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01.

arXiv (quant-ph) 2026-06-19 DOI: arXiv:2512.08393

Single-Step Phase-Engineered Pulse for Active Readout Cavity Reset in Superconducting Circuits

作者:

Ren-Ze Zhao↗Ze-An Zhao↗Tian-Le Wang↗Peng Wang↗Sheng Zhang↗Xiao-Yan Yang↗Hai-Feng Zhang↗Zhi-Fei Li↗Yuan Wu↗Sheng-Ri Liu↗Peng Duan↗Guo-Ping Guo↗…

arXiv:2512.08393v2 Announce Type: replace Abstract: In a circuit QED architecture, we experimentally demonstrate a hardware-efficient and qubit-state-dependent Single-Step Phase-Engineered (SSPE) pulse scheme for actively depopulating a readout cavity. The protocol appends a reset segment with tailored amplitude and phase to a standard square readout pulse. Within the linear-response regime, the optimal reset amplitude scales proportionally with the readout amplitude, while the optimal reset phase remains invariant, significantly simplifying the experimental calibration procedure. Time-resolved measurements of the cavity photon number dynamics demonstrate that the SSPE scheme significantly outperforms the CLEAR protocol in terms of reset speed. Crucially, this approach enables arbitrarily fast, overshoot-free depletion of the cavity photon population, with the ultimate reset rate constrained by the finite analog bandwidth of the measurement chain. Furthermore, a comprehensive evaluation of the QND nature demonstrates that the SSPE scheme introduces no additional non-QND measurement errors. It exhibits non-QNDness comparable to both the free-decay and CLEAR protocols, with residual errors predominantly governed by state switching induced by qubit relaxation during the readout process. Thses results establish the SSPE scheme as a practical and scalable approach for achieving rapid and smooth cavity reset in superconducting quantum circuits.

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02.

arXiv (CS.CL) 2026-06-15 DOI: arXiv:2606.14574

SIMMER: Benchmarking Latent Failures in LLM Executable Planning with a World Model

作者:

Xiaoxin Lu↗Ranran Haoran Zhang↗Rui Zhang↗

Large language models (LLMs) are increasingly deployed as planners for autonomous agents in household environments. While existing benchmarks evaluate whether LLM-generated plans execute successfully, they overlook a critical type of failure: latent failures. Unlike immediate failures that trigger instant feedback at execution time and enable timely correction, latent failures do not immediately halt plan execution but silently compromise goal achievement. In severe cases, they cause irreversible harm. To address this gap, we introduce SIMMER, a benchmark for evaluating latent failures in LLM planning through a human-curated symbolic world model grounded in the kitchen domain. SIMMER defines a world model comprising 77 actions, 262 unique objects, and approximately 46,800 possible interactions that are semantically realistic, derived from real-world cooking scripts. It then leverages a state machine executor that validates plans against the world model and detects immediate precondition violations, latent hazards, and irreversible failures. Experiments across six LLMs show that even frontier models achieve at most 17% error-free plans. Moreover, up to 56% of plans contain latent failures, the majority of which lead to irreversible consequences. We further demonstrate that explicit state reasoning via counterfactual foresight simulation can reduce latent failures by up to 72% and irreversible cases by up to 75%, suggesting a promising direction for more robust LLM planners.

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03.

arXiv (CS.CL) 2026-06-18 DOI: arXiv:2606.18986

Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering

作者:

Yafeng Wu↗Huu Hiep Nguyen↗Thin Nguyen↗Hung Le↗

Recent advances in large language models (LLMs) have given rise to time-series question answering (TSQA), which formulates time-series analysis as natural-language question answering. However, directly feeding raw numerical series into LLMs suffers from a tokenization bottleneck: Byte Pair Encoding fragments continuous values into unstable tokens whose embeddings lack meaningful metric structure, resulting in the loss of magnitude, scale, and trend information. Prior methods use patch-based encoders that split the series into fixed windows, locking in one granularity that breaks patterns and hides exact timesteps, through a separate module that rarely transfers across datasets with different lengths or sampling rates. To address this challenge, we propose CADE (Contrastive Alignment with Direct Embedding), a novel framework for TSQA built upon two key components: direct timestep embedding and semantic alignment. The proposed framework maps each timestep directly into the LLM embedding space through a point-wise linear encoder and MLP projector, preserving exact index-level access while eliminating the need for patching and padding. To further bridge the semantic gap between time-series and language representations, we introduce a novel one-directional supervised contrastive loss that aligns time-series embeddings with frozen class-name text anchors. Experimental results on the public Time-MQA benchmark demonstrate that our framework consistently improves performance across six TSQA tasks, outperforming both open-source and proprietary LLM baselines.

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04.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2606.16560

The BD-LSC Dataset: Facilitating the Benchmarking of Models for Lexical Semantic Change Detection in Slang and Standard Usage

作者:

Afnan Aloraini↗Viktor Schlegel↗Goran Nenadic↗Riza Batista-Navarro↗

Automatic semantic change detection aims to identify how word meanings shift over time, offering insights into both linguistic and societal change. Despite recent progress in computational lexical semantic change (LSC), existing benchmarks and methods struggle to capture bi-directional semantic change, particularly cases where words simultaneously gain and lose senses. This problem is especially challenging for words that have both slang and standard meanings. To address these gaps, we introduce two complementary benchmark datasets. The Bi-Directional Lexical Semantic Change (BD-LSC) dataset captures sense gain, sense loss, and stability across three time periods, enabling the study of complex semantic trajectories. The SlangTrack Word Sense Disambiguation (ST-WSD) dataset provides fine-grained, instance-level sense annotations for words combining slang and standard usages, supporting systematic benchmarking of WSD and semantic change detection models. Using these benchmarks, we systematically evaluate models across different methodological families: unsupervised clustering using contextualised embeddings, supervised machine learning, transformer-based models, and state-of-the-art large language models. Among the evaluated systems, the few-shot GPT-4o model achieved the strongest aggregate performance on Exact Sense Match (ESM) and multi-label accuracy; however, Macro-F1 scores near 0.5 across all systems show that rare slang senses remain difficult, which we identify as the central open challenge.

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05.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2606.19270

Beyond Algorithms: Conceptual Innovation in Medical Imaging AI

作者:

Mark A. Anastasio↗

arXiv:2606.19270v1 Announce Type: cross Abstract: Artificial intelligence has driven rapid progress in medical imaging research, producing increasingly sophisticated algorithms and steady improvements on benchmark tasks. However, this algorithm-centric trajectory has also revealed a growing imbalance: while computational methods advance rapidly, the conceptual foundations that define imaging tasks, evaluation metrics, and clinical meaning sometimes remain underexamined. In this Perspective, we distinguish algorithmic innovation, which focuses on improving computational implementations and performance within a fixed problem definition, from conceptual innovation, which reframes what problems are posed, how success is measured, and why an approach is clinically relevant. We argue that prevailing incentive structures, training pathways, and publication norms disproportionately reward algorithmic novelty, particularly for early-career researchers, while at times undervaluing conceptual contributions that are essential for scientific maturation and clinical translation. Through representative examples from medical imaging AI, we show how insufficient conceptual grounding can lead to misaligned objectives, fragile generalization, and limited real-world impact. We conclude with actionable recommendations for researchers, mentors, reviewers, and journals to better recognize, support, and integrate conceptual innovation alongside algorithmic advances.

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06.

arXiv (quant-ph) 2026-06-16 DOI: arXiv:2603.05627

Classical Explanations in (and of) General Probabilistic Theories

作者:

John Harding↗Alex Wilce↗

arXiv:2603.05627v2 Announce Type: replace Abstract: We introduce a notion of the ``explanation" of one (generalized) probabilistic model by another as particular kind of span in the category $\Prob$ of probabilistic models and morphisms. We show that explanations compose under a standard pullback construction (notwithstanding that $\Prob$ does not support arbitrary pullbacks). We then show that every locally-finite probabilistic model has a canonical, sharp classical explanation. The construction is functorial, so every locally-finite probabilistic theory has a canonical, sharp classical (though of course, usually non-local) representation.

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07.

arXiv (CS.LG) 2026-06-11 DOI: arXiv:2606.11341

Energy-Conserved Neural Pipelines: Attenuating Error Propagation in Modular Neural Networks via Physical Conservation Constraints

作者:

David Young↗Swan Yi Htet↗

arXiv:2606.11341v1 Announce Type: new Abstract: Modular neural network pipelines suffer from error compounding: noise at any module boundary propagates and potentially amplifies through subsequent modules. We introduce energy conservation as a hard physical constraint on inter-module information flow. Activation energy (the squared L2 norm of feature vectors) is enforced to be exactly preserved at every module boundary. Unlike soft energy penalties, conservation is an inviolable law: the network may redistribute energy across neurons but cannot create or destroy it. Four experiments on CIFAR-10 demonstrate: (1) conservation retains 77.4% of clean accuracy at noise sigma=0.2, versus 35.1% for baselines and 30.9% for energy-penalized models (p

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08.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2606.17104

Prefill/Decode-Aware Evaluation of LLM Inference on Emerging AI Accelerators

作者:

Shun Usami↗Venkatram Vishwanath↗E. Wes Bethel↗

arXiv:2606.17104v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed in latency- and cost-sensitive settings, inference efficiency has become a central systems challenge. While GPUs dominate current deployments, a growing number of AI accelerators claim advantages for LLM inference, yet it remains unclear under which conditions such accelerators outperform GPUs in practice. Recent inference systems decompose execution into Prefill and Decode phases, which exhibit distinct computational characteristics and latency metrics, commonly captured by time to first token (TTFT) and time per output token (TPOT). This paper presents a phase-aware evaluation of LLM inference performance across GPUs and emerging AI accelerators using a common model, Llama2-7B. By separately measuring Prefill and Decode performance, we reveal that accelerator advantages differ by phase and metric. Our results show that GPUs consistently excel in the compute-intensive Prefill phase, while GroqRack achieves significantly lower TPOT during Decode (batching not currently supported). However, GPUs regain an advantage in Decode throughput as batch size increases. These findings demonstrate that each platform exhibits distinct phase-dependent strengths. We further analyze heterogeneous Prefill/Decode disaggregation across different accelerator platforms, identifying performance gains and the workload and network conditions under which such gains are realized.

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09.

arXiv (CS.LG) 2026-06-15 DOI: arXiv:2606.13912

Direct/adaptive-mixture phase-gradient learning for neural-network quantum states with complex phase structure

作者:

Yi-Ran Xue↗Rui Wang↗Baigeng Wang↗Chenan Wei↗

arXiv:2606.13912v1 Announce Type: cross Abstract: Neural-network quantum states (NQS) are a leading variational tool for quantum many-body physics, yet their optimization is fragile whenever the ground state carries a non-trivial sign or complex phase structure, a situation generic to gauge fields, broken time-reversal symmetry, and fermionic statistics. We trace this fragility to the stochastic estimator of the phase gradient rather than to network expressiveness. The phase sector of the Monte Carlo energy gradient is a noisy score-function estimator; differentiating the local energy instead yields a direct estimator that is unbiased for the same phase force, has far lower variance, and requires only a separated amplitude–phase ansatz. Demonstrated on a 100-site flux ladder, a small network trained this way reaches $0.89\%$ median error, where tuned standard baselines plateau at $1.8\%$ and wider or deeper standard-gradient networks degrade from $8.4\%$ to $24.6\%$. The advantage carries over to chiral XXX chains: the direct estimator again converges to a markedly lower error than the standard one, across $\alpha$ and size; it grows with flux and vanishes in zero-flux controls. An adaptive-mixture of the two estimators is provably never worse in variance than the better endpoint at the optimal mixing coefficient, with seed-resolved diagnostics tracing much of the gain to eliminating failed runs. Estimator design thus emerges as a first-class lever for complex-valued neural quantum states.

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10.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2509.25148

AAPA: Adversarially Anchored Preference Alignment for Post-Training of Large Language Models

作者:

Faqiang Qian↗Kang An↗Weikun Zhang↗Ziliang Wang↗Xuhui Zheng↗Liangjian Wen↗Yong Dai↗Mengya Gao↗Yichao Wu↗

arXiv:2509.25148v2 Announce Type: replace Abstract: Post-training alignment of large language models often combines supervised fine-tuning (SFT) on expert demonstrations with reinforcement learning (RL) from preference or verifiable feedback. SFT provides a useful behavioral anchor but can overfit to static demonstrations, whereas RL encourages exploration but may drift from expert behavior or exploit imperfect rewards. We propose AAPA (Adversarially Anchored Preference Alignment), a plug-in framework that augments existing post-training objectives with a sentence-level adversarial anchoring signal. AAPA compares policy rollouts with offline, pre-collected expert responses using a fixed lightweight discriminator, and therefore requires neither online teacher inference nor discriminator co-training during policy optimization. The same anchoring term can be added to SFT, GRPO, and CHORD while preserving their original training pipelines. Experiments on instruction-following benchmarks show that AAPA consistently improves the corresponding base objectives across model scales. In particular, the staged AAPA configuration improves over a strong GRPO baseline by 5.77\% on \texttt{Qwen3-0.6B} and 3.75\% on \texttt{Qwen3-4B}. Further analyses on response length, log-probability distributions, and discriminator variants suggest that adversarial anchoring provides a stable semantic grounding signal for preference optimization. Code is available at \url{https://github.com/IsFaqq/AAPA}.

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11.

medRxiv (Medicine) 2026-06-18 DOI: HASH:eb6cb8b86eaee1a53a01e2516d4bf761

Rare Coding Variants Reveal Distinct Genetic Architectures Across Multidimensional Sleep Phenotypes

作者:

Zhang↗Lu↗Kunorozva↗Jones↗S. E↗Maher↗Valliere↗Wood↗A. R↗Weedon↗M. N↗Tubbs↗…

Sleep and circadian traits have been widely studied using common variants, but the contribution of rare coding variation remains unclear. We analyzed rare coding variants in 397,065 whole-exome sequenced UK Biobank participants across 36 sleep phenotypes from self-report, diagnoses, sleep medication use and accelerometry, and meta-analyzed results with 171,536 whole-genome sequenced All of Us participants of diverse ancestries, with replication in the Mass General Brigham Biobank (N = 31,275). We identified 260 genes associated with sleep phenotypes, including novel associations with sleep medication use in 29 genes and 24 out of 29 have not previously been reported with any sleep phenotypes. We observed modest but significant rare variant heritability and strong genetic correlations between sleep medication use, insomnia and fatigue. Temporal gene expression trajectory analyses indicate that genes associated with self-reported sleep traits show constant high prenatal expression, whereas genes linked to sleep medication phenotypes exhibit peak expression in the late prenatal period. These findings highlight distinct biological mechanisms captured by different measurement sources of sleep phenotypes and reveal rare-variant-informed targets for therapeutic discovery.

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12.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2510.21978

Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models

作者:

Hoang Phan↗Xianjun Yang↗Yuanshun Yao↗Jingyu Zhang↗Shengjie Bi↗Xiaocheng Tang↗Madian Khabsa↗Lijuan Liu↗Deren Lei↗

arXiv:2510.21978v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has delivered impressive gains in mathematical and multimodal reasoning and has become a standard post-training paradigm for contemporary language and vision-language models. However, the RLVR recipe introduces a significant risk of capability regression, in which models forget foundational skills after prolonged training without employing regularization strategies. We empirically confirm this concern, observing that open-source reasoning models suffer performance degradation on core capabilities such as perception and faithfulness. While imposing regularization terms like KL divergence can help prevent deviation from the base model, these terms are computed on the current task and therefore do not guarantee preservation of broader knowledge. Meanwhile, commonly used experience replay across heterogeneous domains makes it nontrivial to decide how much training emphasis each objective should receive. To address this, we propose RECAP-a replay strategy with dynamic objective reweighting for general knowledge preservation. Our reweighting mechanism adapts online using short-horizon signals of convergence and instability, shifting the post-training focus away from saturated objectives and toward underperforming or volatile ones. Our method is end-to-end and readily applicable to existing RLVR pipelines without training additional models or heavy tuning. Extensive experiments on benchmarks using Qwen2.5-VL-3B and Qwen2.5-VL-7B demonstrate the effectiveness of our method, which not only preserves general capabilities but also improves reasoning by enabling more flexible trade-offs among in-task rewards.

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13.

arXiv (quant-ph) 2026-06-11 DOI: arXiv:2606.11882

Tensor-Network Algorithm for Many-Body Trace Norms

作者:

Seunghun Lee↗Eun-Gook Moon↗

arXiv:2606.11882v1 Announce Type: new Abstract: Trace norms are fundamental to quantum information theory, yet in many-body systems their evaluation remains a major computational bottleneck, as it generally requires diagonalizing exponentially large operators. Here, we overcome this bottleneck by introducing a controlled tensor-network algorithm for estimating the trace norm of matrix product operators without full diagonalization. The key idea is to combine Zolotarev's rational approximation to the sign function with a variational formulation solved using a density-matrix-renormalization-group-like algorithm. The resulting approximation is systematically improvable, with its accuracy controlled by the rational approximation parameters and the spectral weight near zero. Beyond the reach of exact diagonalization, we demonstrate controlled trace-norm calculations for entanglement negativity, quantum fidelity and quantum Fisher information, achieving substantially improved accuracy over polynomial-based Lanczos approaches. Our results establish trace-norm-based quantities as practical tensor-network observables, opening a route toward tensor-network studies of quantum information in mixed states.

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14.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.16301

One-Step Generalization Ratio Guided Optimization for Domain Generalization

作者:

Sumin Cho↗Dongwon Kim↗Kwangsu Kim↗

arXiv:2606.16301v1 Announce Type: new Abstract: Domain Generalization (DG) aims to train models that generalize to unseen target domains but often overfit to domain-specific features, known as undesired correlations. Gradient-based DG methods typically guide gradients in a dominant direction but often inadvertently reinforce spurious correlations. Recent work has employed dropout to regularize overconfident parameters, but has not explicitly adjusted gradient alignment or ensured balanced parameter updates. We propose GENIE (Generalization-ENhancing Iterative Equalizer), a novel optimizer that leverages the One-Step Generalization Ratio (OSGR) to quantify each parameter's contribution to loss reduction and assess gradient alignment. By dynamically equalizing OSGR via a preconditioning factor, GENIE prevents a small subset of parameters from dominating optimization, thereby promoting domain-invariant feature learning. Theoretically, GENIE balances convergence contribution and gradient alignment among parameters, achieving higher OSGR while retaining SGD's convergence rate. Empirically, it outperforms existing optimizers and enhances performance when integrated with various DG and single-DG methods.

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15.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2605.29151

Real-rootedness of the Poincaré polynomials of $\overline{\mathcal M}_{0,n}$: an AI-assisted proof

作者:

Gergely B\'erczi↗Young-Hoon Kiem↗

arXiv:2605.29151v2 Announce Type: replace-cross Abstract: We prove real-rootedness for the Poincaré polynomial \[ P_n(t)=\sum_{i=0}^{n-3} \dim H^{2i}(\overline{\mathcal M}_{0,n};\mathbb{Q})t^i \] of the Deligne–Mumford moduli space $\overline{\mathcal M}_{0,n}$ of stable $n$-pointed rational curves, proving a conjecture of Aluffi–Chen–Marcolli. The proof starts from the Keel–Manin–Getzler recurrence, but its main new idea is a bivariate deformation $F_m(y,t)$ of the Poincaré polynomial. This deformation reveals a hidden interlacing structure not visible in the one-variable recurrence. For fixed $t

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16.

arXiv (CS.CV) 2026-06-18 DOI: arXiv:2506.13506

Stimulus Motion Perception Studies Imply Specific Neural Computations in Human Visual Stabilization

作者:

David W Arathorn↗Josephine C. D'Angelo↗Austin Roorda↗

Even during fixation the human eye is constantly in low amplitude motion, jittering over small angles in random directions at up to 100Hz. This motion results in all features of the image on the retina constantly traversing a number of cones, yet objects which are stable in the world are perceived to be stable, and any object which is moving in the world is perceived to be moving. A series of experiments carried out over a dozen years revealed the psychophysics of visual stabilization to be more nuanced than might be assumed, say, from the mechanics of stabilization of camera images, or what might be assumed to be the simplest solution from an evolutionary perspective. The psychophysics revealed by the experiments strongly implies a specific set of operations on retinal signals resulting in the observed stabilization behavior. The presentation is in two levels. First is a functional description of the action of the mechanism that is very likely responsible for the experimentally observed behavior. Second is a more speculative proposal of circuit-level neural elements that might implement the functional behavior.

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17.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.16434

Autonomous End-to-End SOH Prediction Services for Battery Systems via Temporal-Contrastive Representation Learning

作者:

Junting Wen↗Dan Li↗Qihao Quan↗Xiwen Wang↗Hang Yang↗Zhaohong Meng↗Zigui Jiang↗Changlin Yang↗Tianle Liu↗Diego Mu\~noz-Carpintero↗Jian Lou↗

arXiv:2606.16434v1 Announce Type: cross Abstract: Accurate state of health (SOH) estimation is a critical diagnostic service for lithium-ion battery management. However, reliance on labor-intensive manual feature engineering and opaque black-box models hinders scalable industrial deployment. To address this, we introduce TC-SOH: a modular, plug-and-play service architecture for autonomous, end-to-end SOH prediction. TC-SOH employs a temporal-contrastive mechanism and a cross-window prediction pretext task to extract degradation-relevant representations directly from raw operational data. To improve transparency, we connect model efficacy with representation diagnostics: visualization, sensitivity analysis, redundancy analysis, bidirectional probing, future-SOH probing, and temporal shuffling show that learned features overlap with selected expert descriptors while retaining additional SOH-relevant variation, and that ordered temporal context improves subsequent-SOH prediction. Across four public datasets, TC-SOH outperforms the considered physics-informed and data-driven baselines, reducing MAPE by 1.91 times and RMSE by 2.13 times.

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18.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2606.12629

Bag of Dims: Training-Free Mechanistic Interpretability via Dimension-Level Sign Patterns

作者:

Varun Reddy Nalagatla↗

arXiv:2606.12629v1 Announce Type: cross Abstract: We show that the standard basis of transformer hidden states already provides a training-free, architecture-general feature basis. Individual dimensions encode semantic content via their signs and confidence via their magnitudes, functioning as independent binary registers. We validate this Bag of Dims framework across three model families (Qwen 3.5-4B, Gemma 3-4B, Mistral 7B) through four progressive experiments. Sign patterns alone carry predictive content: replacing all magnitudes with unity achieves 72-93% top-5 next-token accuracy through the LM head, and pure Hamming scoring without any decoder reaches 80-90% top-4096. These sign patterns organize into semantic features: using a single-token type cache (one forward pass per vocabulary token, no context), we discover 175 categories via per-dimension sign consistency (mean AUC 0.80) from 50 anchors with zero training. A trained probe adds only +0.018 AUC and converges to axis-aligned weights, confirming negligible cross-dimension structure. This structure extends to attention: all 175 categories remain discoverable in K and V projections. On the write side, static FFN weight inspection links 20% of features to individual writer neurons (>0.70 agreement; random controls: 0%), with top-200 neuron coalitions achieving >0.70 agreement on 99.9% of prototypes via majority vote. Fully unsupervised discovery (random seeds, no labels) scales to 1500 features at 100% yield and 99% sparsity across all three models, with pairwise MI of 0.0014 bits confirming low inter-dimension coupling. These results establish that the standard basis already suffices for feature reading throughout the transformer compute pathway, requiring no training, no optimization, and no GPU-days beyond a single forward pass per vocabulary token.

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19.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2606.12424

AI-Automation Tooling in Computer Engineering Education: Mixed-Methods TAM/UTAUT Evidence for a General Acceptance Attitude

作者:

Aung Pyae↗

arXiv:2606.12424v1 Announce Type: cross Abstract: As generative AI and low-code workflow platforms become routine in software practice, a key educational question is whether the next generation of computer engineers will accept these tools as useful, usable, and worthy of sustained engagement. This paper reports a mixed-methods, cross-sectional study of undergraduate computer engineering students' acceptance of AI automation tooling, instantiated through the open-source platform n8n across three identically scripted workshops in Thailand (n = 103). A 12-item, five-point Likert instrument mapped to six TAM/UTAUT constructs - Performance Expectancy (PE), Effort Expectancy (EE), Behavioral Intention (BI), Self-Efficacy (SE), Hedonic Motivation (HM), and Output Quality (OQ) - was complemented by inductive thematic analysis of open-ended feedback. Analyses combined ordinal reliability estimation, bootstrap confidence intervals, non-parametric tests, multiple-comparison-controlled correlations, polychoric dimensionality diagnostics, a common-method-bias check, and between-session comparisons. Acceptance was favorable across all six constructs with large effect sizes, with PE emerging as the strongest construct and HM as the weakest. Dimensionality diagnostics further revealed that canonical TAM/UTAUT sub-facets collapsed into a single general acceptance factor in this short-form post-workshop context, a finding with important methodological and theoretical implications. Qualitative themes converged with the quantitative profile regarding usefulness and enthusiasm but diverged on output quality, revealing a small yet articulate reliability-skeptical minority. The findings support the curricular adoption of AI automation tooling in undergraduate computing education and identify three theory-grounded instructional levers: instruction-sequencing scaffolds, self-efficacy supports, and trust-calibration interventions.

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20.

arXiv (CS.AI) 2026-06-18 DOI: arXiv:2606.18733

SWE-Future: Forecast-Conditioned Data Synthesis for Future-Oriented Software Engineering Agents

作者:

Qiao Zhao↗JianYing Qu↗Jun Zhang↗Yehua Yang↗Hanwen Du↗Zhongkai Sun↗

arXiv:2606.18733v1 Announce Type: cross Abstract: Realistic coding-agent benchmarks often replay public GitHub issues and pull requests, making them vulnerable to overlap with model pretraining, fine-tuning, synthetic-data generation, or benchmark-driven model selection. Fully synthetic tasks avoid direct historical replay, but can drift away from real repository needs. We propose SWE-Future, a forecast-conditioned data synthesis method for future-oriented coding tasks. Given a forecast snapshot at time $T_0$, the method uses only pre-$T_0$ repository evidence to forecast future feature implementation/enhancement, bugfix, and refactor task families. We first validate this forecasting step retrospectively: after forecasts are fixed, later pull requests are used only to measure whether the predicted task families match future repository work. In an 80-repository study, the forecaster achieves 58.1\% future-work relevance under the main semantic matching metric. We then use validated forecast families as conditioning signals to synthesize a 200-task coding-agent dataset across 61 repositories from a task-generation snapshot, rather than replaying the later pull requests used for validation. SWE-Future shows that repository-evolution forecasts can guide realistic, future-oriented coding-task synthesis while reducing direct dependence on historical pull-request replay.

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21.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2605.25160

ScaleWoB: Guiding GUI Agents with Coding Agents via Large-Scale Environmental Synthesis

作者:

Guohong Liu↗Jialei Ye↗Pengzhi Gao↗Wei Liu↗Jian Luan↗Yunxin Liu↗Yuanchun Li↗

arXiv:2605.25160v2 Announce Type: replace Abstract: GUI agents powered by large language models are advancing rapidly, creating urgent needs for evaluation and training based on realistic environments. However, directly doing so in real-world environments introduces some challenges that cannot be overlooked. Real-world environments are complex and uncontrollable, making it difficult to construct verifiable rewards and to save or reset states. Existing works prioritize reproducibility but are often limited to open-source apps or file-operation tasks for reliable reward building, leaving a persistent gap from real-world usage. Furthermore, relying on virtual machines or docker images demand high resource requirements and suffer from slow response speeds, which limit the efficiency. We present \sys, a framework that could produce high-fidelity synthesized interactive environments for GUI agents across platforms with verifiable rewards. These environments behave as backend-free webpages accessible via URL, requiring near-zero setup and low resource cost, making the approach suitable for both large-scale evaluation and downstream agent training. We support multiple GUI platforms including mobile, desktop, and automotive/in-vehicle interfaces based on the same pipeline, covering 100+ environments and 1000+ verifiable tasks. Among them, 120 challenging tasks across 63 simulated mobile applications are released as a fully synthesized mobile GUI agent benchmark. Experiment results on five state-of-the-art mobile GUI agents reveal substantial headroom – the average success rate is only 27.92\%, dropping to 17.82\% on long-horizon subset – while humans reach 92.08\%. A comparison against real-world sample tasks shows that assessments made in our synthetic environments generalize to real apps. The project website is at https://scalewob.github.io.

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22.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2606.16183

LLM-Powered Virtual Population for Demand Simulation and Pricing

作者:

Chengpiao Huang↗Kaizheng Wang↗

We develop an LLM-powered virtual population model that simulates demand for pricing decisions, in settings where products are described by rich unstructured information, such as text descriptions and images, and where decision makers need not only mean-demand predictions but also uncertainty estimates for counterfactual prices. Our model represents exposed customers as draws from a finite mixture of customer personas. For each persona, product, and candidate price, an LLM elicits a persona-level purchase probability using both structured persona information and unstructured product information. These probabilities are aggregated through calibrated mixture weights to form a predictive distribution of aggregate demand. The resulting simulator can evaluate counterfactual prices under various pricing objectives, including expected revenue and risk-aware criteria such as conditional value at risk. We test the framework on an online H&M fashion dataset with product descriptions and images. The calibrated LLM-based simulator achieves the best overall predictive performance among the models considered, and supports sample-efficient pricing decisions. Our framework provides a practical way to use LLMs as demand simulators for products with limited historical demand data but rich product information. By producing a full predictive demand distribution rather than only a point forecast, it enables managers to compare candidate prices, quantify demand uncertainty, and choose prices that target either average-case revenue or risk-aware objectives.

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23.

medRxiv (Medicine) 2026-06-18 DOI: HASH:c71c21826f565dbdc5b5c752c4b75e1a

Automated Airways Characterization and Assessment of Cystic Fibrosis from CT Imaging

作者:

Chong Chie↗J. A. K. H↗Cooper↗M. L↗Persohn↗S. A↗Burton↗C. P↗Salama↗Territo↗P. R↗

Background Advancements in medical imaging have enabled non-invasive diagnosis and staging of cystic fibrosis (CF) using CT scans, revealing dilated airways, an increased number of visible airways, and airway generation splits in these patients. However, manual characterization of airways remains time-consuming and challenging due to the numerous structural changes, thereby limiting clinical feasibility. This study aims to develop an automated algorithm to characterize airways from segmented lung CT scans and apply this to a retrospective population. This approach reduces the time required to analyze images and obtain disease-staging results. Methods This framework consists of two stages. The first stage extracts and skeletonizes the airway tree from lung CTs, while the second stage measures lung features, including airway volumes, branch counts, generation splits, diameters, and cross-sectional areas. This permits comprehensive characterization for use in clinical assessment. Results The airways analysis was performed on 169 CT volumes ranging in age from 6 to 18 years of age, revealing substantial differences in detected airway branches, generation splits, and normalized airway volume between the control and CF groups. The framework also measures airway diameters and cross-sectional areas, revealing an increase in the number of small airways in cystic fibrosis patients, due to early bronchiectasis. These findings align with previous research and demonstrate the framework's ability to accurately quantify airway changes in patients with CF. Discussion The framework extracts entire airway trees, facilitating measurements of volume, branch count, diameters, and cross-sectional areas, which change with CF severity and/or treatment. However, partial lung atelectasis can limit the accuracy of airway detection in moderate-to-severe cases. Funding NIA U54 AG054345 and NIA R21 AG07857501

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24.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2606.18750

Ensuring Trustworthy Online A/B Testing: Addressing Five Key Questions on CUPED

作者:

Yu Zhang↗Bokui Wan↗Yongli Qin↗Jinyong Ma↗Yifan Guo↗

arXiv:2606.18750v1 Announce Type: cross Abstract: A/B testing has become the gold standard for data-driven decision-making in large-scale online experimentation, providing critical guidance for feature launch, pricing optimization, and user experience enhancement. To maximize statistical sensitivity, many technology companies routinely employ Controlled-experiment Using Pre-Experiment Data (CUPED), a technique that achieves substantial variance reduction while preserving the unbiasedness of estimating the average treatment effect. Despite its widespread adoption, several critical methodological and practical nuances of CUPED remain underexplored. This paper systematically addresses five frequently encountered yet overlooked questions regarding the application of CUPED. First, we provide a comparative analysis of various post-CUPED estimators to identify the optimal adjustment specification. Second, we evaluate the validity of regression-based adjustments and delineate robust variance estimation methods tailored for such frameworks. Finally, we extend our investigation to complex but common scenarios, including multi-arm experiments and two-stage sampling designs. Our findings reveal that in these settings, naive reliance on standard variance estimators can lead to severely misleading inferences. By offering rigorous theoretical insights and extensive experimental validation, this work deepens the conceptual understanding of CUPED. Notably, the recommended methodologies have been successfully deployed and integrated into ByteDance's experimentation platform.

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25.

Nature (Science) 2026-06-17 DOI: HASH:adf92af8f70130253db113f31bf13770

Cucurbituril-based anion-conducting membranes with supramolecular nanopores

作者:

Ziang Xu↗

Nanoporous anion-conducting membranes have gained considerable interest for their potential to reduce resistance in electrochemical devices1–4. Current pore-forming methods, such as backbone engineering through polymers of intrinsic microporosity5,6 or covalent organic and metal–organic frameworks7,8, however, suffer from limited structural control, mechanical fragility or demanding synthesis. Here we establish a supramolecular strategy that overcomes these limitations by constructing uniform, dynamic nanopores. Co-assembly of the rigid macrocyclic host cucurbit[7]uril with the cationic polymer guest quaternized poly(piperidinium-terphenyl) yields a robust network of nanometre-scale channels while simultaneously enhancing mechanical and chemical stability. The dynamic host–guest interactions allow the pore structure to fluctuate on picosecond and angstrom scales. This transient environment supports low-friction hydroxide migration through a Grotthuss mechanism, producing a marked enhancement in ionic conductivity. This bottom-up design principle provides a versatile new tool for molecularly engineering transport pathways and promises to advance electrochemical reactors with respect to energy efficiency, operational stability and the production of high-purity products. A supramolecular strategy, in which uniform, dynamic nanopores are constructed, overcomes the limitations of limited structural control, mechanical fragility or demanding synthesis in nanoporous anion-conducting membranes, providing a versatile tool for molecularly engineering transport pathways.

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