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01.
bioRxiv (Bioinfo) 2026-06-20

MIRATS framework: Normative multiscale characterization of brain regulatory systems across sex and age using multimodal MRI

Authors:

Deep brain systems involved in arousal, autonomic regulation, sensory integration, and homeostatic control remain underrepresented in conventional whole-brain neuroimaging frameworks. In particular, diencephalic and brainstem nuclei are often insufficiently represented in cortex-centered analyses, limiting the normative references needed to interpret systems-level variation in health and disease. To address this gap, we developed a unified multiscale framework with explicit representation of deep nuclei. By integrating cerebral, cerebellar, diencephalic, and brainstem atlases in standard space, we constructed a 220-region whole-brain parcellation and extracted complementary features at three analytical scales: nodal properties, edge-wise connectivity, and persistent-homology-based topological descriptors. We applied this framework to healthy adults from the Human Connectome Project-Aging cohort to characterize normative multiscale organization and test sex- and age-related variation. Applied to this cohort, our framework revealed pronounced heterogeneity across anatomical systems. Brainstem and diencephalic nuclei showed multiscale feature profiles distinct from those of cerebral and cerebellar regions across nodal, edge-wise, and higher-order topological scales. Sex comparisons identified selective differences across different scales, whereas age modeling revealed widespread but feature- and system-dependent variation across adulthood. Together, these findings show that normative whole-brain organization in this deep-system-aware space is structured by system-specific rather than globally uniform patterns. These findings establish a normative multiscale framework for characterizing brainstem-diencephalic-cerebellar-cerebral organization in healthy adults and provide a quantitative reference for future translational studies of disease-related abnormalities in deep regulatory systems.

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

FLiP: Towards understanding and interpreting multimodal multilingual sentence embeddings

This paper presents factorized linear projection (FLiP) models for understanding pretrained sentence embedding spaces. We train FLiP models to recover the lexical content from multilingual (LaBSE), multimodal (SONAR) and API-based (Gemini) sentence embedding spaces in several high- and mid-resource languages. We show that FLiP can recall more than 75% of lexical content from the embeddings, significantly outperforming existing non-factorized baselines. Using this as a diagnostic tool, we uncover the modality and language biases across the selected sentence encoders and provide practitioners with intrinsic insights about the encoders without relying on conventional downstream evaluation tasks. Our implementation is public https://github.com/BUTSpeechFIT/FLiP.

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

Pre-Deployment Robustness Stress Testing for CT Segmentation Systems Using Clinically Motivated Multi-Corruption Augmentation

Deep learning-based CT segmentation systems often achieve high accuracy on clean benchmark images, but their performance may degrade under heterogeneous clinical imaging conditions such as noise, resolution loss, contrast variation, intensity shift, and artifacts. This instability can limit reliable deployment in real-world medical imaging workflows. We propose Robustness via Augmented Multi-corruption Pipeline (RAMP), a robustness-oriented augmentation framework for CT segmentation. RAMP combines anatomically constrained spatial perturbations, CT intensity transformations, and stochastic multi-corruption composition to expose models to clinically plausible image degradation during training. Across two CT segmentation evaluation settings, RAMP achieved the strongest corrupted-image performance and the smallest clean-to-corrupted robustness gap. In the five-organ noisy evaluation benchmark, RAMP improved mean corrupted Dice from 0.610 to 0.753 and reduced the robustness gap from 0.264 to 0.064 compared with the nnU-Net baseline. In Abdomen1K, RAMP improved mean corrupted Dice from 0.633 to 0.789 and reduced the robustness gap from 0.290 to 0.070. Although RAMP did not achieve the highest clean-image Dice, it substantially mitigated worst-case segmentation collapse under severe image degradation. These results suggest that multi-corruption augmentation can serve as a practical pre-deployment strategy for improving the reliability of CT segmentation systems in heterogeneous clinical environments.

04.
arXiv (CS.CV) 2026-06-16

A Comprehensive Survey of Knowledge-Based Vision Question Answering Systems: The Lifecycle of Knowledge in Visual Reasoning Task

Knowledge-based Vision Question Answering (KB-VQA) extends general Vision Question Answering (VQA) by not only requiring the understanding of visual and textual inputs but also extensive range of knowledge, enabling significant advancements across various real-world applications. KB-VQA introduces unique challenges, including the alignment of heterogeneous information from diverse modalities and sources, the retrieval of relevant knowledge from noisy or large-scale repositories, and the execution of complex reasoning to infer answers from the combined context. With the advancement of Large Language Models (LLMs), KB-VQA systems have also undergone a notable transformation, where LLMs serve as powerful knowledge repositories, retrieval-augmented generators and strong reasoners. Despite substantial progress, no comprehensive survey currently exists that systematically organizes and reviews the existing KB-VQA methods. This survey aims to fill this gap by establishing a structured taxonomy of KB-VQA approaches, and categorizing the systems into main stages: knowledge representation, knowledge retrieval, and knowledge reasoning. By exploring various knowledge integration techniques and identifying persistent challenges, this work also outlines promising future research directions, providing a foundation for advancing KB-VQA models and their applications.

05.
arXiv (CS.AI) 2026-06-11

Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification

arXiv:2606.12252v1 Announce Type: cross Abstract: Training deep neural networks for clinical time-series analysis is computationally demanding, yet many healthcare settings lack the resources required for repeated model development and deployment. This challenge is particularly evident in electrocardiogram classification, where large datasets and long training schedules make efficiency practically important. Progressive Data Dropout reduces training cost by excluding samples from gradient updates once they are learned, but it relies on model confidence and may retain samples that are difficult due to noise or ambiguity rather than useful signal. In this work, we introduce ERTS, an explainability-based reliability training signal for efficient ECG classification. ERTS uses explanation quality during training to distinguish between informative and unreliable uncertainty. Building on progressive data selection, we compute Grad-CAM attention maps for candidate samples and derive a focus score that measures whether model predictions are supported by coherent and localised patterns. Samples with low focus are filtered out, while those with meaningful attention are prioritised for gradient updates. We evaluate ERTS across three ECG datasets and multiple backbone architectures, showing consistent improvements in macro-F1 alongside reduced effective training cost. These results suggest that explanation quality can serve as a practical signal for improving both efficiency and reliability in clinical time-series learning. Code will be released.

06.
arXiv (CS.CV) 2026-06-18

The Market in the Model: Latent Diffusion as Neural Economy

Valuable critique of generative image models within visual culture and the humanities has emphasized the role of datasets in shaping the images they produce. Yet, close studies of the ideological positions embedded into the mechanism of the models have been neglected, leaving them imagined as "black boxes." In a bid to expand, rather than replace, dataset critique, this paper examines the mechanisms of the latent diffusion model in terms of the problems they were brought in to solve on behalf of computer vision engineers, and the decisions each component was tasked with automating. I interpret that ensemble through the histories of its parts and the theory of vision the system inscribes into every generated image. Drawing on Impett and Offert's notion of neural exchange value, I offer this analysis to argue that the model operates as a neural economy: a contained symbolic system that abstracts social communication into commensurable vectors as it transfers the social sphere into parcels for sale. Tracing the training and generation pipelines component by component reveals what each operation displaces, and how it further entrenches the logics of platform and attention economies over social communication. The paper warns that any critique fixated exclusively on copyright and commodity defenses risks reaffirming the very fetishism the model produces, and argues instead for centering social exchange.

07.
Science (Express) 2026-05-21

DNA polymerization activates RNA cleavage of a reverse transcriptase–like antiviral enzyme | Science

Authors: Unknown Author

Defense-associated reverse transcriptases (DRTs) transcribe noncoding RNAs (ncRNAs) for antiviral defense, but the mechanisms of ncRNA-independent DRTs remain unclear. In this work, we show that a single DRT4 mediates RNA-targeting antiphage defense by integrating DNA polymerase, exonuclease, and RNA endonuclease activities. First, through an equilibrium between its DNA polymerase and exonuclease activities, DRT4 senses phage infection, as elevated dNTP levels shift the equilibrium toward polymerase activity, thereby promoting protein-primed single-stranded DNA (ssDNA) synthesis. Second, ssDNA of sufficient length, phage DNA-binding proteins, and deoxyguanosine triphosphate collectively activate an unusual RNA endonuclease activity of DRT4, excising 3′–guanosine monophosphate from both phage and host RNA to terminate infection. These findings reveal a distinctive immune strategy combining nucleic acid synthesis and degradation, expanding the functional landscape of DRTs for new DNA- and RNA-processing technologies.

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

MODE: Modality-Decomposed Expert-Level Mixed-Precision Quantization for MoE Multimodal LLMs

arXiv:2606.17118v1 Announce Type: cross Abstract: Mixture-of-Experts Multimodal Large Language Models (MoE-MLLMs) offer remarkable performance but incur prohibitive GPU memory costs, making compression essential. Among PTQ methods, expert-level mixed-precision quantization has proven effective for MoE-LLMs, yet suffers notable degradation on MoE-MLLMs due to two overlooked biases in expert importance estimation. (1) At the cross-modal level, the numerical dominance of vision tokens causes expert selection frequency to be dominated by vision tokens, masking experts that are critical to the text modality; (2) at the intra-vision level, the large proportion of redundant vision tokens further skew frequency statistics, obscuring experts critical for informative visual content. To bridge gaps, we propose MODE, a modality-decomposed expert-level mixed-precision quantization framework for MoE-MLLMs that decomposes expert selection frequency by modality, filters redundant vision tokens to obtain denoised visual frequency, and further evaluates quantization sensitivity per modality as a complementary signal to frequency-based estimation. These signals are integrated into an Integer Linear Programming formulation to assign per-expert bit-widths under a given budget. Extensive experiments show that MODE is particularly well-suited for MoE-MLLMs, limiting average performance loss to within 2.9% at W3A16, with larger gains at the extreme 2-bit setting.

09.
arXiv (quant-ph) 2026-06-19

Stalls and Spequlation: Pipelined Execution for Fault Tolerant Quantum Computation

arXiv:2606.19593v1 Announce Type: new Abstract: Fault-tolerant quantum computation requires the coordinated action of three distinct systems: classical control logic, quantum hardware, and classical error decoders. Current scheduling models treat logical operations as atomic, hiding the fact that these subsystems operate sequentially and spend significant time idle. We present a pipelined execution framework that decomposes each logical operation into its component stages i.e. Control, Execute, and Decode. Building on this, we discuss some speculation strategies that allow successor operations to begin processing before their predecessors have completed decoding. We evaluate our framework on several common benchmarks and show that pipelining with speculation reduces total pipeline steps by 20-40% compared to a no-speculation baseline. The most aggressive strategy consistently outperforms conservative alternatives, even though partial rollback is needed at times, because the per-rollback penalty is small relative to the parallelism gained. We further show that speculation facilitates load balancing by distributing work more evenly across the heterogeneous subsystems of a fault-tolerant quantum computer, converting idle time into useful computation while also saving on execution time.

10.
arXiv (CS.LG) 2026-06-17

Dropout Neural Network Training Viewed from a Percolation Perspective

arXiv:2512.13853v2 Announce Type: replace Abstract: In this work, we investigate the existence and effect of percolation in training deep Neural Networks (NNs) with dropout. Dropout methods are regularisation techniques for training NNs, first introduced by G. Hinton et al. (2012). These methods temporarily remove connections in the NN, randomly at each stage of training, and update the remaining subnetwork with Stochastic Gradient Descent (SGD). The process of removing connections from a network at random is similar to percolation, a paradigm model of statistical physics. If dropout were to remove enough connections such that there is no path between the input and output of the NN, then the NN could not make predictions informed by the data. We study new percolation models that mimic dropout in NNs and characterise the relationship between network topology and this path problem. The theory shows the existence of a percolative effect in dropout. We also show that this percolative effect can cause a breakdown when training NNs without biases with dropout; and we argue heuristically that this breakdown extends to NNs with biases.

11.
arXiv (quant-ph) 2026-06-17

Intrinsic Pointer Basis and Irreversible Classicality from Coherence Contraction

Authors:

arXiv:2604.23304v4 Announce Type: replace Abstract: This work analyzes an operational route to classical behavior for reduced quantum states using the intrinsic reference basis (IRB). Relative to a fixed physical conjugation, the IRB separates intrinsic populations from a real antisymmetric cohesion sector. A globally bounded cohesion index is defined and its exponential contraction is proved for phase-free dephasing dynamics aligned with the IRB; for general aligned dephasing, the corresponding modulus-based coherence functional contracts at the same computable rates. The results provide distance bounds to the IRB-diagonal description and a logarithmic upper bound on the time required to reach a prescribed experimental tolerance. The IRB projectors constitute state-derived candidate pointer sectors, and they become dynamically stable pointer sectors when the effective dephasing generator is aligned with them and damps the relevant inter-sector coherences. Degenerate population sectors lead naturally to block-classicality and protected intra-block coherence. In a two-level active sector, the cohesion index equals fringe visibility, giving a direct interferometric test of the contraction law. The construction is independent of any spacetime- or unification-emergence hypothesis and is intended as a channel-level complement to environment-induced einselection.

12.
arXiv (CS.CV) 2026-06-16

Avoiding Exponential Blow-Up in Distributive Lattice Submodular Minimization

Authors:

Submodular function minimization has gained a lot of interest in recent years. They are highly applicable in the area of Computer Vision and Machine Learning. Often such applications require to work with submodular functions defined on distributive lattice. Current best way of dealing with it is using a transformation which extrapolates the submodular function for the respective boolean lattice. It makes optimization system too inefficient due to enlargement of the working space. Quantitatively, the expanded space has additional exponential (in set size) number of elements. We propose a generic framework for dealing with distributive lattice which only works within distributive lattice. Our framework allows one to use already established submodular function minimization algorithms for boolean lattice. In our experiment, we show the huge improvement in terms of running time over tranditional methods for handling distributive lattice.

13.
arXiv (CS.LG) 2026-06-15

Learning Variable-Length Tokenization for Generative Recommendation

arXiv:2605.17779v2 Announce Type: replace Abstract: Generative recommendation reformulates recommendation as next-token prediction over discrete semantic identifiers (IDs). A fundamental yet unexplored design choice is that existing methods employ fixed-length tokenization for all items, implicitly assuming uniform encoding capacity regardless of item characteristics. Through systematic experiments across four datasets, we discover the Popularity-Length Paradox: popular items achieve optimal performance with short IDs, while tail items require substantially longer codes to capture discriminative semantics. This reveals a critical mismatch where popular items benefit from abundant collaborative signals and require minimal semantic detail, whereas tail items must rely on fine-grained content features due to sparse interaction data. To address this, we propose VarLenRec, a framework for learning variable-length tokenization. We develop Popularity-Weighted Information Budget Allocation (PIBA), an information-theoretic framework proving that optimal ID length should scale as a negative power of popularity. Directly implementing variable-length allocation faces two technical challenges: standard Euclidean residual quantization lacks geometric capacity to support diverse code lengths without distortion, and discrete length decisions are non-differentiable. We address these through Hyperbolic Residual Quantization, which leverages the exponential volume growth of the Poincaré ball to naturally stratify encoding capacity, and a Soft Length Controller, which enables differentiable length prediction via continuous layer retention probabilities regularized by PIBA-derived priors. Extensive experiments demonstrate that VarLenRec achieves significant improvements over state-of-the-art methods in recommendation accuracy and training/inference efficiency, revealing the importance of adaptive encoding capacity in generative recommendation.

14.
arXiv (CS.AI) 2026-06-18

Deep-Learning-Based Pixelated Microwave Filter Design and Characterization using Electro-Optical Electric-Field Measurements

arXiv:2606.18402v1 Announce Type: cross Abstract: Traditional microwave filter design typically relies on iterative parameter tuning and predefined topologies, which limits design space and increases development time. This study uses a deep learning approach combining convolutional neural networks with genetic algorithms to automate pixelated microwave filter synthesis. To validate the approach experimentally, both S-parameter and spatial electric-field measurements were analyzed. The synthesized low-pass filter demonstrated excellent agreement between simulated and measured performance, achieving a 7 GHz passband with over 20 dB suppression beyond 9.5 GHz. Electro-optical measurements, for the first time, revealed electric field patterns that resemble coupled transmission-lines or stub structures, providing insight into the emergent characteristics of AI-generated designs.

15.
bioRxiv (Bioinfo) 2026-06-21

Machine learning evaluation of gene expression-based ALS subtypes across brain and blood tissues

The clinical and molecular heterogeneity observed in amyotrophic lateral sclerosis (ALS) presents a challenge for diagnosis, prognosis, and treatment. RNA sequencing of post-mortem brain samples from ALS patients has identified several subtypes with distinct molecular signatures. We sought to evaluate these subtypes across diverse tissues and datasets and assess the feasibility of supervised machine learning models for sample classification. Unsupervised clustering and pathway analysis were performed to confirm the presence of ALS subtypes in motor cortex samples. Three machine learning strategies were then used to create models based on post-mortem motor cortex expression data of 112 people with ALS from the London Neurodegenerative Diseases Brain Bank. These models were subsequently improved through feature selection and evaluated in independent cohorts from motor cortex (n = 257, NYGC ALS Consortium) and blood (n = 96, Macquarie University Neurodegenerative Disease Biobank) samples. Multi-class linear discriminant analysis (LDA) models were then used for subtype classification. Clustering of ALS post-mortem motor cortex samples confirmed the presence of three subtypes: neuroinflammation (ALS-Neu), extracellular matrix organisation and muscle contraction (ALS-OxA), and synaptic and neuropeptide signalling (ALS-SNs). Among all machine learning strategies, random forests produced the most accurate and stable models for binary classification (~93% accuracy across the three subtypes). After feature selection, random forest models were able to classify samples from an independent post-mortem motor cortex cohort in their respective subtypes (AUC of ~0.98 across the three subtypes). When these models were evaluated in blood using LDA, we found consistent clustering patterns, with samples aligning in the same subtype regions of the post-mortem motor cortex samples, with ALS-SNs being the subtype in which samples were classified with the highest confidence (LDA class probability ~86%). Moreover, classification for this subtype improved when blood samples were collected closer to death. Our findings support the presence of three gene expression-based ALS subtypes in motor cortex samples and the utility of machine learning strategies for subtype classification. We also observed that the subtypes identified in the brain partially match those in the blood, with samples from the late stages of the disease more likely to be correctly predicted into the ALS-SNs cluster. This suggests a longitudinal effect in subtype identification that requires further investigation.

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

PACT: Preserving Anchored Cores in Task-vectors for Model Merging

arXiv:2606.18627v1 Announce Type: new Abstract: Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a single multi-task model. Most existing model merging approaches follow the Task Arithmetic paradigm, which decomposes fine-tuned weights into pre-trained parameters and task vectors, and performs merging exclusively in the task-vector space. The effectiveness of this paradigm implicitly relies on the assumption that task-specific knowledge is encoded solely within task vectors. We argue that this assumption generally does not hold due to the intrinsic task preferences of pre-trained models. Specifically, we identify Load-Bearing Wall (LBW) dimensions, namely some task-critical knowledge that remains embedded in the pre-trained weights rather than being fully transferred into task vectors. We characterize LBW dimensions from both scalar-weight and subspace perspectives, thereby covering the major paradigms of existing model merging methods. Our analysis reveals that, by ignoring LBW dimensions, task-vector-based approaches fail to fully resolve task conflicts and may inadvertently damage task-specific knowledge encoded in the pre-trained model, leading to degradation. To address this issue, we propose PACT, which preserves the anchored task-specific cores (i.e., LBW dimensions) within task vectors by aligning their orthogonal complements with the subspace of the pre-trained weights. These aligned subspace components are then removed from the task vectors before applying existing model merging algorithms. Furthermore, we develop an efficient variant based on randomized SVD to improve scalability. PACT can be seamlessly integrated with existing methods. Extensive experiments across multiple benchmarks demonstrate that PACT consistently enhances mainstream model merging approaches and establishes new state-of-the-art performance.

17.
arXiv (CS.AI) 2026-06-18

The Long Delay to Arithmetic Generalization: When Learned Representations Outrun Behavior

arXiv:2604.13082v2 Announce Type: replace-cross Abstract: Grokking in transformers trained on algorithmic tasks is characterized by a long delay between training-set fit and abrupt generalization, but the source of that delay remains poorly understood. In encoder-decoder arithmetic models, we argue that this delay reflects limited access to already learned structure rather than failure to acquire that structure in the first place. We study one-step Collatz prediction and find that the encoder organizes parity and residue structure within the first few thousand training steps, while output accuracy remains near chance for tens of thousands more. Causal interventions support the decoder bottleneck hypothesis. Transplanting a trained encoder into a fresh model accelerates grokking by 2.75 times, while transplanting a trained decoder actively hurts. Freezing a converged encoder and retraining only the decoder eliminates the plateau entirely and yields 97.6% accuracy, compared to 86.1% for joint training. What makes the decoder's job harder or easier depends on numeral representation. Across 15 bases, those whose factorization aligns with the Collatz map's arithmetic (e.g., base 24) reach 99.8% accuracy, while binary fails completely because its representations collapse and never recover. The choice of base acts as an inductive bias that controls how much local digit structure the decoder can exploit, producing large differences in learnability from the same underlying task.

18.
bioRxiv (Bioinfo) 2026-06-13

ADMETron: An AI-driven SaaS platform for comprehensive ADMET prediction and compound prioritisation

ONTOSIGHT(R) ADMETron is an AI-driven platform designed for rapid prediction and visualization of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties to support modern drug discovery. The platform integrates an interactive web interface with a scalable predictive engine, enabling high-throughput virtual screening and batch analysis of chemical compounds. Its core architecture combines recurrent neural network (RNN)-derived molecular embeddings from SMILES representations with physicochemical descriptors, which are subsequently modeled using gradient boosting machines (GBMs). This framework provides predictions across 34 ADMET endpoints, including physicochemical properties, absorption, CYP450 interactions, hERG liability, and mutagenicity. The predictive performance of ADMETron was evaluated using benchmark datasets from the Therapeutics Data Commons (TDC), demonstrating strong performance and generalizability across both classification and regression tasks. Beyond predictive modeling, the platform introduces an interactive radar graph-based structure-activity relationship (SAR) visualization framework that enables real-time comparison of multiple compounds and reference drugs across selected ADMET parameters. This feature facilitates intuitive interpretation of multidimensional molecular profiles and supports lead optimization and compound prioritization. Comparative assessment against widely used online ADMET tools further demonstrated broad endpoint coverage spanning pharmacokinetic, physicochemical, toxicity, and medicinal chemistry properties within a unified environment. Together, these capabilities establish ADMETron as a comprehensive platform for ADMET assessment and data-driven decision-making in drug discovery. (https://admetron.partex.ai/).

19.
arXiv (CS.CV) 2026-06-17

WeaveLA: Event Driven Cross-Subtask Latent Memory Weaving for Repetitive Robot Manipulation

Vision-Language-Action (VLA) policies have achieved remarkable single-step manipulation, yet they remain brittle precisely where each stage depends on what was just completed. The core issue is structural: short-window VLAs lack an explicit channel for rouxting information across sub-task boundaries, and existing memory-augmented variants either write at every frame, retrieve from demonstration-time stages, or fire at sub-goal events without performing an explicit sub-task-to-sub-task hand-off into the action expert. We identify the sub-goal completion event as the natural temporal unit for cross-subtask memory hand-off, and present WeaveLA (Weave Latent memory for Vision-Language-Action policies), a cross-subtask memory interface that, on top of a frozen VLA backbone, compresses each completed segment into latent tokens via query-driven attention pooling and routes them directly into the action-generation path of the next sub-task. This event-triggered, action-side design preserves the base policy's short-window interface while adding a lightweight cross-subtask channel. Through stratified evaluation on RoboMME with a $\pi_{0.5}$ backbone, WeaveLA's gains land exactly where the channel is needed: on the hardest repetition slice (SwingXtimes, $N{=}3$), success rises from $0\%$ to $47.8\%$, while single-execution episodes remain unchanged. Per-episode paired analysis confirms the gains are confined to tasks whose causal structure requires cross-subtask information.

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

Tyler: Typed Latent Reasoning for Language Models – When to Think, What to Compute, and How Much to Allocate

Chain-of-thought (CoT) prompting improves reasoning in large language models (LLMs) by externalizing intermediate computation as discrete text tokens, but this textual interface also introduces redundancy and inference overhead. Latent reasoning offers a promising alternative by carrying part of the computation in continuous representations. However, existing methods typically predefine when latent computation is invoked and how it is allocated during decoding, leaving a key problem unresolved: when to invoke latent computation, what type of computation to perform, and how much budget to allocate. We propose Typed Latent Reasoning (Tyler), a typed and budget-aware framework for latent reasoning during autoregressive decoding. Tyler learns a policy that, at each decoding step, chooses between emitting a text token and switching to a latent computation module specialized for a particular reasoning function. Once invoked, an operator maps the current reasoning state into latent tokens that support global planning, local state updates, or reusable procedural abstraction. Across extensive experiments on three backbone LLMs, Tyler improves accuracy by up to 14.49 points over CoT and by up to 4.30 points over the strongest competing baseline. It further generalizes across diverse reasoning domains and achieves the best final-stage performance with the lowest forgetting.

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

Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale

Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve, and deploy. In this report, we present Ling-2.6 and Ring-2.6, a family of models designed to address this challenge at scale. Ling-2.6 is optimized for instant response generation and high capability per output token, whereas Ring-2.6 is tailored for deeper reasoning and more advanced agentic workflows. Instead of training from scratch, we upgrade the Ling-2.0 base model through architectural migration pre-training and large-scale post-training. This upgrade is guided by a unified co-design of model architecture, optimization objectives, serving systems, and agent training environments, enabling improvements in both model capability and deployment efficiency. At the architectural level, we introduce a hybrid linear attention design that integrates Lightning Attention with MLA, improving the efficiency of long-context training and decoding. To further enhance token efficiency, we optimize capability per output token through Evolutionary Chain-of-Thought, Linguistic Unit Policy Optimization, bidirectional preference alignment, and shortest-correct-response distillation. For agentic capabilities, we propose KPop, a reinforcement learning framework designed to support stable training of Ring-2.6-1T on large-scale environment-grounded data. KPop improves training efficiency through asynchronous scheduling across coding, search, tool use, and workflow execution, enabling scalable learning from complex agent-environment interactions. Together, Ling-2.6 and Ring-2.6 provide a practical pathway toward efficient, scalable, and open agentic systems. We open-source all checkpoints in the 2.6 family to support further research and development in practical agentic intelligence.

22.
medRxiv (Medicine) 2026-06-22

Repeat expansions in Parkinson's disease and parkinsonism across ancestries: insights from a global genetic cohort

Expanded short tandem repeats contribute to a broad spectrum of neurodegenerative diseases, yet their roles in Parkinson's disease (PD) and parkinsonism remain incompletely characterized, especially across diverse ancestries. We analyzed short-read whole-genome (WGS) and clinical exome sequencing (CES) data from 38,365 individuals (28,861 WGS; 9,504 CES), encompassing 23,242 patients with PD, 4,729 patients with atypical parkinsonism and 10,394 healthy controls from 11 genetic ancestries. To determine carrier frequencies and characterize repeat structures across diverse ancestries, we genotyped 12 established pathogenic loci where normal, intermediate, and pathogenic alleles can be reliably differentiated using short-read sequencing data. Additionally, we conducted threshold-based associations to determine the minimum threshold associated with increased PD risk in 15,995 individuals (8,591 PD, 7,404 controls) of European ancestry. Pathogenic repeat expansions were detected in 62 patients (56 PD and 6 atypical parkinsonism) and 5 controls across seven loci (AR, ATXN1, ATXN2, ATXN3, CACNA1A, HTT and THAP11), spanning seven ancestries. Among these, ATXN2 expansions were the most frequently observed in PD and were present in African, East Asian, European and Middle Eastern ancestries. Additionally, intermediate ATXN2 repeat expansions exhibited a strong, length-dependent association with PD risk in the European population, with individuals with [≥]32 repeats having a more than four-fold increased risk (odds ratio 4.25, 95% confidence interval 1.80-12.05). Overall, >92% of expanded alleles harbor CAA interruptions within the CAG tract. Pathogenic expansions at other loci, such as ATXN3 and THAP11, showed more ancestry-specific distributions. Clinically, individuals with pathogenic ATXN2 and ATXN3 expansions most often presented with typical PD features but frequently showed earlier disease onset and a strong family history of PD. This large-scale, multi-ancestry study comprehensively maps the genetic landscape of pathogenic and intermediate repeat expansions in PD. Our findings confirm a length- and structure-dependent risk association for ATXN2 with PD in the European population, and highlight the pleiotropic effects of repeat expansions across the parkinsonian spectrum.

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

EqCollide: Equivariant and Collision-Aware Deformable Objects Neural Simulator

arXiv:2506.05797v2 Announce Type: replace Abstract: Simulating collisions of deformable objects is a fundamental yet challenging task due to the complexity of modeling solid mechanics and multi-body interactions. Existing data-driven methods often suffer from lack of equivariance to physical symmetries, inadequate handling of collisions, and limited scalability. Here we introduce \name, the first end-to-end equivariant neural fields simulator for deformable objects and their collisions. We propose an equivariant encoder to map object geometry and velocity into latent control points. A subsequent equivariant Graph Neural Network-based Neural Ordinary Differential Equation models the interactions among control points via collision-aware message passing. To reconstruct velocity fields, we query a neural field conditioned on control point features, enabling continuous and resolution-independent motion predictions. Experimental results on 2D and 3D scenarios show that \name achieves accurate, stable, and scalable simulations across diverse object configurations. It achieves $24.34\%$ to $57.62\%$ lower rollout MSE, even compared with the best-performing baseline model. Furthermore, \name could generalize to more colliding objects and extended temporal horizons, and stay robust to input transformed with group action. Code is available at: https://github.com/AI4Science-WestlakeU/EqCollide

24.
medRxiv (Medicine) 2026-06-22

Midlife Measures of General Cognitive Performance in the National Longitudinal Study of Adolescent to Adult Health (Add Health)

Objective: The Add Health Cognitive Assessment, Physical, and Sensory Function Protocol (Add CAPS) was developed to assess cognitive, physical, and sensory function in early midlife in a nationally representative sample in the United States. Using Add CAPS, we developed two general cognitive performance measures. Methods: The sample included 2,525 participants from Add Health Wave VI who completed an in- home assessment of cognitive performance. Confirmatory factor analysis (CFA) was used to derive two general cognitive performance (GCP) scores: (1) a five-domain score based on originally designed cognitive domains (Add CAPS GCP), and (2) a modified score aligned with the Harmonized Cognitive Assessment Protocol (HCAP) framework (Add CAPS GCP-H). We evaluated model fit using Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), and Comparative Fit Index (CFI) and tested factor scores for criterion validity. Results: Both models showed good fit (Add CAPS GCP: RMSEA = 0.025, SRMR = 0.031, CFI = 0.968; Add CAPS GCP-H: RMSEA = 0.027, SRMR = 0.033, CFI = 0.962), indicating that they adequately represent the underlying GCP construct. Discussion: The Add CAPS cognitive battery captures a robust, hierarchical structure of GCP across alternative domain specifications. The derived factor scores provide a valuable method for characterizing a person's cognitive baseline during midlife. Importantly, the Add CAPS GCP-H enhances comparability with the HCAP network, supporting cross-cohort analyses of cognitive aging.

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

Evidence of Layered Positional and Directional Constraints in the Voynich Manuscript: Implications for Cipher-Like Structure

The Voynich Manuscript (VMS) exhibits a script of uncertain origin whose grapheme sequences have resisted linguistic analysis. We present a systematic analysis of its grapheme sequences, revealing two complementary structural layers: a character-level right-to-left optimization in word-internal sequences and a left-to-right dependency at word boundaries, a directional dissociation not observed in any of our four comparison languages (English, French, Hebrew, Arabic). We further evaluate two classes of structured generator against a four-signature joint criterion: a parametric slot-based generator and a Cardan grille implementing Rugg's (2004) gibberish hypothesis. Across their full tested parameter spaces, neither class reproduces all four signatures simultaneously. While these results do not rule out generator classes we have not tested, they provide the first quantitative benchmarks against which any future generative or cryptanalytic model of the VMS can be evaluated, and they suggest that the VMS exhibits cipher-like structural constraints that are difficult to reproduce from simple positional or frequency-based mechanisms alone.