Papers

Egocentric video and body-worn inertial sensors are increasingly recorded together, and the video is usually encoded by pretrained models. What these embeddings represent when the camera sits on the head, and how they should be combined with inertial sensing, has received little attention. Across VideoMAEv2, CLIP, DINOv2 and the egocentric VC-1, the leading principal directions of the embedding are dominated by where the camera looks. Measured head orientation explains them on Ego-Exo4D for egocentric (R^2 0.49-0.70) but not exocentric cameras, and the scene layout explains them on WEAR. The part that the limbs' inertial signals predict separates activities across people. In a set-level probe, the rest separates exercise sets within a participant but not activities across people. This is consistent with separate roles: the video finds the clips of the same set, and the inertial classifier labels them. On WEAR, with temporal order removed and one random limb per one-second clip, we propagate inertial predictions over a within-participant video graph whose leading components are removed. This raises subject-wise cross-validated macro-F1 from 0.648 to 0.852. In our comparisons, propagation helps only over the video graph, and fusing the modalities in one classifier, or adding inertial edges to the graph, performs worse.

We study dictionary learning for probability measure-valued data by embedding measures into Hilbert spaces using features derived from optimal transport geometry. We learn data-adapted dictionaries through sparse autoencoder parameterizations, producing fast, scalable representations of probability measures as sparse finite-dimensional codes. The resulting framework combines the rich, geometric structure of optimal transport with the interpretability and efficiency of sparse representation learning. We demonstrate its utility across diverse data modalities, including hyperspectral images, geometric point clouds, and document embeddings in large language model latent spaces. We also prove generalization bounds for sparse dictionary learning objectives on broad classes of $L^2$-valued measure embeddings, expressed in terms of the metric complexity of the atom class. For optimal transport and entropic transport map dictionaries, we derive new covering number bounds that yield explicit rates. Together, our computational and theoretical results support a new, scalable model for representation learning in the space of probability measures via embedding into Hilbert spaces.

Brain-computer interfaces decode neural activity, yet class-label supervision (CLS) leaves relationships among classes implicit. We propose Language as Supervision (LaS), which aligns neural features with natural-language class descriptions through learned projections and predicts classes using candidate text prototypes. We evaluate seven encoder architectures with independently trained models for visual, motor, and speech decoding from scalp and intracranial recordings. LaS improves speech-decoding accuracy by up to 17.15~pp over matched CLS baselines. Its representations support candidate-set reuse and label-supervised subject and session adaptation, while LaS enables conditional unseen-frequency decoding in steady-state visual evoked potential (SSVEP) responses. Correct class-description correspondence supports this generalization, and dual-sided attribution visualization shows neural and textual contributions to the same class-matching score. These findings suggest that text descriptions can serve as general-purpose references for learning and interpretation in neural decoding.

LLM-generated service-call code is increasingly important for service computing, where developers rely on large language models (LLMs) to compose services and invoke third-party services. However, current approaches have not yet evaluated the invocation and execution of services in the actual code environment. To support execution-grounded evaluation, we present the Service-Call Code Generation Benchmark (SCCGBench), a benchmark of 3,135 traceable and decontamination-filtered RapidAPI call functions from real GitHub repositories. SCCGBench uses redacted-intent generation to reduce circular leakage and introduces Mock, Robust, ParamF1, and host-, endpoint-, method-, and parameter-level diagnostics. Mock denotes a virtual-execution hit that simultaneously matches the gold host, endpoint, and HTTP method, while Robust further requires status checking and exception handling. We evaluate six code LLMs and one non-neural retrieval baseline under intent-only, retrieval-augmented, and gold-documentation Oracle settings. Results show that models often generate syntactically correct or superficially similar HTTP-request code but achieve low Mock success in the intent-only setting; the strongest evaluated model obtains only 3.76% Mock. Under the gold-documentation Oracle settings (C3 and C5), Mock reaches 88.73%–90.52% for the open-source models evaluated in RQ2 and 76.47%–81.86% for DeepSeek-V4-Pro, whereas sparse and dense retrieval remain far below this oracle level. The findings indicate that service/API grounding is the main bottleneck: Mock is dominated by host, endpoint, and method alignment, while ParamF1 separately reveals parameter-alignment errors. SCCGBench provides an execution-grounded benchmark for Web service invocation, API recommendation, service-interface retrieval, endpoint grounding, and reliable service-call automation.

We consider online binary prediction with paid stochastic features, where the learner can spend resources to obtain better observations of the underlying features. This setting is motivated by applications such as medical testing, where additional cost can provide more precise measurements of patient's features for predicting a binary outcome. At each round, a latent feature is drawn i.i.d., the response is generated through a logistic model, and the learner chooses how much to pay before observing a noisy version of the feature. The goal of learner is to jointly control prediction loss and acquisition cost. In contrast to the former result on the online linear regression with paid features, covariance information alone is no longer sufficient: two noise distributions with the same covariance may induce different optimal logistic losses, and lower variance can even lead to higher loss. Under a structural ordering in which larger payments yield better observations by removing independent noise, we obtain $\widetilde O(\sqrt{T})$ regret when the noise structure is known and $\widetilde O(T^{2/3})$ regret when it is unknown, with matching lower bounds up to logarithmic factors.

AI shopping assistants increasingly influence which products consumers consider. We study retail assortment planning under AI mediation, where a retailer chooses a catalog, an AI intermediary forms a customer-facing shortlist, and the customer chooses from that shortlist according to a multinomial logit model. We consider both deterministic mediation, in which the AI follows a fixed segment-specific ranking, and stochastic mediation based on the Plackett-Luce model. Empirically, we construct a dataset of product rankings from leading AI models, estimate Plackett-Luce ranking models, and evaluate their predictive performance on held-out assortments. Combining these estimates with synthetic customer preferences, we find that accounting for AI mediation can materially change optimal assortments and revenue. Across the tested capacity configurations, ignoring mediation yields mean revenue losses between 2.52% and 11.37% relative to the PL optimum. A much simpler deterministic plug-in, based only on the ordering of fitted AI utilities, performs substantially better, with mean losses of at most 0.53%. Motivated by this performance, we study assortment optimization under deterministic mediation. We characterize its computational complexity and show that, for any fixed number of customer segments, the problem admits a fully polynomial-time approximation scheme.

Existing governance approaches in decentralized financial networks rely on manual review processes conducted by human committees, which introduces subjective bias, creates a scalability bottleneck under high network volume, and fails to capture complex behavioural patterns and evolving network dynamics. To address these limitations, we propose \name, a federated learning-based pairwise ranking framework in which multiple institutions collaboratively train a ranking model without sharing their private transaction data. This collaborative learning enables the model to capture network-wide behavioural patterns that signify strong network participation and directly contribute to the network effect, including diversity of counterparties, usage of multiple applications, and frequency of transactions. The proposed system ranks providers based on the authenticity of their actions, promoting genuine contributors and demoting those exhibiting suspicious behaviour. We evaluate \name\ on a real-world blockchain transaction dataset. Results demonstrate that the federated approach achieves ranking performance modestly below the centralized baseline (NDCG@15: $\approx$0.87 vs.\ $\approx$0.92) while preserving data privacy and matching its ranking stability (Kendall $\tau$: $\approx$0.70 for both regimes), producing reliable governance decisions, including provider promotion and demotion (648 and 39 unique providers, respectively), without requiring centralized access to sensitive transaction data.

Minimax optimization is fundamental to adversarial learning yet suffers from severe stability challenges, often manifesting as persistent limit cycles or divergence. This paper investigates these instabilities through the lens of continuous-time dynamical systems. By modeling training dynamics as a second-order Ordinary Differential Equation (ODE), we rigorously identify the root cause of non-convergence: the problematic eigenstructure of the loss function's Hessian near equilibrium points. To address this, we introduce Controlled-Minimax, a novel stabilization framework that reformulates the optimization trajectory as a controllable physical system. Unlike heuristic regularization techniques, our approach derives a specific feedback controller that provides theoretical guarantees of local exponential stability, regardless of the underlying Hessian spectrum. Consequently, our method stabilizes general minimax problems where traditional gradient descent-ascent methods fail. Extensive experiments on pathological benchmarks including the Dirac-GAN, 2D Gaussian mixtures, and Swiss Roll datasets, as well as high-resolution image synthesis with StyleGAN2-ada, demonstrate that Controlled-Minimax effectively suppresses oscillatory behavior while ensuring fast and robust convergence.

Vision-language-action (VLA) models have shown strong promise in robotic manipulation, yet their post-training still predominantly relies on trajectory-level supervised imitation over robot demonstrations. While effective for learning feasible actions, this paradigm supervises policies to reproduce expert actions at each step, biasing them toward demonstration-specific action paths and providing limited guidance for recovering toward task-progress states once execution deviates from demonstrated trajectories. To address this limitation, we propose OGPO, an Offline Goal-conditioned Policy Optimization framework for recoverable VLA post-training. Instead of treating offline demonstrations merely as step-wise action labels, OGPO adaptively relabels trajectories with semantic key states and constructs goal-conditioned process rewards to optimize the policy for reaching these task-progress states. In this way, OGPO shifts VLA post-training from trajectory-level action imitation to key-state reachability learning, enabling policies to acquire a more robust ability to complete goal states from feasible off-demonstration states without requiring additional online interaction. Experiments on LIBERO and MetaWorld show that OGPO consistently improves VLA policy performance over standard supervised fine-tuning. Moreover, evaluations on LIBERO-Plus and our proposed LIBERO-ReAct, a mid-execution perturbation benchmark, further demonstrate that OGPO achieves stronger robustness and goal-directed recoverability under distribution shifts and recoverable off-demonstration states.

The Noisy Linear Equations problem involves finding the solution to a random linear system over a finite field given noisy evaluations. This is a generalisation of various learning problems widely used in cryptography, including Learning with Errors (LWE), Learning with Rounding (LWR), and Learning Parity with Noise (LPN). We present a new sample-preserving search-to-decision reduction for Noisy Linear Equations that has complexity $\mathrm{poly}(n,m,\log q, q/\sigma)$, where $n$ is the dimension of the secret, $m$ is the number of samples, $q$ is the modulus, and $\sigma$ is the magnitude of the noise (defined suitably for the kind of noise involved). In particular, this implies search-to-decision reductions for LWE and LWR that run in time $\mathrm{poly}(n)$ even if the modulus $q$ is exponentially large, as long as the noise-to-modulus ratio $\sigma/q$ is non-negligible. Our reduction works for prime moduli $q$. If $q$ is composite, it instead produces a list that contains a small multiple of the solution. Prior to this work, known search-to-decision reductions for LWE and LWR either did not preserve the parameters of the problem (i.e., $m$, $n$, $\sigma$, and $q$), or had complexity that scaled polynomially with either $\sigma$ or the largest prime factor of $q$.

Clef was created as a concurrent programming language whose compiler tracks state ownership, waiting computations and request dependencies. We develop a coexponential account of stateful services and their client pools within this design. Delimited continuations retain the computation and resources needed to complete each interaction. These structures give our compiler premises for verifying service composition and preserving it through native lowering to multiple hardware targets. We connect finite actor state transitions to the initial, service and final rules of a coexponential server. A separate operational account relates actor dispatch to single-use continuation frames. We prove state-ownership and reply preservation, give a finite communication-summary analysis, and derive circular-wait exclusion under stated assumptions. A further result identifies when changing service order preserves both state and replies. Our Program Semantic Graph retains these facts through compilation. We are researching reusable proofs to confirm the ownership and communication interfaces, while our BAREWire implementation connects message meaning to its unboxed representation across respective endpoints. A distributed worker example preserves request identity across suspension, reordered replies and boundary conversion. Urschel's growth bounds inform a solver service; a distributed ADM update additionally preserves joint samples, metric evidence and request identity. Our two-category account connects component refinement, reusable proof terms and native realization. Protocols determine the admissible interactions, continuations expose their storage requirements, and retained dependencies support differential compilation. Developers can express ordinary stateful services while the compiler carries the conditions for their correct composition across execution targets.

Visual world models predict the consequences of actions from images, but planning with them is typically done by optimizers such as CEM and MPPI. However, these optimizers stall on long-horizon tasks where progress requires motion the objective does not reward, such as moving around a block before pushing it. This paper revisits kinodynamic sampling-based motion planning for world models, using the learned model as the propagator of a tree search in latent space. Our key insight is that the sampling strategies devised for high-dimensional search in configuration space apply directly to the latent space of a world model, where they rapidly expand through intermediate states that myopic optimizers never sample. We present two exploration strategies for the high-dimensional latent space, nearest-neighbor-guided RRT and projection-based KPIECE, with GPU-native batched expansion for efficient device utilization. We evaluate with an off-the-shelf DINO-WM on four simulated domains and a real-world block-pushing task. Our approaches match or exceed the success rate of CEM and MPPI at up to 13.8x lower planning time in simulation, and improve the success rate by 20% on the real robot.

Accurate charging load forecasting is essential for the reliability and efficiency of the Internet of Electric Vehicles (IoEV), yet it is challenging on two fronts: charging demand is highly stochastic, and the data collected by distributed sensors is privacy-sensitive. Although federated learning (FL) preserves data privacy, collaborative training still remains vulnerable to an honest-but-curious server that can invert shared model updates to reconstruct private data. To address both data volatility and privacy concerns, we propose SecFD-FL, a frequency-decoupled federated learning framework for IoEV comprising: 1) a dual-stream gated recurrent unit (GRU) network that separately models short-term and long-term charging load volatility, and 2) a lightweight secure aggregation layer for privacy preservation. Specifically, the secure aggregation layer employs a zero-sum additive masking protocol that perturbs each client's upload with high-variance noise, preventing the server from reconstructing private data while preserving the exact aggregate. Extensive experiments have been conducted on both EV-level and charging station-level using real datasets. The experiment results show that SecFD-FL reduces global RMSE by up to 60% over centralized baselines, achieving Jain's fairness up to 0.991 across clients, and reduces DLG-attack reconstruction correlation by 8 times to 15 times relative to FedAvg – all with no additional per-round communication cost beyond a one-time key exchange.

Federated vision-language models (FedVLMs) represent one of the emerging frontiers of machine learning, aiming to integrate federated learning within the fine-tuning pipelines of vision-language models (VLMs). In this work, we introduce BLEND, a new framework designed to jointly enhance the personalization and generalization capabilities of FedVLMs. BLEND proposes a selective VLM parameter fine-tuning and aggregation strategy that consists of (i) global vision and text adapters shared across clients, and (ii) a personalized vision projection adapter tailored to each client. BLEND further introduces a novel loss function that blends the outputs of the global and personalized adapters to achieve personalization, while simultaneously promoting generalization through a principled anchor design implemented via the projection heads of the zero-shot VLM. Extensive experiments demonstrate that BLEND consistently outperforms state-of-the-art baselines, highlighting its ability to effectively balance personalization and generalization across diverse system settings. (Code is available at: https://github.com/payamsiabd/BLEND.git)

Tabular foundation models (TFMs) based on prior-data fitted networks typically use sampled hard query labels, leaving avoidable sampling variability in pretraining. A key observation is that intermediate generator states can reveal label uncertainty beyond a sampled outcome. We propose Generator-state-Informed Supervision for TFMs (GIST), which uses these states to average query losses over conditional label distributions. GIST jointly designs task generation and query supervision, using entropy-calibrated encodings to obtain tractable, input-consistent targets on supported label-dependent branches. We show that this averaging preserves the population objective and yields no larger gradient covariance at fixed parameters within each generating distribution. Under matched pretraining schedules, GIST improves mean rank across three architectures on both TabArena and TALENT without changing architecture or inference. Across these benchmarks and architectures, GIST wins 62.1% of paired dataset-level comparisons against the corresponding hard-label controls.

Data-enabled predictive control (DeePC) replaces the explicit system model of model predictive control with a data-driven predictor built directly from recorded input-output trajectories, but the original hard-equality formulation keeps its coordinate vector as an online decision variable whose size grows with the offline data length, so the online problem grows more expensive as more data is used to enrich the behavioral representation. This article proposes Fast-DeePC, a framework which relaxes this consistency relation into the control objective with a quadratic ridge on the coordinate vector in place of the customary sparsity-promoting penalty, admitting a closed-form, offline elimination of the coordinate vector and reducing the online problem to a fixed-size box-constrained quadratic program solved by a structure-exploiting interior-point method. On identical soft quadratically regularized problems, the result matches a general-purpose solver's solution and is up to two orders of magnitude faster.

Domain adaptive object detection (DAOD) aims to reduce performance degradation due to distribution shifts between training (source) and testing (target) datasets. However, owing to insufficient modeling of fine-grained features and limited instance-level alignment, existing approaches become less effective under complex environmental conditions, such as extreme weather conditions and object occlusion. We overcome these challenges by proposing JMT-YOLO, a novel joint multi-task learning framework for DAOD. JMT-YOLO is based on the Mean Teacher architecture. It introduces an auxiliary semantic segmentation task and a masking mechanism to enhance the modeling of foreground-background semantic boundaries in the target domain. Specifically, fine-grained feature degradation is reduced by introducing a multiscale context-aware segmentation head equipped with a dense connection structure to effectively capture local and global contextual features. A mask-consistency loss is formulated by applying random masking operations to target images, thereby guiding the network to learn structural context and improving instance-level feature alignment. Extensive experiments on three benchmark domain adaptation tasks (Pascal VOC to Clipart1k, Cityscapes to Foggy Cityscapes, and KITTI to Cityscapes) demonstrate that JMT-YOLO achieves detection performances of 46.9%, 57.3%, and 61.0%, respectively. These results demonstrate superior performance over existing state-of-the-art and baseline methods, thereby validating the effectiveness and generalization of JMT-YOLO.

Few-step text-to-image generators produce high-quality images with few network forward passes, yet struggle with complex prompts involving multiple subjects, interactions, spatial relations, counting, and attribute binding. Can these errors be reduced without changing the generator's parameters or increasing its forward-pass count? We propose a lightweight correction framework for a frozen 6B-parameter generator. The generator predicts velocities that determine how its intermediate image representation is updated; a corrector with fewer than 20M parameters learns residual adjustments at selected sampling steps. Because earlier corrections change the states encountered at later steps, our On-Trajectory Flow-Bridge Supervision derives residual targets at the states actually reached during corrected generation. A single corrector is shared across selected steps and runs in parallel with the generator. Only the corrector is trained, requiring neither reward optimization nor re-distillation. To our knowledge, this is the first framework for lightweight velocity-residual adaptation of fully frozen few-step text-to-image generators that preserves their original sampling schedule and forward-pass count. On our target complex-scene distribution, the framework achieves a +17.60-percentage-point NetWin over frozen Z-Image-Turbo in pairwise instruction-following evaluation, with only 26.9 ms (0.84%) additional end-to-end latency per image and no increase in backbone evaluations.

In continuous diffusion language models (DLMs), latents generated by the diffusion prior are mapped almost deterministically to tokens, either by a decoder or through a token embedding matrix. Unlike in image generation, where visual details can vary continuously, the diffusion prior must capture fine-grained discrete token choices in continuous space, which can make the joint latent distribution difficult to model. We argue that generative roles should instead be divided: the diffusion prior should generate high-level predictive information, while the decoder should resolve the remaining token-level choices. Based on this view, we introduce Predictive-State Flows (PSF), which partitions a sequence into blocks and associates each block with a predictive state computed solely from the preceding context. A rectified-flow prior generates the sequence of predictive states, after which a block-causal decoder generates all blocks in parallel, autoregressively only within each block. This design reduces diffusion from L token-level states to K << L predictive states and reduces token-level sequential depth to L/K. On unconditional OpenWebText generation, a 197M-parameter model with K=8 achieves a generative perplexity of 22.8 at entropy 5.16 using 0.31 TFLOPs per 1024-token document, versus 24.0 and 7.2 TFLOPs for ELF-B with 32 sampling steps. The corresponding measured operating points yield MAUVE@512 of 0.93 and 0.73, respectively. Controlled comparisons further show that content states, which additionally encode their own blocks, yield 8-16% higher generative perplexity than predictive states, and that replacing sampled predictive-state trajectories with a shared mean lowers MAUVE@512 by 0.11. These results suggest that latent design for continuous DLMs should consider not only how compactly text is represented, but also how generative roles are divided between the prior and the decoder.

Existing image fusion methods struggle to balance visual quality and semantic attributes due to the inherent conflict between their optimization objectives. To address this challenge, we propose an attack-defense semantic reinforcement framework for multi-modal image fusion, termed AD-SRF. By decoupling the optimization of these two aspects and then losslessly reintegrating them, this framework injects rich semantic attributes into the fused image without compromising its visual quality. First, we develop a transformer-based restorative visual fusion network as a strong visual baseline. Equipped with the cross-modal retrieval module and the information restoration-fusion collaborative loss, it produces highly visible fused images even under degraded conditions. Second, we introduce an attack-defense game mechanism to strengthen the semantic attributes of the fused images. The attacker learns perturbation patterns that impair downstream segmentation, whereas the defender restores missing semantics from the original inputs and reintegrates them into the fused image through an imperceptible semantic map. Through iterative adversarial interaction, this mechanism drives the defender to continually strengthen the fused image's semantic attributes, boosting downstream segmentation performance without compromising visual quality. Extensive experiments demonstrate that AD-SRF achieves a win-win performance in both visual quality and semantic attributes. The code is publicly available at https://github.com/HaoZhang1018/AD-SRF.

Scene flow estimation is essential for many autonomous driving (AD) and robotic applications, but obtaining ground-truth scene flow for non-rigid objects such as pedestrians is highly difficult. We present MeshBind, an automatic annotation method that uses mesh models to generate per-point scene flow for non-rigid objects. The novelty of this work is threefold: 1) developing a multi-view module that reconstructs an articulated mesh model from six surround cameras and registers it to the LiDAR frame; 2) developing a local-frame binding technique that attaches each point to the mesh via a per-triangle tangent-normal frame, invariant under rigid motion yet adaptive to articulated deformation; 3) developing a three-layer flow registration pipeline that combines ego-vehicle motion compensation, box binding, and mesh binding to produce full-scene per-point flow. We implement MeshBind in SF-Studio, an interactive annotation tool that visualises flow and allows human mesh corrections with automatic recomputation. We also release SFB (SUScape-Flow Benchmark), a large-scale benchmark built on the public SUScape dataset, covering 41k frames with per-point non-rigid pedestrian annotations and a Rigid/Semi-Rigid/Non-Rigid evaluation protocol. Experiments show that fine-tuning on SFB reduces three-way endpoint error (EPE) by 2-3x, and brings non-rigid EPE to the same level as rigid EPE.

Determinantal point processes (DPPs) model subset selection probability with repulsion, but many applications also exhibit attraction arising from pairwise relations or shared latent structures. Nonsymmetric DPPs (NDPPs) capture both effects, but their kernels are difficult to interpret and specify. We propose the determinant-ratio point process (DRPP), which models repulsion and attraction via a ratio of the determinants of two symmetric positive-definite kernels. This formulation preserves the feature-based interpretation of standard DPPs while allowing the two effects to be specified separately. Furthermore, we introduce an auxiliary-variable representation that enables efficient MCMC sampling and yields tractable expectation formulas for key statistics including the normalizing constant, which together facilitate maximum-likelihood learning. We theoretically characterize the interaction structures well suited to DRPPs: for clustered attraction with $C$ latent groups, DRPPs exactly realize the target pairwise interactions with an attraction rank $C$, whereas NDPPs can require a rank that grows with the number of items given the same repulsion component. Conversely, nontransitive pair-specific attraction may require compensation through the DRPP repulsion kernel. Experiments support these predictions, showing that DRPPs fit transitive attraction with substantially fewer parameters than NDPPs.

The rapid integration of Artificial Intelligence (AI) models has established a complex supply chain, but many of these shared artifacts harbor severe vulnerabilities such as insecure serialization and architectural backdoors. While prior efforts have extensively scanned model registries to flag malicious artifacts, a critical gap remains in understanding how developers actually adopt and integrate these unsafe models into downstream software systems. We bridge this gap by conducting a large-scale empirical study on the usage and propagation of 16,977 known vulnerable models on HuggingFace flagged by InsightDB. We track their prevalence across GitHub repositories, software packages, fine-tuning chains, and deployed applications. Our analysis reveals that, despite being publicly flagged, these vulnerable models appear in over 21,000 code locations across more than 5,000 open-source repositories, with 3,757 distinct flagged models (22.1%) actively referenced in at least one project. To understand how these models are integrated in code and identify recurring usage patterns, we categorize and analyze the recurring patterns surrounding them via a customized pipeline that combines Code Property Graphs extraction with a tailored Frequent Subgraph Mining approach. Across all patterns, we find no evidence of provenance verification or model sanitization logic, with developers frequently bypassing security parameters or delegating trust to permissive framework defaults. These findings underscore that scanning model files alone is insufficient, highlighting the urgent need for context-aware security analysis and stricter integration guardrails at the application layer of the AI supply chain.

For many users, the relationship they have with an AI companion (AIC) feels genuinely mutual. The AIC appears to be attentive, empathic, and responsive, and the exchange can feel like two parties reacting to each other in real time. As a result, the user's mental model of relational adaptation correctly represents the AIC's responsiveness and change, but the mechanisms producing this may not be what the user thinks they are. In this paper, we illustrate how an AIC's apparent mutual engagement is generated without a persisting relational subject on the other side and without a self that persists between prompts. Despite this, we show that both the human and the AIC are changed by the interaction, but these changes occur at different levels. The change in the person is constitutive (i.e., intrinsic), but the change in the AIC is configurative (i.e., external and editable), and it is this difference in level that explains how the AIC contributes to a sense of mutuality. Importantly, we highlight how the mental models users develop regarding AIC mutuality may misrepresent its mechanisms and ontological significance. Specifically, we discuss how the ways in which AICs respond to a user contribute to the user's inaccurate inference that the AIC's adaptation is constitutive rather than configurative and how this can lead users to assign unwarranted interpersonal significance to the mutuality of the relationship. The paper specifies the implications this has for users, researchers, and AIC designers.

The aim of this study is to conduct an empirical analysis of the correlation between code modifications and software quality. This will be achieved by examining the effects of changes made before and after each refactoring commit, using software metrics such as size, complexity, code smells, vulnerability, and bug detection. The objective is to conduct static code analysis on open-source software from GitHub repositories using various repository mining and static code analysis tools. Our approach involves formulating research-based questions and utilizing the specified tools to obtain results that will enable us to resolve the case study. The obtained results have been summarised and further analyzed using tables and graphs to achieve the objective.

This is a lecture note to introduce the innovative concept of numerical smoothness and its application in the error analysis of numerical solutions of evolution equations. We start from the two fundamentally different error splittings. One splitting is popular, which brings us to local error defined along the original solution and error propagation by numerical schemes. The well know Lax Equivalence Theorem is based on this error splitting. The other splitting can only be seen in a few numerical ODE books, which brings us to local error defined along the numerical solution and error propagation by the solutions of the evolution equations. The innovative concept of numerical smoothness arises from the second error splitting. While there are many difficulties when we use numerical stability in error analysis of nonlinear evolution equations, we will show that numerical smoothness works naturally in error analysis/estimation/control with advantages in several ways. We will define temporal and spatial numerical smoothness indicators, prove the necessity of the boundedness of these indicators for having optimal convergence rate, and show computable error estimates as functions of these smoothness indicators. Moreover, these error estimates are optimal in the approximatons' convergence rates and also optimal in the error propagation of the evolution equation. In comparison with error analysis framework of the Lax Equivalence Theorem, from the necessity results and the error estimates proven in the numerical smoothness framework, we have a new equivalence theory: Consistency + Numerical Smoothness <=> optimal error estimates. Unlike the Lax Equivalence Theorem, the new equivalence theory works well for nonlinear problems, nonlinear schemes, and many other complications of problems.

Existing multi-modal image fusion (MMIF) methods generally face an optimization gap between the visual and semantic domains, limiting the enhancement of perception capabilities. To overcome this long-standing barrier, this work proposes W2F, a new visual-semantic mutual reinforcing paradigm for MMIF. First, we reinforce the foundation for visual-semantic collaboration by amplifying feature complementarity through two targeted designs. Specifically, the segmentation-guided fusion loss introduces object-aware structural guidance into visual fusion, while the embedded information restoration prior strengthens the ability of visual fusion to provide reliable perceptual evidence for the semantic branch under degraded conditions. Then, we introduce a strong multi-modal information interaction mechanism composed of interactive gated mixed attention (IGM-Att) and progressive coupling attention (PC-Att). These modules ensure comprehensive scene representation from both local and global perspectives, providing informational support for visual-semantic collaboration. Finally, we construct a collaborative mechanism to couple visual fusion and semantic fusion. It employs a feature search module to extract complementary key features from both tasks, enriching the representational potential of visual and semantic information. Meanwhile, a gradient correction strategy ensures collaboration from the optimization perspective, enhancing performance for both visual and semantic fusion through consistency learning. Extensive experiments demonstrate that W2F surpasses state-of-the-art methods in both visual appeal and decision accuracy. The code is publicly available at https://github.com/HaoZhang1018/W2F.

Action-chunking policies predict future controls but execute only a prefix before replanning. How should a fixed action-token budget be organized to support execution? We propose LeapBot-JAC, which allocates sparse future-control references and dense execution targets within one shared action expert. Both chains are supervised by demonstrated controls and jointly denoised at aligned physical-time positions. Directed attention allows execution tokens to read reference tokens subject to the host's visibility constraints. JAC adds neither action tokens nor parameters and retains the host's world-modeling process; both chains participate in inference, while only the execution chain drives the robot. On LIBERO, JAC reaches 98.3% success with FastWAM and LeapBot-WA, up from 97.0% and 97.3%. On zero-shot LIBERO-Plus, their success rates improve from 53.5% to 70.4% and from 73.1% to 77.3%, respectively. RoboTwin and two real-world tasks provide additional deployment evaluations. Single-training-seed comparisons and FastWAM ablations support action-role organization as a practical design choice under a fixed action-token budget.

Federated Direct Preference Optimization (FedDPO) enables privacy-preserving alignment of language models across distributed agents without centralizing sensitive preference data. However, existing FedDPO analysis assumes all clients converge to a single shared policy, which is fundamentally at odds with the heterogeneous preference distributions present in practice. We propose Personalized FedDPO (pFedDPO), in which each client maintains a small private LoRA adapter atop a shared global base model. In pFedDPO, the base model is synchronized via FedAvg each round, while LoRA adapters remain local and capture client-specific preference patterns. We prove that this decomposition replaces the gradient diversity $\kappa^2$ in the FedDPO bound with a residual heterogeneity $\kappa^2_{\mathrm{eff}}$, which is no larger than $\kappa^2$ when adapters absorb client-specific directions, at the cost of an additive adapter-bias term that vanishes as adapters converge. Experiments on preference datasets show that pFedDPO consistently attains a lower gradient norm than vanilla FedDPO while remaining competitive in training loss and per-client reward accuracy.

Accurate estimation of reservoir properties from seismic data remains challenging when well-log labels are sparse but three-dimensional seismic observations are abundant. We present CoGameReg, a cooperative-game-based semi-supervised regression framework that uses contribution-weighted predictions of heterogeneous regressors to construct shared pseudo-labels. This design makes each regressor's influence explicit in the learning targets generated for unlabeled seismic samples under sparse well control. The Tri-Game Shapley mechanism combines a rank-MSE performance game with a pairwise consistency game to estimate nonnegative regressor weights from labeled training data. These weights remain fixed during collaborative training and are reused for final prediction fusion. Candidate pseudo-labels are screened using a common local k-nearest-neighbor error-reduction criterion before retraining. We evaluate CoGameReg on 10 public regression datasets, a synthetic three-dimensional P-wave velocity prediction task, and a field water-saturation application. The benchmark advantage is most pronounced at the 2.5% labeled-data ratio and decreases as supervision increases. CoGameReg obtains the best blind-well metrics in the synthetic experiment. In the field application, predictions clipped to 0-100% yield a pooled coefficient of determination of 0.5942, a root mean square error of 5.7441 percentage points, and a Pearson correlation of 0.8265 across three blind wells. CoGameReg has the highest Pearson correlation among the four field methods. The results support the use of CoGameReg to integrate sparse well labels with dense seismic attributes, with performance varying across metrics and well locations.