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

Probes of chaos over the Clifford group and approach to Haar values

作者:
Stefano Cusumano↗Gianluca Esposito↗Alioscia Hamma↗

arXiv:2603.29695v3 Announce Type: replace Abstract: Chaotic behavior of quantum systems can be characterized by the adherence of the expectation values of given probes to moments of the Haar distribution. In this work, we analyze the behavior of several probes of chaos using a technique known as Isospectral Twirling [1]. This consists in fixing the spectrum of the Hamiltonian and picking its eigenvectors at random. Here, we study the transition from stabilizer bases to random bases according to the Haar measure by T-doped random quantum circuits. We then compute the average value of the probes over ensembles of random spectra from Random Matrix Theory, the Gaussian Diagonal Ensemble and the Gaussian Unitary Ensemble, associated with non-chaotic and chaotic behavior respectively. We also study the behavior of such probes over the Toric Code Hamiltonian.

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02.
arXiv (CS.CV) 2026-06-16

Segmentation-based Detection for Efficient Multi-Task Spacecraft Perception

作者:
Sivaperuman Muniyasamy↗Surendar Devasundaram↗

Vision-based perception is fundamental to Space Situational Awareness and autonomous on-orbit operations such as rendezvous, docking, servicing, and navigation. However, progress in this area is limited by the scarcity of annotated space imagery and by challenging visual-domain characteristics including severe illumination changes, low signal-to-noise ratio, and high contrast. We address Stream 1 of the SPARK 2026 Challenge, which requires a single model for spacecraft classification, detection, and fine-grained component segmentation across multiple target types. We propose a compact architecture that integrates a MobileNetV3 encoder with a U-Net-style decoder, combining computational efficiency with accurate dense prediction. Detection is derived analytically from the union of predicted component masks, avoiding a separate bounding-box regression head in the single-spacecraft setting. Our method achieved an overall leaderboard score of 0.9482, with task-specific scores of 1.0000 in classification, 0.9788 in detection, and 0.8917 in segmentation. The proposed approach ranked second overall in the SPARK 2026 Challenge, demonstrating that lightweight encoder-decoder architectures can deliver strong multi-task performance for practical onboard space vision systems.

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03.
medRxiv (Medicine) 2026-06-17

Waning protection of long-acting RSV monoclonal antibodies in infants: a Bayesian analysis of clesrovimab and nirsevimab trial data

作者:
Gong↗Flasche↗Hodgson↗

Clesrovimab and nirsevimab are long-acting monoclonal antibodies used to prevent respiratory syncytial virus (RSV) disease in infants, but waning protection in the first year of life is incompletely characterised. We applied a published Bayesian inference framework to clesrovimab and pooled nirsevimab trial data to estimate time-varying efficacy against medically attended RSV lower respiratory tract infection (LRTI) and RSV-associated hospitalisation, accounting for differences in placebo-arm event timing between trials. Estimated clesrovimab efficacy declined from 60.7% (95% CrI: 46.3-72.6) shortly after dosing to 38.3% (8.6-52.9) at six months against medically attended RSV LRTI, and from 87.1% (71.2-96.2) to 49.6% (10.4-70.7) against RSV-associated hospitalisation. For nirsevimab, corresponding estimates declined from 86.9% (75.4-95.0) to 53.8% (27.4-69.7) against LRTI, and from 77.5% (52.6-91.8) to 49.7% (15.7-68.3) against hospitalisation. After accounting for differences in RSV exposure timing and LRTI endpoint definitions between trials, we found no evidence of a difference in efficacy or waning between clesrovimab and nirsevimab.

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04.
arXiv (CS.CV) 2026-06-12

On the Reliability of Cue Conflict and Beyond

作者:
Pum Jun Kim↗Seung-Ah Lee↗Seongho Park↗Dongyoon Han↗Jaejun Yoo↗

Understanding how neural networks rely on visual cues offers a human-interpretable view of their internal decision processes. The cue-conflict benchmark has been influential in probing shape-texture preference and in motivating the insight that stronger, human-like shape bias is often associated with improved in-domain performance. However, we find that the current stylization-based instantiation can yield unstable and ambiguous bias estimates. Specifically, stylization may not reliably instantiate perceptually valid and separable cues nor control their relative informativeness, ratio-based bias can obscure absolute cue sensitivity, and restricting evaluation to preselected classes can distort model predictions by ignoring the full decision space. Together, these factors can confound preference with cue validity, cue balance, and recognizability artifacts. We introduce REFINED-BIAS, an integrated dataset and evaluation framework for reliable and interpretable shape-texture bias diagnosis. REFINED-BIAS constructs balanced, human- and model- recognizable cue pairs using explicit definitions of shape and texture, and measures cue-specific sensitivity over the full label space via a ranking-based metric, enabling fairer cross-model comparisons. Across diverse training regimes and architectures, REFINED-BIAS enables fairer cross-model comparison, more faithful diagnosis of shape and texture biases, and clearer empirical conclusions, resolving inconsistencies that prior cue-conflict evaluations could not reliably disambiguate.

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

DoubtProbe: Black-Box Jailbreak Defense via Structural Verification and Semantic Auditing

作者:
Xuanyu Yin↗Yilin Jiang↗Jun Zhou↗Kai Chen↗Zhengfu Cao↗Xiaolei Dong↗

As large language models (LLMs) are increasingly deployed in user-facing systems, black-box jailbreak defense has become an important practical problem. Existing defenses often rely on known-attack coverage, prompt-level semantic judgment, or local runtime control, yet these paths can become unstable under evolving prompt packaging, expression rewriting, and structure manipulation. We observe that many black-box jailbreaks do not remove the harmful goal, but reorganize the information needed to express and execute it, thereby evading safety alignment while remaining recoverable during generation. Motivated by this observation, we propose DoubtProbe, a dual-branch inference-time defense framework that combines structural verification with semantic auditing and formulates black-box jailbreak defense as consistency checking under controlled transformation. The structural branch extracts a structured representation from the original request, reconstructs the request under representation constraints, and detects information-preservation failures between the original and reconstructed requests; the semantic branch audits the original prompt directly. We evaluate DoubtProbe against representative black-box defenses on jailbreak and benign-request benchmarks, and further test backbone transfer from Qwen2.5-72B to Llama-3.1-70B. Results show that DoubtProbe achieves a stronger and more stable defense-utility trade-off: on Qwen2.5-72B, it reduces the JBB attack success rate from 0.293 to 0.100 and the CodeAttack attack success rate from 0.152 to 0.001, while maintaining false positive rates of 0.022 and 0.016 on AlpacaEval and OR-Bench; the same pattern remains stable on Llama-3.1-70B. These findings show that structural inconsistency signals provide a practical and generalizable basis for black-box jailbreak defense, especially when combined with semantic auditing.

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

Imitating What Works: Simulation-Filtered Modular Policy Learning from Human Videos

作者:
Albert J. Zhai↗Kuo-Hao Zeng↗Jiasen Lu↗Ali Farhadi↗Shenlong Wang↗Wei-Chiu Ma↗

The ability to learn manipulation skills by watching videos of humans has the potential to unlock a new source of highly scalable data for robot learning. Here, we tackle prehensile manipulation, in which tasks involve grasping an object before performing various post-grasp motions. Human videos offer strong signals for learning the post-grasp motions, but they are less useful for learning the prerequisite grasping behaviors, especially for robots without human-like hands. A promising way forward is to use a modular policy design, leveraging a dedicated grasp generator to produce stable grasps. However, arbitrary stable grasps are often not task-compatible, hindering the robot's ability to perform the desired downstream motion. To address this challenge, we present Perceive-Simulate-Imitate (PSI), a framework for training a modular manipulation policy using human video motion data processed by paired grasp-trajectory filtering in simulation. This simulation step extends the trajectory data with grasp suitability labels, which allows for supervised learning of task-oriented grasping capabilities. We show through real-world experiments that our framework can be used to learn precise manipulation skills efficiently without any robot data, resulting in significantly more robust performance than using a grasp generator naively.

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07.
Nature (Science) 2026-06-10

Molecular glue degraders of HuR suppress BRAF-mutant colorectal cancer

作者:
Xiaocui Lu↗

BRAF gain-of-function mutations, particularly BRAF(V600E), affect roughly 10% of all patients with colorectal cancer (CRC), and portend poor prognosis with limited therapeutic interventions. BRAF inhibitors such as encorafenib are ineffective due to MAPK pathway reactivation driven by BRAF dimerization. Combined inhibition of BRAF and EGFR, although approved therapies, results in short survival benefits and frequent treatment resistance and relapse1–3. Here, through rational chemical library design coupled with parallel proteomic screening, we identified dHuR as a molecular glue degrader of human antigen R (HuR), an RNA-binding protein that drives tumour growth, invasion and therapy resistance. dHuR binds to the CRBN ubiquitin ligase to create a unique benzofuran-tethered composite surface to recruit HuR as a neosubstrate by engaging its β-hairpin G-loop degron, as revealed by the cryo-electron microscopy structure of the ternary complex. dHuR abrogated BRAF expression by inducing its exon 18 skipping, and demonstrated superior suppression of BRAF-mutant CRC tumours including those gaining resistance to BRAF inhibitors. Finally, we performed kinome library CRISPR screening and revealed that inactivation of EGFR or MEK enhanced dHuR cytotoxicity, thus establishing a combinatorial strategy to treat patients with refractory BRAF-mutant CRC. Molecular glue degraders of the RNA-binding protein HuR have therapeutic potential for BRAF-mutant cancers.

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08.
arXiv (CS.AI) 2026-06-15

Learning Developmental Scaffoldings to Guide Self-Organisation

作者:
Milton L. Montero↗Elias Najarro↗Jakob Schauser↗Sebastian Risi↗

arXiv:2605.14998v3 Announce Type: replace Abstract: From subcellular structures to entire organisms, many natural systems generate complex organisation through self-organisation: local interactions that collectively give rise to global structure without any blueprint of the outcome. Yet a significant portion of the information driving such processes is not produced by self-organisation itself, instead, it is often offloaded to initial conditions of the system. Biological development is a prime example, where maternal pre-patterns encode positional and symmetry-breaking information that scaffolds the self-organising process. From maternal morphogen gradients in early embryogenesis to tissue-level morphogenetic pre-patterns guiding organ formation, this transfer of information to initial conditions, analogous to a memory-compute trade-off in computational systems, is a fundamental part of developmental processes. In this work, we study this offloading phenomenon by introducing a model that jointly learns both the self-organisation rules and the pre-patterns, allowing their interplay to be varied and measured under controlled conditions: a Neural Cellular Automaton (NCA) paired with a learned coordinate-based pattern generator (SIREN), both trained simultaneously to generate a set of patterns. We provide information-theoretic analyses of how information is distributed between pre-patterns and the self-organising process, and show that jointly learning both components yields improvements in robustness, encoding capacity, and symmetry breaking over purely self-organising alternatives. Our analysis further suggests that effective pre-patterns do not simply approximate their targets; rather, they bias the developmental dynamics in ways that facilitate convergence, pointing to a non-trivial relationship between the structure of initial conditions and the dynamics of self-organisation.

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

When Do LLMs Reason? A Dynamical Systems View via Entropy Phase Transitions

作者:
Wei Xia↗Haoqing Wang↗Zhi-Hong Deng↗Yehui Tang↗

Chain-of-thought (CoT) reasoning has become the default strategy for enhancing LLM capabilities, yet its application raises a fundamental question: when is explicit reasoning actually beneficial? Empirical evidence reveals a striking paradox: CoT often provides marginal or even negative gains on factual and open-ended tasks while multiplying token consumption. In this work, we show that LLM reasoning is not a static property of tasks or models, but a dynamic decoding state that emerges during generation. Through systematic analysis, we find early-stage entropy dynamics provide a reliable signal of this state: tasks benefiting from CoT exhibit consistent entropy reduction, while others display unstable or increasing patterns. This behavior can be interpreted as a phase-transition-like shift from a high-entropy exploratory regime to a low-entropy structured reasoning regime. Based on these insights, we propose EDRM (Entropy Dynamics-based Reasoning Manifold), a lightweight and training-free routing framework that leverages early decoding entropy to adaptively select inference strategies. EDRM embeds entropy trajectories into a compact and interpretable manifold representation, enabling both zero-shot deployment and fine-grained instance-level adaptation. Across 15 benchmarks and 4 LLMs of varying scales and architectures, EDRM consistently outperforms static baselines. At the dataset level, EDRM achieves 41–55\% token reduction while improving accuracy with as few as 50 calibration samples. At the instance level, it further improves accuracy by up to 4.7\% while maintaining 27–45\% token savings. These results suggest that reasoning should be invoked selectively rather than by default, and demonstrate the effectiveness of entropy-driven decoding control for efficient and adaptive LLM inference.

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10.
arXiv (CS.AI) 2026-06-12

Mapping AI Programs in the U.S: A Status Report from Early 2026 and an Analysis of AI Majors and Minors

作者:
Felix Muzny↗Carolyn Jones↗Carter Ithier↗Hasnain Sikora↗Hrutika Harshadbhai Patel↗Carla E. Brodley↗

arXiv:2606.12428v1 Announce Type: cross Abstract: We present a report on the status of undergraduate Artificial Intelligence (AI) programs in the United States in Spring 2026. In so doing, we 1) describe our scraping and mapping tools, which dynamically update to track the state of AI education in the U.S., and 2) create a historic record at a time of great upheaval. The tool we developed, available at https://cicmap.ai, detects, scrapes, and displays data from more than 350 undergraduate AI programs–majors, minors, concentrations, and certificates–at 4-year universities. Our tool searched over 560 institutions to locate these programs, a sample that represents 86\% of all undergraduate Computer Science (CS) graduates in the U.S. This tool allows prospective students, guidance counselors, administrators, and faculty to easily access AI program requirements and is designed to continually update as new programs emerge. To the best of our knowledge, this survey represents the most comprehensive snapshot of the state of AI programs in the U.S. to date. With this work we offer three important contributions: 1) a record of AI programs in the U.S. at a time of great upheaval; 2) a tool to explore AI programs and their requirements; and 3) an analysis of the courses required for 66 AI majors and 87 AI minors. Our analysis of majors and minors shows great variability in the size and the requirements of these degrees, but we note two takeaways. First, not all majors require a general AI course, but if they don't, they do require a Machine Learning (ML) course. Second, while more than a third of majors require an Ethics in AI course, just under a quarter of AI minors do.

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11.
arXiv (CS.CV) 2026-06-12

Zero-Shot Captioning for Cultural Heritage: Automated Image Analysis of Traditional Indonesian Clothing

作者:
Anugrah Aidin Yotolembah↗Novanto Yudistira↗Gembong Edhi Setyawan↗

This paper presents Custom ZeroCLIP, a retrieval-augmented vision-language framework for zero-shot captioning of Indonesian traditional garments. The dataset contains 3,800 expert-annotated images from all 38 Indonesian provinces. Using a province-level inductive zero-shot protocol, the model is trained on 24 seen provinces, validated on 6 seen provinces, and evaluated on 8 unseen provinces. The framework combines a frozen CLIP ViT-B/32 image encoder, a CLIP text encoder, a BERT text encoder, and an LSTM caption decoder. During inference, unseen-province labels and captions are unavailable, and retrieval uses only captions from training provinces. No unseen-province image, label, or caption is used during training, validation, or retrieval-bank construction. Custom ZeroCLIP achieves a CLIPScore of 0.8536, BLEU-4 of 0.3342, and METEOR of 0.4859, outperforming existing baselines. Ablation results show that retrieval improves cultural vocabulary recovery with a 19.3\% METEOR gain, while human evaluation confirms stronger cultural accuracy and fluency. The results demonstrate the effectiveness of retrieval-augmented domain adaptation for culturally grounded caption generation in low-resource heritage settings. The dataset is publicly available at https://github.com/AnugrahAidinYotolembah/Traditional-Indonesian-Clothing-Captioning-Dataset.

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12.
arXiv (CS.AI) 2026-06-15

The Journal of Prompt-Engineered (Moral) Philosophy Or: Why AI-Assisted Ethics Research Requires Process Transparency

作者:
Michele Loi↗

arXiv:2511.08639v4 Announce Type: replace-cross Abstract: Existing AI disclosure mandates in scholarship require that AI assistance be reported but leave transparency philosophically unspecified: they fix the duty without explaining what the duty serves. We argue that ethical inquiry is essentially contested at two independent levels – about what it is, and about what it demands of the inquirer – defeating output-only evaluation and welfare-economic dismissal of the transparency question, and, by extension, reproducibility framings imported from the empirical sciences. The transparency duty is grounded instead in agent-integrity: the legibility, before a community of inquiry, of the identity-constituting commitments that the author's mode of philosophising expresses. Because the standards for evaluating such work are not communally settled, the achievable goal for transparency is not evaluation against agreed criteria but tracking – accumulating the evidentiary record that lets each tradition assess the work on its own terms and makes future normative judgments possible. We develop a documentation-adequacy framework that operationalises Meaningful Human Control through five transparency elements – declaration, navigation, documentation account, process documentation, and development records – demonstrated by the paper itself, whose full documentation record is archived at a persistent identifier. The framework is a first iteration subject to revision, not a settled standard.

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

An Integrated System for Real-Time Student Assessment and Career Guidance Using Neural Networks in Computing Disciplines

作者:
Sakir Hossain Faruque↗Md. Jubair Hossain↗Sharun Akter Khushbu↗

arXiv:2606.15831v1 Announce Type: new Abstract: Many undergraduate students in Computer Science (CS) and Software Engineering (SWE) struggle to identify suitable career paths, particularly when their academic performance, abilities, and interests do not fully align. To address this issue, this study proposes an AI-driven Student Assessment and Career Prediction System that integrates a Career Guidance Expert (CGE) system with a Web-Based Student Assessment (WBSA) platform. Within the integrated framework, CGE enhances personalized career recommendations using AI while also assisting students after graduation in identifying suitable jobs, research domains, and higher study opportunities aligned with their skills and interests. The WBSA platform further strengthens interaction between students and faculty through assessments, personalized tasks, mentorship activities, and a secure real-time chat application. The CGE system employs a Multilayer Perceptron (MLP) model trained on real-world academic and extracurricular data collected using the snowball sampling method from the students of universities, achieving a validation accuracy of 94.71% in predicting personalized career paths. A pre-survey was conducted across universities to evaluate the proposed model before deployment. The WBSA system was developed as a modern web application using technologies such as Node.js, Next.js, and PostgreSQL to ensure scalability, responsiveness, and secure data management. The overall system is supported by a secure cloud-based infrastructure, the platform provides reliable performance while assisting graduates to select suitable career path in IT sector. In addition, a post-survey involving both students and faculty was conducted to gather feedback and further improve the overall effectiveness and usability of the system.

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14.
arXiv (CS.AI) 2026-06-19

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

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

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

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15.
arXiv (CS.LG) 2026-06-18

Task-Restricted Symmetries in Recurrent Weight Space

作者:
Simon Dr\"ager↗

arXiv:2606.18457v1 Announce Type: new Abstract: Recurrent networks can contain substantial functional redundancy in weight space: changing a recurrent matrix may leave the input-output rollout nearly unchanged on a task distribution, while similar-scale changes can destroy the same behavior. We study this redundancy in one-layer tanh RNNs using ordered real Schur coordinates. The Schur form separates spectral blocks from directed nonnormal couplings, giving a diagnostic basis for structured ablations that keep the input and readout maps fixed. In a fixed-length copy task, selected nonnormal Schur couplings can be removed with little loss in some trained solutions, whereas other couplings are necessary for accurate autonomous replay. Across flip-flop, sine generation, and context-dependent integration, the loss-preserving ablation profile varies across tasks and trained solutions. These results identify candidate approximate functional invariances, not universal symmetries of recurrent weight space. Schur-coordinate ablations provide a practical diagnostic for which structured perturbations preserve a trained recurrent solution and which ones disrupt its computation.

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

MASK: Multi-Agent Semantic K-Scheduling for Risk-Sensitive 6G Robotics

作者:
Ahmet Gunhan Aydin↗Elif Tugce Ceran↗

arXiv:2606.11249v1 Announce Type: cross Abstract: Realizing the vision of 6G connected robotics requires reconciling high-performance collaborative control with the rigid spectral limitations of physical wireless channels. In realistic collaborative sensing scenarios, spectral resources are quantized into finite physical resource blocks or orthogonal subcarriers, rendering simultaneous transmission by all agents infeasible. To address this, we propose Multi-Agent Semantic K-Scheduling (MASK), a control architecture designed to sustain robust, risk-aware coordination under strict instantaneous bandwidth caps. We introduce Arbiter-Assisted Semantic Information Gating (A-SIG), a lightweight coordination mechanism that enforces hard access constraints by scheduling only the top-K agents based on locally computed semantic importance scores. By aggregating these prioritized observations into a compact latent state, a self-supervised global encoder enables a distributional policy to mitigate tail risks despite data sparsity. We evaluate MASK across diverse benchmarks, demonstrating that it matches the performance of communication-unconstrained baselines even when channel access is restricted to a small fraction of the swarm size. Furthermore, the framework exhibits inherent resilience to packet erasures, validating semantic scheduling as a critical enabler for resource-constrained 6G systems.

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17.
bioRxiv (Bioinfo) 2026-06-19

Tox21mer, A transformer foundation model for Tox21 high-throughput concentration-response curves data

作者:
Li↗Hwang↗Shockley↗Motsinger-Reif↗Hsieh↗J.-H↗Auerbach↗S. S↗Reif↗

The U.S. Tox21 collaboration has generated a large reference library of high-throughput concentration-response assays. Here we present Tox21mer, a 43.5-million-parameter transformer that encodes each Tox21 concentration-response curve together with assay metadata into a 768-dimensional representation. Tox21mer was pretrained on ~2.5 million curves from 102 assay protocols and 6,727 compounds using masked-response reconstruction as the primary objective, with low-weight auxiliary supervision on assay outcome and AC50. To evaluate the learned representation, we trained lightweight probes on frozen embeddings from concentration-response curves of held-out compounds. The representation supported a macro-F1 of 0.985 for three-class outcome prediction (agonist, antagonist, inactive), a binary F1 of 0.994 for active/inactive prediction, and an R2 of 0.87 for log10(AC50). The learned embeddings formed coherent groupings by curve-class category. A masked-only pretraining variant retained near-baseline probe performance, indicating that the representation is learned largely from the self-supervised objective rather than from auxiliary labels. Ablation analyses further showed that predictive performance depends mainly on curve-level response-value distributions conditioned on assay context, with limited reliance on detailed within-curve ordering. Tox21mer thus provides a reusable foundation representation for Tox21 concentration-response data that can support extrapolation to untested compounds through integration with chemical features or distillation into chemistry-only student models for large-scale external screening.

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

Structured vs. Unstructured Pruning: An Exponential Gap

作者:
Davide Ferre'↗Fr\'ed\'eric Giroire↗Frederik Mallmann-Trenn↗Emanuele Natale↗

arXiv:2603.02234v3 Announce Type: replace-cross Abstract: The Strong Lottery Ticket Hypothesis (SLTH) states that large, randomly initialized neural networks contain sparse subnetworks capable of approximating a target function at initialization without training, suggesting that pruning alone is sufficient. Pruning methods are typically classified as unstructured, where individual weights can be removed from the network, and structured, where parameters are removed according to specific patterns, as in neuron pruning. Existing theoretical results supporting the SLTH rely almost exclusively on unstructured pruning, showing that logarithmic overparameterization suffices to approximate simple target networks. In contrast, neuron pruning has received limited theoretical attention, despite its practical appeal for direct hardware speedups. In this work, we consider the problem of approximating a single bias-free ReLU neuron by pruning hidden units of a randomly initialized two-layer ReLU network, effectively isolating the intrinsic limitations of neuron pruning. We show that achieving an $\varepsilon$-approximation requires a starting network size of $\Omega(1/\varepsilon)$ for neuron pruning, whereas weight pruning succeeds with only $O(\log(1/\varepsilon))$ hidden units, revealing an exponential separation between the two approaches.

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19.
medRxiv (Medicine) 2026-06-16

The Target48 Neurodegeneration Panel: A Novel Tool for Profiling Protein Signatures in Neurodegenerative Disorders

作者:
Hok-A-Hin↗Y. S↗Vermunt↗de Jong↗del Campo↗Vijverberg↗Lemstra↗A. W↗Pijnenburg↗Y. A. L↗Van Harten↗A. C↗…

Introduction: Novel tools for absolute quantification of established and emerging fluid neuro-biomarkers are required to advance diagnostic studies and improve biological insights. Methods: We conducted an extensive analytical and clinical validation of the Olink Target 48 Neurodegeneration panel (T48 Neuropanel) in 352 paired CSF and plasma samples from cognitively unimpaired controls (CU), Alzheimer dementia (AD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB), n=44 per group. Comparisons with benchmark assays were performed. Results: Good detectability (CSF: 31 out of 42 assays; plasma: 38 out of 42 assays) and technical performance was observed. Benchmark assays showed good correlations, supporting method transformation formulas. Next to emerging biomarkers (MMP10, ITGB2), discriminative performance was excellent in AD: CSF pTau217: AUC=1; FTD: plasma NfL: AUC=0.952; and DLB: CSF DDC: AUC=0.901. Discussion: This analytical and clinical validation of the T48 Neuropanel highlights initial cut-offs and emerging biomarkers to aid clinical studies for the diagnosis, prognosis, and monitoring of neurodegenerative diseases. Highlights: The T48 Neuropanel shows robust analytical performance, with high detectability across both plasma and CSF matrices. The T48 Neuropanel validates established (i.e., pTau217, Abeta42, NfL, and GFAP) and emerging biomarkers (i.e., DDC, MMP10, ITGB2, ITGAM, NPTX2, NPTXR, SMOC1, sTREM1, and sTREM2) in CSF and plasma. CSF NfL, GFAP, ITGB2, and ITGAM and plasma GFAP were dysregulated across AD, FTD, and DLB dementias. -The multiplex design of the T48 Neuropanel enables rich biological interpretation by simultaneously quantifying established and emerging neurodegeneration biomarkers. Importantly, the inclusion of absolute quantification facilitates the establishment of cut-offs, supporting its potential for clinical translation.

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20.
arXiv (math.PR) 2026-06-12

Branching-selection particle systems and inverse first passage problems

作者:
Jacob Mercer↗

arXiv:2606.13487v1 Announce Type: new Abstract: A generalised inverse first passage problem asks whether, given a probability measure $p$ on $[0,\infty]$, one can find a boundary $b:[0,\infty]\to \mathbb{R}$ such that the stopping time:\[\tau:=\inf\left\{t:\Lambda\int_0^t \omega(W_s-b(s))ds \geq U\right\}\] has distribution $p$, where $U\sim Exp(1)$, $\Lambda\in(0,\infty)$ and $\omega$ is a monotonic decreasing function. We construct a branching-selection particle system whose hydrodynamic limit is governed by a free boundary problem and connect this to the generalised inverse first passage problem. In the $N$-particle system, particles move as independent Brownian motions, branch at a prescribed rate, and are removed at a rate proportional to their location relative to a position $b^N(t)$ which is a function of the empirical distribution. We identify the limit of $b^N$ as the solution of the inverse first passage problem.

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21.
arXiv (CS.LG) 2026-06-15

The Program Is Still There: A Conservation Law for Program Discovery

作者:
Jorge Miguel Silva↗

arXiv:2606.13799v1 Announce Type: cross Abstract: Finding the shortest program that generates a sequence is uncomputable, and for six decades that fact has been mistaken for a wall around finding any generating program. It is not a wall but a price, and this paper measures it. For every algorithm that learns about a candidate program only through its score, a class spanning Levin search, evolutionary methods, simulated annealing, and the cross-entropy method, we define the coupling width of a search problem and prove an unconditional worst-case lower bound, exponential in that width with base one less than the domain size. From it follows a conservation law: structural knowledge injected into a search trades one for one against the search it removes, and their sum can never fall below the length of the program sought. Levin's 1973 upper bound and the lower bound proved here are the two ends of one conserved quantity, closing on each other as the instruction set grows. The only escape is to read a candidate's structure rather than its score, and its price, which we prove for generic targets, is incompleteness. A deterministic engine built on this theory recovers a generating program, certified by compressing its data and predicting an unseen continuation, for 2,383 of 3,914 sequences across four independent populations, including 244 of the 256 elementary cellular automata, with measured discovery cost rising along program length more than an order of magnitude inside the score-oracle worst case.

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22.
Nature (Science) 2026-06-17

A mosaic of whole-body representations on the human precentral gyrus

作者:
Darrel R. Deo↗

Understanding how the body is represented in the motor cortex is key to understanding how the brain controls movement. Although the motor cortex has been mapped in animal models at a fine scale1–10, characterization in humans remains primarily limited to low-resolution recording11–16 and stimulation techniques17–20. Here we created a comprehensive map of the human motor cortex at single-neuron resolution, spanning microelectrode array recordings from 20 arrays across 8 individuals with paralysis from spinal cord injury, amyotrophic lateral sclerosis or brainstem stroke, all enrolled in brain–computer interface clinical trials. These arrays broadly sample the crown of the precentral gyrus (PCG; thought to be composed largely of the premotor cortex (Brodmann area 6)). We found that body parts were highly intermixed, such that the entire body was represented in all sampled locations of the PCG, although the relative strength of body parts was roughly consistent with the motor homunculus17,18. We also found two speech-preferential areas with a broadly tuned, orofacial-dominant area in between them. Throughout the PCG, movement representations of the four limbs were interlinked, with homologous movements of different limbs (for example, toe curl and hand close) having correlated representations. These data provide evidence consistent with an intermixed, interrelated and behaviour-centred organization of the motor cortex3,21. The resulting map also provides important targeting information for brain–computer interfaces that seek to restore motor function. A comprehensive map of the human motor cortex at single-neuron resolution is described.

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23.
arXiv (quant-ph) 2026-06-12

Influence-solvability: a systematic theory of $(1+1)D$ solvability and its application to brickwork circuits

作者:
Friedrich H\"ubner↗Sun Woo P. Kim↗

arXiv:2606.12538v1 Announce Type: cross Abstract: `Solvable' circuits, such as dual unitaries and its generalisations, have arisen as paradigmatic examples of tractable chaotic non-equilibrium dynamics, both in classical and quantum systems. However, while increasingly more complicated sufficient conditions have been proposed, a systematic theory classifying and understanding general features of solvable circuits is missing. We develop such a theory by introducing influence-solvable circuits, a class of $(1+1)D$ circuits whose influence matrix, which represents the `bath' generated by its own evolution, is given by a uniform MPS with finite bond-dimension $\chi$. This property allows for efficient computation of subsystem dynamics and essentially contains all known examples of solvable circuits. We derive a set of necessary and sufficient local conditions by using a version of the fundamental theorem of MPS for open boundary conditions. Next we apply our theory to brickwork circuits with $\chi=1$ influence-solvability and perform a systematic classification of classical brickwork circuits with local dimension up to $d=3$ and quantum brickwork circuits with $d=2$. Our search reveals new solvable circuits that are not captured by known solvability conditions.

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24.
arXiv (CS.AI) 2026-06-16

A Model-Free Universal AI

作者:
Yegon Kim↗Juho Lee↗

arXiv:2602.23242v3 Announce Type: replace Abstract: In general reinforcement learning, all established optimal agents, including AIXI, are model-based, explicitly maintaining and using environment models. This paper introduces Universal AI with Q-Induction (AIQI), the first model-free agent proven to be asymptotically $\varepsilon$-optimal in general RL. AIQI performs universal induction over distributional action-value functions, instead of policies or environments like previous works. Under a grain of truth condition, we prove that AIQI is strong asymptotically $\varepsilon$-optimal and asymptotically $\varepsilon$-Bayes-optimal. We also apply our novel proof techniques to show asymptotic $\varepsilon$-optimality of Self-AIXI without any ad-hoc assumptions. Our results significantly expand the diversity of known universal agents.

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

MoonSplat: Monocular Online Gaussian Splatting with Sim(3) Global Optimization

作者:
Guo Pu↗Yixuan Han↗Haofeng Li↗Yao Zhang↗Hui Zhou↗Zhouhui Lian↗

Online 3D reconstruction from monocular image sequences is a challenging and ongoing research topic. 3D Gaussian Splatting (3DGS), leveraging its high-quality real-time rendering capability, empowers online 3D reconstruction to represent dense scenes with enhanced expressiveness, and thus holds great promise for a wide range of applications such as robotics and AR/VR. However, existing online 3DGS methods still suffer from some key challenges: fragile camera pose estimation due to the lack of global optimization, and low optimization efficiency in large-scale or long-sequence scenarios. To address these issues, we propose a robust and efficient online voxelized 3DGS reconstruction framework integrated with global $Sim(3)$ optimization, which enables reliable camera tracking and efficient global loop closure for both camera poses and voxelized 3DGS. To accelerate the convergence of the voxelized 3DGS, we further introduce a color residual learning strategy, which not only boosts optimization speed but also enhances rendering quality. Extensive experiments on diverse indoor and outdoor datasets demonstrate that our method achieves state-of-the-art performance in both camera pose estimation accuracy and rendering quality, while retaining real-time efficiency. Additionally, we develop and deploy a real-world UAV-based active reconstruction system grounded on our proposed method, validating its robustness and generalizability for practical online 3D reconstruction tasks. Our code and data are available at https://github.com/TrickyGo/MoonSplat.

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