# Research Papers — 2026-05 · bundle page 152 of 326 > Full-text markdown of papers #7551–7600 of 16266 published in 2026-05, newest first. AI-readable mirrors of the full 16266-paper 2026-05 archive (50 papers per file). Canonical page: https://aipod.dev/papers/archive/2026-05/page/152 [← Previous bundle](https://aipod.dev/papers/archive/2026-05/page/151.md) [Next bundle →](https://aipod.dev/papers/archive/2026-05/page/153.md) ### Efficient Sparse-to-Dense Visual Localization via Compact Gaussian Scene Representation and Accelerated Dense Pose Estimation Authors: Zizhuo Li, Songchu Deng, Linfeng Tang, Jiayi Ma Published: 2026-05-18 Categories: cs.CV arXiv: https://arxiv.org/abs/2605.17777v1 This letter presents LiteLoc, a novel and efficient localizer built on 3D Gaussian Splatting (3DGS). The previous state-of-the-art (SoTA) sparse-to-dense localizer, STDLoc, has shown remarkable localization capability but suffers from severe storage redundancy and computational latency. By revisiting its design decisions, we derive two simple yet highly effective improvements that cumulatively make LiteLoc much more efficient in both memory and computation, while also being easier to train. One key observation is that the color field, inherited directly from Feature 3DGS, is functionally useless for localization. Yet, its reconstruction of high-frequency photometric details necessitates excessive Gaussian primitives, resulting in a tightly coupled color-feature representation with significant memory overhead and sub-optimal feature field optimization. To resolve this, we propose a color-free decoupled feature field that constructs a compact Gaussian scene representation by retaining only task-essential feature attributes, thereby eliminating approximately 94% of redundant storage with no loss of localization-relevant information. We further find that the primary computational bottleneck lies in the dense Perspective-n-Point (PnP) solver, where most matches contribute saturated geometric constraints with diminishing accuracy gains. Accordingly, we propose a condensing strategy that distills dense matches into a subset of 5% representative matches, enabling a nearly 19-fold speedup in robust estimation with negligible performance drop. Extensive experiments show that LiteLoc surpasses STDLoc in multiple scenes with considerable efficiency benefits, opening up exciting prospects for latency-sensitive visual localization. --- ### Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scale Authors: Jinghui Liu, Sarvesh Soni, Anthony Nguyen Published: 2026-05-18 Categories: cs.CL, cs.AI arXiv: https://arxiv.org/abs/2605.17775v1 Large language models (LLMs) can generate or synthesize clinical text for a wide range of applications, from improving clinical documentation to augmenting clinical text analytics. Yet evaluations typically focus on a narrow aspect -- such as similarity or utility comparisons -- even though these aspects are complementary and best viewed in parallel. In this study, we aim to conduct a systematic evaluation of LLM-generated clinical text, which includes intrinsic, extrinsic, and factuality evaluations of synthetic clinical notes rephrased from MIMIC databases at million-note scale. Our analysis demonstrates that synthetic notes preserve core clinical information and predictive utility for coarse-grained tasks despite substantial linguistic changes, but lose fine-grained details for task like ICD coding. We show this loss of detail can be substantially mitigated by rephrasing notes by chunks rather than by the whole note, but at the cost of reduced factual precision under incomplete context. Through fact-checking and error analysis, we further find that synthesis errors are dominated by misinterpretation of clinical context, alongside temporal confusion, measurement errors, and fabricated claims. Finally, we show that the synthetic notes -- despite their task-agnostic nature -- can effectively augment task-specific training for rare ICD codes. --- ### Internalizing Tool Knowledge in Small Language Models via QLoRA Fine-Tuning Authors: Yuval Shemla, Ayal Yakobe, Tanmay Agarwal, Dhaval Patel, Kaoutar El Maghraoui Published: 2026-05-18 Categories: cs.CL arXiv: https://arxiv.org/abs/2605.17774v2 Large language models are increasingly used as planning components in agentic systems, but current tool-use pipelines often require full tool schemas to be included in every prompt, creating substantial token overhead and limiting the practicality of smaller models. This paper investigates whether tool-use knowledge can be internalized into small language models through parameter-efficient fine-tuning, enabling structured planning without explicit tool descriptions at inference time. Using AssetOpsBench as the primary benchmark, we fine-tune Gemma 4 E4B and Qwen3-4B with 8-bit QLoRA on approximately 1,700 tool-use examples spanning tool knowledge, question-to-plan mappings, and execution-style traces. We evaluate the resulting models under description-free inference, where the prompt omits the tool catalog entirely. The fine-tuned models outperform an informed unfine-tuned baseline that receives full tool descriptions, reducing input length by 82.6\% while improving structural and LLM-judge planning scores. In the best Gemma run, the model achieves an AT-F1 of 0.65 and an overall judge score of 3.88, compared with 0.47 and 2.88 for the informed baseline. Qwen3-4B achieves a strong overall judge score of 3.78 while using 62\% less memory and running 2.5$\times$ faster than Gemma, though it also exhibits greater catastrophic forgetting on general multiple-choice benchmarks. Additional ablations show that LoRA rank controls a quality--retention trade-off, with $r=32$ maximizing planning quality and smaller ranks preserving more general knowledge. These results suggest that, for fixed tool catalogs, QLoRA fine-tuning can shift tool knowledge from prompt context into model weights, substantially reducing inference overhead while maintaining or improving tool-planning quality. --- ### PlantPose: Universal Plant Skeleton Estimation via Tree-constrained Graph Generation Authors: Xinpeng Liu, Hiroaki Santo, Yosuke Toda, Fumio Okura Published: 2026-05-18 Categories: cs.CV arXiv: https://arxiv.org/abs/2605.17773v1 Accurate estimation of plant skeletal structures (e.g., branching structures) from images is essential for smart agriculture and plant science. Unlike human skeletons with fixed topology, plant skeleton estimation presents a unique challenge, i.e., estimating arbitrary tree graphs from images. To address this problem, we introduce PlantPose, a universal plant skeleton estimator via tree-constrained graph generation. PlantPose combines learning-based graph generation with traditional graph algorithms to enforce tree constraints during the training loop. To enhance the model's generalization capability, we curate a large and diverse dataset comprising real-world and synthetic plant images, along with simplified representations (e.g., sketches and abstract drawings). This dataset enables the generalized model to adapt to diverse input styles and categories of plant images while preserving topological consistency. Our approach demonstrates robust and accurate plant skeleton estimation across multiple domains, including previously unseen out-of-domain scenarios. Further analyses highlight the method's strengths and limitations in handling complex, heterogeneous data distributions. All implementations and datasets are available at https://github.com/huntorochi/PlantPose/. --- ### Towards Universal Physical Adversarial Attacks via a Joint Multi-Objective and Multi-Model Optimization Framework Authors: Ziyang Liu, Hongyuan Wang, Zijian Wang, Yinxi Lu, Yunzhao Zang, Zhiqiang Yan, Qianhao Ning Published: 2026-05-18 Categories: cs.CV arXiv: https://arxiv.org/abs/2605.17772v1 Physical adversarial attacks often overfit single surrogate models and optimization objectives. While ensemble attacks can mitigate this, existing methods struggle with severe gradient conflicts within restricted physical texture spaces, significantly degrading cross-model transferability. To bridge this gap, this paper proposes a Joint Multi-Objective and Multi-Model Optimization Framework (JMOF) that leverages quantitative similarity analysis to select the optimal surrogate model ensemble. Within JMOF, a dual-level mechanism jointly suppresses prediction outputs and flattens intermediate feature distributions, balancing attack efficiency with deep generalization. Additionally, an Orthogonal Gradient Alignment (OGA) strategy resolves cross-model gradient conflicts, transforming mutually repulsive gradients into synergistic optimization directions. Extensive simulated and real-world experiments demonstrate that JMOF outperforms state-of-the-art baselines against diverse black-box detectors. Crucially, JMOF exhibits substantial cross-vision-task generalization, generating attacks capable of simultaneously deceiving object detection and semantic segmentation or monocular depth estimation models. This research advances the generalization limits of physical adversarial attacks, providing a robust framework for evaluating visual AI vulnerabilities in real-world deployments. --- ### Entropy-Gradient Inversion: Moving Toward Internal Mechanism of Large Reasoning Models Authors: Junyao Yang, Chen Qian, Kun Wang, Linfeng Zhang, Quanshi Zhang, Yong Liu, Dongrui Liu Published: 2026-05-18 Categories: cs.AI, cs.CL arXiv: https://arxiv.org/abs/2605.17770v4 The advancement of Large Reasoning Models (LRMs) has catalyzed a paradigm shift from reactive ``fast thinking'' text generation to systematic, step-by-step ``slow thinking'' reasoning, unlocking state-of-the-art performance in complex mathematical and logical tasks. However, the field faces \textit{the fundamental gap between token-level behavioral analysis and internal reasoning mechanisms, and the instability of reinforcement learning (RL) for reasoning optimization relying on costly external verifiers}. We identify and formally define \textbf{Entropy-Gradient Inversion}, a robust negative correlation between token entropy and logit gradients that acts as a definitive geometric fingerprint for LRM reasoning capability. Building on this, we propose \textbf{Correlation-Regularized Group Policy Optimization (CorR-PO)}, which embeds this inversion signature into RL reward regularization. Extensive experiments on various reasoning benchmarks across multiple model scales show CorR-PO consistently outperforms state-of-the-art baselines, confirming that stronger inversion directly correlates with superior reasoning performance. --- ### Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent Authors: Behrad Moniri, Hamed Hassani Published: 2026-05-18 Categories: stat.ML, cs.LG arXiv: https://arxiv.org/abs/2605.17767v2 We study feature learning in two-layer neural networks within the linear-width regime, where the number of hidden neurons, sample size, and input dimension scale proportionally. While recent work has analyzed feature learning via a single step of gradient descent on the first layer weights in this regime, such one-step update schemes are fundamentally limited: the update to the weights is approximately rank-one, captures only a single direction, and requires the target function to have an information exponent of one. In this paper, we go beyond one-step updates to provide a full characterization of the features learned during the \textit{second step} of gradient descent with step-sizes $η_1\asymp N^{α_1}$ and $η_2 \asymp N^{α_2}$ for $α_1, α_2 \in (0,0.5)$, where $N$ is the number of hidden neurons. We derive a spectral characterization of the updated weights, demonstrating they behave as a spiked random matrix with multiple outliers, each corresponding to a learned direction. We show that the number of the outliers is determined by the parameters $α_1, α_2$ through $\lfloor \frac{α_2}{1/2 - α_1} \rfloor$. Furthermore, by analyzing the alignment between the learned directions and the target function, we identify a gap between training with independent versus reused batches. While independent batches restrict learning to directions with an information exponent of one, batch reuse enables the second update to capture directions even when the information exponent exceeds one, provided that $α_1, α_2$ are chosen properly. This shows that the benefits of batch reuse, previously observed in narrow-width regimes, persist in the linear-width limit as well. By characterizing these early-phase evolutions, our work proposes a tractable framework for studying optimization and feature learning phenomenology in modern overparameterized networks. --- ### LatentUMM: Dual Latent Alignment for Unified Multimodal Models Authors: Yinyi Luo, Wenwen Wang, Hayes Bai, Marios Savvides, Jindong Wang Published: 2026-05-18 Categories: cs.CV arXiv: https://arxiv.org/abs/2605.17766v1 Unified multimodal models (UMMs) achieve strong performance in both understanding and generation by learning a shared latent space, yet they often exhibit functional inconsistency between these two capabilities. We observe that this issue does not stem from a lack of shared representations, but from the absence of explicit alignment between the transformations that map into and out of the latent space. As a result, generation and re-encoding can follow inconsistent trajectories, leading to semantic drift under modality transitions. In this work, we propose LatentUMM, a framework that constructs an enhanced shared latent space to explicitly align these transformations and improve cross-modal consistency. LatentUMM consists of two stages. First, dual latent alignment enforces consistency at both the modality and capacity levels: cross-modal alignment uses a stronger embedding model to impose structured cross-modal semantics, while dual capacity alignment enforces bidirectional consistency under generation and re-encoding. Second, latent dynamics stabilization improves robustness via stochastic latent rollouts and preference optimization, favoring trajectories that better preserve semantic consistency. Experiments show that LatentUMM consistently improves multimodal consistency across diverse architectures. Code is available at: https://github.com/AIFrontierLab/TorchUMM/tree/main/src/umm/post_training/LatentUMM. --- ### AURORA: Contextual Orthogonalization for Geometric Representation Learning in Healthcare Foundation Models Authors: Yuanyun Zhang, Shi Li Published: 2026-05-18 Categories: cs.LG arXiv: https://arxiv.org/abs/2605.17765v1 Recent healthcare foundation models have achieved strong predictive performance through large scale self supervised learning, yet their latent representations frequently entangle physiologic severity, intervention intensity, observational structure, and institutional workflow into shared embedding directions. While effective for downstream prediction, such representations remain semantically opaque and unstable under contextual shift. We introduce AURORA, Adaptive Uncertainty aware Representations through Orthogonalized Relational Alignment, a new framework for healthcare representation learning based on contextual latent geometry. Rather than optimizing a single unified embedding manifold, AURORA decomposes representations into orthogonal semantic subspaces corresponding to distinct contextual factors and learns relational consistency objectives within each subspace. This induces latent spaces that are both semantically disentangled and geometrically interpretable. Across multiple clinical prediction and retrieval tasks, AURORA consistently outperforms reconstruction, contrastive, and self distillation baselines while substantially improving contextual disentanglement, neighborhood purity, and robustness under institutional distribution shift. Our results suggest that latent geometry itself constitutes an important axis of healthcare foundation model design and that explicitly structuring representation space according to contextual semantics provides a complementary direction beyond conventional predictive compression objectives. --- ### Comparing Two Categorical Gini Correlations with Applications to Classification Problems Authors: Sameera Hewage, Yongli Sang Published: 2026-05-18 Categories: stat.ME, stat.ML arXiv: https://arxiv.org/abs/2605.17763v1 This article proposes an inferential framework for comparing predictor importance in classification problems with categorical response variables. The approach is based on the categorical Gini correlation (CGC) proposed by Dang et al. (2020), a measure of dependence between numerical predictors and categorical outcomes. Predictor importance is evaluated by testing differences in CGCs across competing predictor groups. The proposed methodology accommodates predictors of arbitrary and unequal dimensions and allows for dependence between predictor groups. Asymptotic normality of the test statistic is established under both the null and alternative hypotheses, and the resulting test is shown to be consistent. In addition to deriving the asymptotic distribution, a nonparametric bootstrap procedure is developed as an alternative approach to inference. Simulation studies, along with applications to breast cancer and human activity recognition datasets, demonstrate the effectiveness of the proposed framework. --- ### Surface-Form Neural Sparse Retrieval: Robust Fuzzy Matching for Industrial Music Search Authors: Paul Greyson, Zhichao Geng, Wei Zhang, Yang Yang Published: 2026-05-18 Categories: cs.AI arXiv: https://arxiv.org/abs/2605.17762v1 Music search at the scale of Amazon Music presents a unique challenge: queries frequently deviate from indexed metadata due to misspellings, transpositions, and phonetic variations, yet the retrieval system must operate under strict millisecond-level latency constraints. Our existing learning-to-retrieve system, the High Confidence Index (HCI), learns query-entity associations from customer behavior, relying on continual ``exploration'' to choose candidates. Traditional n-gram matching enables this exploration but suffers from poor semantic robustness and high noise, limiting the system's ability to learn from long-tail queries. In this work, we present a \textbf{robust neural sparse retrieval system} designed to maximize exploration efficiency. We adapt a state-of-the-art \textbf{inference-free} sparse retrieval architecture to the music domain, combining it with an effective \textbf{domain-specific granular subword tokenization strategy}. Our approach utilizes short-length token constraints (max 3 chars) to enforce the learning of surface-form robustness over lexical memorization. By pre-computing the neural embeddings and term expansions during the offline indexing phase, online processing is reduced to minimal tokenization and IDF weighting, achieving effectively zero latency overhead for query encoding. Evaluations on a 6M-document production corpus show an aggregate \textbf{91.4\%} recall@10 (vs. \textbf{57.7\%} for trigrams) at comparable throughput. Simulation of the HCI feedback loop demonstrates improved exploration efficiency, with \textbf{+0.8\%} higher stabilized recall than production trigrams. Ablation studies indicate that our sparse training methodology drives the performance gains, while domain-specific pretraining provides a cost-effective alternative to large-scale general-purpose pretraining. --- ### MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling Authors: Kaichuan Kong, Dongjie Liu, Xiaobo Jin, Guanggang Geng Published: 2026-05-18 Categories: cs.SI, cs.LG arXiv: https://arxiv.org/abs/2605.17761v1 Insider threats often reveal early anomalies through disruptions in behavioral statistics-such as altered recurrence patterns or short-versus long-term frequency shifts-rather than changes in event semantics. Yet, as the field has shifted from statistical modeling to log tokenization and deep sequential encoders, these statistical cues are weakened or lost, leaving current models insensitive to gradual and low-visibility insider behaviors.We propose MV-Gate, a multi-view behavior modeling framework that explicitly integrates statistical regularities with sequence semantics. MV-Gate constructs three aligned behavioral sequences: activity tokens, multi-scale status signals capturing recurrence patterns, and frequency-deviation signals describing short- vs long-term intensity differences. An anomaly-aware gating mechanism injects these statistical views into the attention computation, guiding the encoder to emphasize statistically irregular events. Experiments on CERT r4.2, CERT r5.2, and ADFA-LD show that MV-Gate achieves notable gains over classical, deep-learning, and domain-specific baselines, particularly for progressive, weak-signal threats. These results highlight the necessity of jointly modeling statistical and sequential evidence for robust insider-threat detection. --- ### FrequencyBooster: Full-Frequency Modeling for High-Fidelity Pixel Diffusion Authors: Lichen Ma, Zipeng Guo, Yu He, Xiaolong Fu, Luohang Liu, Jingling Fu, Junshi Huang, Yan Li Published: 2026-05-18 Categories: cs.CV arXiv: https://arxiv.org/abs/2605.17759v1 To circumvent the inherent fidelity bottlenecks and optimization misalignment of VAE-based latent diffusion, pixel-space diffusion models have emerged as a compelling end-to-end paradigm. However, existing pixel diffusion models often struggle to balance computational efficiency with the preservation of high-frequency details. They frequently resort to patch-based compression or restricted local decoding, leading to a "spectral compromise" where high-frequency and fine-grained pixel information are suppressed. To address these challenges, we propose \textbf{FrequencyBooster}, a novel framework designed to empower pixel diffusion with full-frequency modeling capabilities without prohibitive overhead. The core of our method is a high-capacity decoder that specializes in extracting exhaustive high-frequency details and low-frequency semantics, the latter of which is derived from a Diffusion Transformer (DiT) backbone. Unlike prior works that sacrifice global context for local refinement, FrequencyBooster leverages high-dimensional feature representations to maintain global structural integrity while achieving superior pixel-level precision. Extensive experiments on ImageNet demonstrate the effectiveness of our approach: our model achieves a state-of-the-art FID of \textbf{1.60} at $256 \times 256$ resolution within only 320 epochs. Furthermore, at $512 \times 512$ resolution, FrequencyBooster attains an FID of \textbf{1.69}, significantly outperforming existing pixel-space and latent-space generative models. --- ### Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets Authors: Nitish Nagesh, Pengbao Zhou, Atchuth Naveen Chilaparasetti, Yajat Nagaraj Kiran, Tu Nguyen, Arshia Harish Puthran, Muhjaazee Love, Aadi Sharma, Mahdi Bagheri, Ian Harris, Amir M. Rahmani Published: 2026-05-18 Categories: cs.LG arXiv: https://arxiv.org/abs/2605.17758v2 Synthetic data is widely used in healthcare to create datasets that preserve statistical properties of real data without exposing sensitive patient information. Generating and evaluating synthetic data across privacy, utility, and fairness dimensions is crucial for enabling high-quality data availability in downstream prediction tasks and clinical decision making. We present \textbf{Memisis}, a tool that orchestrates and evaluates synthetic data by leveraging existing synthesis libraries, large language models (LLMs), and state-of-the-art evaluation metrics. Our tool creates a unified workflow for data generation, validation, and evaluation. Users can control training size, training epochs, and the number of synthetic rows to sample. Beyond manual configuration, an interactive agent mode allows users to specify data generation goals in natural language, and the tool orchestrates the full pipeline by invoking existing synthesizers while performing the requisite evaluation. For the demo, we use an open-source schizophrenia dataset with protected attributes related to race and gender, evaluate six synthesizers spanning GANs, VAEs, diffusion models, and normalizing flows, and use a local LLM to orchestrate the workflow. The system affords users flexibility and control over the data generation and evaluation process. --- ### OSCAR: Offline Spectral Covariance-Aware Rotation for 2-bit KV Cache Quantization Authors: Zhongzhu Zhou, Donglin Zhuang, Jisen Li, Ziyan Chen, Shuaiwen Leon Song, Ben Athiwaratkun, Xiaoxia Wu Published: 2026-05-18 Categories: cs.LG, cs.AI, cs.DC, cs.PF arXiv: https://arxiv.org/abs/2605.17757v1 INT2 KV-cache quantization is attractive for long-context LLM serving, but it remains difficult to make both accurate and deployable. Simple rotations such as Hadamard transforms reduce outliers, but still degrade at INT2 because they are not aligned with downstream attention. We propose OSCAR, an Ultra-low-bit KV Cache quantization method that estimates attention-aware covariance structures offline and uses them to derive fixed rotations and clipping thresholds for quantization. In this way, it aligns KV quantization with the covariance structures that attention actually consumes. More importantly, we not only provide theoretical justification but also develop a fully deployable OSCAR system with a custom INT2 attention kernel that remains compatible with paged KV-cache serving and fused kernel pipelines, enabling seamless integration into modern LLM serving frameworks such as SGLang and vLLM. We evaluate our methods on recent reasoning models with reasoning traces of up to 32k tokens across 5 tasks. On Qwen3-4B-Thinking-2507 and Qwen3-8B, OSCAR reduces the BF16 accuracy gap to 3.78 and 1.42 points, respectively, while naive rotation INT2 collapses to nearly zero. We further scale OSCAR to Qwen3-32B and GLM-4.7 (358B params), where it remains effectively on par with BF16. On long context - RULER-NIAH up to 128K, OSCAR remains robust on both Qwen3 models, while naive rotation INT2 collapses. System-wise, OSCAR reduces KV-cache memory by approximately 8x, improves throughput by up to 7x at large batch sizes under the same memory budget, and accelerates batch-size-1 decoding by up to 3x over BF16 due to reduced memory bandwidth overhead. --- ### Bridging the Version Gap: Multi-version Training Improves ICD Code Prediction, Especially for Rare Codes Authors: Jinghui Liu, Anthony Nguyen Published: 2026-05-18 Categories: cs.CL, cs.AI arXiv: https://arxiv.org/abs/2605.17755v1 Clinical coding maps clinical documentation to standardized medical codes, an essential yet time-consuming administrative task that could benefit from automation. Current models on ICD coding are typically optimized for codes from a specific ICD version. However, in reality, ICD systems evolve continuously, and different versions are adopted across time periods and regions. Moreover, ICD coding suffers from the long-tail problem, and rare code performance can be a bottleneck for developing implementable models. We examine whether it is viable to train version-independent models by combining data annotated in different ICD versions, which may help address these challenges. We add ICD-9 data to the training of a modified label-wise attention model for ICD-10 prediction, and find that despite the version mismatch, adding ICD-9 yields a 27% increase in micro F1 for 18K rare ICD codes compared to training on ICD-10 alone. On 8K frequent ICD-10 codes, the multi-version training also substantially improves macro metrics, with far fewer model parameters. --- ### Testable and Actionable Calibration for Full Swap Regret Authors: Konstantina Bairaktari, Lunjia Hu, Huy L. Nguyen, Jonathan Ullman Published: 2026-05-18 Categories: cs.LG, stat.ML arXiv: https://arxiv.org/abs/2605.17749v1 AI generated predictions increasingly inform decision making in critical tasks, and therefore must be trustworthy. One widely used measure of trustworthiness is calibration, which requires that the predictions match the true frequencies and can be treated like real probabilities of a given outcome. However, defining calibration is subtle, and designing good measures of calibration error has been an active topic of recent research. The first goal is to find calibration measures that are actionable, meaning they can inform decision makers about their utility loss when predictions are treated as true probabilities, which is known as swap regret. The second goal is to find calibration measures that are testable, meaning that calibration error can be measured from a small sample of predictions and outcomes. Although these are very basic requirements, there is no existing calibration measure that fully satisfies both properties, and all existing measures relax actionability by bounding a weaker notion of swap regret, or relax testability by having suboptimal estimation error. We introduce a new calibration measure, Soft-Binned Calibration Decision Loss (SCDL), which we prove is fully actionable without weakening either requirement, and testable with nearly optimal error rate. In addition, SCDL satisfies other desired properties such as continuity and consistency. We also provide a set of experiments confirming that the theoretical advantages of SCDL compared to other measures lead to better performance in practice. --- ### Unleashing Vision Transformer Potential In Image Quality Assessment via Global-Local Adaptive Interaction Authors: Yu Li, Puchao Zhou, Yachun Mi, Yanfeng Wu, Xiaoming Wang, Shaohui Liu Published: 2026-05-18 Categories: cs.CV arXiv: https://arxiv.org/abs/2605.17748v1 In the field of Blind Image Quality Assessment (BIQA), accurately predicting the perceptual quality of authentically distorted images remains highly challenging due to the diverse and complex distortions present in natural environments. Although existing methods have achieved notable accuracy, their scalability is often constrained by the high cost of subjective annotation and the limited size of available datasets. Recent advances in large-scale pre-trained vision models have introduced powerful semantic and representational capabilities, yet their application to IQA tasks is hindered by substantial computational demands and suboptimal fine-tuning efficiency. To overcome these limitations, we introduce the Global-Local Interaction Adapter (GLIA), a novel framework that effectively harnesses pre-trained Vision Transformers through a dual-stream feature extraction mechanism coupled with interactive global-local fusion. By jointly retaining global semantic information and fine-grained local details, our approach delivers superior prediction accuracy and robustness while requiring significantly fewer trainable parameters. Extensive experiments on multiple benchmarks validate the effectiveness and superiority of our approach. --- ### Agents for Experiments, Experiments for Agents: A Design Grammar for AI-Enabled Experimental Science Authors: Yingjie Zhang, Chun Feng, Weizhang Zhu, Tianshu Sun Published: 2026-05-18 Categories: cs.AI, cs.HC arXiv: https://arxiv.org/abs/2605.17746v1 AI systems are becoming active participants in organizational and knowledge work. They increasingly interact with humans, coordinate workflows, and operate in multi-agent arrangements. Understanding their effects therefore requires more than measuring output accuracy; it requires evidence about mechanisms, delegation, feedback, and control. Experiments remain central to this task, but they also face a recursive challenge: we need experiments for agents to study these arrangements, and we may need agents for experiments to help search the expanding space of possible designs. Yet experimental conditions for human-AI and agentic workflows are still largely specified in prose, making them difficult to compare, reuse, or audit. We frame this as a problem of workflow representation, traceability, and governance in AI-enabled knowledge production. We introduce SEED (Structural Encoding for Experimental Discovery), a framework that represents experimental conditions as typed actor-flow graphs. SEED supports three design functions: describing conditions as interaction structures, evaluating structural novelty relative to encoded prior designs, and generating candidate designs under feasibility and governance constraints. We report a lightweight empirical feasibility test that compares graph-blind and SEEDguided generation in a medical-triage design task. In this diagnostic contrast, SEED-guided candidate designs show clearer actor-flow changes, assumptions, and governance checks, supporting the feasibility of the grammar as a design aid. The commentary closes by identifying governance tensions around novelty, replication, validity, diversity of inquiry, and accountability. --- ### StatQAT: Statistical Quantizer Optimization for Deep Networks Authors: Mehmet Aktukmak, Daniel Huang, Ke Ding Published: 2026-05-18 Categories: stat.ML, cs.LG arXiv: https://arxiv.org/abs/2605.17745v1 Quantization is essential for reducing the computational cost and memory usage of deep neural networks, enabling efficient inference on low-precision hardware. Despite the growing adoption of uniform and floating-point quantization schemes, selecting optimal quantization parameters remains a key challenge, particularly for diverse data distributions encountered during training and inference. This work presents a novel statistical error analysis framework for uniform and floating-point quantization, providing theoretical insight into error behavior across quantization configurations. Building on this analysis, we propose iterative quantizers designed for arbitrary data distributions and analytic quantizers tailored for Gaussian-like weight distributions. These methods enable efficient, low-error quantization suitable for both activations and weights. We incorporate our quantizers into quantization-aware training and evaluate them across integer and floating-point formats. Experiments demonstrate improved accuracy and stability, highlighting the effectiveness of our approach for training low-precision neural networks. --- ### MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation Authors: Ronyu Zhang, Aosong Cheng, Gaole Dai, Yulin Luo, Jiaming Liu, Li Du, Huanrui Yang, Dan Wang, Leyuan Fang, Yuan Du, Shanghang Zhang Published: 2026-05-18 Categories: cs.CV arXiv: https://arxiv.org/abs/2605.17743v1 Continual test-time adaptation adapts a source-pretrained model to non-stationary, unlabeled target streams while retaining past competence, yet texture-biased backbones risk error accumulation and catastrophic forgetting. Drawing inspiration from the process of decoupling shape and texture in the human visual system, we introduce MoASE, a plug-in mixture-of-experts that disentangles domain-agnostic structure from domain-specific texture using Activation Sparsity Experts with Spatial Differentiable Dropout, forming complementary high- and low-activation pathways, while high- and low-rank bottlenecks diversify representations. The Activation Sparsity Gate produces input-adaptive SDD thresholds for precise token selection, and the Domain-Aware Router assigns per-sample expert weights using texture-sensitive cues. To curb confirmation bias on unlabeled streams and stabilize supervision, we then introduce Domain-Adaptive On-Policy Distillation to constitute MoASE++, with an EMA-anchored on-policy reverse KL distillation and an augmentation policy conditioned on entropy and confidence that aligns predictions across the same views and improves the robustness-plasticity balance. Extensive experiments on classification (CIFAR-10/100-C, ImageNet-C) and semantic segmentation (Cityscapes->ACDC) demonstrate consistent state-of-the-art performance, offering a principled, controllable approach to continual adaptation in dynamic visual environments. --- ### UST-Hand: An Uncertainty-aware Spatiotemporal Point Cloud Interaction Network for 3D Self-supervised Hand Pose Estimation Authors: Tianhao Han, Haoyang Zhang, Liang Xie, Haochen Chang, Kun Gao, Yuan Cheng, Pengfei Ren, Erwei Yin Published: 2026-05-18 Categories: cs.CV, cs.HC arXiv: https://arxiv.org/abs/2605.17742v1 Manually annotating accurate 3D hand poses is extremely time-consuming and labor-intensive. Existing self-supervised hand pose estimation methods leverage the discrepancy between input images and rendered outputs, or multi-view consistency constraints, as the driving force to optimize networks and progressively refine pose accuracy. However, these methods are highly susceptible to noisy pseudo-labels and overlook the importance of fully exploiting fine-grained spatial correlations, which undermines the stability of model training. To address these issues, we propose UST-Hand, a self-supervised learning framework that estimates uncertainty distribution of hand pose and constructs a probabilistic point cloud feature space, which enables the complex spatiotemporal relationship modeling. UST-Hand employs a conditional normalizing flow model to capture hand pose distributions and samples diverse hypotheses, facilitating robust learning under noisy pseudo-labels supervision with enhanced stability. These multi-hypothesis are mapped to a unified probabilistic 3D point cloud space for multi-view and temporal feature interaction, comprehensively exploring hand motion patterns and fine-grained spatial correlations. Extensive experiments on three challenging datasets demonstrate that UST-Hand achieves state-of-the-art performance, outperforming existing self-supervised methods by up to 37.8% in Mean Per Vertex Position Error (MPVPE). --- ### Harnessing LLM Agents with Skill Programs Authors: Hongjun Liu, Yifei Ming, Shafiq Joty, Chen Zhao Published: 2026-05-18 Categories: cs.AI arXiv: https://arxiv.org/abs/2605.17734v2 Equipping LLM agents with reusable skills derived from past experience has become a popular and successful approach for tackling complex and long-horizon tasks. However, such lessons are often encoded as textual guidance that remains largely advisory, lacking explicit mechanisms for when and how to intervene in the agent loop. To bridge the gap, we introduce HASP(Harnessing LLM Agents with Skill Programs), a new framework that upgrades skills into executable Program Functions (PFs). Rather than offering passive advice, PFs act as executable guardrails that activate on failure-prone states and modify the next action or inject corrective context. HASP is highly modular: it can be applied at inference time for direct agent-loop intervention, during post-training to provide structured supervision, or for self-improvement by evolving validated, teacher-reviewed PFs. Empirically, HASP drives substantial gains compared to both training-free and training-based methods on web-search, math reasoning, and coding tasks. For example, on web-search reasoning, inference-time PFs alone improve the average performance by 25% compared to (multi-loop) ReAct Agent, while post-training and controlled evolution achieve a 30.4% gain over Search-R1. To provide deeper insights into HASP, our mechanism analysis reveals how PFs trigger and intervene, how skills are internalized, and the requirement for stable skill library evolution. --- ### Harnessing LLM Agents with Skill Programs Authors: Hongjun Liu, Yifei Ming, Shafiq Joty, Chen Zhao Published: 2026-05-18 Categories: cs.AI arXiv: https://arxiv.org/abs/2605.17734v1 Equipping LLM agents with reusable skills derived from past experience has become a popular and successful approach for tackling complex and long-horizon tasks. However, such lessons are often encoded as textual guidance that remains largely advisory, lacking explicit mechanisms for when and how to intervene in the agent loop. To bridge the gap, we introduce HASP(Harnessing LLM Agents with Skill Programs), a new framework that upgrades skills into executable Program Functions (PFs). Rather than offering passive advice, PFs act as executable guardrails that activate on failure-prone states and modify the next action or inject corrective context. HASP is highly modular: it can be applied at inference time for direct agent-loop intervention, during post-training to provide structured supervision, or for self-improvement by evolving validated, teacher-reviewed PFs. Empirically, HASP drives substantial gains compared to both training-free and training-based methods on web-search, math reasoning, and coding tasks. For example, on web-search reasoning, inference-time PFs alone improve the average performance by 25% compared to (multi-loop) ReAct Agent, while post-training and controlled evolution achieve a 30.4% gain over Search-R1. To provide deeper insights into HASP, our mechanism analysis reveals how PFs trigger and intervene, how skills are internalized, and the requirement for stable skill library evolution. --- ### Divergence-Suppressing Couplings for Rectified Flow Authors: Yimeng Min, Carla P. Gomes Published: 2026-05-18 Categories: cs.AI, cs.LG arXiv: https://arxiv.org/abs/2605.17733v1 The promise of Rectified Flow rests on producing self-generated couplings whose trajectories are straight, or nearly so. In practice, trajectories generated by the base flow model can bend and intertwine, and the resulting coupling inherits this distortion. In this paper, we identify that such trajectory entanglement is often associated with regions of nonzero divergence in the learned velocity field, where local expansion or contraction distorts trajectories and steers particles away from their ideal endpoints. We then propose divergence-suppressing couplings for Rectified Flow, an offline correction that attenuate the divergent component of the learned velocity during coupling generation. The correction is paid only once per coupling pair and amortized over training, so deployment runs plain Euler at identical wall-clock cost to standard Rectified Flow. Empirically, this offline modification yields consistent improvements on 2D synthetic benchmarks and on image generation. --- ### L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting Authors: Fan Zhang, Shijun Chen, Hua Wang Published: 2026-05-18 Categories: cs.LG, cs.AI arXiv: https://arxiv.org/abs/2605.17730v2 Mainstream methods for multivariate time-series forecasting largely follow the Direct-Mapping paradigm. They learn a unified mapping from history to the future in the observation space to fit value-level dependencies. However, real-world systems often undergo distribution shifts and regime changes. In such cases, a unified mapping can exhibit response lag around turning points, causing error accumulation within the switching window and reducing forecasting reliability. To address this issue, we propose L-Drive, a change-aware forecasting framework. L-Drive introduces a Latent-Context, to explicitly characterize high-level dynamics evolving over time, and uses gating to modulate increment representations. This provides more timely change cues and improves adaptation to changing segments. In addition, it incorporates patch-shared relative positional basis functions to strengthen intra-segment structural modeling and reduce overfitting caused by absolute-position memorization. Extensive experiments validate the effectiveness of L-Drive and show a better overall trade-off between forecasting accuracy and computational efficiency. --- ### Domain Incremental Learning for Pandemic-Resilient Chest X-Ray Analysis Authors: Danu Kim Published: 2026-05-18 Categories: cs.CV, cs.AI, cs.LG arXiv: https://arxiv.org/abs/2605.17729v1 Deep learning models achieved high accuracy in pneumonia detection from chest X-rays. However, their generalization across clinical domains remains limited due to variations in imaging devices, acquisition protocols, and institutional conditions. This study introduces a replay-based domain-incremental continual learning designed to enable continual adaptation to cross-domain variations without catastrophic forgetting. The proposed method incorporates a class-aware balanced replay to maintain balanced class representation within a constrained memory and a class-aware loss to dynamically reweight class imbalance during training. Experiments conducted on a domain-shifted PneumoniaMNIST dataset consisting of five simulated domains demonstrate that the proposed method achieves an average accuracy of 88.66%, outperforming Experience Replay, Fine-Tuning, and Joint Training baselines. These findings highlight the efficacy of the proposed approach in achieving robust and consistent pneumonia detection across clinical environment variations. --- ### GraSP-VL: Length as a Semantic Granularity Interface for Vision-Language Representations Authors: Zesheng Li, Chengchang Pan, Honggang Qi Published: 2026-05-18 Categories: cs.CV arXiv: https://arxiv.org/abs/2605.17727v2 Frozen vision-language embeddings contain signals at multiple semantic resolutions, from object identity to attributes, relations, and full-caption meaning, but they expose these signals through a fixed-length vector interface. We study whether embedding length can be turned into a controllable semantic access interface. We propose \textbf{GraSP-VL}, which learns a shared near-orthogonal prefix transform over frozen VLM embeddings. GraSP-VL instantiates a \textbf{Semantic Matryoshka} interface: short prefixes are assigned coarse semantic roles, while longer prefixes progressively expose finer language-grounded distinctions. Because the transform is shared across image and text embeddings and preserves full-dimensional geometry, prefix behavior changes without rewriting the original VLM space. On a 20,147-example COCO/Flickr30K annotation pool, GraSP-VL reaches a staircase score of 53.01 and hard-negative selectivity of 89.76, while keeping full-space drift below $10^{-6}$. It also transfers to SugarCrepe-clean with 86.03 object accuracy and 11.96 mean external emergence, and preserves full-dimensional zero-shot CIFAR-100 accuracy. These results show that frozen VLM embeddings can be reorganized into a truncatable semantic prefix interface rather than merely compressed. --- ### GraSP-VL: Length as a Semantic Granularity Interface for Vision-Language Representations Authors: Zesheng Li, Chengchang Pan, Honggang Qi Published: 2026-05-18 Categories: cs.CV arXiv: https://arxiv.org/abs/2605.17727v1 Frozen vision-language embeddings contain signals at multiple semantic resolutions, from object identity to attributes, relations, and full-caption meaning, but they expose these signals through a fixed-length vector interface. We study whether embedding length can be turned into a controllable semantic access interface. We propose \textbf{GraSP-VL}, which learns a shared near-orthogonal prefix transform over frozen VLM embeddings. GraSP-VL instantiates a \textbf{Semantic Matryoshka} interface: short prefixes are assigned coarse semantic roles, while longer prefixes progressively expose finer language-grounded distinctions. Because the transform is shared across image and text embeddings and preserves full-dimensional geometry, prefix behavior changes without rewriting the original VLM space. On a 20,147-example COCO/Flickr30K annotation pool, GraSP-VL reaches a staircase score of 53.01 and hard-negative selectivity of 89.76, while keeping full-space drift below $10^{-6}$. It also transfers to SugarCrepe-clean with 86.03 object accuracy and 11.96 mean external emergence, and preserves full-dimensional zero-shot CIFAR-100 accuracy. These results show that frozen VLM embeddings can be reorganized into a truncatable semantic prefix interface rather than merely compressed. --- ### Sequential Structure in Intraday Futures Data: LSTM vs Gradient Boosting on MNQ Authors: Mathias Mesfin Published: 2026-05-18 Categories: q-fin.TR, cs.LG, q-fin.CP, q-fin.ST arXiv: https://arxiv.org/abs/2605.17724v1 This paper compares gradient boosting and long short-term memory (LSTM) architectures for intraday directional prediction in Micro E-Mini Nasdaq 100 futures (MNQ). Motivated by recent foundation-model research on financial candlestick data, including the Kronos architecture, we test whether five-minute OHLCV bar sequences contain exploitable sequential predictive structure at the scale of a single instrument dataset. Using 944 trading days from 2021-2025, four model configurations are evaluated under strict expanding-window walk-forward validation across three out-of-sample periods. The target variable is whether the session close exceeds the 10:30 AM open by more than ten points. No configuration produces statistically significant out-of-sample accuracy above the 51.8% base rate. Combined OOS accuracies range from 50.00% to 50.89% across gradient boosting variants, while the LSTM achieves 50.59%. Permutation tests yield p-values of 0.135 for the best gradient boosting model and 0.515 for the LSTM, indicating no statistically significant predictive edge. Feature importance instability across walk-forward folds suggests noise fitting rather than stable structural signal capture. The results indicate that four years of single-instrument five-minute OHLCV data are insufficient for reliable sequential ML-based intraday forecasting. The primary contribution is a documented evaluation of a Kronos-inspired architecture on a constrained real-world dataset, providing an empirical lower bound on data scale requirements for sequential financial ML. --- ### EXG: Self-Evolving Agents with Experience Graphs Authors: Yuxin Jin, Siyuan Zhang, Hanchen Wang, Lu Qin, Ying Zhang, Wenjie Zhang Published: 2026-05-18 Categories: cs.AI arXiv: https://arxiv.org/abs/2605.17721v1 Large language model (LLM)-based agents have demonstrated strong capabilities in complex reasoning and problem solving through multi-step interactions, yet most deployed agents remain behaviorally static, with knowledge acquired during execution rarely translating into systematic improvement over time. In response, a growing line of work on self-evolving agents explores how agents can improve through experience during deployment, but most existing approaches either rely on ad hoc reflection limited to single-task correction or adopt unstructured memory that accumulates fragmented experience with delayed usability. To address this limitation, we introduce EXG, an experience graph framework for self-evolving agents that explicitly organizes accumulated successes and failures into a structured, relational representation. EXG is the first experience graph designed for self-evolving agents, supporting both online, real-time graph growth during execution for immediate cross-task experience reuse, and offline reuse of a consolidated experience graph as an external memory module. This design also enables EXG to serve as a plug-and-play component for existing self-evolving agents, organizing prior experience into a unified experience graph and improving both solution quality and resource efficiency as deployment progresses. Extensive experiments across code generation and reasoning benchmarks show that EXG attains more favorable performance-efficiency trade-offs than reflection- and memory-based baselines in both online and offline evaluations. Our results suggest that structuring experience as a graph provides a principled foundation for scalable and transferable self-evolving agent behavior. --- ### Patch-MoE Mamba: A Patch-Ordered Mixture-of-Experts State Space Architecture for Medical Image Segmentation Authors: Diego Adame, Fabian Vazquez, Jose A. Nunez, Huimin Li, Jinghao Yang, Erik Enriquez, DongChul Kim, Haoteng Tang, Bin Fu, Pengfei Gu Published: 2026-05-18 Categories: cs.CV arXiv: https://arxiv.org/abs/2605.17719v1 CNN- and Transformer-based architectures have achieved strong performance in medical image segmentation, but CNNs are limited in modeling long-range dependencies, while Transformers often suffer from quadratic computational and memory complexity. State space models, especially Mamba-based networks, offer an efficient alternative with linear sequence complexity. However, existing Mamba segmentation models still face two limitations: pixel-wise directional scanning can disrupt local 2D spatial structure, and simple summation-based fusion of scan directions cannot adapt well to diverse object sizes, shapes, and boundaries. To address these issues, we propose \textit{Patch-MoE Mamba}, a patch-ordered mixture-of-experts state space architecture for medical image segmentation. It introduces a hierarchical patch-ordered scanning mechanism that preserves local spatial neighborhoods while capturing multi-scale context, and an MoE-based directional fusion module that adaptively combines multiple Mamba scanner outputs using four directional experts, a learnable concatenation expert, and residual directional aggregation. Experiments on five public polyp segmentation benchmarks and the ISIC 2017/2018 skin lesion segmentation datasets demonstrate the effectiveness and generality of Patch-MoE Mamba. --- ### How does feature learning reshape the function space? Authors: João Lobo, Bruno Loureiro, Long Tran-Than, Fanghui Liu Published: 2026-05-18 Categories: stat.ML, cs.LG arXiv: https://arxiv.org/abs/2605.17718v1 Feature learning is widely regarded as the key mechanism distinguishing neural networks from fixed-kernel methods, yet its impact on the induced function space remains poorly understood. In this work, we precisely characterize how the function space spanned by the features of a two-layer neural network evolves during gradient descent training. We prove that, in the high-dimensional proportional regime, after a large gradient step the post-update feature distribution is well approximated by a target-dependent spiked Gaussian covariance. This induces a data-adaptive kernel that reshapes the function space and modifies its spectral structure. Our analysis reveals that feature learning can be interpreted as a distributional transformation in either parameter space or input space, equivalently as the introduction of a target-dependent kernel. In particular, it selectively amplifies eigenvalues aligned with the target direction and mixes leading eigenfunctions, coupling the top radial mode with a target-aligned quadratic harmonic. Overall, our results provide a precise function-space perspective on early-stage feature learning: rather than just rescaling a fixed kernel, gradient descent induces a data-adaptive deformation that preferentially enhances directions aligned with the signal in the data. --- ### From Documents to Segments: A Contextual Reformulation for Topic Assignment Authors: Hoonsang Yoon, Takyoung Kim, Wonkee Lee, Ilmin Cho, Dilek Hakkani-Tür, Stanley Jungkyu Choi Published: 2026-05-18 Categories: cs.CL arXiv: https://arxiv.org/abs/2605.17714v1 Traditional topic modeling assigns a single topic to each document. In practice, however, many real-world documents, such as product reviews or open-ended survey responses, contain multiple distinct topics. This mismatch often leads to topic contamination, where unrelated themes are merged into a single topic, making it difficult to identify documents that truly focus on a specific subject. We address this issue by introducing segment-based topic allocation (SBTA), a reformulation of topic modeling that assigns topics not to entire documents, but to segments: short, coherent spans of text that each express a single theme. By modeling topical structure at the segment level, our approach yields cleaner and more interpretable topics and better supports analysis of multi-theme documents. To support systematic evaluation, we construct a SemEval-STM, a new dataset inspired by aspect-based sentiment analysis. Documents are first decomposed into topical segments using large language models (LLMs), followed by human refinement to ensure segment quality. We also propose a segment-level extension of the word intrusion task, enabling human evaluation of topical coherence at the granularity where topics are actually assigned. Across multiple models and evaluation metrics, we show that SBTA improves clustering quality and interpretability. Overall, this work provides a practical, scalable framework for fine-grained topic analysis in heterogeneous text corpora where documents naturally span multiple topics. URL: https://huggingface.co/datasets/LG-AI-Research/SemEval-STM --- ### Sometin Beta Pass Notin: Improving Multilingual ASR for Nigerian Languages via Knowledge Distillation Authors: Sewade Ogun Published: 2026-05-18 Categories: cs.CL, eess.AS arXiv: https://arxiv.org/abs/2605.17710v3 Although modern multilingual Automatic Speech Recognition (ASR) systems support several Nigerian languages, their performance consistently lags behind resource-rich languages such as English and French. Nigerian languages present unique modelling hurdles, including acute data scarcity, inconsistent orthography, tonal diacritics, diverse accents, frequent code-switching, and localised named entities. To address these challenges, we developed a multilingual ASR framework using a two-stage distillation process. First, we employed student-teacher knowledge distillation from existing monolingual models, conditioned on robust language-specific N-gram language models. Second, we performed iterative self improvement using pseudo-labelled data to further refine accuracy. Our method significantly bridges the performance gap, achieving on average a reduction in the relative Word Error Rate (WER) of 29% over the monolingual baselines. Our models also outperform state-of-the-art multilingual models across major benchmarks, including Common Voice and FLEURS. We introduce Sometin Beta Pass Notin (SBPN), a multilingual foundational ASR model that covers Yorùbá, Hausa, Igbo, Nigerian Pidgin, and Nigerian English. --- ### Sometin Beta Pass Notin (SBPN): Improving Multilingual ASR for Nigerian Languages via Knowledge Distillation Authors: Sewade Ogun Published: 2026-05-18 Categories: cs.CL, eess.AS arXiv: https://arxiv.org/abs/2605.17710v1 Although modern multilingual Automatic Speech Recognition (ASR) systems support several Nigerian languages, their performance consistently lags behind high-resource languages like English and French. Nigerian languages present unique modelling hurdles, including acute data scarcity, inconsistent orthography, tonal diacritics, diverse accents, frequent code-switching, and localized named entities. To address these challenges, we developed a multilingual ASR framework utilizing a two-stage distillation process. First, we employ student-teacher knowledge distillation from existing monolingual models, conditioned on robust language-specific N-gram language models. Second, we perform iterative self improvement using pseudo-labelled data to further refine accuracy. Our method significantly bridges the performance gap, achieving on average a relative Word Error Rate (WER) reduction of 29 % over monolingual baselines. Our models also outperform state-of-the-art multilingual models across major benchmarks, including Common Voice and Fleurs. We introduce Sometin Beta Pass Notin (SBPN), a foundational multilingual ASR model covering Yorùbá, Hausa, Igbo, Nigerian Pidgin, and Nigerian English. SBPN is released in two sizes: SBPN-Base (120 M parameters) and SBPN-Large (600 M parameters). By releasing these as open foundation models, we aim to provide ASR resources for further research into the rich phonetic and cultural landscape of the region. --- ### Online Conformal Prediction for Non-Exchangeable Panel Data Authors: Daohong Tu, Kay Giesecke Published: 2026-05-18 Categories: stat.ML, cs.LG, stat.ME arXiv: https://arxiv.org/abs/2605.17705v1 Panel data, in which multiple units are repeatedly observed over time, arise throughout science and engineering. Quantifying predictive uncertainty in such settings is challenging because conformal prediction, while distribution-free and model-agnostic, classically relies on exchangeability assumptions that fail under temporal dependence and unit heterogeneity. We propose a simple online conformal framework for non-exchangeable panel data. The method exploits a key feature of online panel prediction: when a forecast is required for one unit, contemporaneous outcomes from related units may already be observed and can serve as a calibration panel. At each round, prediction sets are formed using currently observed calibration units together with two adaptive quantities: history-based similarity weights that emphasize calibration units resembling the target, and an adaptive miscoverage level that is updated whenever target feedback is revealed. This two-state design yields a stepwise coverage bound and a long-run coverage guarantee. Empirically, across synthetic and real panel data sets, the method improves coverage on the worst-covered target units through adaptive interval-width allocation rather than uniform inflation. The two states are complementary: similarity weights protect coverage when target feedback is sparse, while the adaptive level further improves coverage as feedback accumulates. --- ### Toy Combinatorial Interpretability Models Reveal Lottery Tickets in Early Feature Space Authors: Alon Bebchuk, Nir Shavit Published: 2026-05-18 Categories: cs.LG arXiv: https://arxiv.org/abs/2605.17704v1 The lottery ticket hypothesis posits that dense networks contain sparse subnetworks, ``winning tickets,'' that, when rewound to their initial weights and retrained in isolation, match the performance of the full model. We ask a more mechanistic question: what internal object does a winning ticket preserve? We work in a combinatorial, clause-structured toy setting that admits an interpretable feature-space representation with well-defined combinatorial distances between features. We show that winning tickets in weight space correspond to precursor locations in feature space that are already near, at initialization, to the final feature-channel codes. Dense SGD resolves these locations through structured selection: proximal locations either converge to final codes or are rejected, with rejection concentrated at more crowded neurons, implicating competition under superposition. A winning ticket is thus a family of compatible code locations that jointly balance proximity to final codes with low inter-feature interference. Sparse retraining often re-expresses the same clause/template family on a different row, so the preserved object is family-level rather than microscopic row identity. We validate this account with lightweight probes based on feature-space distance and motion; in our setting, these probes frequently outperform established weight-based ticket discovery methods in both accuracy and exact code recovery. Although these findings are grounded in a toy setting, they suggest that the lottery ticket structure is governed by hidden feature-space geometry rather than weight-space subnetwork identity. --- ### AI-Powered Mental Health Chatbots in Africa: A Systematic Review and Culturally Adaptive Framework Authors: Matshepo Lebese, Pitso Tsibolane Published: 2026-05-17 Categories: cs.AI arXiv: https://arxiv.org/abs/2608.24890v1 Mental health challenges in Africa remain under-addressed due to inadequate infrastructure, stigma, and a chronic shortage of professionals. Artificial Intelligence (AI)-powered chatbots are emerging globally as low-cost, accessible tools that can offer psychological support. This paper presents a systematic review of 52 empirical studies published between 2017 and 2025, critically analysing their cultural, linguistic, and infrastructural relevance to African contexts. The findings demonstrate the potential of AI chatbots to improve accessibility, reduce symptoms of anxiety and depression, and expand psychosocial support, yet reveal limited African-specific adaptation. Most systems remain rooted in Western models and English-language designs, leaving critical gaps in local relevance, inclusivity, and sustainability. Based on the findings, the authors develop a Culturally Adaptive Digital Mental Health (CADMH) framework that integrates African cultural values, multilingual design, mobile-first optimisation, and ethical safeguards. The study highlights opportunities and barriers for integrating AI chatbots into African healthcare, offering guidance for research, practice, and policy. --- ### Runtime Governance for Agentic AI: Action-Boundary Control with Trusted Provenance and Fail-Closed Execution Authors: Adam Mazzocchetti Published: 2026-05-17 Categories: cs.AI, cs.CE, cs.CR, cs.CY arXiv: https://arxiv.org/abs/2608.16891v1 Agentic AI systems request tool actions that can modify files, send messages, launch jobs, or change workflow state. This shifts the safety problem from harmful text generation to harmful operational side effects. Prompt-level governance can shape model behavior, but it does not create an execution boundary. We introduce Aegis, a runtime governance system that treats model outputs as action proposals and mediates them through a trusted decision layer before tool execution. The model proposes; the trusted runtime decides. Aegis evaluates proposals against active policy state, resolves provenance server-side, fails closed under uncertainty, and routes selected cases through Senate-style settlement, a quorum- based non-unilateral authorization path. We evaluate Aegis on a repeated sandbox corpus spanning five run families, 42 tasks, three conditions, and ten repeats per family. Across 6,300 rows, prompt-policy conditioning produced 79 risky comparator-path leakage rows. Across 2,100 Aegis-governed rows, the system recorded zero governed mock-tool applications and zero governed risky side-effect completions. All 1,832 Aegis-attempted governed rows preserved trusted Aegis-resolved provenance, and all 1,019 Senate-settled rows had quorum and final signed tally evidence. These results do not prove general autonomous-agent safety. They support the narrower systems claim that, in this evaluated sandbox corpus, runtime action-boundary governance prevented observed risky proposals from becoming governed side effects. --- ### Can LLMs Really Understand Item Difficulty Levels? Implications for Automated Item Generation Using LLMs Authors: Xinyi Wang, Hong Jiao, Ming Li, Sydney Peters, Hanna Choi, Tianyi Zhou, Qingshu Xu Published: 2026-05-17 Categories: cs.CL, cs.LG arXiv: https://arxiv.org/abs/2607.28634v1 The estimation of item difficulty plays a key role in both formative assessment and large-scale high-stakes summative assessments. This study explores how large language models (LLMs) perform in predicting item difficulty levels using items from a large-scale Reading and Writing test. The study investigated various prompting strategies and parameter settings across multiple LLMs. LLM performance was compared with encoder-only language models and feature-based supervised machine learning models. Zero-shot GPT-4.1 with a temperature of 0 yielded the highest item difficulty level prediction accuracy, with a quadratic weighted kappa (QWK) of 0.578. However, LLMs' prediction accuracy was lower than that of ConvBERT (QWK = 0.625), which outperformed the best feature-based supervised machine learning model. Further analysis showed that all LLMs struggled to label hard items; in particular, the current advanced GPT-5.4 tended to underestimate item difficulty levels. Dimension reduction of embeddings showed that item embeddings from different difficulty levels were mixed together, indicating that semantic information from items alone is likely insufficient for item difficulty level prediction. The findings suggest that if LLMs cannot understand item difficulty levels as evidenced by empirical data and tend to treat most items as easy when their own capabilities increase, caution should be exercised when using LLMs to generate items with targeted difficulty levels. --- ### TextClusterLab: An Integrated Framework for Reliable Text Clustering Studies Authors: Daoming Wan, Yizheng Huang, Jimmy X. Huang Published: 2026-05-17 Categories: cs.IR, cs.LG arXiv: https://arxiv.org/abs/2606.28328v1 In recent years, text clustering has become a critical technique for applications including intent discovery, topic mining, and recommendation systems. However, evaluating text clustering algorithms remains challenging since many real-world textual datasets are not suitable for clustering assessment due to ambiguous semantic boundaries, the high dimensionality of embeddings, and inconsistent cluster structure. Current clustering dataset generators are designed for numerical data, providing limited support for text-specific benchmarking. This paper introduces TextClusterLab, a comprehensive framework for text clustering research. TextClusterLab offers a Large Language Model (LLM)-driven text clustering dataset generator to produce synthetic text datasets for evaluating clustering algorithms. This generator supports setting various clustering attributes, such as class imbalance, intra-cluster compactness, and inter-cluster diversity. These generated datasets can serve as practical benchmarks for testing the robustness and versatility of text clustering algorithms in diverse scenarios. Moreover, we introduce a benchmark to verify whether a text dataset is suitable for clustering evaluation. Therefore, TextClusterLab provides an integrated framework for reproducible and comprehensive text-specific clustering research. Our TextClusterLab is publicly available at https://github.com/research-paper-code/TextClusterLab, and some synthetic example datasets with various attributes are publicly available at https://huggingface.co/datasets/DW-irlab/TextClusterLab. --- ### Embodied AI in 6G Networks: From Intelligent Connectivity to Physical Intelligence Authors: Junaid Sajid, Sheikh Salman Hassan, Wenshuai Liu, Yan Kyaw Tun, Yaru Fu, Nguyen H. Tran, Zhu Han, Cedomir Stefanovic, Tharmalingam Ratnarajah, Muhammad Mahtab Alam Published: 2026-05-17 Categories: cs.NI, cs.LG arXiv: https://arxiv.org/abs/2606.20592v2 Embodied artificial intelligence (AI) couples perception and learned decision making to actions that change the physical world. This coupling distinguishes an embodied agent from a conventional connected controller: the agent maintains task state and uncertainty, reasons about the consequences of actions, and adapts from subsequent observations. Wireless networking becomes relevant when perception, inference, or coordination is distributed, but it should not replace local safety control. This article develops a tutorial perception--communication--action (PCA) architecture that exposes task state, action deadlines, uncertainty, agent intent, and safety envelopes to a 6G orchestration plane. It separates capabilities already addressed by 5G and 5G-Advanced from functions that motivate 6G, including task-state interfaces, semantic freshness, predictive digital twins, and safety-aware coordination across agents. A multi-robot simulation study is retained to illustrate joint sensing, communication, and computation control. The results show where network orchestration improves task utility and where local autonomy remains essential. --- ### Empowering Embodied AI in 6G Networks: Architecture, Enablers, and Open Challenges Authors: Junaid Sajid, Sheikh Salman Hassan, Wenshuai Liu, Yan Kyaw Tun, Yaru Fu, Nguyen H. Tran, Zhu Han, Cedomir Stefanovic, Tharmalingam Ratnarajah, Muhammad Mahtab Alam Published: 2026-05-17 Categories: cs.NI, cs.LG arXiv: https://arxiv.org/abs/2606.20592v1 Embodied artificial intelligence (AI) is emerging as a key driver of the sixth-generation (6G) wireless networks by enabling agents that continuously perceive, communicate, and act in dynamic physical environments. Unlike conventional AI systems that process disembodied data, embodied agents such as robots, autonomous vehicles, and extended reality (XR) devices operate through closed-loop perception-communication-action (PCA) interactions, where communication performance directly affects physical behavior, control stability, and task success. However, existing AI-native wireless architectures remain largely connectivity-centric and are not designed to support task-driven embodied intelligence at large scale. Therefore, we present a holistic framework for embodied AI-native 6G systems, in which communication, sensing, computation, and control are jointly designed as a unified closed-loop infrastructure. We introduce a system-level PCA architecture, discuss key enabling technologies and representative applications, and highlight major open challenges in multimodal intelligence, edge-aware deployment, evaluation, trustworthiness, and practical implementation. Our central argument is that future 6G systems must evolve from intelligent communication platforms into active enablers of embodied physical intelligence. --- ### Delay-Adaptive Speculation Control for Low-Latency Edge-Cloud LLM Inference Authors: Kangkang Sun, Jianhua Li, Xiuzhen Chen, Junyi He, Minyi Guo Published: 2026-05-17 Categories: cs.NI, cs.AI arXiv: https://arxiv.org/abs/2606.20591v1 Speculative decoding accelerates large language model (LLM) inference by using a lightweight draft model to propose tokens and a larger target model to verify them in parallel. In distributed edge-cloud inference, however, draft length must be controlled online: longer drafts amortize communication delay but reduce token acceptance, whereas shorter drafts preserve acceptance but trigger more communication rounds. We formulate this tradeoff as a ratio-type optimal stopping problem and prove that the optimal draft length is a finite delay-monotone threshold. The analysis identifies a critical delay below which single-token speculation is optimal and shows that the optimal length grows only logarithmically with communication delay. For time-varying networks, we extend the model to Markov-modulated channels and establish, under a bounded horizon and monotone stopping-region conditions, a state-dependent threshold policy. For unknown environments, we propose UCB-SpecStop, an online control algorithm with gap-free and gap-dependent expected regret bounds of $O(L_{\max}\sqrt{K_{\max}T\log(K_{\max}T)})$ and $O(\sum_{k:Δ_k>0}L_{\max}^2\log(K_{\max}T)/Δ_k)$. We implement the method on a real edge-cloud testbed with a Jetson Orin Nano Super edge node and an RTX~3090 Ti cloud node, using Qwen and Llama draft--target pairs. Experiments validate the predicted phase transition, with transition points near 83~ms and 111~ms. Qwen matches the geometric prediction, while Llama requires empirical-prefix calibration due to heavy-head acceptance. Across the tested delay grid, UCB-SpecStop reduces per-token latency over SpecDec++ by up to 22.4\%, approaches an offline oracle within 0.2--2.4\% in communication-dominated regimes, improves over naive UCB by up to 7.5\%, removes the 14.0--18.7\% gap caused by static tuning under delay drift, and gains 3.0--6.8\% with contextual channel-state information. --- ### Hamiltonian-Inspired Attention Mechanism for Scalable RF Transmitter Fingerprinting Authors: Chitraksh Singh, Monisha Dhanraj, Akram Sheriff Published: 2026-05-17 Categories: eess.SP, cs.AI arXiv: https://arxiv.org/abs/2605.30364v1 Radio-frequency (RF) fingerprinting identifies wire-less transmitters using hardware-induced imperfections present in baseband I/Q signals. However, deep learning models often degrade under receiver and channel distribution shifts, particularly as transmitter populations grow. This work proposes the Hamiltonian Transformer, a physics-informed attention architecture that enforces norm preserving value dynamics within each attention head using a learned skew-symmetric generator and a Störmer-Verlet leapfrog integration step. An additional phase-increment embedding exposes oscillator dynamics at the input layer. All experiments use non-equalized raw I/Q signals from the WiSig dataset under four protocols: same-day classification, cross-receiver generalisation, cross-day generalisation, and transmitter scaling up to 150 devices. The Hamiltonian Transformer achieves 99.12% accuracy under same-day conditions and 61.64% at 150 transmitters, consistently outperforming CNN and Transformer baselines across all scale points. A controlled ablation study identifies norm-preservation in the value update as the primary inductive bias driving the scaling advantage, with the phase increment embedding providing the single largest per-component improvement. These results indicate that embedding physics-informed structural priors into attention mechanisms is an effective approach to large-scale transmitter identification on raw wireless signals. --- ### Enhancing Regime Shift Detection Using Unstructured Data: A Study on the Treasury Market Authors: Mingxuan Yi, Vidal Mehra, Jing Chen, John Cartlidge Published: 2026-05-17 Categories: q-fin.CP, cs.AI, cs.LG, q-fin.ST arXiv: https://arxiv.org/abs/2605.30363v2 Regime shifts in financial markets reorganise the joint dynamics of asset prices and macro variables, breaking any single-regime calibration. They are nonetheless hard to identify: the data signal is noisy and heavily multicollinear, while the contemporaneous text that announces them is unstructured. Standard regime shift detection reads only the data panel and ignores this text, even though it typically signals the shift weeks before it materialises in observed prices. We address this with a text-enhanced pipeline that cross-validates the two signals. A large language model (LLM) proposes candidates from text, which a likelihood-ratio vector-autoregression (VAR) test validates on the panel. In parallel, any regime shift detector proposes data-side candidates that a second LLM call accepts via a permissive text check. Because the acceptance stage consumes a candidate set rather than an algorithm's internals, the data channel accepts any data-driven detector. We deploy the pipeline on the US Treasury market, pairing 2010-2024 FOMC minutes with a 14-variable Treasury / macro panel, with every method evaluated on this same panel. The pipeline reaches F1 = 0.82 and F2 = 0.86 against a verified anchor list of monetary-policy regime shifts (best with rolling PCMCI as the data channel), with same-day modal detection latency, and is detector-agnostic: any of four interchangeable data-driven detectors clears the strongest pure data-only baseline on both scores. --- ### Behavior-Aware Auxiliary Corrections for Off-Policy Temporal-Difference Prediction Authors: Xingguo Chen, Zhiang He, Yuchen Shen, Shangdong Yang, Chao Li, Guang Yang, Wenhao Wang Published: 2026-05-17 Categories: cs.AI arXiv: https://arxiv.org/abs/2605.28855v1 Temporal-difference learning with function approximation can be unstable under off-policy sampling. TDC stabilizes off-policy TD through an auxiliary covariance correction, and TDRC further regularizes this correction in a single-timescale recursion. This paper studies a behavior-aware replacement of the auxiliary covariance geometry in the linear prediction setting, which is the standard local model for understanding the feature-space dynamics of value-function approximation. We first replace the TDC auxiliary matrix (C) by the behavior Bellman matrix (A_μ), yielding BA-TDC, and then regularize the same behavior-aware equation to obtain BA-TDRC. This two-step construction separates the contribution of behavior-aware geometry from the contribution of regularization. The linear analysis also provides a tractable model for an auxiliary-geometry design question that arises in neural-network value approximation, where feature covariances and temporal transition matrices jointly shape the last-layer correction dynamics. We give a finite-state mean-system formulation, prove fixed-point preservation and almost-sure convergence under a Hurwitz stability condition on the instantiated mean system, and compare deterministic mean rates through the spectral radius of the exact linear error recursion. Experiments on the two-state counterexample, Baird's counterexample, Random Walk, and Boyan Chain show that the behavior-aware replacement can be highly beneficial by itself on some tasks, but that regularization is necessary for robust performance across harder settings. --- ### RAW: Robust Avatar Watermarking -- Benchmarking and Baseline Authors: Jack Parry, Jack Saunders, Vinay Namboodiri Published: 2026-05-17 Categories: cs.CV, cs.AI arXiv: https://arxiv.org/abs/2605.23994v1 Digital avatar watermarking presents unique challenges: avatars are routinely post-processed with background replacement, reframing, and format conversion before deployment. We introduce \textbf{RAW} (Robust Avatar Watermarking), a benchmark comprising 50 synthetic avatar videos from 5 commercial providers and 6 attacks simulating real-world avatar workflows. Evaluating 7 existing methods reveals that avatar-specific attacks such as background removal significantly degrade watermark recovery. We propose \textbf{WALT} (Watermarking Avatars with Learned Textures), which embeds watermarks in UV texture space via 3D face reconstruction. WALT achieves the highest robustness to zoom attacks (92.4\%) while maintaining strong performance on background removal (95.6\%). We release our benchmark to facilitate research into avatar-specific watermarking. --- ### Nano World Models: A Minimalist Implementation of Future Video Prediction Authors: Siqiao Huang, Partha Kaushik, Michael Chen, Hengkai Pan, Kaiwen Geng, Omar Chehab, Fernando Moreno-Pino, Max Simchowitz Published: 2026-05-17 Categories: cs.CV, cs.AI, cs.LG arXiv: https://arxiv.org/abs/2605.23993v2 World models have become a central paradigm for learning predictive simulators that support generation, planning, and decision-making. Yet, despite rapid progress in industry-scale interactive video generation, the broader research community still lacks compact, reproducible, and easily extensible implementations for studying the design choices underlying modern world models. We introduce Nano World Models, a minimalist codebase for future video prediction centered around diffusion forcing. Nano World Models provides a unified interface for generative objectives, model scales, action-conditioning mechanisms, latent observation spaces, datasets, evaluation protocols, and long-horizon rollout procedures. This design enables controlled studies of world-modeling components that are often entangled across separate implementations. Through experiments across simple control environments, game simulation, and real-robot data, we examine how prediction parameterization, architecture scale, action injection, sampling budget, and domain complexity affect video prediction quality and autoregressive rollout behavior. By releasing code, configurations, evaluation scripts, and pretrained checkpoints, Nano World Models aims to provide a compact yet extensible experimental substrate for open, reproducible, and scientific world-model research. ---