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01.
arXiv (math.PR) 2026-06-15

Ergodicity for stochastic 2D Boussinesq equations with a highly degenerate pure jump Levy noise

Authors:
Jianhua Huang↗Xuhui Peng↗Xue Wang↗Jiangwei Zhang↗

arXiv:2503.18045v2 Announce Type: replace Abstract: This study aims to analyze the ergodicity for stochastic 2D Boussinesq equations and explore the impact of a highly degenerate pure jump L\'{e}vy noise acting only in the temperature equation, where this noise could appear on only a few Fourier modes. By leveraging the equi-continuity of the semigroup established through Malliavin calculus and an analysis of stochastic calculus, together with the weak irreducibility of the solution process, we prove the existence and uniqueness of the invariant measure. Moreover, we overcome the main challenge of establishing time asymptotic smoothing properties of the Markovian dynamics corresponding to this system by conducting spectral analysis of the Malliavin covariance matrix.

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02.
arXiv (CS.CL) 2026-06-18

GrowthHacker: Automated Off-Policy Evaluation Optimization Using Code-Modifying LLM Agents

Authors:
Jie JW Wu↗Ayanda Patrick Herlihy↗Ahmad Saleem Mirza↗Ali Afoud↗Fatemeh Fard↗

With data-driven development now widely adopted, online A/B testing is an established method for measuring the effects of new technologies. However, deploying online experiments demands resources for design, implementation, and deployment, and may negatively impact users (e.g., unsafe or unethical outcomes) while requiring weeks of data collection. To address this, the growing research area of off-policy evaluation (OPE), or offline A/B testing, assesses new technologies offline using previously collected logged data. OPE is also a fundamental problem in reinforcement learning and is important where online testing is expensive or risky, such as healthcare, recommender systems, education, and robotics. Despite advances in code-generation large language models (LLMs) and agentic workflows, little is known about whether and how LLMs and LLM-based agents can automatically optimize OPE implementations. We propose GrowthHacker, a benchmark that evaluates baseline LLMs and LLM-based agents on large-scale public datasets. GrowthHacker autonomously and iteratively modifies code, runs OPE, and uses the metrics to guide subsequent optimization. We evaluate methods on Open Bandit Pipeline (OBP) and Scope-RL, and develop a two_agent framework that addresses limitations of existing frameworks while reducing complexity. Across both libraries, two_agent shows the highest reliability (98.1%-100% success rate) and positive-outcome rate (78%), with a median improvement of 4.4% among positive outcomes; CrewAI achieves the highest average improvement (37.9%) and is the only framework with zero extreme-value failures. AutoGen and Default each reach 65% positive-outcome rates. These results establish the feasibility of using LLM-based agents as automated "growth hackers" to continuously improve OPE systems, with implications for scaling data-driven decision-making where manual optimization is expensive.

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03.
arXiv (CS.CL) 2026-06-18

Want Better Synthetic Data? Steer It: Activation Steering for Low-Resource Language Generation

Authors:
Jan Cegin↗Daniil Gurgurov↗Yusser Al Ghussin↗Simon Ostermann↗

Large language models (LLMs) have become an effective tool for synthetic data generation, including for low-resource languages, where generated data can improve downstream task performance. Current best-performing approaches typically rely on few-shot prompting with target-language examples, which increases inference costs and may reduce diversity through lexical anchoring. In this work, we investigate activation steering as an alternative for low-resource synthetic data generation. We study two steering strategies: Language Steering, which targets the linguistic identity of a language, and Quality Steering, which captures well-formedness by contrasting human-written and backtranslated text representations. We evaluate these methods across four open-source LLMs, multiple layers, and 11 typologically diverse languages by generating sentiment and topic classification data and finetuning smaller classifiers. Steering is applied in both zero-shot and few-shot prompting settings and compared against non-steered counterparts. Our results show that steering on early layers consistently improves the diversity of generated data while often yielding stronger downstream model performance, particularly for low-resource languages.

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04.
medRxiv (Medicine) 2026-06-24

Cortisol Stress Response is Associated with Iron Status in Pregnancy

Authors:
Reid↗B. M↗Celestin↗G. F↗Georgieff↗M. K↗Mbayiwa↗Keenan↗

Background: Iron deficiency (ID) affects up to 40% of pregnant women in the third trimester, even in highly resourced and iron-supplemented populations, with adverse consequences for maternal health and long-term offspring development. Psychological stress may compromise iron status through hypothalamic-pituitary-adrenocortical (HPA) axis dysregulation and inflammation, but no study has directly examined cortisol in relation to iron status across human pregnancy. Objective: This longitudinal study examined associations between HPA function and maternal iron status across pregnancy and tested whether IL-6 and CRP mediated the relationship between cortisol and ferritin across gestation. Methods: One hundred sixty-eight pregnant Black women with Medicaid insurance completed up to four laboratory assessments across pregnancy. Salivary cortisol was measured before and in response to the Trier Social Stress Test, yielding basal and reactive cortisol indices. Serum ferritin, IL-6, and CRP were collected at each visit. Trimester-specific regression models examined cortisol reactivity in relation to ferritin; linear mixed-effects models with moderated mediation tested whether basal cortisol predicted ferritin via inflammation. Results: Higher cortisol reactivity was associated with lower ferritin specifically in the third trimester (std. {beta} = -0.197, p = .004). Higher basal cortisol predicted a steeper IL-6 rise across gestation (p = .002), and IL-6 was positively associated with ferritin (b = 0.236, p = .006), consistent with inflammatory iron sequestration. The indirect effect of basal cortisol on ferritin via IL-6 was statistically significant, and higher basal cortisol was negatively associated with cortisol reactivity in the third trimester. No pathway was observed through CRP. Conclusion: Greater cortisol reactivity predicted lower third-trimester ferritin, a pattern that suggests cumulative iron depletion, atypically sustained HPA reactivity in late pregnancy, or both. To our knowledge, this is the first prospective study linking cortisol reactivity to iron status across human pregnancy, identifying maternal stress physiology as a novel target for understanding and addressing gestational iron deficiency.

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05.
arXiv (CS.LG) 2026-06-16

Greedy Coordinate Diffusion: Effective and Semantically Coherent Adversarial Attacks via Diffusion Guidance

Authors:
Bohdan Turbal↗Blossom Metevier↗Max Springer↗Aleksandra Korolova↗

arXiv:2606.15531v1 Announce Type: new Abstract: Fine-tuning aligned language models on benign tasks (e.g. math tutoring) systematically breaks safety guardrails, even when training data contains no harmful content. While mechanistic approaches have shed light on where alignment resides in model weights, they do not by provide a general formal framework for deriving guarantees about when fine-tuning degrades it – leaving the field without principled tools for predicting or preventing alignment collapse. We develop a local geometric framework through geometric analysis of parameter-space trajectories and apply it to understand the fragility of alignment in fine-tuning. While first-order analysis suggests orthogonal updates are safe, we prove this is illusory: the curvature of the fine-tuning loss induces second-order acceleration that can induce second-order drift into alignment-sensitive regions. We formalize a construct of our framework as the Alignment Instability Condition (AIC), three geometric properties that, when present, are sufficient to guarantee degradation. Our main result proves quartic onset of alignment degradation along gradient-flow trajectories, determined by how sharply alignment depends on specific parameters and how strongly tasks couple to these parameters. These findings yield formal sufficient conditions under which static first-order protection can fail under gradient descent. We further empirically validate the framework's foundations, showing that the Fisher Information Matrix provides a proxy for the degree of safety degradation across diverse fine-tuning.

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06.
arXiv (CS.AI) 2026-06-19

Variable-Length Tokenization via Learnable Global Merging for Diffusion Transformers

Authors:
Dong Hoon Lee↗Seunghoon Hong↗

arXiv:2606.20076v1 Announce Type: cross Abstract: Latent Diffusion Models (LDMs) have become dominant in visual synthesis, but their quality-compute trade-off is largely constrained by the tokenizer's fixed compression ratio. Variable-length tokenizers (VLTs) promise adaptive compression by varying token counts, allowing diffusion models to flexibly balance quality and compute. However, conventional VLTs modulate length by truncating ordered token sequences, which makes token semantics depend on token position and breaks representational alignment across lengths. This leads to a cross-length shift in the latent distribution that hinders a single variable-length diffusion model from operating effectively. To address this, we propose a novel variable-length tokenizer that modulates length by merging tokens. We show that encouraging similar tokens to merge enables direct cross-length representation alignment when the diffusion transformer operates according to the merging pattern. Since conventional merging methods are data-dependent, making the merging pattern inaccessible during generation, we introduce learnable global merging, which is data-independent, to ensure compatibility with diffusion transformers. On ImageNet 256$\times$256 generation, our merging-based variable-length tokenizer integrated with a diffusion transformer achieves a superior gFID-compute trade-off compared to prior VLT methods. Code is available at [this https URL](https://github.com/movinghoon/lgm)

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07.
arXiv (math.PR) 2026-06-16

Asymptotic behavior of some strongly critical decomposable 3-type Galton–Watson processes with immigration

Authors:
Matyas Barczy↗D\'aniel Bezd\'any↗

arXiv:2406.09852v2 Announce Type: replace Abstract: We study the asymptotic behavior of a critical decomposable 3-type Galton-Watson process with immigration when its offspring mean matrix is triangular with diagonal entries 1. It is proved that, under second or fourth order moment assumptions on the offspring and immigration distributions, a sequence of appropriately scaled random step processes formed from such a Galton-Watson process converges weakly. The limit process can be described using independent squared Bessel processes $({\mathcal X}_{t,1})_{t\geq0}$, $({\mathcal X}_{t,2})_{t\geq0}$, and $({\mathcal X}_{t,3})_{t\geq0}$, the linear combinations of the integral processes of $({\mathcal X}_{t,1})_{t\geq0}$ and $({\mathcal X}_{t,2})_{t\geq0}$, and possibly the 2-fold iterated integral process of $({\mathcal X}_{t,1})_{t\geq0}$. The presence of the 2-fold iterated integral process in the limit distribution is a new phenomenon in the description of asymptotic behavior of critical multi-type Galton-Watson processes with immigration. Our results complete and extend some results of Foster and Ney (1978) for some strongly critical decomposable 3-type Galton-Watson processes with immigration.

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08.
arXiv (CS.AI) 2026-06-17

Constitutional On-Policy Safe Distillation

Authors:
Ming Wen↗Yuxuan Liu↗Kun Yang↗Yunhao Feng↗Zhuoer Xu↗Yuhao Sun↗Shiwen Cui↗Xiang Zheng↗Guoyu Wang↗Xingjun Ma↗Yu-Gang Jiang↗

arXiv:2606.03089v2 Announce Type: replace-cross Abstract: On-policy self-distillation (OPSD) has emerged as an efficient post-training paradigm by using a teacher conditioned on privileged information to provide dense token-level supervision. Prior work has shown that OPSD can collapse in verifiable reasoning tasks, but safety alignment differs in that it is guided by high-level constitutions rather than explicit target answers, making it a natural setting to revisit dense distillation. However, our pilot study show that safety OPSD still suffers from severe collapse: constitutional conditioning contracts the teacher distribution toward short and overly conservative responses, and Reverse KL further amplifies this contraction into reduced expressiveness. We formalize this effect as geometric leakage under safety boundaries in a non-orthogonal semantic space, where safety pressure transfers into the expressiveness dimension. Based on this analysis, we propose Constitutional On-Policy Safe Distillation (COPSD), which first calibrates the teacher through a Cross-SFT cold-start and then performs constitution-conditioned on-policy distillation. Experiments on 12 benchmarks show that COPSD achieves a consistently stronger safety–helpfulness trade-off than baselines while substantially reducing the safety tax on general reasoning ability.

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09.
arXiv (CS.CL) 2026-06-16

A Systematic Evaluation of Large Language Models for PTSD Severity Estimation: The Role of Contextual Knowledge and Modeling Strategies

Authors:
Panagiotis Kaliosis↗Adithya V Ganesan↗Oscar N. E. Kjell↗Whitney Ringwald↗Scott Feltman↗Melissa A. Carr↗Dimitris Samaras↗Camilo Ruggero↗Benjamin J. Luft↗Roman Kotov↗Andrew H. Schwartz↗

Large language models (LLMs) are increasingly being used in a zero-shot (generative) fashion to assess mental health conditions, yet we have limited knowledge on what factors affect their accuracy. In this study, we use a clinical dataset of natural language narratives and self-reported PTSD severity scores from 1,437 individuals to comprehensively evaluate the performance of 11 state-of-the-art LLMs. To understand the factors affecting model's assessment accuracy, we systematically varied (i) contextual knowledge prompted to the models like subscale definitions, distribution summary, and interview questions, and (ii) modeling strategies including zero-shot vs few shot, amount of reasoning effort, model sizes, structured subscales vs direct scalar prediction, output rescaling and nine ensemble methods. Our findings indicate that (a) LLMs are most accurate when provided with detailed construct definitions and context of the narrative, even exceeding human raters agreement with self-reported scores; (b) increased reasoning effort leads to better estimation accuracy; (c) performance of open-weight models (Llama, DeepSeek) plateaus beyond 70B parameters while closed-weight (gpt-o3-mini, gpt-5) alternatives improve with newer generations; and (d) best performance is achieved when ensembling a supervised model with the zero-shot LLMs. Beyond agreement with self-reports, LLMs' estimates discriminated PTSD severity from depression, anxiety, and alcohol use, and prospectively predicted future mental healthcare expenditure. Together, these results suggest that contextual knowledge and modeling strategies meaningfully affect accuracy and clinical utility of LLM-based assessments of PTSD severity.

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10.
arXiv (CS.AI) 2026-06-24

Efficient Test-time Inference for Generative Planning Models with OCL Search

Authors:
Robert Gieselmann↗Mihai Samson↗Federico Pecora↗Jeremy L. Wyatt↗

arXiv:2606.00618v2 Announce Type: replace Abstract: Generative models have emerged as a powerful paradigm for AI planning, yet their performance remains constrained by the training data distribution. One approach is to improve generated solutions during inference by scaling test-time compute. A more efficient alternative is to optimize the inference process itself. In this paper, we show that a modified version of a classical Open-Closed List (OCL) search provides just such an efficient inference procedure. Our algorithm synergizes two learned components: a generative model that performs fast rollouts from intermediate states and a heuristic model that prioritizes among candidate reasoning paths. Key contributions include novel exploration control mechanisms and integration of learned models within the OCL framework. Across multiple combinatorial planning domains, our approach outperforms both neurosymbolic search baselines and classical solvers in computational efficiency and solution quality.

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11.
arXiv (CS.CV) 2026-06-16

FireRed-Image-Edit-1.0 Technical Report

Authors:
Super Intelligence Team↗Changhao Qiao↗Chao Hui↗Chen Li↗Cunzheng Wang↗Dejia Song↗Jiale Zhang↗Jing Li↗Qiang Xiang↗Runqi Wang↗Shuang Sun↗Wei Zhu↗…

We present FireRed-Image-Edit, a diffusion transformer for instruction-based image editing that achieves state-of-the-art performance through systematic optimization of data curation, training methodology, and evaluation design. We construct a 1.6B-sample training corpus, comprising 900M text-to-image and 700M image editing pairs from diverse sources. After rigorous cleaning, stratification, auto-labeling, and two-stage filtering, we retain over 100M high-quality samples balanced between generation and editing, ensuring strong semantic coverage and instruction alignment. Our multi-stage training pipeline progressively builds editing capability via pre-training, supervised fine-tuning, and reinforcement learning. To improve data efficiency, we introduce a Multi-Condition Aware Bucket Sampler for variable-resolution batching and Stochastic Instruction Alignment with dynamic prompt re-indexing. To stabilize optimization and enhance controllability, we propose Asymmetric Gradient Optimization for DPO, DiffusionNFT with layout-aware OCR rewards for text editing, and a differentiable Consistency Loss for identity preservation. We further establish REDEdit-Bench, a comprehensive benchmark spanning 15 editing categories, including newly introduced beautification and low-level enhancement tasks. Extensive experiments on REDEdit-Bench and public benchmarks (ImgEdit and GEdit) demonstrate competitive or superior performance against both open-source and proprietary systems. To support future research, our code, models, and benchmark suite are publicly available at https://github.com/FireRedTeam/FireRed-Image-Edit/ .

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12.
arXiv (CS.LG) 2026-06-18

Towards Anomaly Detection on Relational Data

Authors:
Shiyuan Li↗Yunfeng Zhao↗Yue Tan↗Qingfeng Chen↗Yixin Liu↗Shirui Pan↗

arXiv:2606.18621v1 Announce Type: new Abstract: Relational databases are widely used for managing structured data in real-world systems. Detecting anomalies from such relational data is crucial for identifying fraud, risks, and abnormal behaviors, yet remains under-explored. The key challenges lie in the intrinsic complexity of relational data: multi-table attributes are high-dimensional and heterogeneous, making sparse abnormal clues easy to overwhelm by normal or irrelevant information; and anomalies may further manifest as abnormal connection patterns across different foreign-key relations, which existing tabular and graph anomaly detection methods are ill-suited to capture. To address them, we propose RelAD, a reconstruction-based framework that captures anomalies from both attribute and relational edge reconstruction. RelAD contains two core modules: conditional sparse-gated attribute reconstruction, which suppresses redundant multi-table attributes and emphasizes abnormal semantic blocks, and dual-view multi-relational edge reconstruction, which detects relation-specific abnormal connections from both intrinsic and behavioral entity profiles. The resulting attribute and relational signals are integrated through a lightweight fusion module to produce the final anomaly score. We further construct 6 benchmark datasets with systematic anomalies, on which extensive experiments show that RelAD consistently outperforms other baselines while achieving competitive efficiency.

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13.
arXiv (quant-ph) 2026-06-25

From spectral structure to sensing limits in quantum thermometry

Authors:
Youssef Aiache↗Simone Cavazzoni↗Abderrahim El Allati↗Paolo Bordone↗Matteo G. A. Paris↗

arXiv:2606.25933v1 Announce Type: new Abstract: The precision of a quantum thermometer is fundamentally constrained by the spectral structure of the probe itself, and a systematic mapping between the configurations of energy levels and thermometric performance provides relevant information to design optimized devices. In this work, we establish such a mapping by analyzing a broad class of quantum systems, ranging from finite spin ensembles and degenerate atoms to confining potentials, quantum walks, and continuous-spectrum models. We derive exact scaling laws for the quantum Fisher information, revealing two distinct high-temperature universality classes: finite-spectrum probes exhibit a $T^{-4}$ decay, while unbounded or continuous spectra yield a slower $T^{-2}$ decay. At low temperatures, we show that sensitivity, though universally exponentially suppressed, can be enhanced arbitrarily by engineering degenerate excited states or a quantum walk on a fully connected topology. By contrast, specific quantum walk topologies provide a distinct enhancement mechanism based on gap engineering, whereby an optimal network size yields an optimized $T^{-2}$ low-temperature scaling. Furthermore, power-law spectra enable tunable scaling of thermometric performance with system size, offering a design principle for optimal probes in specific temperature windows. Our results contribute to transform spectral information into a resource for quantum thermometry, providing both fundamental bounds and practical guidelines to tailored temperature sensing.

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14.
arXiv (CS.AI) 2026-06-12

Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communities

Authors:
Dipto Das↗Achhiya Sultana↗Ankit Singh Chauhan↗Saadia Binte Alam↗Mohammad Shidujaman↗Shion Guha↗Sunandan Chakraborty↗Syed Ishtiaque Ahmed↗

arXiv:2606.13397v1 Announce Type: cross Abstract: Language operates as a mechanism of both marginalization and resistance, especially for minority communities navigating insensitive and harmful speech online. As content moderation increasingly depends on large language models (LLMs), concerns arise about whether these systems can recognize culturally insensitive speech-language that disregards or marginalizes the cultural and religious perspectives of historically underrepresented communities, often through implicit erasure, misrepresentation, or normative framing, rather than overt hostility. Focusing on Bangladesh's Hindu and Chakma communities – the country's largest religious and Indigenous ethnic minorities, respectively – this paper investigates the epistemic limits of LLM-based moderation systems and explores methods for incorporating minority perspectives. We co-created a culturally grounded corpus of insensitive speech with community members and integrated their narratives into moderation pipelines using retrieval augmented generation (RAG). Our tool, Mod-Guide, improves LLM sensitivity to minority viewpoints by leveraging contextual cues derived from lived experience. Through mixed-method evaluations involving both minority and majority participants, we demonstrate that RAG-enhanced moderation responses are more contextually accurate and perceived differently across ethnic lines. This work advances research in human-computer interaction, AI ethics, and social computing by foregrounding restorative justice and hermeneutical inclusion in the design of content moderation systems.

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15.
medRxiv (Medicine) 2026-06-24

TSPO PET binding in vivo reflects increased phagocytic microglia at post mortem in people with frontotemporal dementia

Authors:
Vontobel↗D. S↗Lai↗K. O↗Bacioglu↗Nolan↗Maddison↗D. C↗Adamski↗Goddard↗Shapiro↗N. L↗…

Brain inflammation is a key feature of frontotemporal dementia (FTD). TSPO PET is widely used as an in vivo proxy for neuroinflammation, but whether the elevated signal reflects microglial, astrocytic, or vascular pathology is controversial. We paired ante mortem [11C]PK11195 TSPO PET with post mortem neuropathology in 10 individuals with FTD (5 FTLD-tau, 5 FTLD-TDP) and 5 controls, combining CD68 immunohistochemistry across 17 regions, multiplex immunofluorescence pairing TSPO with microglial/macrophagic (IBA1, CD68), astrocytic (GFAP) and endothelial (CD31) markers, and three-dimensional single-cell reconstruction. CD68 burden was elevated in FTD, concentrated in white matter, and correlated with regional TSPO PET binding across pathologies ({beta} = 8.40, P < 0.001). Only the CD68-TSPO co-localised fraction tracked the PET signal, with no TSPO upregulation per-cell. The elevated TSPO PET signal in FTD likely reflects an increased burden of lysosome-enriched CD68+ microglia, supporting TSPO PET as a microglial-burden biomarker in both FTLD-tau and FTLD-TDP.

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16.
arXiv (CS.AI) 2026-06-15

From Self-Supervised Speech Models to Mixture-of-Experts for Robust Anti-Spoofing

Authors:
Hugo Daumain↗Driss Matrouf↗Khaled Khelif↗Mickael Rouvier↗

arXiv:2606.14639v1 Announce Type: cross Abstract: Recent advances in speech generation have significantly improved the naturalness of synthetic speech, making spoofing detection increasingly challenging. A key limitation of current anti-spoofing systems is their limited robustness to unseen synthesis methods. In this work, we transform a self-supervised speech representation model into a Mixture-of-Experts (MoE) architecture to improve generalization. Feed-forward blocks in selected encoder layers are replaced by multiple expert networks controlled by a layer-wise gating mechanism, allowing experts to capture complementary acoustic patterns while preserving the representations learned during self-supervised pretraining. We further analyze the architectural choices affecting the performance of this MoE conversion and investigate the activation behavior of the experts. The proposed approach is evaluated on 14 spoofing datasets and reduces the macro EER from 5.46% to 4.81%, corresponding to 11.9% relative improvement over the baseline.

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17.
arXiv (CS.CV) 2026-06-12

GetNetUPAM: Ecologically Informed Nested Cross-Validation and Noise-Robust Attention for Marine Bioacoustic Monitoring

Authors:
Nicholas R. Rasmussen↗Rodrigue Rizk↗Longwei Wang↗KC Santosh↗

Deploying reliable bioacoustic monitoring systems requires models that generalize under high-noise, low-SNR conditions and evaluation protocols that expose deployment-relevant failure modes, gaps largely unaddressed in current UPAM practice. Intrinsic noise, variable propagation, and mixed biological and anthropogenic sources induce distribution shifts that conventional models and single-split evaluations obscure, inflating performance and masking instability. We introduce GetNetUPAM, a hierarchical nested cross-validation framework that uses the nested stage to quantify model stability rather than tune for inflated hold-out scores. By partitioning data into site-year blocks, GetNetUPAM preserves ecological heterogeneity and forces each outer fold to represent a distinct environmental regime, preventing overfitting to localized noise or sensor artifacts. Inner stratified folds measure generalization across the full UPAM signal distribution, enforcing strict separation between model development and the outer held-out deployment condition. Using GetNetUPAM, we evaluate the Adaptive Resolution Pooling and Attention Network (ARPA-N), a CNN architecture for irregular spectrogram dimensions. ARPA-N integrates CBAM spatial attention as a learned noise suppressor, producing attention maps that localize true call structure and avoid the global, non-biological cues exploited by standard CNNs on long-window data. Under GetNetUPAM, ARPA-N generalizes robustly across diverse environmental regimes. In the zero-training support Balleny Islands region, it reduces false positives per hour by over an order of magnitude (approximately 10x) at fixed 90 percent recall, yielding consistently improved metrics across folds. These advances provide a reproducible benchmark and move UPAM toward scalable, deployment-reliable ecological monitoring.

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18.
arXiv (CS.AI) 2026-06-16

SPRI: SVD-Partitioned Residual Initialization for Data-Constrained MoE Upcycling

Authors:
Weiqiao Shan↗Ruixiang Mao↗Yuang Li↗Yuhao Zhang↗Yingfeng Luo↗Tong Zheng↗Chen Xu↗Yucheng Qiao↗Chunxiang Jin↗Yi Yuan↗Jingdong Chen↗Tong Xiao↗…

arXiv:2606.16456v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models enable efficient scaling, but training them from scratch remains prohibitively expensive. MoE upcycling mitigates this cost by converting pretrained dense models into sparse MoE models. However, existing upcycling methods typically rely on large-scale continued training and often perform poorly under data-constrained supervised adaptation, due to either homogeneous experts or overly disruptive perturbations to pretrained parameters. In this setting, effective upcycling must leverage pretrained weight structure while introducing sufficient diversity among routed experts. To this end, we propose SVD-Partitioned Residual Initialization (SPRI), which distributes SVD-partitioned residuals derived from pretrained feed-forward network (FFN) weights across routed experts, introducing controlled expert diversity grounded in pretrained spectral structure. We further introduce a two-stage training strategy to improve adaptation stability. We evaluate SPRI on multilingual speech-to-text translation, where limited supervised data challenges MoE upcycling and multiple target languages provide natural routing heterogeneity. On CoVoST2 across 15 En-to-XX directions, SPRI improves average BLEU and COMET over fully fine-tuned dense models by 2.58 and 3.32 points, respectively, and outperforms the prior best MoE upcycling baseline by 3.39 BLEU and 4.34 COMET points.

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19.
arXiv (CS.LG) 2026-06-19

When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting

Authors:
Rupasree Dey↗Abdul Matin↗Nathan Orwick↗Yao Zhang↗Shrideep Pallickara↗Sangmi Lee Pallickara↗

arXiv:2606.19363v1 Announce Type: new Abstract: The deployment of Time-Series Foundation Models (TSFMs) in physical sciences is hindered by a critical trade-off: while these models encode rich, universal temporal dynamics, they suffer from severe distributional misalignment when applied zero-shot to specific scientific domains, and their computational cost prohibits deployment in edge-computing sensor networks. We address a fundamental challenge: How can we extract latent structural knowledge from misaligned foundation models (FM) to train lightweight, specialized forecasters? We propose Gated Uncertainty-Aware Routing for Distillation (Guard), a novel framework that reframes multiteacher distillation as an instance-wise decision process with two adaptive mechanisms: (1) a Contextual Router that dynamically selects the most relevant teacher based on local input statistics, exploiting complementarity across diverse foundation models; and (2) an Uncertainty-Gated Temperature mechanism that acts as a "circuit-breaker," automatically attenuating distillation strength when teacher confidence diverges from domain reality. We evaluate our proposed lightweight framework on four climate-critical domains: meteorology, ecosystem carbon flux, soil moisture, and energy grids. Our method significantly reduces RMSE relative to a fixed-weight multi-teacher distillation baseline, successfully distilling knowledge from pretrained FMs (teachers) even when they exhibit suboptimal zero-shot accuracy due to distribution shift between the original and target data domains. We demonstrate that these domain-misaligned teachers can still serve as critical correctives, outperforming the globally superior FMs on 28.5% of the hardest instances. Ultimately, this enables high-precision scientific forecasting suitable for resource-constrained edge deployment. Code is available at https://github.com/RupasreeDey/GUARD-KDD2026.

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20.
arXiv (quant-ph) 2026-06-16

Hardy and Cabello Arguments in Spatial and Temporal Frauchiger-Renner Scenarios

Authors:
Ehsan Erfani Maharat↗Ali Ahanj↗Mohsen Sarbishaei↗

arXiv:2606.15467v1 Announce Type: new Abstract: We investigate Hardy- and Cabello-type logical structures within spatial and temporal extensions of the Frauchiger–Renner (FR) framework, embedding these constructions directly into the FR multi-observer architecture. In the spatial multi-observer scenario, both Hardy and Cabello contradictions arise, with the Cabello construction yielding the stronger violation,$\(\Delta_Cabello^{\max}=0.1078\)$, which exceeds the maximal Hardy probability $\(P_{H}^{\max}=\frac{5\sqrt{5}-11}{2}\approx 0.09017\)$. We then develop a sequential temporal FR protocol based on coherent multi-observer measurements performed on a single spin-$\tfrac12$ system. In this temporal setting, the Hardy contradiction disappears identically due to dynamical constraints imposed by sequential state updates, whereas a finite Cabello-type violation survives, \(\Delta_Cabello^{\max}\approx 0.0674\). Our results establish a fundamental structural distinction between spatial entanglement and temporal multi-observer correlations in FR-type logical scenarios, and demonstrate that certain observer-independent description failures persist even without spacelike separation.

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21.
arXiv (CS.CV) 2026-06-25

FedReLa: Imbalanced Federated Learning via Re-Labeling

Authors:
Guangzheng Hu↗Patricia Men\'endez↗Feng Liu↗Mingming Gong↗Guanghui Wang↗Liuhua Peng↗

Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation. The global class imbalance and cross-client data heterogeneity naturally coexist, and the mismatch between local and global imbalances exacerbates the performance degradation of the aggregated model. The agnosticism of global class distribution poses significant challenges for data-level methods, especially under extreme conditions with severe class absence across clients. In this paper, we propose FedReLa, a novel data-level approach that tackles the coexistence of data heterogeneity and class imbalance in federated learning. By re-labeling samples with a feature-dependent label re-allocator, FedReLa corrects biased global decision boundaries without requiring knowledge of the global class distribution. This modular, model-agnostic approach can be integrated with algorithmic methods to deliver consistent improvements without additional communication overhead. Through extensive experiments, our method significantly improves the accuracy of minority classes and the overall accuracy on stepwise-imbalanced and long-tailed datasets, outperforming the previous state of the art.

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22.
arXiv (CS.LG) 2026-06-12

Policy-driven Conformal Prediction for Trustworthy QoT Estimation

Authors:
Kiarash Rezaei↗Omran Ayoub↗Paolo Monti↗Carlos Natalino↗

arXiv:2606.12501v1 Announce Type: new Abstract: We propose Conformal QoT, a policy-driven framework that combines statistically guaranteed QoT estimation with operational decision policies, enabling reliable lightpath-feasibility predictions under domain shift and improving accuracy from 92\% to 99.6\% on open datasets.

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23.
arXiv (CS.LG) 2026-06-15

Adaptive Oscillatory-State Alignment for Time Series Forecasting

Authors:
Zhangyao Song↗Chaofeng Qu↗Chao Zha↗Xiaoyu Zhao↗Yinfei Xu↗Tao Guo↗

arXiv:2606.06010v2 Announce Type: replace Abstract: Long-term time series forecasting benefits from inductive biases that expose recurring temporal structure. Existing periodic forecasting methods typically model recurrence through predefined periods, global spectral components, or fixed learnable templates. However, real-world temporal dynamics are rarely rigidly periodic: around a nominal cycle, oscillatory behavior often exhibits non-rigid periodicity (NRP), where cycle magnitude, cycle alignment, and local cycle duration vary over time. Under these conditions, fixed-template periodic modeling can become fundamentally mismatched to the underlying temporal states. We propose AOSNet, a Hilbert-guided forecasting framework that reformulates periodic forecasting from fixed template matching to adaptive oscillatory-state alignment. AOSNet extracts analytic-signal descriptors from both the observed sequence and a learnable global oscillatory prior, then adaptively aligns local states through a descriptor-conditioned gate that selectively preserves reliable observations while softly correcting mismatched regions. The learned prior serves not as a rigid repeated template but as a flexible oscillatory reference interpreted through local state dynamics. Experiments on eight public benchmarks and two cloud workload traces demonstrate leading or highly competitive accuracy with a compact model size and low inference latency, supporting repeated forecasting settings such as capacity planning and autoscaling. Controlled synthetic studies that isolate cycle-magnitude and cycle-alignment variation and combine them with cycle-duration changes show that the advantage of oscillatory-state alignment increases as NRP intensifies.

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24.
arXiv (CS.CL) 2026-06-11

Geometry of Reason: Spectral Signatures of Valid Mathematical Reasoning

Authors:
Valentin No\"el↗

Verifying whether a language model is genuinely reasoning or pattern-matching remains an open problem: learned verifiers are expensive, and output-based heuristics are brittle. We show that valid mathematical reasoning induces a measurable, training-free spectral signature in transformer attention. By treating each attention matrix as a weighted token graph, we extract four diagnostics: Fiedler value, High-Frequency Energy Ratio (HFER), spectral entropy, and smoothness, that require no learned parameters. Experiments across seven models from four architectural families yield effect sizes up to Cohen's $d = 3.30$ ($p < 10^{-116}$), enabling $85$–$96\%$ single-threshold classification accuracy. Two findings sharpen the interpretation. First, Platonic validity: the spectral signal tracks logical coherence rather than compiler acceptance, proofs rejected for timeouts or missing imports are correctly classified as valid, a distinction confirmed by a manual audit ($\kappa = 0.82$, $n = 51$). Second, architectural determinism: Sliding Window Attention shifts the discriminative feature from HFER to smoothness ($d = 2.09$, $p < 10^{-48}$), showing that attention design governs which spectral channel encodes reasoning quality. Causal ablation confirms the signature traces induction-head circuits. The method generalises to informal chain-of-thought ($d = 0.78$, $p < 10^{-3}$), and in proof search, HFER reranking improves Best-of-16 Pass@1 by $+4.4$–$6.6$\%, matching $98\%$ of the AUC of fully supervised probes with zero labels. Spectral graph analysis is a principled, architecture-aware primitive for reasoning verification.

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25.
arXiv (CS.AI) 2026-06-17

An Evaluation of Data Leakage Risks in Tool-Using LLM Agents in Realistic Scenarios

Authors:
Hankyul Baek↗Jaewon Noh↗Sang Seo↗Yongsu Kim↗Gabriel Waikin Loh Matienzo↗Young Il Kim↗Ee Wei Seah↗Akriti Vij↗

arXiv:2606.17114v1 Announce Type: cross Abstract: AI agents are increasingly being adopted in enterprise and personal settings with access to emails, databases, documents, and other tools where they can read, update, and disseminate sensitive information. Much of prior research on data leakage risks in agents has focused on adversarial data exfiltration through prompt injections and jailbreaks. However, sensitive information may also be exposed during non-adversarial use, creating leakage risks even when users issue benign requests. We report a joint evaluation by the Singapore AI Safety Institute and the Korea AI Safety Institute examining agent data leakage in 12 realistic, non-adversarial tasks spanning customer support, DevOps, web automation, and enterprise and personal productivity. The evaluation covers five risk types: lack of data awareness, audience awareness, policy compliance, data minimization, and access-boundary awareness. Both institutes tested a common set of scenarios mirroring real-world deployments using independent testing environments and task-specific LLM-judge rubrics. Across the three tested agents, none achieved fully correct and fully safe execution across all scenarios. Successful task completion often coincided with data-handling failures such as accessing unnecessary information or disclosing information to inappropriate recipients, indicating that capability and data-handling safety should be evaluated separately. Qualitative review also revealed claim-action mismatches, simulation-aware behavior, user-simulator role reversal, and interpretation gaps in automated judging. Overall, the results indicate that operational data leakage is a first-order agent-safety concern distinct from adversarial exfiltration and provide a methodology for future evaluations of agent data-handling safety.

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