Papers

Magnetic symmetry can lift spin degeneracy in momentum space without producing net magnetization, generating spin textures classified by their parity under momentum reversal. Even-parity textures have well established in altermagnets. Odd-parity spin textures have recently emerged in electronic bands of compensated magnets, but their counterpart in collective bosonic excitations remains experimentally unresolved. Magnons provide a natural setting for this extension because they carry spin angular momentum through insulating magnets without accompanying charge flow. Beyond realizing this missing state, a broader challenge is to program spin splitting with voltage at room temperature. Here we report room-temperature transport evidence for ferroelectric switching odd-parity magnon spin splitting in multiferroic BiFeO3. Symmetry analysis and spin-wave calculations reveal that the cycloidal chirality splits opposite-spin magnon branches with an odd-in-momentum dependence and sets the sign. Experimentally, an injected in-plane-polarized spin current generates an out-of-plane magnon spin component during propagation. This component exhibits the crystalline angular dependence predicted by theory and reverses upon ferroelectric switching, providing a transport fingerprint of the spin-split magnon state. We use the programmed magnon spin to drive deterministic field-free switching of a perpendicular ferromagnet and demonstrate XNOR logic-in-memory. Our results extend odd-parity spin splitting from fermionic electronic states to bosonic collective modes and establish nonvolatile electrical control at room temperature.

Standard valuation methods, including discounted cash flow, the income approach standard IDW S 1 of the Institute of Public Auditors in Germany, and market multiples, compress milestone probabilities, continuation options, and risk shifts into opaque aggregate parameters; none provides a structured protocol for decomposing AI integration into auditable option-level assumptions. We propose an industry-agnostic taxonomy separating AI Integrators from AI Providers. AI Integrators are further classified by their Integration Depth Level, ranging from no integration to AI at the core of the product or process. A milestone-gated real-options overlay decomposes milestone state value into five components, and an Analytic Hierarchy Process-based Success Readiness Index derives per-option probabilities from structured pairwise comparisons for scenario analysis. Applied to an AI-native energy software-as-a-service firm, the framework yields a coherent valuation band traceable to identifiable option-level assumptions. Risk concentrates in later-stage continuation options, matching the structural prediction for AI Providers. The protocol applies across the firm lifecycle, including mergers and acquisitions due diligence. The case is a single-firm demonstration of protocol coherence, not empirical validation; multi-case testing against realised post-exit valuations is left to future research.

Visual grasp proposal generation has advanced rapidly, yet converting a selected proposal into a stable physical grasp remains a central execution-stage challenge. This paper introduces GraspTune, a tactile-driven execution-stage refinement framework that starts from a nominal proposal and applies bounded residual TCP motions during approach, contact formation, and final grasp execution. GraspTune learns control-facing contact semantics from local depth, tactile signals, state, and history using state-conditioned expert contact queries and multi-task supervision for contact change, contact risk, and post-close readiness. The representation conditions a diffusion-pretrained residual policy and is aligned with PPO for closed-loop execution. Across more than 60,000 simulated executions over 20 object categories, GraspTune establishes an execution-layer benefit across four proposal generators, raising stable grasp success by +19.22, +9.55, +12.45, and +20.70 percentage points for GraspNet, Contact-GraspNet, AnyGrasp, and VGN. A four-fold held-out category study raises unseen-object execution from 54.58% to 70.33%, showing category-disjoint generalization of contact correction. Across more than 1,000 real-robot trials on a UR5e setup with Xense fingertip sensors, GraspTune raises GraspNet execution from 71.0% to 84.3%, validating direct transfer without realworld policy fine-tuning. Together, these results turn visually plausible proposals into stable physical grasps for downstream contact-rich manipulation. A supplementary video is available at https://youtu.be/kcq7fSLNtzU.

In this paper, we study the separation and stability properties of radial solutions to a Hénon-type equation with exponential nonlinearity on the hyperbolic space. By transforming the radial hyperbolic equation into a weighted Euclidean equation and applying well-established separation results in Euclidean space, we classify the solution structures or establish sharp alternatives for radial solutions throughout the full range $n\ge2$ and $\alpha>-2$. We also give an affirmative answer to the open question recently posed by Huang and Zhao.

In this paper, we study remote state estimation under unreliable forward and feedback communication between a smart sensor and a remote estimator. When the sensor cannot perfectly reconstruct the filtering state maintained by the remote estimator, an information asymmetry arises between the two sides of the network. To analyze this asymmetry, we first characterize the internal state maintained by the remote estimator and its evolution under the packet reception process. The sensor's uncertainty about this state is then captured through a belief conditioned on its information set, and we derive a recursive update under noisy acknowledgment feedback. Finally, we show that the resulting belief admits a finite Gaussian mixture representation whose number of components grows at most linearly over time, ensuring computational tractability. Simulation results illustrate the impact of information asymmetry on the estimation performance and demonstrate the effectiveness of the proposed framework.

Legal information in Bangladesh is inaccessible to most citizens. Statutory text is English-only, trained lawyers are concentrated in urban centres, and cloud-dependent AI fails where mobile connectivity is unreliable, a setting in which hallucinated legal text causes direct harm. The system addresses statutory interpretation only; queries that require judicial precedent or case-law reasoning fall outside its scope. We target the statutory access gap by compressing a 9-billion-parameter Gemma-2 teacher into a 2-billion-parameter student through two-phase progressive knowledge distillation. Phase 1 performs supervised fine-tuning on 9,429 quality-gated legal question-answer pairs (65% acceptance from 14,514 generated queries); Phase 2 minimises sparse Kullback-Leibler divergence against the teacher's top-50 per-token logits at temperature tau = 4.0, implemented via QLoRA (4-bit NF4, rank-32 LoRA adapters). Prior legal language models target general legal English; this system specialises in Bangladeshi statutory law. Every response is grounded through hybrid retrieval combining dense semantic search (60%) and BM25 (40%) across 36,029 statutory passages from the Bangladesh Constitution and national legislation. On a 50-query English benchmark, the distilled model reaches ROUGE-L 0.4715 and BERTScore F1 0.5679, a 103% ROUGE-L and 143% BERTScore gain over the retrieval-augmented undistilled baseline (ROUGE-L 0.2323, BERTScore 0.2340). The adapter quantises to 1.6 GB (GGUF Q4_K_M) and runs at 4-8 tokens per second on a Pixel 6 with no network access. Cross-lingual evaluation on 50 Bangla queries yields ROUGE-L 0.4083 and BERTScore 0.8133, showing effective retrieval from Bangla input against an English-only corpus. In a single-evaluator pilot, a practising lawyer rated 50 responses at a weighted mean of 4.16/5 (90% rated 4 or 5), supporting utility beyond text-overlap metrics.

Whether a public blockchain is "decentralized" is routinely settled by citing a concentration statistic. On two ERC-20 ledgers, Chainlink (LINK) and Uniswap (UNI) over a 90-day window, we show that verdict depends on measurement specification rather than the ledger itself. Four discretionary choices (holder population, address type, temporal aggregation, and entity resolution) move the balance HHI for UNI from 109 to 2,336 (a factor of 21), with every specification defensible. Over the same range, the Gini coefficient moves by less than 0.003 and does not change under entity resolution, demonstrating that Gini and HHI answer different questions and cannot substitute for one another. We further document an implementation choice - summing versus overwriting repeated transfers - that discards roughly 85% of volume and overturns a finding on wealth and structural position. Substantively, both ledgers are extraordinarily unequal in ownership (balance Gini = 0.990 and 0.998) yet unconcentrated in routing (weekly flow HHI = 421 and 386), with the two dimensions close to statistically independent across addresses. A parameterized criterion for hidden brokers identifies 30 and 18 zero-balance intermediaries, 12 shared across ledgers; a matched control confirms that degree thresholding, rather than learned embeddings, drives the discovery. Finally, on the governance ledger, proposal-eligible addresses and routing intermediaries are almost disjoint, so routing contestability is held at the pleasure of a rule layer with a Nakamoto coefficient of two. We conclude that on-chain concentration should be reported as a specified range rather than a point estimate.

While Large Language Models (LLMs) excel at understanding unstructured financial contexts, their direct use in portfolio optimization is limited by a mismatch between next-token prediction and the listwise ranking objectives required for asset allocation. They also struggle with precise numerical forecasting, leading to instability and arithmetic hallucinations. To bridge this gap, we propose FinRankGRPO, a framework that shifts LLM based portfolio construction from direct numerical prediction to listwise ranking of financial assets. We introduce a two-stage training process, supervised finetuning on Chain-of-Thought reasoning data, followed by our Financial Asset Ranking via Group Relative Policy Optimization with a Spearman rank correlation reward that aligns generated asset rankings with ground truth market orderings. The second stage uses a novel Spearman rank correlation reward to explicitly align the model's generative preferences with ground truth market orderings. Experimental results show that FinRankGRPO outperforms traditional quantitative and state-of-the-art commercial models, achieving a Sharpe ratio of 0.636 and a Spearman correlation of 0.023.

Long-horizon language agents receive sparse terminal feedback, while intermediate rubrics provide structured but potentially misspecified assessments of progress. In resettable training environments, counterfactual continuation rollouts can measure local credit, but exhaustive replay is costly. We propose Credo, a framework that couples evolving semantic rubrics with selective, execution-based credit correction. A frozen judge maps visible transitions to rubric features, and a credit head predicts the change in expected terminal reward associated with the realized transition. Independently sampled two-sided replays correct prediction residuals using their recorded inclusion probabilities. We derive conditional unbiasedness and a variance decomposition that connects two design choices: which rubric features to retain, and where to allocate a fixed expected replay budget. The resulting criterion weights prediction errors by policy-score sensitivity and missing replay coverage; its allocation rule additionally accounts for continuation cost. We also describe a practical mixture with terminal leave-one-out advantages and distinguish its clipped, token-normalized PPO implementation from the ideal policy-gradient estimator. This preliminary report provides the method, proofs, an exact finite-model audit, and a controlled evaluation protocol. It makes no claim of empirical superiority on language-agent benchmarks.

The growing capability of Wi-Fi devices to identify human activities using channel state information (CSI) raises privacy concerns. To counter this threat, we propose GRAW, an adversary system, acting as a privacy defender, that degrades the human activity recognition (HAR) system at the user device by perturbing the router's signals that the device uses to estimate CSI. GRAW employs generative adversarial imitation learning (GAIL) to construct perturbation signals, and thereby eliminates the need for any information on the target HAR systems and their inputs (i.e., zero-knowledge operation). We evaluate GRAW against seven representative HAR models, using datasets collected in five environments, including our own dataset. We observe that GRAW is the only remote attack scheme that degrades every tested HAR model to a random-selection level. At the same perturbation level, GRAW achieves an attack success ratio up to 76.7% higher than comparison methods, while maintaining over 99% packet success rate on regular Wi-Fi communication. We demonstrate the feasibility of GRAW through real-time, over-the-air experiments with software-defined radios.

Legends are fundamental to chart understanding, as reliable interpretation requires correctly binding legend entries to corresponding visual marks. While vision-language models (VLMs) are increasingly applied to chart understanding, their legend understanding is poorly diagnosed by aggregate accuracy, which can be satisfied by superficial shortcuts and confound legend-specific errors with other reasoning failures. To enable fine-grained diagnosis and controlled testing, we introduce LegendBench, a parametric benchmark and generation pipeline that produces targeted legend-centric test cases. LegendBench contributes (1) a capability-task taxonomy spanning legend parsing, legend grounding, legend-conditioned reasoning, and legend-aware abstention to localize failures, and (2) counterfactual group generation, where each base chart yields multiple variants under controlled legend interventions to probe model invariance and sensitivity. Using LegendBench, we evaluate both general-purpose VLMs and specialized chart models and generate their capability profiles, revealing persistent bottlenecks in reliable legend-to-mark binding and counterfactual consistency. We then use these capability profiles to guide targeted fine-tuning, demonstrating that bottleneck-specific interventions can effectively close the localized capability gaps and generalize to unseen data. We further leverage our counterfactual design to conduct fine-grained diagnostic experiments, analyzing encoding-channel effects, legend-order shortcuts, and abstention under varying visibility.

We present a non-paraxial design strategy based on the full ideal aberration-free phase function for a dual-layer rotational zoom metalens. By adopting the exact lens phase expression rather than the conventional parabolic approximation, this approach preserves the higher-order phase contributions critical to large-NA operation. Zooming is achieved by engineering complementary phase distributions on two metasurface layers and dynamically modulating their combined phase via controlled rotation. To realize the target phase profiles, a dielectric metasurface operating at 10 GHz is designed using ceramic cylindrical resonators arranged in a triangular lattice. A systematic parametric optimization of lattice period, cylinder diameter, and height ensures a complete 2pi phase coverage while maintaining a transmission above 85%. The resulting phase-geometry mapping establishes a reliable unit-cell library for device implementation. Leveraging this library, the dual-layer metalens is designed and its performance evaluated through finite-difference time-domain simulations. A prototype with a 300 mm aperture is fabricated and experimentally characterized using three-dimensional near-field scanning measurements. Both simulations and experiments confirm that continuous focal-length tuning is achieved as the rotation angle varies from -60deg to 60deg, corresponding to a focal-length range of 100 to 300 mm and a zoom ratio of 3:1. The device achieves a maximum NA of 0.83, and the measured focal spots remain nearly diffraction-limited. The experimentally extracted focal-length variation agrees closely with theoretical predictions and simulations.

Embodied AI systems are often organized into System 1 and System 2. System 1 is typically a pretrained policy that generates actions at high frequency, whereas System 2 is often instantiated as a vision-enabled language model for high-level planning. We ask whether a large language model (LLM) can act as the policy for robot manipulation without task-specific finetuning. We call this setting LLM as policy. We evaluate three LLMs on all 42 RoboDojo tasks and compare their scores with 40 public policies. Astra and GPT-5.5 use the official 50-episode-per-task protocol; DeepSeek-Flash uses 10 episodes per task. GPT-6 Astra achieves 22.48% average success rate and 28.97 Score over 2,100 trials, ranking above every public entry. Yet GPT-5.5 and DeepSeek-Flash reach only 0.88% and 1.92% average success rate with the same post-processing. We find that Astra exhibits a sharply polarized capability profile. It generalizes well to tasks that require semantic understanding but not high-precision control. In contrast, it performs poorly on tasks that require precision, dynamic control, or complex bimanual coordination. In-context experiments show no aggregate benefit from one-shot demonstrations, while selected interaction traces show within-episode corrections under perturbations. Overall, the evaluated LLMs vary substantially in manipulation performance. Astra stands out and provides initial evidence for the potential of a general-purpose manipulation model, although reliable precision and dynamic control remain limitations in the evaluated setting.

A generalisation of the Leggett-Garg inequality (LGI) and the Bell-CHSH inequality is proposed in this work, by replacing a classical probability notion with an extended probability notion. The extended probability serves as an underlying layer of reality to the classical probability. Within the consistent history framework, the underlying layer is so-called a non-settleable history. Hence, the bounds of the LGIs and Bell-CHSH inequalities are extended. The resulting generalised inequalities are satisfied without violation for arbitrary measurement settings. Furthermore, generalised Macro- realism and generalised Local realism are introduced and analysed within the extended probability framework without relying on an explicit measurement. This opens up further possibilities on one of the great pursuits in physics, understanding on how a quantum system can possess classical properties.

We obtain a complete characterization of eventually entanglement breaking (EEB) unital channels in terms of some simple commutation properties of their eigenvectors. Specifically, the following properties are equivalent for a unital channel: (i) it is EEB; (ii) it is eventually positive partial transpose (PPT); (iii) it is eventually mixed twisted dephasing; and (iv) its eigenvectors corresponding to non-zero eigenvalues commute with all its peripheral eigenvectors. Similar results hold for one-parameter semigroups of unital quantum channels, where continuity forces such semigroups to become primitive. We employ a matrix integral technique inspired by a result of Watrous. We also characterize the extreme points of the convex set of unital EB channels and, using this characterization and the theory of equiangular tight frames (ETFs), identify several extreme points beyond the twisted dephasing channels.

Let $C_0=\frac32-\frac1{\sqrt2}\cot\big(\frac1{\sqrt2}\big) =0.67250\ldots$ and $C_1=\frac{C_0+1}{2}= 0.83625\ldots$. Recently, it is obatained by Alpöge and Furman that more than 67.25% of the non-trivial zeros of the Riemann zeta function are simple and on the critical line, and more than 83.62% are distinct. Later, Lamzouri gave a different and more direct proof. In this article, by refining the method of Lamzouri, we slightly improve the bounds $C_0$ and $C_1$ in these two results to $C_0+\delta_0$ and $C_1+\frac{\delta_0}2$ with $\delta_0=6.66624\ldots\times10^{-8}$, respectively.

Identifying a low-entanglement eigenstate inside a many-body spectrum and preserving it during measurement are distinct tasks. We construct an explicit local ring Hamiltonian with an exact cluster-state eigenvector and study continuous monitoring of its stabilizer defects. For arbitrary mixed inputs, the conditional cluster fidelity is the initial target weight divided by the no-observed-click probability. A positive defect-operator gap gives finite-time bounds that hold for noncommuting Hamiltonian dynamics, nonnormal effective generators and imperfect detection. At fixed total monitoring rate, the guaranteed exponent falls inversely with system size; high conditional fidelity does not remove the preparation cost set by the initial overlap. Exact diagonalization up to eleven qubits gives finite-size evidence for a cluster-state outlier in a chaotic spectral background. Independent matrix and trajectory calculations verify the dynamics and a conservative coherent-error bound. This construction specializes established scar embedding and nondemolition verification frameworks, with explicit measurement assumptions, finite-time guarantees and resource limitations.

LLM agents can make expert scientific workflows accessible through natural language, but a plausible response does not establish that the requested computation ran. This matters at synchrotron facilities, where calibration and integration are multi-step operations over terabyte-scale datasets. We present APEXA, a deployed framework coordinating 61 tools for synchrotron data reduction. A deterministic execution-integrity guard prevents it from returning results for tool calls that did not execute, and the same tool layer enforces motor safety: across 50 adversarial scenarios and four models, it produced 0/200 violations against a simulated EPICS controller, versus 15/200 for a prompt-only baseline. We also introduce APEXA-Bench, 58 facility tasks organized by scientific correctness and physical consequence. On real APS beamline data, one prompt recovered detector geometry and integrated a full attenuation and exposure sweep. Trustworthy scientific agents need deterministic validation of execution, not model-side assurances. We release the framework, benchmark, and traces.

Dependency scanning tools surface hundreds of vulnerabilities but provide no exploitation-aware ordering, leaving practitioners to decide which upgrades to perform first with no principled guidance. The dominant practice, ordering by CVSS severity, is structurally misaligned with active exploitation: in our npm corpus, 186 non-KEV vulnerabilities carry CVSS scores greater than 8, all outranking three CISA Known Exploited Vulnerability (KEV)-listed packages and causing CVSS-first tools to defer the first actively exploited fix by 17 upgrade actions. KEVGraph is an eight-stage pipeline that frames remediation as a KEV-aware set-cover problem: it constructs per-repository dependency graphs from lockfiles, joins them against OSV and the CISA KEV catalogue, and produces a minimum-cardinality upgrade plan ordered to eliminate actively exploited vulnerabilities as early as possible via exact Integer Linear Programming (ILP) or a KEV-aware greedy algorithm. Evaluated on 924 real-world npm repositories (1,046 vulnerabilities, 5 KEV-listed), the ILP planner achieves AUCCKEV = 0.997 versus a random-baseline mean of 0.663 (95 percent CI [0.519, 0.831], n = 30), resolves the first KEV vulnerability at plan step 1, and requires only 417 upgrade actions, 15.9 percent fewer than the random mean of 495.4. CVSS-first and Dependabot-style ordering are strictly dominated: they defer the first KEV fix to step 18 while requiring more actions (419 and 421, respectively). The framework generalises: Maven (1,200 repos) achieves AUCCKEV = 0.988 versus random mean 0.486; PyPI (300 repos) achieves AUCCKEV = 1.000. Each plan is accompanied by a machine-verifiable certificate enabling automated compliance verification under CISA BOD 22-01.

Large Audio Language Models (LALMs) can follow diverse instructions to synthesize speech in specified styles. However, complex instructions that require simultaneous control over pitch dynamics, speaking rate, and emotional tone often exceed what a single-pass generation can faithfully realize. While recent reasoning models have shown that intermediate "thinking" tokens improve output quality, this paradigm has been confined to the text modality. In this work, we extend reasoning to the audio token space by training a LALM with reinforcement learning to reason over its own speech output. The model first generates a draft speech as a form of audio-token reasoning, critiques its own generation by reflecting on the acoustic realization in text, and then produces a refined version conditioned on both the first-pass speech and the critique, all within a single model. After RL training, the refined two-hop outputs achieve a relative improvement of 7.15% on the InstructTTSEval benchmark, demonstrating the model's reflective ability.

Next-generation aircraft, rotorcraft, and wind turbines demand improved aerodynamic efficiency, making drag reduction a central design objective; in transition-sensitive configurations, the extent of laminar flow strongly affects viscous drag and performance. Accurate prediction of laminar-turbulent transition is therefore essential. Transport-equation-based transition models, however, increase computational cost and implementation complexity. Neural-network-based closures can be difficult to interpret and integrate into independent flow solvers, while a single compact correction trained on heterogeneous transition data may fail to preserve mechanism-specific behavior. This study develops a mechanism-separated transition model using field inversion and symbolic regression. The framework treats natural, crossflow, and separation-induced transition with separate correction branches and yields explicit, closed-form corrections to the Spalart-Allmaras production term, without additional transport equations or runtime neural-network inference. The closed-form model is implemented in an independent flow solver to assess implementation portability and is evaluated on canonical cases and complex three-dimensional configurations, including a natural-laminar-flow transport wing and a hovering rotor. Across the tested cases, the model captures the principal transition-front trends and associated aerodynamic-performance changes. For the hovering-rotor case, it requires about 55% of the wall-clock time of the comparable transport-equation transition model.

As LLM agents increasingly interact with external tools through standardized protocols such as MCP, tool-interface design becomes a critical yet underexplored factor. How fun{\psi}tionality is decomposed into tools affects whether an agent can select the right tool and construct valid arguments. This choice is especially consequential at the edge, where resource constraints limit which models can run locally and scaling up is often not an option. We present MCP-GRANITE, an open-source extensible benchmark framework that treats tool-interface granularity as a controlled variable for MCP-based agents, evaluated under edge and IoT scenarios. It comprises 81 multi-step scenarios across 9 domains, instantiated at 4 granularity levels from fine-grained primitive tools to a single tool. We evaluate 9 locally deployed models (268M-20.9B parameters) across 8,748 trials using task completion, tool selection F1, argument accuracy, latency, and resource-usage metrics. Results show that a 4-tool interface offers the best trade-off, improving task completion by 16.4% over fine-grained primitives and 33.6% over a single monolithic tool, while nearly doubling argument accuracy. Model size is only weakly correlated with task completion and strongly with latency, while its association with argument accuracy is less robust, and a 3.2B model at the optimal granularity outperforms a 20.9B model at a mismatched one. These findings identify tool-interface granularity as a key design parameter for MCP-based agents.

We use the Mori-Zwanzig formalism to derive an exact generalized Langevin equation governing the dynamics of magnetic skyrmions. Using standard approximations, this generalized Langevin equation is found to reduce to the usual Thiele equation. Our approach provides a general route for obtaining extensions of the Thiele equation applicable to magnetic textures and degrees of freedom not covered by the standard derivation route. It also provides some insights into the microscopic justification of the standard Thiele equation, specifically concerning contributions of fast modes to the form of the drift and diffusion terms in the Fokker-Planck equation for skyrmions.

In this paper, we study Einstein-Dirac system based on a Dirac fermion coupled with a nonlinear chiral field on static spherically symmetric spacetime. Gravitational effects on the dynamical mass of fermions are examined. The chiral quark soliton model (CQSM) originally was a model of hadron inspired by the low-energy regime of large-$N_\textrm{c}$ QCD, realizing localized fermions with the full inclusion of the Dirac-sea, which is derived from a regularized one-fermion loop. We extend the CQSM in spherically symmetric curved spacetime and coupled with the Einstein gravity. We successfully solve the CQSM and the Einstein equation self-consistently, and obtain the spectral flow of the fermion energies and also of the ADM mass. The Dirac-sea dilutes the effect of the energy-momentum tensor, which induces an inactive point for the impact of gravity even when the localizing matter exists. The Dirac-sea effect becomes dominant for larger dynamical mass $M\sim \langle\bar{\psi}\psi\rangle$, leading to the emergence of the negative ADM mass.

Reconstructing expressive and relightable 3D head avatars from monocular videos remains challenging in computer vision, as it requires accurate modeling of both non-rigid facial motion and illumination-dependent appearance. Existing Gaussian avatar methods commonly rely on globally coupled representations, in which Gaussian primitives share a unified motion or illumination response model. Such uniform modeling neglects the distinct motion patterns and material/reflectance properties of different facial semantic regions, thereby limiting fine-grained animation accuracy and reducing relighting plausibility. To address this limitation, we propose SAMIRA, a 3D Gaussian avatar framework for semantic-adaptive motion-illumination response modeling. For motion response modeling, the Semantic-Adaptive Motion Response module rasterizes current-to-reference mesh displacements into a topology-consistent UV space and leverages facial semantics to route displacement features through semantic-specific modulators, predicting localized Gaussian geometric residuals beyond coarse mesh binding. For illumination response modeling, the Semantic-Adaptive Illumination Response module learns compact diffuse and specular response factors for each facial region, allowing Gaussians in different regions to adapt their illumination responses to novel environment lighting. These response factors are incorporated into deferred physically based shading, providing a lightweight approximation of semantic-dependent illumination effects. Extensive experiments on self-reenactment, cross-reenactment, and relighting demonstrate that SAMIRA improves both fine-grained expression reconstruction and relighting realism over existing methods.

The enzyme turnover number (kcat) defines catalytic efficiency and constrains quantitative models of metabolism, yet the molecular determinants governing kcat and its response to mutation remain poorly understood. Measurements are sparse and labor-intensive, and most computational approaches provide numerical predictions without explaining how enzyme-substrate interactions shape catalytic outcomes. A central challenge is therefore to identify the topological principles that determine where mutations act and how their functional outcomes are encoded within the enzyme-substrate interaction network. Here, we show that catalytic efficiency and mutational effects can be interpreted through enzyme-substrate interaction topology. We developed Interkcat, an interpretable bidirectional cross-attention framework that captures reciprocal coordination between protein residues and substrate atoms. Optimized on a unified benchmark, Interkcat achieves state-of-the-art predictive performance (R2 = 0.701). From its learned representations, we derive an Interaction Topology Score (ITS) that identifies sequence regions statistically enriched for mutation-sensitive sites without explicit structural inputs. We further demonstrate that higher-order topological features distinguish opposing mutational outcomes: lethal mutations disrupt coordinated networks, whereas activity-preserving or enhancing mutations retain sparse, globally organized coupling. These findings establish interaction topology as a unifying principle linking enzyme sequence, catalytic efficiency, and evolutionary perturbation.

Most existing time series forecasting methods rely solely on numerical observations, overlooking rich contextual information from auxiliary texts. Recent multimodal approaches attempt to incorporate textual signals, but they often treat text as static features or use large language models as forecasting backbones, limiting their ability to capture temporal dynamics and increasing computational cost. To address these challenges, we propose TAC-Time, a unified framework that transforms textual information into additional temporal channels. By modeling text features jointly with numerical sequences in a shared temporal backbone, TAC-Time preserves temporal continuity and periodic structures while remaining efficient and scalable. This formulation also enables systematic interpretability analyses. We show strong cross-modal dependencies through attention and frequency-domain analyses, and identify predictive textual signals whose correlation-aware alignment yields partial forecasting improvements. Extensive experiments on real-world multimodal benchmarks demonstrate that TAC-Time outperforms prior methods.

Robot visuomotor policies are commonly formulated as autoregressive, diffusion-based, or more recently, flow matching models. Among them, Action-to-Action (A2A) flow matching improves inference efficiency by initializing generation from historical action priors rather than stochastic noise. However, stale historical motion patterns and entangled global visual representations can jointly reduce robustness under spatial out-of-distribution (OOD) shifts and visual distractors. In this work, we propose SlotFlow, an object-centric flow matching policy for robust visuomotor manipulation. SlotFlow decouples scene observations into semantic ("what") features and lightweight image-plane spatial ("where") cues to provide object-aware policy conditioning and current-state grounding. The semantic representation suppresses irrelevant background correlations, while the spatial cue improves adaptation to shifted object configurations. Extensive simulation and real-world experiments demonstrate improved robustness under visual distractors and severe spatial perturbations while preserving the low-step inference efficiency of A2A. Controlled initialization and perception ablations further identify object-centric grounding as a major source of the gains and show that it complements, rather than replaces, useful historical motion priors.

We consider exponential polynomials of the form $F_l(z)=P_1(z)\exp(Q_1(z))+P_2(z)\exp(Q_2(z))+\cdots+P_{l-1}(z)\exp(Q_{l-1}(z))+P_l(z)$, where $l\geq2$, each $Q_i$ is a non-constant polynomial and each $P_i$, $i=1,2,3\ldots,l-1$, is a polynomial (possibly constant), while $P_l$ is a non-constant polynomial. This article studies the topology of the preimages under $F_l$ of the neighborhoods of the essential singularity at infinity. We prove that for each disk $D$ (with respect to the spherical metric) centered at infinity, the set $F_l ^{-1} (D) $ is connected and, in fact, is an infinitely connected domain with all its boundary components bounded. In such a situation, the point at $\infty$ is called the Baker omitted value of the function. Our methods extend those developed by Das and Nayak for the function $\exp(z)+P(z)$ for any non-constant polynomial $P$ (Complex Var. Elliptic Equ. 70 (2025), 1831-1847), providing a broader framework for studying this phenomenon. We further show that these functions do not admit any Baker wandering domain. Finally, we conclude by posing some problems arising out of this work.

Object-valued time series, including distributions, covariance matrices, networks, and compositions, lack the linear structure required by conventional autocovariance-based definitions of long memory. We develop an intrinsic framework for defining and estimating long memory in metric spaces of negative type. An isometric embedding yields a centered Hilbert-valued process, and the trace of its lag-covariance operator provides a signed, additive measure of temporal dependence. Crucially, this trace equals the difference between the marginal mean pairwise distance and expected lagged distance, so the framework and its estimators use only distances between the original objects. We define the memory parameter through the nonsummable decay of this trace and show that it coincides with the usual parameter in Hilbert-valued settings. Estimation uses Bartlett aggregates of distance-based lag measures. Estimating the common marginal distance from the same dependent sample induces a common-centering bias in these aggregates. We derive its finite-sample form and propose iterated block-difference corrections, log-ratio and multi-bandwidth log-slope estimators, and a localized self-consistency refinement. We establish consistency, document substantial bias reduction in simulations, and find long-memory evidence in foreign-exchange return distributions and U.S. electricity-generation compositions.