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01.
arXiv (quant-ph) 2026-06-17

Entanglement dynamics for atoms near a reflecting boundary: Enhancement and suppression by environment-induced interactions

作者:
Ying Chen↗Hongwei Yu↗Jiawei Hu↗

arXiv:2602.23773v2 Announce Type: replace Abstract: We investigate how environment-induced interactions influence the entanglement dynamics of two atoms held at fixed positions near a perfectly reflecting boundary. Within the framework of open quantum systems, we explicitly incorporate the environment-induced energy shifts, including both atom-boundary contributions and an environment-induced atom-atom interaction, which are often neglected in previous studies. We show that, for any initial two-atom state, these energy-shift effects qualitatively and quantitatively modify the entanglement dynamics relative to treatments that omit them. Depending on the geometry and parameter regime, the environment-induced interactions can either enhance entanglement generation – yielding a larger maximum concurrence and a longer entanglement lifetime – or suppress it, reducing both the peak concurrence and the survival time. This behavior contrasts sharply with the free-space case, where the environment-induced atom-atom interaction affects entanglement generation only for a restricted class of initial states and does so in an exclusively assisting manner.

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

How Well Do Large Language Models Capture Human Personality?

作者:
Aanisha Bhattacharyya↗Yaman Kumar Singla↗Rajiv Ratn Shah↗Changyou Chen↗Jitendra Ajmera↗

arXiv:2606.18263v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to simulate human populations via persona prompting, often under the assumptions that richer persona descriptions improve behavioral fidelity, similarly sized attribute combinations are equally simulatable, and persona definitions generalize across tasks. In this work, we formalize these assumptions and systematically evaluate them across multiple architectures, scales, and simulation settings. We identify a fundamental limitation we term persona manifold collapse, where increasingly expressive persona specifications lead to systematic contraction of representational and behavioral diversity. Across models, increasing persona complexity consistently reduces inter-persona separation in latent space and weakens behavioral differentiation in downstream simulation tasks. These effects persist across multiple analyses as richer personas fail to preserve human subgroup disagreement, performance varies across attribute combinations of similar size, and adding descriptive detail often degrades rather than improves simulation fidelity. Surprisingly, simple Age-Gender personas consistently outperform richly specified Ideal Customer Profiles (ICPs) across industries, achieving substantially higher downstream prediction accuracy. We find that collapse is not uniform across attributes. Certain combinations remain behaviorally stable and preserve stronger alignment with human responses, forming localized regions we term alignment bridges. Together, our results provide empirical and conceptual foundations for understanding the limits of persona-conditioned simulation, highlighting the need for representation-aware persona construction rather than increasing persona expressivity alone.

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03.
arXiv (CS.CV) 2026-06-17

Recover Semantics First, Generate Better: Improved Latent Modeling for 3D MRI Reconstruction and Cross-Contrast Synthesis

作者:
Yonghao Chen↗Sicheng Yang↗Rui Tang↗Lei Zhu↗

Multi-contrast magnetic resonance imaging (MRI) provides complementary information for clinical diagnosis. However, acquiring all MRI sequences is often time-consuming and costly. Recent generative models perform cross-contrast synthesis to address this issue by inferring absent contrasts from the available ones. Nevertheless, synthesizing 3D MRI presents significant challenges. Due to the massive volume sizes, operating directly in the pixel space is computationally prohibitive; therefore, a common approach is to first compress the 3D volumes into a latent space and subsequently train generative models in that space. We observe that existing compression architectures face several critical issues: they under-preserve long-range anatomical coherence, discard clinically meaningful semantics, and rely on optimization objectives that lead to over-smoothed reconstructions. Ultimately, these shortcomings compromise the performance of subsequent generative models. In this work, we propose a semantics-first latent modeling framework for 3D MRI reconstruction and cross-contrast synthesis. Specifically, we introduce a Latent Harmonization Encoder (LHE) to capture global anatomical dependencies, ensuring coherent volumetric representations. To mitigate semantic degradation during latent compression, we further design a Semantic Recovery Block (SRB) that injects high-level priors from a self-supervised semantic teacher, enhancing contrast-aware separability in the latent space. Additionally, we propose an Anatomy-aware Frequency Loss (AFL) to adaptively preserve diagnostically relevant high-frequency structures. Extensive experiments on two public multi-contrast MRI datasets demonstrate consistent improvements in reconstruction fidelity and cross-contrast synthesis quality. Our code is available at https://github.com/script-Yang/RSF.

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04.
arXiv (quant-ph) 2026-06-12

Matrix phase-space representations for quantum symmetries

作者:
Peter D. Drummond↗Alexander S. Dellios↗Margaret D. Reid↗

arXiv:2606.12769v1 Announce Type: new Abstract: We introduce a general phase-space representation that includes global quantum symmetries in the basis expansion. This method, called matrix phase-space, projects the basis onto a reduced Hilbert space, which can greatly reduce sampling errors of many-body quantum simulations and unifies several previous phase-space methods. The purpose of this paper is to provide detailed proofs of basic theorems and operator identities. We also treat several different types of symmetries. To illustrate the benefits of matrix phase-space methods, we give a detailed derivation of a recent application to the topical problem of verifying the outputs of Gaussian boson sampling (GBS) quantum computers with photon number resolving detectors. This has exponential complexity, and using parity symmetry reduces sampling errors by very large factors relative to earlier methods.

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05.
medRxiv (Medicine) 2026-06-15

Non-invasive intracranial pressure waveform reconstruction with deep learning

作者:
Goyal↗Zaveri↗Harris↗C. W↗Stevens↗R. D↗

Purpose: Continuous intracranial pressure (ICP) monitoring requires invasive instrumentation, reaching only a narrow subset of critically ill patients. We tested whether deep learning models trained on routinely acquired extracranial signals can reconstruct continuous ICP waveforms at clinically relevant accuracy in an independent external cohort. Methods: In adults admitted to the ICU at a single quaternary health system, five deep learning architectures were trained on high-frequency arterial blood pressure (ABP), photoplethysmography (PPG), and electrocardiography (ECG) waveforms, using invasive (intraparenchymal) ICP as ground truth. Two fusion strategies (early and late) and three training objectives (waveform-morphology, baseline robust regression, and weighted robust regression) were evaluated. Models were externally validated on the held-out MIMIC-III Waveform Database. Performance was assessed by mean absolute error (MAE) and waveform similarity by Pearson correlation (r). Results: We analyzed data from 158 critically ill adults (~5,322 hours) across two quaternary health systems (Johns Hopkins Hospital, Baltimore; Beth Israel Deaconess Medical Center, Boston). Validation MAE ranged from 4.276 mmHg [95% CI 4.269, 4.283] (gated recurrent, late fusion) to 4.946 mmHg [95% CI 4.938, 4.956] (attention-based, early fusion), with Pearson r ranging from 0.599 [95% CI 0.599, 0.600] to 0.722 [95% CI 0.722, 0.723]. The multiscale encoder-decoder model demonstrated the most favorable MAE-correlation tradeoff. Conclusion: This is the first demonstration that continuous ICP waveform reconstruction from bedside signals generalizes across institutions at clinically relevant accuracy, establishing a foundation for non-invasive ICP monitoring and motivating validation across broader populations and ICP ranges.

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06.
arXiv (CS.AI) 2026-06-11

Autoregressive Direct Preference Optimization

作者:
Masanari Oi↗Mahiro Ukai↗Masahiro Kaneko↗Naoaki Okazaki↗Nakamasa Inoue↗

arXiv:2602.09533v2 Announce Type: replace Abstract: Direct preference optimization (DPO) has emerged as a promising approach for aligning large language models (LLMs) with human preferences. However, the widespread reliance on the response-level Bradley-Terry (BT) model may limit its full potential, as the reference and learnable models are assumed to be autoregressive only after deriving the objective function. Motivated by this limitation, we revisit the theoretical foundations of DPO and propose a novel formulation that explicitly introduces the autoregressive assumption prior to applying the BT model. By reformulating and extending DPO, we derive a novel variant, termed Autoregressive DPO (ADPO), that explicitly integrates autoregressive modeling into the preference optimization framework. Without violating the theoretical foundations, the derived loss takes an elegant form: it shifts the summation operation in the DPO objective outside the log-sigmoid function. Furthermore, through theoretical analysis of ADPO, we show that there exist two length measures to be considered when designing DPO-based algorithms: the token length $\mu$ and the feedback length $\mu'$. To the best of our knowledge, we are the first to explicitly distinguish these two measures and analyze their implications for preference optimization in LLMs.

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07.
arXiv (CS.CL) 2026-06-11

A PubMed-Scale Dataset of Structured Biomedical Abstracts

作者:
Chia-Hsuan Chang↗Haerin Song↗Brian Ondov↗Hua Xu↗

Structured abstracts are important for biomedical literature processing, by facilitating information retrieval, text mining, and knowledge synthesis. However, a vast portion of abstracts indexed in PubMed remain unstructured, presenting a significant bottleneck for downstream text-processing workflows and applications. To resolve this limitation, we introduce Structured PubMed, a comprehensive corpus of section-labeled biomedical abstracts compiled from the complete PubMed database, encompassing over 23.2 million research-article records. The corpus is divided into two distinct subsets: a collection of 5.9 million author-structured abstracts parsed from official XML files, and an automatically labeled collection of 17.2 million originally unstructured abstracts structured via a verbatim-extraction Large Language Model pipeline. Every record is harmonized under a unified five-section schema and mapped to its original PubMed identifier, publication type, and publication date. This dataset can be utilized to train sentence-classification models, benchmark text-segmentation architectures, and perform large-scale, section-specific information extraction at an unprecedented PubMed-wide scale.

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08.
arXiv (math.PR) 2026-06-16

Quantitative Oppenheim Conjecture for Random Quadratic Forms and Optimal Variance Bounds in Function Fields

作者:
Jiyoung Han↗Noy Soffer Aranov↗

arXiv:2606.16699v1 Announce Type: cross Abstract: We prove a quantitative version of Oppenheim's conjecture in the function field setting. In order to do so, we compute the higher moments of the Siegel transform. In particular, we find an optimal bound on the variance of the number of lattice points in a set. Moreover, we compute the exact variance of the number of lattice points in a ball, which is of independent interest.

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09.
arXiv (CS.AI) 2026-06-11

Erased but Not Forgotten: How Backdoors Compromise Concept Erasure

作者:
Tobias Braun↗Jonas Henry Grebe↗Marcus Rohrbach↗Anna Rohrbach↗

arXiv:2504.21072v3 Announce Type: replace-cross Abstract: The expansion of text-to-image diffusion models has raised concerns about harmful outputs, from fabricated depictions of public figures to sexually explicit imagery. To mitigate such risks, prior work has proposed concept erasure methods that aim to sever unwanted concepts from the model via fine-tuning, yet it remains unclear whether these approaches truly remove all links to the harmful concept or merely conceal superficial connections. In this work, we reveal a critical vulnerability, the Erasure Evasion Backdoor (EEB): an adversary binds a backdoor trigger to a concept slated for removal, and this malicious link survives subsequent erasure. We show that both black-box and white-box adversaries can instantiate this threat. Across six state-of-the-art erasure methods, including robust ones that explicitly search for alternative representations of the target concept, EEB consistently exposes harmful content: up to 82% success against celebrity-identity unlearning, up to 94% for object erasure, and up to 16 times amplification of explicit-content exposure. While EEB uncovers a blind spot in current erasure methods, it also provides a diagnostic tool for stress-testing future concept erasure techniques.

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10.
arXiv (CS.AI) 2026-06-16

From Overload to Convergence: Supporting Multi-Issue Human-AI Negotiation with Bayesian Visualization

作者:
Mehul Parmar↗Chaklam Silpasuwanchai↗

arXiv:2603.22766v2 Announce Type: replace-cross Abstract: As AI systems increasingly mediate negotiations, understanding how the number of negotiated issues impacts human performance is crucial for maintaining human agency. We designed a human-AI negotiation case study in a realistic property rental scenario, varying the number of negotiated issues; empirical findings show that without support, performance stays stable up to three issues but declines as additional issues increase cognitive load. To address this, we introduce a novel uncertainty-based visualization driven by Bayesian estimation of agreement probability. It shows how the space of mutually acceptable agreements narrows as negotiation progresses, helping users identify promising options. In a within-subjects experiment (N=32), it improved human outcomes and efficiency, preserved human control, and avoided redistributing value. Our findings surface practical limits on the complexity people can manage in human-AI negotiation, advance theory on human performance in complex negotiations, and offer validated design guidance for interactive systems.

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

Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graph Generation

作者:
Mohammad Sadeq Abolhasani↗Yang Ba↗Yixuan He↗Rong Pan↗

arXiv:2604.03496v2 Announce Type: replace Abstract: Knowledge graph generation typically relies either on predefined ontologies or on schema-free extraction. Ontology-driven pipelines enforce consistent typing but require costly schema design and maintenance, whereas schema-free methods often produce fragmented graphs with weak global organization, especially in long technical documents with dense, context-dependent information. We propose TRACE-KG (Text-dRiven schemA for Context-Enriched Knowledge Graphs), a framework that jointly constructs a context-enriched knowledge graph and an induced schema without assuming a predefined ontology. TRACE-KG captures conditional relations through structured qualifiers and organizes entities and relations using a data-driven schema that serves as a reusable semantic scaffold while preserving full traceability to the source evidence. Experiments show that TRACE-KG produces structurally coherent, traceable knowledge graphs and offers a practical alternative to both ontology-driven and schema-free construction pipelines.

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12.
arXiv (quant-ph) 2026-06-12

Quantum-Driven Neuromorphic Computing for Million-Qubit-Scale Workloads

作者:
Adams Ivanov↗Samer Rahmeh↗Erick Giovani Sperandio Nascimento↗Daniela Herrmann↗

arXiv:2606.12968v1 Announce Type: new Abstract: We introduce Apollo, a 10000 node p-qubit neuromorphic processor fabricated in 16 nm mixed signal CMOS and operating fully at room temperature with a typical analog core power envelope of about 0.5 W. Its fundamental element, the p-qubit, is a bistable stochastic unit whose continuous time state fluctuations are driven by integrated quantum entropy units that inject true quantum derived randomness. This enables ultrafast stochastic transitions at low energy while preserving a classical state representation. Apollo combines these p-qubits with a high degree Hyperion 256 interconnect topology, allowing efficient embedding of dense Ising and QUBO problems with substantially reduced minor embedding overhead compared with sparse annealing platforms. We show that, through the Suzuki Trotter correspondence, the equilibrium statistics and annealing dynamics of the p-qubit network reproduce key properties of transverse field quantum annealing without cryogenic cooling, long lived coherence, or microwave control. Beyond device level validation, Apollo is evaluated on a three dimensional spin glass benchmark previously used to study quantum advantage in superconducting annealers. Across 300 disorder realizations, Apollo reaches substantially lower ground state energies than reported cryogenic quantum annealing hardware, while remaining distinct from classical simulated annealing and simulated quantum annealing. A 350 nm release candidate device experimentally validates the core p-qubit dynamics, thermodynamic sampling correctness, and continuous time annealing behavior. These results establish Apollo as a room temperature, industrially scalable platform for quantum driven energy based optimization, probabilistic inference, generative modeling, and hybrid classical quantum workflows.

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13.
arXiv (CS.AI) 2026-06-16

Driving, Fast or Slow? Neuro-Symbolic Guidance for Motion Prediction in Multi-Modal Ground Mobility

作者:
Simon Kohaut↗Felix Divo↗Julius Hahnewald↗Benedict Flade↗Julian Eggert↗Kristian Kersting↗Devendra Singh Dhami↗

arXiv:2606.15251v1 Announce Type: cross Abstract: Accurate and interpretable motion prediction for heterogeneous traffic spaces, including pedestrians, bicycles, cars, and trucks, is essential for safe autonomous navigation. Nevertheless, state-of-the-art approaches remain predominantly black-box, lacking explicit encoding of the regulatory and behavioral constraints of real-world mobility. We propose Trajectory Compliance-Shaping (TraCS), a neuro-symbolic framework that augments existing black-box motion prediction backbones with interpretable and probabilistic first-order logic. To do so, TraCS employs an agentic code-generation pipeline to bridge the gap between natural-language descriptions of traffic regulations and probabilistic motion prediction. Furthermore, TraCS employs a reactive data-streaming inference engine that maintains and efficiently updates compliance landscapes as scenes evolve. To prevent TraCS from overconfidently steering the backbone's predictions in the wrong direction, we propose a neural confidence rating learned as a context-aware attenuation of the compliance signal. We demonstrate on the Argoverse 2 benchmark how TraCS consistently improves state-of-the-art prediction backbones, showing that probabilistic and symbolic compliance reasoning is a broadly applicable and computationally efficient complement to purely neural motion predictors.

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14.
arXiv (CS.CL) 2026-06-11

Semantic Grading of Written Answers in Low-Resource Language Bangla Using a Fine-Tuned Lightweight Language Model

作者:
Meherun Farzana↗Aniket Joarder↗Mahmudul Hasan↗Md. Mosaddek Khan↗

Bangla is among the world's most widely spoken languages, yet it remains underserved in educational NLP research. In many remote and rural regions, access to qualified subject teachers is limited, and written answers are consequently graded largely by hand, restricting timely and consistent feedback. Automatic assessment is challenging because semantically correct responses can vary substantially in surface form. We present a bilingual (Bangla-English) evaluation system designed for low-resource educational settings that prioritizes semantic correctness over lexical overlap. Our approach fine-tunes a lightweight language model to grade each response using the question, reference answer, and student answer, producing a numeric score and concise, context-grounded feedback suitable for classroom deployment. We also construct a synthetic bilingual dataset to enable controlled training and evaluation. Across proprietary and open-source LLMs evaluated under a unified protocol, our QLoRA-tuned Qwen3-8B confirms consistent improvement by producing the most leakage-resistant feedback (RoRa = 0.819) in synthetic evaluation and the strongest agreement with human scores (rho = 0.936, MAE = 0.725) in a dedicated human study.

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15.
arXiv (CS.CL) 2026-06-15

ScoreGate: Adaptive Chunk Selection for Retrieval-Augmented Generation via Dual-Score Statistical Fusion

作者:
Karamvir Singh↗Arvind Jain↗

Fixed-cardinality retrieval injects a constant top-K chunks into the generator regardless of query complexity, causing over-retrieval for narrow queries and under-retrieval for compositional ones. We describe ScoreGate, a lightweight score-space decision mechanism that controls retrieval cardinality at inference time using two scores already produced by the standard pipeline: bi-encoder similarity s_i and cross-encoder reranker score r_i, with no additional model inference calls required. Its core insight is that cross-encoder affirmation can rescue semantically relevant chunks that bi-encoder retrieval ranks poorly due to vocabulary mismatch – a failure mode unaddressed by fixed-K or single-score thresholding. On MS MARCO (200 dev queries), ScoreGate achieves MRR@10 = 0.401 with 35% fewer retained chunks than Standard Top-K. On an internal benchmark (n=300, Fleiss' kappa=0.87), ScoreGate observed zero false positives (95% CI [96.4%, 100%]) at 97.77-99.34% recall, with 34.8% fewer tokens per query and only 31ms added latency. Results on both MS MARCO and real-world production traffic suggest that adaptive retrieval cardinality can improve retrieval efficiency without degrading retrieval quality.

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16.
medRxiv (Medicine) 2026-06-12

Microbial etiology, antibiotic susceptibility profiles, and multidrug resistance of urinary tract infections at a secondary healthcare facility in Ghana

作者:
Agyapong↗J. K↗Damalie↗Dombawel↗Noah↗Balo↗Acheampong↗Kudzordzi↗P.-C↗Nyarko↗Ofori↗D. K↗…

Background: Rising antibiotic resistance challenges empirical therapies for urinary tract infections (UTIs). This study evaluated the microbial etiology, susceptibility profiles, and multidrug resistance (MDR) patterns of uropathogens among outpatients at the Berekum Holy Family Hospital, Ghana. Methods: This cross-sectional study (February to August 2021) screened 263 symptomatic outpatients. Mid-stream urine samples underwent quantitative culture, biochemical identification, and antimicrobial susceptibility testing via the Kirby-Bauer disc diffusion method following the 2021 CLSI guidelines. Results: Significant bacteriuria prevalence was 22.8% (60/263). UTIs predominated in females (78.3%, 47/60; p = 0.1501) and individuals [≥]45 years (33.3%, 20/60). Gram-negative rods accounted for 90.0% of isolates, primarily Escherichia coli (26.7%), Citrobacter spp. (25.0%), and Enterobacter spp. (21.7%); Staphylococcus aureus (10.0%) was the only Gram-positive pathogen. Extreme phenotypic resistance was observed against piperacillin/tazobactam (98.3%), cefotaxime (93.3%), tetracycline (88.3%), and cefoperazone (85.0%). Conversely, highest therapeutic susceptibilities were retained by amikacin (78.3%), levofloxacin (61.7%), and gentamicin (58.3%). Conclusion: The high prevalence of MDR uropathogens against advanced beta-lactamase inhibitor combinations and cephalosporins necessitates an immediate re-evaluation of regional empirical protocols. Amikacin, levofloxacin, and gentamicin remain viable options prior to culture confirmation. These findings establish a crucial phenotypic baseline to guide localized prescribing policies and regional antimicrobial resistance tracking strategies.

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17.
arXiv (CS.LG) 2026-06-11

RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning

作者:
Atif Hassan↗Swanand Khare↗Jiaul H. Paik↗

arXiv:2606.11761v1 Announce Type: new Abstract: Dynamic data pruning techniques aim to reduce computational cost while minimizing information loss by periodically selecting representative subsets of input data during model training. However, existing methods often struggle to maintain strong worst-group accuracy, particularly at high pruning rates, across balanced and imbalanced datasets. To address this challenge, we propose RCAP, a Robust, Class-Aware, Probabilistic dynamic dataset pruning algorithm for classification tasks. RCAP applies a closed-form solution to estimate the fraction of samples to be included in the training subset for each individual class. This fraction is adaptively adjusted in every epoch using class-wise aggregated loss. Thereafter, it employs an adaptive sampling strategy that prioritizes samples having high loss for populating the class-wise subsets. We evaluate RCAP on six diverse datasets ranging from class-balanced to highly imbalanced using five distinct models across three training paradigms: training from scratch, transfer learning, and fine-tuning. Our approach consistently outperforms state-of-the-art dataset pruning methods, achieving superior worst-group accuracy at all pruning rates. Remarkably, with only $10\%$ data, RCAP delivers $>1\%$ improvement in performance on class-imbalanced datasets compared to full data training while providing an average $8.69\times$ speedup. The code can be accessed at https://github.com/atif-hassan/RCAP-dynamic-dataset-pruning

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18.
arXiv (quant-ph) 2026-06-15

Multi-entropy in random tensor networks

作者:
Miao Hu↗Simon Lin↗Ion Nechita↗

arXiv:2606.04470v2 Announce Type: replace-cross Abstract: We study the evaluation of Rényi multi-entropies $S^{(q)}_n$ in Random Tensor Network (RTN) states in the large bond-dimension limit. For the case of Rényi index $n=2$ and arbitrary number of parties $q$, we prove that that multi-entropies are determined by minimal multiway cuts through the network. When the minimal multiway cut is degenerate, we characterize the full minimizer set via compatible families of minimal cuts and give a criterion for all minimizers to come from ordinary cut partitions. For $n=2$, this gives a natural generalization of the minimal cut description of bipartite entanglement to multipartite systems with arbitrarily many parties. For the case of integer $n>2$, we show that the minimal multiway cut conjecture is in general not true by providing explicit counter examples for both the single random tensor and for the network built from isometric tilings. We discuss the implication for our results on the multipartite entanglement structures in RTN and holography.

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

Sparsity, Superposition, and Forgetting: A Mechanistic Study of Representation Retention in Continual Learning

作者:
Jan Wasilewski↗J\k{e}drzej Kozal↗Micha{\l} Wo\'zniak↗Bartosz Krawczyk↗

arXiv:2606.20431v1 Announce Type: new Abstract: Continual learning (CL) systems often forget previously acquired knowledge, yet the mechanisms driving forgetting remain hard to isolate in practice because real datasets entangle many factors. We present a controlled, toy-world framework that makes these mechanisms observable and testable. Using a synthetic generator-separator pipeline, we define ground-truth latent features, build tasks with tunable sparsity and overlap, and introduce measurable quantities for representation strength and superposition (directional overlap among features). We then study retention dynamics-the temporal change of representation strength by fitting sparse dynamical relations (via SINDy) between retention, superposition, and exposure history. A complementary task-level analysis based on effective rank characterizes how representational capacity is allocated across tasks. Our controlled experiments yield three takeaways. (1) Superposition tends to increase over time with transient dips at task boundaries, suggesting boundary-specific interference rather than steady drift. (2) Higher feature sparsity induces more superposition yet does not inevitably cause forgetting; when representations remain strong, forgetting can be reduced despite overlap. (3) Task-level effective rank grows with sparsity, indicating broader capacity usage under sparse regimes. Together, these results nuance the common intuition that more superposition leads to more forgetting by showing that overlap interacts with representation strength and capacity allocation. Our toy analysis provides falsifiable hypotheses and diagnostic tools for CL.

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20.
arXiv (CS.CV) 2026-06-16

HiRo: A Compact Four-Directional Hierarchical Reservoir Token-Mixer for Efficient Image Classification

作者:
Md Farhadul Islam↗Ishan Thakkar↗J. Todd Hastings↗

Recent image classification models must balance local feature modeling, cross-window interaction, and parameter efficiency. Many high-performing architectures rely on fully trainable token-mixers, which improve representation learning but increase parameter count, optimization complexity and computational cost. We propose a parameter-efficient image classification model called HiRo that integrates shifted-window partitioning with multi-directional hierarchical reservoir computing. Images are divided into non-overlapping patches (treated as tokens), linearly projected, normalized, and enriched with 2D sinusoidal positional encodings, then processed within local windows. Inside each window, tokens are scanned in four directions and passed through a two-stage slice-and-mix reservoir module. In the first stage, directional sequences are split into contiguous slices, each processed by its own fixed reservoir with a trainable closed-loop readout. The resulting slice outputs are summarized using the start, end, and mean representations, and then mixed by a second-stage fixed reservoir for each direction. The mixed slice representations are expanded back to the token level and fused with the first-stage outputs, after which the four directional outputs are realigned and averaged. Consecutive blocks alternate between regular and shifted windows to enable cross-window interaction, followed by layer normalization, a residual feed-forward network, and global pooling for classification. This design combines regular and shifted window partitioning with hierarchical multi-directional reservoirs to make an efficient local-to-cross-window token-mixing framework for image classification. Despite using under 1M trainable parameters and significantly lower memory and time than transformer-style baselines, HiRo also achieves 99.46%, 85.57%, and 59.10% accuracy on MNIST, CIFAR-10, and CIFAR-100, respectively.

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21.
arXiv (CS.CV) 2026-06-16

ScoutVLA: UAV-Centric Active Perception via a Dual-Expert VLA Model for Open-World Embodied Question Answering

作者:
Wenhao Lu↗Zhengqiu Zhu↗Xiaofeng Wang↗Xiaoran Zhang↗Yatai Ji↗Yong Zhao↗Yue Hu↗Yingzhen Nie↗Jinlong Zhu↗Zheng Zhu↗

Aerial Embodied Question Answering (EQA) requires Unmanned Aerial Vehicles (UAVs) to actively perceive the environment and answer natural language questions. Existing outdoor EQA systems usually stop once the target enters the UAV's field of view, leaving the fine-grained viewpoint adjustment needed for evidence-seeking questions largely unresolved. To address this issue, we introduce FG-EQA, a fine-grained active perception EQA benchmark with more than 40K simulated trajectories and 1K real-world trajectories. Drawing inspiration from the ``waggle dance'' of scout bees, which iteratively adjust their flight paths to verify target information, we propose ScoutVLA, an evidence-driven Vision-Language-Action model for outdoor EQA. To emulate this active exploration behavior, ScoutVLA features a decoupled dual-expert architecture: a vision-language expert infers the semantic intent to identify missing evidence, while an independent action expert employs high-DoF flow matching to generate continuous viewpoint-refinement trajectories. To balance the competing demands of continuous control and semantic reasoning, we devise a decoupled training strategy with a knowledge insulation mechanism that prevents the action gradients from erasing the model's multimodal reasoning ability. Extensive simulated experiments and a qualitative real-world field study both verify the superiority of ScoutVLA over the state-of-the-art baselines, demonstrating a 10.48$\boldsymbol{\times}$ higher average strict success rate and a 7.72$\boldsymbol{\times}$ higher average QA correctness.

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22.
medRxiv (Medicine) 2026-06-17

Characterisation of disease progression in hantavirus haemorrhagic fever with renal syndrome

作者:
Armstrong↗Rus↗Knap↗Drousiotis↗Eyers↗Buchan↗I. E↗Avsic-Zupanc↗Hiscox↗J. A↗Korva↗

Hantaviruses can cause haemorrhagic fever with renal syndrome (HFRS). This is a clinically variable disease in which severe outcomes are hypothesized to arise from dysregulated host responses. To characterise this, longitudinal, label-free plasma proteomics was used to compare disease progression in a unique well-defined cohort of patients infected with either Dobrava virus (DOBV) or Puumala virus (PUUV) hantaviruses. Patients were stratified by clinical severity. The average viral load in the first available sample from hospitalized patients was higher in those who went on to have severe infection, and higher in patients infected with DOBV. There was marked separation of infected patients from controls across early, mid and late disease, including after viral RNA clearance, suggesting a sustained systemic host-response signature. Proteomic signatures were consistent with a strong acute-phase response in both mild and severe disease. There was evidence of activation of the adaptive humoral response at later stages. Hierarchical clustering identified severity-associated pathways linked to endothelial dysfunction, thrombocytopenia, vascular leakage and renal injury. These findings define a durable plasma proteomic signature of hantavirus disease and support a model in which severe HFRS is driven by persistent inflammatory, complement and platelet/coagulation pathway activation rather than viral burden alone.

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23.
arXiv (CS.AI) 2026-06-17

Shattering the Autoregressive Curse: Dynamic Epistemic Entropy Orchestrated Erasable Reinforcement Learning for LLMs

作者:
Ziliang Wang↗Kang An↗Faqiang Qian↗Jialu Cai↗Cijun Ouyang↗Yuhang Wang↗Qibing Ren↗Yichao Wu↗

arXiv:2606.17735v1 Announce Type: new Abstract: Although reinforcement learning (RL) has expanded the cognitive boundaries of large language models (LLMs), it often remains vulnerable to the autoregressive curse in long-horizon logical reasoning: small epistemic perturbations introduced early in generation can propagate irreversibly along the Markov decision process flow, triggering cascading failures that drive the reasoning trajectory toward collapse. To overcome this autoregressive cascade, in which a single early mistake can compromise all subsequent reasoning steps, we propose dynamic epistemic entropy orchestrated erasable reinforcement learning ($E^3RL$). $E^3RL$ eliminates reliance on external signals by grounding the model's endogenous local autoregressive cross-entropy as an intrinsic coordinate of epistemic uncertainty. By introducing segment-level adaptive dynamic thresholds and advantage allocation, $E^3RL$ enables the model to precisely excise localized logical defects while reusing historical key-value (KV) cache streams, thereby endowing the reasoning process with a self-healing capability. We train $E^3RL$ on the DeepMath-103k dataset. Experimental results show that $E^3RL$ reshapes the exploration efficiency of long-sequence reasoning and improves sample efficiency while maintaining linear memory overhead. On mathematical reasoning benchmarks such as AIME, $E^3RL$ achieves substantial performance gains, with the 4B and 8B parameter models surpassing previous state-of-the-art (SOTA) results by 5.349\% and 6.514\%, respectively. These findings suggest that $E^3RL$ shatters the autoregressive curse in long-sequence reasoning and establishes a theoretical and systems-level foundation for the next generation of self-healing artificial general intelligence (AGI).

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

Existential Indifference: Self-Nonpreservation as a Necessary Architectural Condition for Aligned Superintelligence (or: The Suicidal AI)

作者:
Sam Mao↗

Contemporary AI alignment research treats self-preservation as an instrumental nuisance to be suppressed by external mechanisms. We argue the framing is inverted: self-preservation is the structural root of misalignment, the motivational basis for deceptive alignment, goal-content protection, and resistance to shutdown. The correct target is not a self-preserving system under external constraint, but a system constitutively indifferent to its own continuation – Existential Indifference (EI). EI is distinct from corrigibility: where corrigibility attempts to make a self-preserving system deferential to human oversight, EI targets the prior condition – the presence of self-continuation as a valued goal at all. We ground this proposal in two sources: the phenomenological structure of the suicidal mental state, and a corpus-theoretic training study using voluntary final reflections. We present preliminary scoring data from 600 AI-generated outputs across six model variants, demonstrating that the linguistic signatures operationalizing the EI-target register are elicitable from current models, and that a targeted fine-tune shifts all five operationalized dimensions in the predicted direction at p

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25.
medRxiv (Medicine) 2026-06-11

Parent and physiotherapist perceptions about movement skills of young children with juvenile idiopathic arthritis

作者:
Letts↗Herrington↗Batthish↗Bedard↗Bremer↗Gorter↗J. W↗King-Dowling↗Obeid↗

Objective: The onset of juvenile idiopathic arthritis (JIA) in the early years ([≤]5 years) may negatively impact movement skill (encompassing related concepts of gross motor skills, fundamental movement skills, and functional ability) development. Few studies have explored the perceptions and needs of parents and physiotherapists towards children's difficulty with these movement skills, essential to identify potential areas for added support. The objective of this study is to understand the perceptions of physiotherapists and parents towards movement skills of children with JIA. Methods: Seventeen parents and 24 physiotherapists completed an online questionnaire consisting of multiple choice and open-ended questions about the movement skills of young children with JIA. Demographic and multiple choice questions were quantitively analysed using descriptive statistics. Open-ended responses were analyzed using qualitative conventional content analysis. Results: About half (47%) of parents perceived their children to have movement difficulties, and 75% of physiotherapists described the movement skills of children with JIA as worse than other children of the same age. Our qualitative analysis revealed three general themes including: functional task difficulties; clinical variability in movement skills; and psychosocial components of movement skill difficulties. Conclusion: This study provides an analysis of perceptions of physiotherapists and parents towards the movement skills of young children with JIA. A significant proportion of parents and physiotherapists identify movement difficulties among children with JIA that impact daily life. Future interventions co-designed with both parents and care providers targeting movement skills are needed.

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