. After studying all the 65 possible combinations of these models, we have proposed an innovative method for effective automobile insurance fraud detection. For the given dataset, our results show that using a combination of MWMOTE as a data imbalance handling technique (Phase I), TNFSE2 as data embedding (Phase II) and Random Forest as classification (Phase III) provides the best result in comparison to all other combinations. This work also highlights the efficacy of TNF based spectral embedding in automobile insurance dataset

The transition from vocoder-based to neural-codec speech synthesis makes generalization more difficult for speech deepfake detectors, particularly those relying on speech-oriented representations. It remains unclear which acoustic representations retain discriminative information when the generation mechanism changes. We therefore compare 12 acoustic representations using a shared low-capacity linear classifier to identify effective evidence under codec shift. The analysis shows that hierarchical XLS-R leads on the pooled test set, while pooled no-vocals residual statistics perform best on the unseen-codec condition, revealing complementary behavior across generation conditions. Building on this finding, we propose MN-P, a dual-view detector that integrates an utterance-level pooled no-vocals representation with token-level XLS-R features through adaptive gating. The proposed MN-P reduces EER by 54.2% overall and by 60.9% on the codec-unseen condition relative to the best-performing retrained state-of-the-art system, with consistent gains across different detector backends. These results indicate that pooled no-vocals residual statistics provide effective complementary evidence for cross-generation speech deepfake detection.

A long-standing conjecture in non-Kähler geometry states that a compact Hermitian manifold with constant Chern holomorphic sectional curvature should be Kähler when the constant is nonzero and Chern flat when the constant is zero. In this article, we study the corresponding problem for a Hermitian connection $\nabla$ with $\nabla T=0$. We first show that if the $\nabla$-holomorphic sectional curvature is constant, then the $(1,1)$-part of the curvature is $\nabla$-parallel. Our main result states that a nonzero constant forces $T=0$; consequently, the metric is Kähler and locally a complex space form. The proof is pointwise and requires neither compactness nor completeness. We also obtain applications to the Chern and Bismut connections.

Tracking speech sources remains a challenge due to ambiguous data association arising from intermittent speech, close spatial proximity, and complex acoustic conditions. To address these issues, we propose an identity-assisted association that maps unordered direction-of-arrival (DOA) estimates to speaker-consistent source trajectories for reliable speech source tracking. Specifically, speaker identity embeddings are directly integrated into the model input as a complementary cue to spatial features. This enables maintaining identity consistency by combining long-term time-invariant vocal identity characteristics with the short-term continuity of spatial cues. To effectively process these heterogeneous inputs while accommodating their distinct characteristics, we design a unified neural tracker. Within this model, time self-attention modules capture the temporal evolution of each source, while source self-attention modules distinguish between competing source tracks. Experimental results demonstrate the superiority of the proposed neural tracker in mitigating association confusion for speech source tracking.

A special semimartingale admits a unique decomposition $X=X_0+M+A$ into a local martingale $M$ and a predictable finite-variation part $A$. We consider the identification of $A$ when $X$ is observed only through repeated cross-sections. The estimand is then the projection of the sampled predictable compensator onto the observable feature filtration, namely the current state together with whatever randomness is shared across the population, so that at a fixed diffusion coefficient the marginal flow identifies the drift only up to a Markovian projection. If the drift is an affine functional of an observed lag window, the joint problem is a convex quadratic programme whose solution is the pseudo-panel regression of econometrics. Our principal concern is the case, which we believe not to have been treated before, in which the drift is the output of a hidden linear dynamical system whose dynamics are themselves to be identified from the marginals. The joint problem is then a bilinear quadratically constrained programme, which we solve to certified global optimality by spatial branch and bound; with unpenalised state disturbances and a drift basis growing with the grid it is NP-hard already in latent dimension one, by reduction from $\ell^1$ rank-one matrix approximation, whereas the complexity of the deterministic system at fixed latent dimension remains open. A block-coordinate decomposition offers a cheaper alternative. For the estimator itself, we obtain rates at a fixed mesh, separated into Monte-Carlo, estimation and grid contributions.

Tool-using large language model (LLM) agents turn credential hygiene from a storage problem into an execution-security problem. A key pasted into a prompt, or embedded in a system prompt or tool configuration, crosses from an authentication boundary into a data pipeline, where it may persist in conversation history, logs, memory stores, generated code, and error payloads. Prompt injection and excessive agency then convert passive disclosure into unauthorized action. This paper formalizes the credential-exposure threat chain for agentic systems; synthesizes evidence from a platform secret-store incident, vendor-reported secret-sprawl measurement, and OWASP and NIST guidance; and describes a vault-mediated execution architecture in which the model selects a connector identifier while a trusted request boundary supplies authentication. We evaluate a production implementation, Corvic Security Vault, in two controlled black-box experiments. Across 16 probes spanning seven control domains, every probe met its expected outcome: an authenticated GitHub API request succeeded while the credential stayed absent from process environment values, caller-visible request headers, tested filesystem locations, three third-party echo services, and two unrelated API origins; both cloud instance-metadata endpoints were unreachable. We also report a negative result, a connector whose stored header mapping did not satisfy its provider's authentication contract, showing that centralized custody does not by itself guarantee correct configuration. Vault mediation removes several disclosure paths but is necessary rather than sufficient: least privilege, deterministic action authorization, human approval, telemetry redaction, and rotation remain independently required. The study is purposive and small, a functional security evaluation rather than a certification.

Open-set graph anomaly detection trains on a few labeled anomalies from one class and must also find anomaly classes that were never labeled. Published results share three conventions: the test score is read at the best epoch on the test set, baseline numbers are copied from earlier papers, and most anomalies are minority classes relabeled as anomalous. We ask how much of the reported ranking these conventions decide. We re-run two recent methods, DEMO and NSReg, together with OUTPOST, a small first-order detector built for this study. All three use one protocol with identical seeds and splits on eight graphs (seven for the baselines, which cannot run on ogbn-mag), ten seeds each, and every run is scored under both the best-epoch rule and a deployable validation rule. Before the runs that test them, we registered 40 predictions. Three findings hold. First, the rule changes the leader: under the best-epoch rule, OUTPOST and NSReg each lead three of seven graphs, while under the validation rule, NSReg leads five. Second, the best-epoch bonus depends on how the benchmark was built: 0.045–0.080 AUC-ROC on the three small relabeled-class graphs and 0.002–0.014 on the three real fraud graphs. Third, pseudo-labeling in OUTPOST is worth 0.038–0.065 AUC-ROC on the same three graphs but gives no benefit on any real fraud graph. We also show that a 0.002 tie band for hyperparameter selection lies below the paired standard error on all six graphs tested, even at ten seeds. Twelve of our 40 predictions were falsified, and we report them. We close with a short reporting checklist.

The deployment of unmanned aerial vehicle (UAV) systems relies on high-performance yet lightweight wireless links between UAVs and ground stations (GSs). This paper presents a geometry-aware multi-UAV in-band full-duplex (MU-IBFD) communication system that uses high-gain directional antennas and separated uplink/downlink channels to convert self-interference into controllable co-channel interference (CCI) between UAVs, thereby avoiding energy-intensive self-interference cancelers on UAVs. We also derive a geometry-aware CCI model and define a reliable operating region (ROR) in the 3D airspace, within which the SINR requirement is satisfied. A prototype consisting of two UAVs and a GS is developed, and field trials are conducted. The measured CCI as a function of UAV positions agrees well with the theoretically predicted non-ROR region, and the downlink capacity significantly exceeds that of a conventional time-division duplex (TDD) with omni-directional scheme and higher transmit power and approaches that of ideal IBFD in most of the airspace. A proof-of-concept 4K/60p video transmission further demonstrates the practical potential of the proposed MU-IBFD system.

Real-world time series contain evolving underlying dynamics with irregular variations that lack stable temporal patterns and are often referred to as noise. Existing methods address this mixture by filtering frequencies or suppressing noisy observations. They either miss temporal evolution or risk suppressing useful dynamics. We propose DrafTS, a model-agnostic framework that aims to reduce noise while preserving evolving dynamics through time-aware Decomposition with ResiduAl correction For Time Series. DrafTS uses features derived from instantaneous amplitude and frequency to guide decomposition into a primary component intended to capture underlying dynamics. A task-specific backbone models the primary component, while a lightweight correction module uses residual information to correct the backbone output. Across four time series modeling tasks, DrafTS improves six diverse backbones, demonstrating its effectiveness. Code is at https://github.com/Autumn61q/DrafTS

Artificial intelligence (AI) is reshaping labor markets, yet its effects on wage distribution and the underlying mechanisms remain insufficiently understood. Conventional analytical approaches are limited in their ability to directly examine the dynamic evolution of worker behavior and income distribution under sustained AI shocks and counterfactual policy scenarios. To address this limitation, we present a hybrid agentic framework that aims to challenges of scalability of rule-based models and the limited explainability in LLM-agentic frameworks. Using this framework and sociodemographic data from China, we simulate changes in wage distribution under repeated AI shocks. The results show that both the average-wage ratio between workers in the top and bottom income deciles (T10/B10) and the Gini coefficient increase persistently, suggesting that AI shocks widen the wage gap and exacerbate income inequality. This pattern of a widening wage gap remains robust across alternative large language model decision engines and 30 Monte Carlo simulations. We further conduct counterfactual policy experiments. The results show that education subsidies targeted at low-income workers increase both the number of skill-upgrading attempts and the number of successful upgrades, with particularly pronounced improvements in the upskilling success probability of workers in the bottom income decile. These effects enable the policy to partially mitigate wage inequality. The proposed framework provides an interpretable simulation approach for examining the effects and mechanisms of AI shocks on wage distribution. It also offers policymakers a complementary analytical tool for evaluating policy interventions.

Theories of consciousness often explain different pieces of the phenomenon, leaving open how to construct an account of the whole. If consciousness is functional, a basic question is what awareness of a content changes in how a system can use it. We review experiments comparing how information X is used in a process Y with and without awareness of X, while constraining differences in the information available about X. The resulting preliminary functional profile links awareness most clearly to attentional control, with narrower evidence for metacognitive evaluation, while simple feature–location binding and retrieval can persist without awareness. But these associations constrain rather than explain consciousness. A full-stack theory must also account for the structure and manner of presentation of perceptual and affective experience, subject–world organisation, awareness judgments, and problem reports. Attention Schema Theory illustrates a possible bridge: using a functional association to motivate an internal process model, then proposing that its information also underlies awareness and associated reports. Generalising this approach, we propose using both functional and explanatory profiles to guide the abductive construction of candidate process architectures, implementing and intervening on them, and progressively testing and refining them against further evidence.

We establish new bounds for incidences between a point set P and a family L of n-flats in PG(n+d,q). For fixed dimensions, our bound on the incidence discrepancy has an explicit piecewise-linear exponent in $\log_q|L|$, improving the classical estimate of Haemers and the Kong-Tamo bound in specified ranges of the number of flats. Matching constructions establish sharpness up to constant factors in several parameter ranges. The proof is combinatorial and avoids spectral and Fourier analytic methods. As applications, we obtain improved estimates for rich flats and exceptional orthogonal projections, together with stronger lower bounds for Furstenberg sets in certain ranges where the fraction of prescribed directions is small.

The cosmic-ray (CR) knee at a few petaelectronvolts (PeV) is commonly associated with the acceleration limit of Galactic protons. Recent detection of multi-PeV photons from the microquasar Cygnus X-3 suggests proton acceleration to tens of PeV. However, the contribution of such type of sources to CRs remains unclear. Here we analyse proton and helium spectral data spanning from 0.1 to 1000~PeV and find that the knee strongly disfavours a single terminal cutoff. Full-spectrum analyses incorporating measurements down to tens of gigaelectronvolts (GeV) require a proton contribution extending beyond $100\,\mathrm{PeV}$. This contribution either extends continuously from GeV to 100~PeV energies or belongs to a distinct component whose spectrum has a spectral break at the knee. Within source-population interpretations, its connection to lower-energy Galactic cosmic rays suggests a Galactic origin. The component from GeV energies is conventionally associated with supernova remnants, but extending it beyond $100\,\mathrm{PeV}$ challenges acceleration theory and $\gamma$-ray observations of ordinary remnants . A spectral break, by contrast, can arise naturally from rigidity-dependent transport. This interpretation makes the knee a probe of Galactic CR transport, and connects the extended proton spectrum to extreme Galactic accelerators, such as super-Eddington X-ray binaries, that can also produce PeV $\gamma$ rays and neutrinos.

Generating differentially private synthetic data with meaningful Wasserstein utility guarantees is challenging in high dimensions. For datasets of size \(n\) on $[0,1]^d$ with $d\ge2$, existing pure \(\varepsilon\)-differentially private mechanisms achieve expected $1$-Wasserstein error of order $(\varepsilon n)^{-1/d}$, reflecting the curse of dimensionality. While this rate is optimal in the worst case, it can be overly pessimistic when the data are supported on a lower-dimensional set. We formalize this through a multiscale packing-growth dimension $k$, which captures the geometric complexity of the support via the growth of packing numbers across scales. We propose Adaptive Pruned-PMM, a pure $\varepsilon$-differentially private mechanism that combines private depth selection with our pruned variant of the Private Measure Mechanism (PMM) of He et al.\ (2023). The mechanism supports deeper, geometry-adapted hierarchies with expected running time $O\!\left(d(n+d)\log(\varepsilon n)\right)$, which is near-linear in $n$ for fixed dimension and privacy budget. Under an external multiscale packing-growth condition with dimension $k$, we show that, for fixed positive privacy budgets and fixed geometry, the expected $1$-Wasserstein error is of order $(\varepsilon n)^{-1/k}$ for $k>1$ as $n$ grows. We also prove a lower bound under a corresponding internal packing-growth condition, showing that the exponent $1/k$ is sharp within this framework.

Multi-speaker dialogue TTS requires natural speech generation, consistent speaker identity, coherent cross-turn transitions, and fine-grained control of expressive attributes such as emotion, speaking rate, and loudness. These requirements are difficult to satisfy reliably with one-shot generation, especially in long-form dialogue. We propose a controllable multi-speaker dialogue TTS framework that formulates synthesis as critique-driven iterative refinement. Its speech backbone, ControlEdit-TTS, unifies instruction-following synthesis and natural-language-guided attribute editing, enabling correction of expressive errors without full regeneration. The framework further performs hierarchical utterance-level and scene-level critique, routing detected issues to editing, resynthesis, or timing adjustment. Experiments on a bilingual Chinese–English dialogue benchmark show improved utterance-level instruction following, better dialogue-level preference than direct dialogue models and agentic baselines, and more effective refinement than regeneration-only alternatives while preserving speaker identity. Ablations further confirm the benefits of scene-level critique and edit-based correction.

Criticality in the Ising model is conventionally generated by Hamiltonian dynamics and controlled by temperature. Here we show that critical like behavior can emerge in an Ising system whose dynamics is completely independent of the Ising Hamiltonian and contains no temperature like parameter. We introduce an adaptive cluster dynamics in which spin connectivity is controlled by a local quiet time variable, the time elapsed since a spin was last updated. The Ising Hamiltonian enters only as an observable characterizing the resulting configurations. As the parameter $\alpha$, controlling the connectivity strength, is varied, the system undergoes spontaneous symmetry breaking accompanied by strong collective fluctuations. The cluster size distribution develops an extended power law regime, terminated by a small finite size hump at the largest scales, while the Ising energy exhibits a singular response to the variation of $\alpha$. These results show that memory dependent connectivity alone can generate collective critical behavior, revealing a route to nonequilibrium criticality without energy based dynamics.

We determine the exact worst-case function-value suboptimality after $N$ first-order oracle calls on $L$-smooth, $\mu$-strongly convex functions under a nonnegative weighted combination of the initial squared distance, function-value suboptimality, and squared gradient norm. Excluding the case where no strict improvement in function-value suboptimality can be guaranteed, the exact deterministic minimax risk in dimension $d\geq2N+1$ is characterized by the unique solution of a single scalar equation involving an $N$-step recurrence. The proof constructs an explicit hard instance for the lower bound and a constant-memory first-order method, ITEM-wf (previously called ITEM-w), whose worst-case performance matches this lower bound. On the standard initial conditions, the result recovers the exact convex bound attained by the Optimized Gradient Method of Kim and Fessler, and establishes the minimax optimality of the recently introduced ITEM-f method of Kim, Ryu, and Das Gupta for the initial function-value condition.

Geometric view synthesis can expand a single wine-label photograph into a training set, but its value with pretrained image encoders is unclear. We study this on a public WineSensed-derived benchmark of 1,000 classes, one enrollment photograph per class, and 4,295 real queries. With the earlier DINO vision transformer (ViT-S/16) recipe, geometric views raise top-1 accuracy from 34.1% to 62.6-63.7%, about three times the gain from two-dimensional (2D) augmentation. Frozen SigLIP 2-B already reaches 94.7%. A linear head over its frozen features gains 1.2-1.3 percentage points with the two geometric pipelines localized by the Segment Anything Model (SAM), while the other pipelines gain an inconclusive 0.3-0.6 points. Low-rank adaptation (LoRA) and validation-selected full fine-tuning show no clear gain within the reported confidence intervals; fixed-budget full fine-tuning loses 9-24 points. SAM localization supplies all six views for 99% of sources, compared with 43% for the edge-based front end. Recognition differences between the two cylinder constructions depend on the training recipe and are confounded by their crop and canvas conventions. Rendered-cylinder tests show different responses to source tilt, but an uncalibrated rim-ratio proxy establishes no corresponding trend in recognition on real photographs. An author-confirmed audit of 50 residual errors identifies 21 query-enrollment appearance mismatches, without establishing an irreducible error rate. These results support geometric synthesis for the tested self-supervised recipe and a smaller benefit through frozen-feature adaptation of the text-supervised encoder.

We establish a local well-posedness theory for the $n-$dimensional Boussinesq equation with a linear restoring force in a modified Sobolev spaces $H_\omega^s$ and Bourgain-type spaces $X_\omega^{s,b}$, which are adapted to its modified dispersion relation. The restoring force introduces a new phase function whose behavior in the low-frequencies is different from ones of the classical Boussinesq equation, that in turn, affects the associated energy functional. We prove bilinear and trilinear estimates for quadratic and cubic nonlinearities, respectively, which in turn imply local well-posedness under suitable conditions on $s$ and $\omega$. To our knowledge, this is the first well-posedness result for this model.

Reliable automated research requires agents to vet data, verify analyses, and generate hypotheses grounded in trustworthy evidence, potentially reducing routine scientific workload while allowing scientists to focus on interpretation and discovery. Existing benchmarks often only assess analytical task completion or hypothesis generation separately rather than testing whether reliable evidence supports valid and novel claims. We introduce DISCERN (Data Integrity and Scientific Capability: Evidence, Reasoning, and Novelty), a controlled benchmark on real, publicly available datasets that evaluates three key levels of an automated research workflow. The first two levels test data integrity and analysis verification under confounds and tool traps, while the third tests hypothesis generation and revision under adversarial review, including counterfactual cases in which evidence consistent with real data and documented scientific phenomena conflicts with established expectations, motivating alternative explanations and testable hypotheses. Across 203 tasks, eight life-science tracks, and eight models, DISCERN shows that strong aggregate performance can mask level-specific weaknesses. Agents earn perfect scores in only 60.8% of Level 1, 34.2% of Level 2, and 0.6% of Level 3 evaluations, with penalties attributed to rejection of sound data, failure to carry recognized limitations into conclusions, and wide variation in hypothesis production. Cross-track rankings by token and code use are substantially more stable than rankings by evidence judgment, suggesting greater consistency in computational effort than in evidence-based reasoning. These profiles identify opportunities for supervised scientific assistance, but current agents do not yet demonstrate reliable autonomous analysis or discovery. Code and data: https://huggingface.co/datasets/discern-bench-anon/discern-benchmark