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01.

arXiv (quant-ph) 2026-06-24 DOI: arXiv:2603.29568

Phase-space microscopes for quantum gases: Imaging conjugate variables and momentum-weighted densities

作者:

N. R. Cooper↗Y. Yang↗C. Weitenberg↗

arXiv:2603.29568v2 Announce Type: replace-cross Abstract: Quantum gas microscopes offer unprecedented insights into quantum many-body states of cold atomic gases. Here we introduce concrete protocols for extending quantum gas microscopes to measure in phase space, by mapping momentum onto auxiliary degrees of freedom and using positive operator-valued measures. We distinguish between two distinct operational modes. In the Husimi-Q phase space microscope, position and momentum are jointly measured; in this mode the fundamental quantum noise is distributed between position and momentum. Conversely, the averaged-mode phase space microscope extracts the spatial dependence of averages of the momentum density (and its moments); these averages can be retrieved with arbitrary spatial resolution. We illustrate the utility of these techniques in diverse physical settings.

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02.

arXiv (CS.AI) 2026-06-11 DOI: arXiv:2606.11990

Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation

作者:

Amir El-Ghoussani↗Michele De Vita↗Ronald Naumann↗Valiseios Belagiannis↗

arXiv:2606.11990v1 Announce Type: cross Abstract: Remaining Useful Life (RUL) prediction is essential for industrial predictive maintenance, yet many learning-based approaches rely on extensive feature engineering or large labeled datasets to train task-specific sequence models. In this work, we introduce a lightweight learning approach, in which we leverage a frozen pretrained time-series foundation model (TSFM) and combine it with a small regression head for RUL estimation from multivariate sensor streams. More specifically, we use Chronos-2 as a frozen backbone to extract context window features and train a lightweight regression neural network for RUL prediction. Experiments on real-world industrial sensor data from two device types show that Chronos-2 features consistently improve over recurrent, convolutional, Transformer-based, and gradient-boosting baselines under the same preprocessing and evaluation protocol. We further analyze the impact of context length and find that performance improves significantly with longer histories, indicating that TSFM representation offer a practical and data-efficient alternative for RUL estimation in industrial settings.

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03.

arXiv (CS.LG) 2026-06-12 DOI: arXiv:2606.12483

Scalable anomaly detection via a univariate Christoffel function

作者:

Florian Grivet↗Didier Henrion↗Jean-Bernard Lasserre↗Louise Trav\'e-Massuy\`es↗

arXiv:2606.12483v1 Announce Type: new Abstract: Anomaly detection plays a critical role in identifying unusual patterns across domains such as fraud detection, network intrusion, and system fault diagnosis. Recently, Christoffel function-based methods, rooted in polynomial optimization, have emerged as promising alternatives to deep learning due to their strong mathematical foundations and computational frugality. However, their practical applicability is hindered by the need to invert a matrix whose size grows exponentially with the data dimension, rendering the method intractable even for moderate-dimensional datasets. This paper addresses the dimensionality limitations of Christoffel function-based anomaly detection while preserving its key theoretical properties, i.e., the on-off support dichotomy behavior and the accurate support shape capture. We introduce UCF, a univariate Christoffel function which is based on the squared distance between the query point and the support points. Extensive experiments on the ADBench benchmark demonstrate that UCF consistently outperforms 14 state-of-the-art baselines in terms of Average Precision. By resolving the scalability bottleneck of the Christoffel Function, this work expands the toolkit of anomaly detection methods with a robust, theoretically grounded, and universally applicable approach.

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04.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2606.18539

TS-Fault: Benchmarking Time Series Forecasters Against Structural Faults

作者:

Yuyang Zhao↗Lian Xu↗Hao Miao↗Chenxi Liu↗Hao Xue↗

arXiv:2606.18539v1 Announce Type: new Abstract: Time series forecasting (TSF) underpins consequential decisions in energy, transportation, finance, and healthcare, yet TSF models are almost universally ranked by a single number (e.g., average error) on clean held-out data, under the implicit assumption that it predicts deployed reliability. However, real faults are not i.i.d noise but structured events with temporal shape, broken cross-variable dependencies, regime change coupled with missingness, and causal propagation across a sensing pipeline. Treating TSF robustness as a data-quality problem, we present TS-Fault, a benchmark that evaluates forecasting models under explicit, parameterized fault scenarios with controllable semantic difficulty. TS-Fault organizes recurring failures into four modes along two orthogonal axes (observation- vs mechanism-level; univariate vs multivariate) and injects each fault into the most prediction-critical window via a unified importance score. This design enables robustness to be tested against the structures models actually rely on, rather than reduced to generic noise sensitivity. We evaluate 21 models across 6 datasets, 4 modes, and 5 difficulty levels under a paired clean/corrupt protocol. The results reveal three findings that contradict common leaderboard intuition: (i) clean-data accuracy anti-correlates with robustness; (ii) clean rankings are preserved under observation-level faults but reshuffled under mechanism-level faults; and (iii) all catastrophic failures occur under mechanism-level faults, with foundation models achieving the highest clean-data accuracy yet exhibiting the greatest fragility. The code is publicly available at https://github.com/Ray-zyy/TS-Fault.

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05.

arXiv (CS.AI) 2026-06-24 DOI: arXiv:2606.23888

E-MRL: Cross-view Aligned Evidence-driven Multimodal Reinforcement Learning for Reliable 3D Tumor Analysis

作者:

Sijing Li↗Zhongwei Qiu↗Zhuoya Wang↗Boxiang Yun↗Zhenyu Yi↗Jianwei Xu↗Wenqiao Zhang↗Yingda Xia↗Ling Zhang↗

arXiv:2606.23888v1 Announce Type: cross Abstract: While Vision-Language Models (VLMs) show great promise in volumetric medical report generation, they frequently suffer from visual hallucinations and a lack of grounding in 3D CT data. Current Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) strategies typically optimize text fidelity alone, essentially rewarding correct diagnoses derived from language priors rather than genuine visual perception. To address this, we propose cross-view aligned Evidence-driven Multimodal Reinforcement Learning (Evidence-MRL, noted as E-MRL), a reliable RL reasoning framework that formulates the generation process as a Markov Decision Process of "diagnosis-localization-verification". Unlike standard approaches, our model is explicitly trained to identify a "key evidence slice" alongside the global diagnostic report, grounding its findings in verifiable visual evidence. Crucially, we introduce a novel cross-view consistency reward, which validates the semantic alignment between the golden-standard report and a local visual re-query of the selected key slice, providing additional rewards for correctly-localized reasoning. Experiments on large-scale 3D CT tumor datasets demonstrate that E-MRL significantly reduces hallucinations and improves diagnostic accuracy compared to SFT and RL baselines, offering a clinically interpretable solution for visually-grounded and tumor analysis.

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06.

arXiv (CS.CL) 2026-06-15 DOI: arXiv:2606.13995

Dialogue SWE-Bench: A Benchmark for Dialogue-Driven Coding Agents

作者:

Brendan King↗Jeffrey Flanigan↗

AI coding agents have rapidly transformed software engineering, powering widely used interactive coding assistants. Despite their interactive real-world use, existing benchmarks evaluate them as fully-autonomous systems. In this work, we introduce Dialogue SWE-Bench, an automatic benchmark dataset for evaluating the ability of coding agents to resolve real-world software engineering problems through dialogue with a user. We design a novel, persona-grounded user simulator to support our task evaluation, and augment our task evaluation with automatic evaluations of dialogue quality. We also propose a new schema-guided agent, aimed at improving the dialogue capabilities of off-the-shelf coding agents, which improves over strong baselines by 3-14%. Our results indicate that better coding models do not always correspond to better dialogue models, suggesting that dialogue capability is a distinct and currently understudied dimension of coding agent performance.

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07.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2606.16497

daVinci-kernel: Co-Evolving Skill Selection, Summarization, and Utilization via RL for GPU Kernel Optimization

作者:

Dayuan Fu↗Mohan Jiang↗Tongyu Wang↗Dian Yang↗Jiarui Hu↗Liming Liu↗Jinlong Hou↗Pengfei Li↗

GPU kernel optimization represents a paradigm where functional correctness is assumed and execution efficiency is the objective. We present daVinci-kernel, a reinforcement learning framework that couples skill discovery with skill exploitation through a dynamically evolving skill library. daVinci-kernel jointly trains three agents sharing one LLM backbone: a Skill Selection Agent that retrieves relevant techniques via BM25 and LLM reranking, a Policy Agent that generates multi-turn CUDA/Triton kernels conditioned on selected skills, and a Skill Summary Agent that distills successful rollouts into reusable skills. Candidate skills are added only after execution-based verification confirms reproducible speedups. All three agents share a single LLM backbone, are initialized via a structured SFT cold start on diversity-filtered data, and are then jointly optimized end-to-end with multi-turn REINFORCE and per-agent advantage estimation. On KernelBench, daVinci-kernel-14B achieves 37.2%, 70.6%, and 32.2% on Level 1, Level 2, and Level 3 under the Fast$_1$ threshold, outperforming the strongest prior RL-trained model, Dr.Kernel-14B.

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08.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2412.08610

Competition and Diversity in Generative AI

作者:

Manish Raghavan↗

arXiv:2412.08610v3 Announce Type: replace-cross Abstract: Recent evidence, both in the lab and in the wild, suggests that the use of generative artificial intelligence reduces the diversity of content produced. The use of the same or similar AI models appears to lead to more homogeneous behavior. Our work begins with the observation that there is a force pushing in the opposite direction: competition. When producers compete with one another (e.g., for customers or attention), they are incentivized to create novel or unique content. We explore the impact competition has on both content diversity and overall social welfare. Through a formal game-theoretic model, we show that competitive markets select for diverse AI models, mitigating monoculture. We further show that a generative AI model that performs well in isolation (i.e., according to a benchmark) may fail to provide value in a competitive market. Our results highlight the importance of evaluating generative AI models across the breadth of their output distributions, particularly when they will be deployed in competitive environments. We validate our results empirically by using language models to play Scattergories, a word game in which players are rewarded for answers that are both correct and unique. Overall, our results suggest that homogenization due to generative AI is unlikely to persist in competitive markets, and instead, competition in downstream markets may drive diversification in AI model development.

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09.

arXiv (quant-ph) 2026-06-15 DOI: arXiv:2605.11895

Universal Speed Limit in a Far-from-Equilibrium Bose Gas: Symmetry and Dynamical Decoherence

作者:

Jun-Cheng Liang↗Bo Chen↗

arXiv:2605.11895v2 Announce Type: replace-cross Abstract: Predicting universal transport coefficients in far-from-equilibrium quantum systems remains a fundamental challenge. A paradigmatic example is the non-thermal fixed point (NTFP) of isolated Bose gases, where coherence spreads as $\ell^2(t) = C\hbar t/m$ with a universal constant $C$. While the scaling exponent $z=2$ is well established, the amplitude $C$ has remained elusive because the underlying particle cascade $n(k)\sim k^{-4}$ leads to a divergent kinetic energy, threatening the very existence of a constant speed limit. Here we resolve this paradox and present the first analytical, parameter-free prediction of a universal amplitude $C$. A deep interplay between symmetry and dissipation is uncovered. The emergent weak U(1) symmetry at the NTFP enforces a conserved total current, forcing the low-energy phase dynamics to obey a diffusive Langevin equation with noise entering as the divergence of a stochastic current. This structure, combined with dynamical decoherence of high-momentum modes, yields a universal power-law momentum distribution $\tilde{f}(v)\sim(1+v^2)^{-3}$ (with $v=k\ell$) that naturally regularizes the ultraviolet divergence. From this, a parameter-free geometric baseline $C=3$ is obtained, independent of microscopic details. The experimental value $C=3.4(3)$ [Martirosyan et al., Nature 647, 608 (2025)] is then shown to be quantitatively consistent with universal logarithmic corrections arising from a marginally irrelevant coupling at the fixed point. A new paradigm is thus established for predicting transport coefficients in strongly correlated non-equilibrium systems: symmetry constraints determine the low-energy effective theory, dynamical decoherence provides a natural ultraviolet completion, and scaling analysis delivers testable predictions moving beyond scaling exponents to quantitative amplitude prediction.

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10.

medRxiv (Medicine) 2026-06-22 DOI: HASH:4a65a4d531f20dd3c5de1793f22ab221

Reform of the intermediate level of the health system in the Democratic Republic of the Congo: Adaptations and limits in the stabilization of the personnel of the Provincial Health Division: A cohort study.

作者:

Mushagalusa↗C. R↗Mayeri↗D. G↗Gaylord↗A. N↗Chimanuka↗C. M↗Mwene-Batu↗P. L↗Albert↗M. T↗…

Background: Human resources are one of the pillars of health systems. Since the World Health Organization's report on human resources issues, several countries have integrated this component into the various reforms aimed at strengthening their health systems. This study aims to explore the effects of reforming the intermediate level of a health system operating in a fragile state context. Methodology Our study was conducted in the Democratic Republic of Congo (DRC). It was a cohort study of the staff of the 14 Provincial Health Divisions (PHD) out of the 26 existing in the DRC. We established a database of the staff of these 14 PHD from 2016, just after the implementation of the intermediate level reform and the allocation of this staff by the Ministry of Health. We did a recall in 2021, in each of these PHD to survey this staff through a structured questionnaire and supplemented by the files of the agents available in each PHD. Sociodemographic, economic and academic variables were collected and analyzed. Data were entered into an Excel 2016 database and processed with SPSS software version 25. The chi-square test was used for comparison of proportions with a statistical significance level of p < 0.05. Risk ratios ratios (RR) and their 95% confidence intervals were calculated as measures of association. The error threshold was set at 5%. Results A total of 657 agents with an average age of 45.2 years had been identified in 2016 at the start of the survey and in 2021, 118 or 18% of them were no longer part of the PHD agents. Among the causes of absence noted: 48% of agents placed on leave, 16% promoted to other functions within the health system, 16% desertion and dismissal and 11% cases of death. 19.8% of absentees are executives, 19.5% men against 10.3% women; 22.3% of absentees in unstable provinces against 16.6% in stable ones. The factors associated with the absence of agents in the PHD remain the reaching of retirement age [RR (95% CI) = 5.5 (1.2-24.9) ]and male agents [RR (95% CI) = 3.2 (1.3-7.9)]. Among the agents who remained, 92% kept their initial position, 6% were subject to an internal permutation accompanied by a promotion. The factors associated with the stability of human resources at the level of the Provincial Health Division are: female gender, manager with experience or seniority > 5 years, Age > 35 years, Stable province, Presence of a partner bonus. Conclusion Even in a crisis and fragile context, health system reform is possible. It is possible to organize staff recruitment through a selection process independent of the political authorities of the Ministry of Health and supported by the technical services of the Ministry and partners . Experience and the presence of a financial bonus are motivating factors for staff stability. The involvement of Technical and Financial Support Partners in the recruitment process helped the Ministry of Health to minimize political influence in the recruitment of middle-level executives.

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11.

arXiv (CS.CL) 2026-06-11 DOI: arXiv:2606.11898

GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs

作者:

Hengyi Feng↗Zeang Sheng↗Meiyi Qiang↗Wentao Zhang↗

Research on Text-Attributed Graphs (TAGs) has gained significant attention recently due to its broad applications across various real-world data scenarios, such as citation networks, e-commerce platforms, social media, and web pages. Inspired by the remarkable semantic understanding ability of Large Language Models (LLMs), there have been numerous attempts to integrate LLMs into TAGs. However, existing methods still struggle to generalize across diverse graphs and tasks, and their ability to capture transferable graph structural patterns remains limited. To address this, we introduce the GraspLLM, a framework that combines Graph structural comprehension with semantic understanding prowess of LLMs to enhance the cross-dataset and cross-task generalizability. Specifically, we represent node texts from different graphs in a unified semantic space with a frozen general embedding model, on top of which we perform motif-aware contrastive learning across multiple motif-induced adjacency matrices to extract dataset-agnostic structural information. Then, with our proposed optimal contextual subgraph, we extract the most contextually relevant subgraph for each target node and align these subgraphs to the token space of LLM via an alignment projector. Extensive experiments on TAG benchmark datasets spanning diverse domains reveal that GraspLLM consistently outperforms previous LLM-based methods for TAGs, especially in zero-shot scenarios, highlighting its strong generalizability across different datasets and tasks. Our code is available at https://github.com/Heinz217/GraspLLM.

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12.

arXiv (CS.AI) 2026-06-18 DOI: arXiv:2603.10827

Speaker Verification with Speech-Aware LLMs: Evaluation and Augmentation

作者:

Thomas Thebaud↗Yuzhe Wang↗Laureano Moro-Velazquez↗Jesus Villalba-Lopez↗Najim Dehak↗

arXiv:2603.10827v2 Announce Type: replace-cross Abstract: Speech-aware large language models (LLMs) can accept speech inputs, yet their training objectives largely emphasize linguistic content or specific fields such as emotions or the speaker's gender, leaving it unclear whether they encode speaker identity. First, we propose a model-agnostic scoring protocol that produces continuous verification scores for both API-only and open-weight models, using confidence scores or log-likelihood ratios from the Yes/No token probabilities. Using this protocol, we benchmark recent speech-aware LLMs and observe weak speaker discrimination (EERs above 20% on VoxCeleb1). Second, we introduce a lightweight augmentation that equips an LLM with ASV capability by injecting frozen ECAPA-TDNN speaker embeddings through a learned projection and training only LoRA adapters. On TinyLLaMA-1.1B, the resulting ECAPA-LLM achieves 1.03% EER on VoxCeleb1-E, approaching a dedicated speaker verification system while preserving a natural-language interface.

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13.

arXiv (quant-ph) 2026-06-19 DOI: arXiv:2412.01871

Quantifying Imaginarity in Neutrino Systems

作者:

Ashutosh Kumar Alok↗Trambak Jyoti Chall↗Neetu Raj Singh Chundawat↗Yu-Feng Li↗

arXiv:2412.01871v2 Announce Type: replace-cross Abstract: It is a fundamental question why quantum mechanics employs complex numbers rather than solely real numbers. In this work, we conduct the first analysis of imaginarity quantification in neutrino flavor and spin-flavor oscillations. As quantum systems in coherent superposition, neutrinos are ideal candidates for quantifying imaginarity within the resource theoretic framework, using measures such as the $\ell_1$-norm and the relative entropy of imaginarity. We show that in the case of two-flavor mixing, these measures of imaginarity are nonzero. The measures of imaginarity reach their extreme values when the probabilistic features of quantum theory are fully maximized, i.e., both the transitional and survival probabilities are approximately equal. Our study reveals that the imaginarity, as a resource, can be harnessed not solely from the presence of a complex phase in the mixing matrix but also from the intrinsic quantum dynamics of time evolution itself. We further extend our analysis to explore the dynamics of three-flavor neutrino mixing, incorporating the effects of a nonzero $CP$ phase.

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14.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.15207

Controlled Dynamics Attractor Transformer

作者:

Cheng Zhang↗Minnan Luo↗Zesheng Yang↗Ming Li↗Yong-Jin Liu↗Qinghua Zheng↗

arXiv:2606.15207v1 Announce Type: cross Abstract: Transformer architectures have dramatically advanced representation learning and inference in deep models through self-attention mechanisms. In parallel,associative memory (AM) frameworks map representations onto energy landscapes, offering interpretable retrieval mechanisms. However, their continuous-time inference dynamics lack the biological plausibility of classical Continuous Attractor Neural Networks (CANNs). To bridge this gap, we propose Controlled Dynamics Attractor Transformer (CDAT), which couples a mixture von Mises-Fisher (Mo-vMF) attention energy with a Hopfield refinement energy, while augmenting energy descent with a CANN-inspired excitation-inhibition modulation. CDAT instantiates a topology-constrained dynamical system whose couplings encode relational structure among tokens, thereby linking attractor-style dynamics to modern energy-based attention. We further provide a constructive dissipation analysis to formally establish their controlled inference dynamics. Benefiting from these robust and structured dynamics, CDAT achieves state-of-the-art performance across multiple benchmarks in graph anomaly detection and graph classification.

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15.

medRxiv (Medicine) 2026-06-22 DOI: HASH:f5680d1e007a4e526aa757f376d8a62a

Histologically validated diffusion MRI signatures of neuroinflammation and neurodegeneration in Alzheimer disease

作者:

Wang↗Jiang↗Luna↗Niu↗Nan↗Sun↗Perrin↗R. J↗Franklin↗Cruchaga↗Flores↗Benzinger↗…

Noninvasive neuroinflammation measurement remains a major barrier for Alzheimer disease (AD) therapeutics. We present generalized diffusion basis spectrum imaging (g-DBSI), a diffusion MRI framework that decomposes the tissue signal into biologically interpretable microstructural compartments. In postmortem Knight ADRC brains, g-DBSI-derived restricted isotropic fraction (RIF) and restricted anisotropic fraction (RAF) mapped cellularity and neurofilament density, while their ratio (RIF/RAF) tracked inflammatory cell density and peri-plaque amyloid-beta with higher specificity and regional consistency than RIF alone. In 112 living Knight ADRC participants stratified by PET amyloid, g-DBSI metrics showed amyloid-dependent trajectories: in low-amyloid individuals, RIF and RAF rose together with amyloid, consistent with early neuropil expansion and glial elaboration, whereas in high-amyloid individuals, RIF/RAF increased, and RAF declined, indicating established neuroinflammatory remodeling and neurofilament loss. CSF proteomics linked RIF/RAF to glia-enriched immune and vascular pathways, supporting g-DBSI as a clinically compatible MRI biomarker of neuroinflammation and neurodegeneration in AD.

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16.

arXiv (CS.AI) 2026-06-25 DOI: arXiv:2605.29129

Governing Technical Debt in Agentic AI Systems

作者:

Muhammad Zia Hydari↗Raja Iqbal↗Narayan Ramasubbu↗

arXiv:2605.29129v2 Announce Type: replace Abstract: Agentic AI systems are increasingly being explored as production infrastructure: they reason over multiple steps, call tools, act through workflows, and adapt through memory and feedback. These systems create governance challenges that are not fully captured by traditional software or predictive ML technical debt. We define Agentic Technical Debt as the accumulated liability created when prompts, memory, tool schemas, orchestration graphs, control policies, and observability routines are patched together faster than they can be validated, standardized, and governed. We define Stochastic Tax as the recurring operating burden of keeping probabilistic agent behavior within acceptable bounds. The distinction matters: debt is a stock of design and governance liability, while the tax is a flow of operating cost that arises because stochastic agents act through tools and workflows. We outline how managers can make both visible through lightweight dashboards and governance controls.

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17.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2606.18621

Towards Anomaly Detection on Relational Data

作者:

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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18.

arXiv (CS.CV) 2026-06-11 DOI: arXiv:2606.11853

Task-Aware Structured Memory for Dynamic Multi-modal In-Context Learning

作者:

Zhirui Chen↗Ziwei Chen↗Ling Shao↗

Multi-modal large language models (MLLMs) depend on in-context learning (ICL) for rapid task adaptation, but their scalability is severely limited by finite context windows and the growing cost of key-value (KV) caches in long multi-modal sequences. Existing memory compression approaches typically rely on rigid token removal or sample-dependent importance estimation, which introduces bias, disrupts semantic structure, particularly for visual representations, and yields static memories that cannot adapt to new queries. We introduce TASM (Task-Aware Structured Memory), a training-free framework that addresses these limitations through task-aware, structure-preserving, and dynamically accessible memory construction. TASM employs task-vector guided compression to replace sample-specific signals with a task-level direction that captures shared relevance across demonstrations. To preserve the underlying manifold, it applies semantics-aware token merging via bipartite graph matching, aggregating tokens without destructive pruning. Finally, TASM structures memory into a hierarchy comprising a compact Core Memory and a Latent Bank, facilitating query-adaptive dynamic retrieval. Evaluations confirm TASM maintains high performance under heavy compression, effectively balancing efficiency with adaptability.

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19.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2603.08999

Learning When to Sample: Confidence-Aware Selective Sampling for Efficient Chain-of-Thought Reasoning

作者:

Juming Xiong↗Kevin Guo↗Congning Ni↗Wexin Liu↗Chao Yan↗Katherine Brown↗Avinash Baidya↗Xiang Gao↗Bradley Malin↗Zhijun Yin↗

Large language models (LLMs) can achieve strong reasoning performance through chain-of-thought (CoT) reasoning, yet they often generate unnecessarily long reasoning paths that incur high inference cost. Self-consistency-based approaches push accuracy higher still, but they require sampling and aggregating multiple reasoning trajectories, leading to substantial computational overhead. In this paper, we introduce a confidence-aware selective sampling framework that, at inference time, analyzes a single reasoning trajectory to adaptively determine whether to rely on that trajectory alone or trigger multi-path sampling. The framework uses trajectory-level numeric features and sentence-level linguistic features extracted from reasoning states to guide selective multi-path reasoning. We train it on MedQA and evaluate it in-domain on MedQA and under calibration-only transfer on MathQA, MedMCQA, and MMLU, without further fine-tuning. Experimental results show that the proposed framework maintains comparable performance to full and efficient multi-path reasoning baselines, with accuracy changes of $-0.41 \pm 0.58$ and $-0.31 \pm 0.58$ percentage points, respectively, while reducing token usage by $71.7 \pm 5.0%$ and $36.6 \pm 9.1%$. These findings demonstrate that reasoning trajectories contain rich signals for uncertainty estimation, enabling a simple, transferable mechanism to balance accuracy and efficiency in LLM reasoning.

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20.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.15767

Visualizing Uncertainty: Spatial Maps of Missing and Conflicting Evidence in Deep Learning

作者:

Dong Hyun Jeong↗Feng Chen↗Jin-Hee Cho↗Lance M. Kaplan↗Audun J{\o}sang↗Soo-Yeon Ji↗

arXiv:2606.15767v1 Announce Type: cross Abstract: Understanding when and why deep neural networks are uncertain is crucial for deploying reliable machine learning systems in safety-critical domains. While existing uncertainty quantification methods provide scalar measures of model confidence, they offer limited insight into which spatial regions of an input contribute to different types of uncertainty. We propose a novel visualization framework, Uncertainty Activation Map (UAM), that combines Evidential Deep Learning (EDL) with Full-Gradient Class Activation Mapping (FullGrad) to generate interpretable spatial uncertainty activation maps. Our approach distinguishes between two fundamental types of uncertainty: vacuity, representing lack of evidence, and dissonance, capturing conflicting evidence between competing hypotheses. By leveraging the complete gradient decomposition property of FullGrad and the principled uncertainty quantification of Subjective Logic, our method produces theoretically grounded visualizations that highlight specific image regions responsible for model uncertainty. With this framework, vacuity and dissonance activation maps are generated by computing belief-weighted attributions, enabling identification of where models lack knowledge versus where they encounter ambiguous evidence. Extensive evaluations across multiple benchmark datasets demonstrate that the proposed framework effectively addresses the critical gap between uncertainty quantification and explainability, providing intuitive visual feedback to assess model reliability in complex visual recognition tasks.

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21.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2606.15416

Encode Errors: Representational Retrieval of In-Context Demonstrations for Multilingual Grammatical Error Correction

作者:

Guangyue Peng↗Wei Li↗Wen Luo↗Houfeng Wang↗

Grammatical Error Correction (GEC) involves detecting and correcting the wrong usage of grammar. While large language models (LLMs) with in-context learning (ICL) capabilities have shown significant progress on various natural language processing (NLP) tasks, their few-shot performance on GEC remains suboptimal. This is mainly due to the challenge of retrieving suitable in-context demonstrations that capture error patterns instead of semantic similarity. In this paper, we demonstrate that LLMs can inherently capture information related to grammatical errors through their internal states. From these states, we extract the Grammatical Error Representation (GER), an informative and semantically neutral encoding of grammatical errors. Our novel GER-based retrieval method significantly boosts performance in ICL settings on multilingual GEC datasets, improving the precision of correction. For high-resource languages, our results on 8B-sized open-source models match those of closed-source models such as Deepseek2.5 and GPT-4o-mini. For low-resource languages, our $F_{0.5}$ scores surpass the baseline by up to a factor of 1.20. This method provides a more precise and resource-efficient solution for multilingual GEC, offering a promising direction for interpretable GEC research.

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22.

arXiv (quant-ph) 2026-06-24 DOI: arXiv:2406.07884

Reinforcement Learning to Disentangle Multiqubit Quantum States from Partial Observations

作者:

Pavel Tashev↗Stefan Petrov↗Matthew T. Diaz↗Friederike Metz↗Alaina M. Green↗Norbert M. Linke↗Marin Bukov↗

arXiv:2406.07884v3 Announce Type: replace Abstract: Using partial knowledge of a quantum state to control multiqubit entanglement is a largely unexplored paradigm in the emerging field of quantum interactive dynamics with the potential to address outstanding challenges in quantum state preparation and compression, quantum control, and quantum complexity. We present a deep reinforcement learning (RL) approach using an actor-critic algorithm for constructing short disentangling circuits for states with up to 16 qubits. With access to only two-qubit reduced density matrices, our agent decides which pairs of qubits to apply two-qubit gates on; requiring only local information makes it directly applicable on modern NISQ devices, as we demonstrated experimentally on a trapped-ion quantum computer. Utilizing a permutation-equivariant transformer architecture, the agent can autonomously identify qubit permutations within the state, and adjusts the disentangling protocol accordingly. Once trained, it provides circuits from different initial states without further optimization. We demonstrate the agent's ability to identify and exploit the entanglement structure of multi-qubit states. We analyze the disentangling circuits constructed by the agent for 4- and 5-qubit Haar-random states, and observe strong correlations between consecutive gates and among the qubits involved. Through extensive benchmarking, we show the efficacy of the RL approach to find disentangling protocols with minimal gate resources. We explore the resilience of our trained agents to noise, highlighting their potential for real-world quantum computing applications. Analyzing optimal disentangling protocols, we report a general circuit to prepare an arbitrary 4-qubit state using at most 5 two-qubit (10 CNOT) gates.

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23.

arXiv (quant-ph) 2026-06-19 DOI: arXiv:2606.19748

Variational Polaron Theory for Ground States of Strongly Coupled Light-Matter and Electron-Phonon Systems

作者:

Nguyen Thanh Phuc↗

arXiv:2606.19748v1 Announce Type: cross Abstract: Strong light-matter and electron-phonon coupling generate ground states dressed by virtual bosonic excitations, making bare-state truncations and perturbative treatments unreliable in the ultrastrong-coupling regime. We introduce a nonperturbative variational ground-state framework based on a state-dependent polaron transformation, combined with a product-state ansatz and a second-order perturbative correction for residual matter-boson entanglement. We show that the optimized transformed frame becomes asymptotically decoupled at infinite coupling, because the leading linear coupling is canceled while off-diagonal matter transitions are suppressed by displaced-oscillator overlaps. The approach is asymptotically correct in both weak- and strong-coupling limits and remains accurate in the intermediate regime, where fixed polaron transformations are least reliable. Dicke-model benchmarks reproduce ground-state energies, fidelities, and the superradiant transition, with second-order energy errors below 0.2%. Holstein-model benchmarks yield errors below 0.5% and clarify how translational symmetry affects wave-function quality. This dressed-basis framework enables nonperturbative modeling of strongly coupled light-matter and electron-phonon systems.

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24.

arXiv (CS.CV) 2026-06-18 DOI: arXiv:2606.18788

HandwritingAgent: Language-Driven Handwriting Synthesis in Scalable Vector Space

作者:

Jaward Sesay↗Yue Yu↗B\"orje F. Karlsson↗

Teaching machines to emulate natural handwriting styles remains an open challenge, as it requires synthesizing stroke sequences that dynamically vary in shape, texture, pressure and script - not only across individuals, but also within a single person's handwriting. Attempts at this challenge have largely explored deep learning methods in both online and offline settings. However, these approaches are often constrained by style-specific architectural choices, heavy reliance on large datasets, high compute costs, and a lack of flexible control over writing styles through natural language. To this end, we introduce HandwritingAgent, a language-driven agent that can synthesize natural handwriting sequences directly in Scalable Vector Graphics (SVG) format with no need for style-specific training. The agent leverages a large reasoning model to geometrically analyse and autoregressively generate target handwritten glyphs as stroke sequences in a discrete grid canvas environment. Generation is conditioned on texts provided in either conversational or non-conversational mode, along with a reference handwriting-style image. Experiments on diverse handwriting tasks spanning imitation, recognition, multi-lingual handwriting synthesis, and generation of complex handwritten maths and science expressions indicate substantial improvement in performance, with HandwritingAgent matching or surpassing state-of-the-art generative handwriting models, while providing a more efficient, controllable, and generalizable synthesis method.

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25.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2606.15335

Privacy-Preserving Text Sanitization for Distributed Agents Collaboration via Disentangled Representations

作者:

Xuan Liu↗Hefeng Zhou↗Sicheng Chen↗Chao Yang↗Xingcheng Xu↗Jingjing Qu↗Jiong Lou↗Jie LI↗Xia Hu↗

When distributed agents exchange text across organizational boundaries, privacy leakage arises not only from explicit identifiers but also from distributional signatures such as formatting conventions, vocabulary choices, and syntactic patterns. We propose DiSan(Disentangled Sanitization), a privacy-preserving sanitization framework and a built-in component of Intern-Shannon for multi-agent collaboration. DiSan uses a two-stream encoder to factorize text into a source-invariant role subspace that preserves task semantics and a source-identifying style subspace that remains local. Federated proto-type alignment and adversarial regularization enable joint training without centralizing raw text. Experiments show that identifier-level masking is insufficient: masking 19.2% of tokens reduces TF-IDF stylometric attribution by only 18.6%. By contrast, DiSan reduces answer-level PII exposure by 20 times while maintaining 83% answer faithfulness on a distributed multi-agent RAG benchmark, and lowers Enron stylometric attribution by 73.2% under TF-IDF and 70.6% under a neural probe.

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