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01.
arXiv (CS.CV) 2026-06-16

You Don't Need Strong Assumptions: Visual Representation Learning via Temporal Differences

Progress in AI has largely been driven by methods that assume less. As compute and data increase, approaches with weaker inductive biases generally outperform those with stronger assumptions. This is particularly characteristic of the field of Visual Representation Learning, where approaches have gone from being dominated by Supervised Learning, to Weakly Supervised Learning, to the now widespread success of Self-Supervised Learning without human labels. Yet, even modern Self-Supervised Learning approaches still depend on strong inductive biases such as augmentations, masking, or cropping. If this trend holds, even these remaining biases should become bottlenecks at scale – and our experiments confirm this: the optimal strength of inductive biases decreases as data grows. This motivates the search for approaches that rely on fewer assumptions. To this end, we introduce Temporal Difference in Vision (TDV), a new paradigm for self-supervised learning from video that avoids existing inductive biases, relying instead on a causal assumption that the past causes the future. TDV functions by jointly training an image encoder and a motion encoder so that the current frame's representation plus the encoded motion equals the next frame's representation. Despite not leveraging any strong inductive biases, TDV matches state-of-the-art recipes on dense spatial tasks, laying the foundation for representation learning without strong assumptions.

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

MoECa: Aligning Feature Reuse with Expert Decomposition in Diffusion Transformers

Diffusion Transformers with Mixture-of-Experts (DiT-MoE) improve model capacity under sparse activation, but diffusion inference is still bottlenecked by redundant computation across timesteps. Existing caching methods mainly operate at the token level, which becomes suboptimal in DiT-MoE because each token update is internally decomposed into multiple routed expert branches. Our analysis shows that cross-timestep redundancy in DiT-MoE is better characterized at the expert-branch level than at the whole-token level. Based on this observation, we propose MoECa, a fine-grained caching framework that performs branch-level feature reuse across timesteps. MoECa further introduces expert-aware adaptive control and synchronized cache updates across MoE and attention paths to maintain stable intermediate states. Experiments on multiple DiT-MoE models show that MoECa consistently achieves a better speed-quality trade-off than prior caching methods, with up to 2.83$\times$ inference speedup and minimal quality degradation.

03.
medRxiv (Medicine) 2026-06-15

Efficacy of Painhunting Therapy for Event-Related Depression: A Randomized Controlled Trial with Crossover Replication

Background. Depression affects an estimated 332 million people worldwide and is a leading cause of disability, with up to 80% of major depressive episodes preceded by an identifiable adverse life event [17,18]. First-line treatments target symptoms rather than the precipitating event and are resource-intensive: standard CBT averages roughly 12 sessions, and antidepressant discontinuation carries relapse rates near 35% at six months [8]. These limitations create a clear rationale for brief, structured interventions that address the cognitive and somatic sequelae of adverse life events directly. Painhunting therapy is one such intervention, in which each session targets a discrete adverse event through a structured incident-processing procedure. Methods. We conducted a two-arm, parallel-group, single-site randomised controlled trial comparing Painhunting therapy (Arm A, immediate; n=42) with a waitlist control (Arm B, delayed; n=42) in adults with PHQ-9 >= 9 and active psychological distress related to an adverse life event. After the primary endpoint at T2 (approximately two weeks post-randomisation), Arm B crossed over to active treatment, with T3 as the post-crossover endpoint at approximately four weeks. The primary outcome was PHQ-9 at T2 (between-arm contrast); secondary outcomes were ICG, GAD-7, WHO-DAS 2.0 (12-item), and the Global Impression of Change (GIC). Pre-specified analyses included intention-to-treat, per-protocol, and single-exclusion sensitivity populations. Results. Eighty-four participants were randomised (198 applications, 134 completed screening questionnaire, 119 passed psychometric screening). At T2, mean PHQ-9 was 2.32 (SD 2.59) in Arm A and 16.56 (SD 6.76) in Arm B, yielding an ITT between-arm Cohen d = 2.78 (95% CI 2.19-3.76, p < 0.001). Within-arm paired reductions during each arm's active-treatment window reproduced this magnitude (Arm A T0 to T2 change 14.71, Morris d = 2.80; Arm B T2 to T3 change 14.19, Morris d = 2.77, eligible n=26). Treatment gains were durable at the T4 follow-up (week 8). Aligning each arm to its own end-of-treatment timepoint, the off-treatment drift to week 8 was almost identical between arms: Arm A rose 0.78 points from T2 to T4 (2.19 to 2.97, n=37) and Arm B rose 1.59 points from T3 to T4 (4.74 to 6.33, n=27), the latter falling to 0.77 points once a single documented relapse case (R59) is excluded (4.81 to 5.58, n=26). This small off-treatment rebound then stabilised rather than continuing: Arm A was essentially unchanged from T3 to T4 (change +0.05), with concordant maintenance on ICG, GAD-7, and WHO-DAS. At T4, 68% of Arm A and 41% of Arm B remained in remission (PHQ-9 < 5). Secondary measures (ICG, GAD-7, WHO-DAS) moved in the same direction and to comparable magnitude at every timepoint. The waitlist window in Arm B showed essentially no change on any measure (PHQ-9 change 0.22, p = 0.81). Sensitivity analyses excluding six sub-threshold T2 cases, the single treated-in-error case (R82), the R59 relapse case, and one late T2 submitter left all conclusions unchanged. Conclusions. Painhunting therapy produced large and statistically robust reductions in depression, complicated grief, anxiety, and functional disability over a brief course of three to four sessions, with effect sizes substantially exceeding benchmarks reported for established first-line psychotherapies including CBT and EMDR. Critically, these gains persisted at the week-8 follow-up: depression scores in the immediate-treatment arm were essentially unchanged from four weeks to eight weeks post-randomisation, indicating that the benefit reflects durable change rather than a transient post-session dip. Treatment-window concordance between arms, durability of gains at one month off-treatment, and the flat waitlist trajectory together strengthen the evidence for genuine efficacy rather than spontaneous remission. Baseline covariates including therapeutic alliance, treatment expectancy, self-efficacy, age, and sex showed near-zero associations with outcome, reducing the plausibility of allegiance bias or expectancy effects as primary drivers. The differential retention between arms (88% vs 64% at T3) is attributable to the waitlist design and is discussed as a limitation. These findings support proceeding to a confirmatory active-comparator trial against manualized CBT. Trial registration: ClinicalTrials.gov NCT07490691, prospectively registered.

04.
medRxiv (Medicine) 2026-06-17

Characterizing the genetic basis of Cardio-Renal-Metabolic multimorbidity using multivariate genomic modelling

Cardio-renal-metabolic multimorbidity (CRMM) encompasses interrelated conditions affecting the heart, kidneys, and metabolic systems. Although the genetics of individual components are well studied, their shared architecture remains unclear. Here, we performed the largest multi-ancestry multivariate GWAS of CRMM across seven biobanks, including individuals of European (EUR; neff = 353,130), African (AFR; neff = 75,436), and East Asian (EAS; neff = 164,373) ancestry. We identified 287 lead loci in EUR, 30 in AFR, and 202 in EAS. Cross-ancestry analyses revealed ancestry-specific signals and 24 shared loci mapping to FTO and TCF7L2. Drug-repurposing highlighted candidates used for type 2 diabetes and hypertension. Mendelian randomization supported causal links with diverse diseases, while polygenic risk scores showed improved prediction across ancestries. Collectively, these findings advance understanding of CRMM genetics and inform precision medicine.

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

Hierarchical Modeling of ICD Codes in EHR Foundation Models

arXiv:2606.15447v1 Announce Type: new Abstract: Electronic health record foundation models typically treat ICD diagnosis codes as flat tokens, overlooking the clinically meaningful hierarchical structure that captures disease families, subcategories, and fine-grained diagnostic detail. As a result, existing EHR representation learning methods do not explicitly exploit the hierarchical structure already present in the coding system. In this work, we study ICD-10-CM hierarchy as a general inductive bias for clinical representation learning. We investigate two complementary mechanisms for incorporating hierarchy: first, by augmenting diagnosis sequences in a BERT-style transformer with tokens corresponding to different levels of the ICD hierarchy, and second, by injecting hierarchy into graph-based code representations through hierarchy-aware edges combined with diagnosis co-occurrence structure. Across these settings, we evaluate whether explicit hierarchy improves downstream prediction, which levels of the hierarchy are most useful, whether hierarchy encoding improves transfer across datasets, and how hierarchy reshapes embedding similarity structure. We conduct experiments on two large-scale real-world clinical datasets: MIMIC-IV, used for pretraining and in-domain evaluation, and eICU, used to assess cross-dataset transfer via frozen encoder probing. Our findings show that explicitly encoding ICD hierarchy improves over flat code representations in both in-domain and cross-dataset settings, while revealing that the most useful level of hierarchy depends on both the task and the modeling approach. More broadly, we focus on hierarchy-aware EHR representation learning and show that the benefits of encoding hierarchy are generalizable across modeling settings and hierarchy levels.

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

Structure-Oriented Randomized Neural Networks for Poisson-Nernst-Planck and Poisson-Nernst-Planck-Navier-Stokes Systems

arXiv:2606.19912v1 Announce Type: cross Abstract: We develop a structure-oriented randomized neural network framework, termed SO-RaNN, for the Poisson-Nernst-Planck (PNP) system and the Poisson-Nernst-Planck-Navier-Stokes (PNP-NS) system. The decoupled linearized subproblems are solved iteratively by randomized neural networks in a space-time framework. For the concentration variables, a pointwise cut-off is used to enforce positivity at the value level, and discrete mass-scaling factors are computed at selected correction instants and interpolated in time, so as to ensure exact mass matching at those instants and to promote approximate mass preservation between them. To introduce an auxiliary discrete dissipation mechanism, we further employ an SAV-type post-processing correction, which yields monotonicity of the SAV auxiliary variable under the ideal SAV update. For the PNP-NS system, a structure-preserving randomized neural network (SP-RaNN) is used for the velocity field, so that the velocity approximation satisfies the incompressibility constraint pointwise by construction. On the theoretical side, we derive residual-based estimates for the raw, uncorrected RaNN solvers of the linearized subproblems, formulate a conditional local-in-time convergence result for the raw outer Picard iteration of the PNP system, and analyze the value-level positivity correction together with the mass-correction and SAV post-processing steps. For the PNP-NS system, we establish an approximation result for the SP-RaNN space and provide a conditional error statement for the corresponding linearized Oseen-type problem. Numerical experiments demonstrate approximation accuracy in the source-driven manufactured tests and illustrate the intended value-level positivity correction, selected-time mass matching, computed free-energy curves based on the final gauge-fixed potential, and divergence-free approximation in benchmark tests.

07.
arXiv (CS.AI) 2026-06-16

SPARK: Security Knowledge Priming and Representation-Guided Knowledge Activation for LLM-based Secure Code Generation

arXiv:2606.16244v1 Announce Type: cross Abstract: Large language models routinely generate code with exploitable security flaws. Prior literature attributes this limitation to a lack of security expertise, steering current defense mechanisms toward heavy fine-tuning or external knowledge retrieval, which introduces significant computational overhead and data bias through redundant code examples. Contrary to this view, we argue that pretraining corpora are already rich in security material. The bottleneck is activation: without an explicit and brief cue, statistical pressure toward common training-distribution patterns suppresses the model's safety-relevant representations. We present SPARK, an inference-time security harness that activates this latent knowledge without any retraining. The harness has two parts. Component~I retrieves a few of the relevant Common Weakness Enumeration (CWE) entries for each coding task and appends a short structured cue to the prompt; this alone is enough to surface the model's existing security representations. Component~II adds a precomputed token bias to the logits at every decoding step. We obtain the bias by projecting a safe-direction vector, the unit difference between the mean safe and mean unsafe last-layer hidden states, through the language model head. The bias is computed once offline; applying it costs a single vector addition per generated token. We evaluate SPARK on 9 open-source models across C++, Java, and Python, and compare with 7 baselines spanning fine-tuning and retrieval-augmented methods. SPARK matches or improves on the best baseline in every setting while preserving HumanEval utility. We further test Component~I in a black-box setting on 7 of today's strongest models, including Claude, DeepSeek, and GPT, demonstrating the bottleneck of insecure code generation and the improvements enabled by our method.

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

Fusion of Pervasive RF Data with Spatial Images via Vision Transformers for Enhanced Mapping in Smart Cities

In this paper, we present a deep learning-based approach that integrates the DINOv2 architecture to improve building mapping by combining (possibly erroneous) maps from open-source platforms with pervasive radio frequency (RF) data collected from multiple wireless user equipments and base stations. Unlike prior methods, our approach leverages a vision transformer-based architecture to jointly process both RF and map modalities within a unified framework, effectively capturing spatial dependencies and structural priors for enhanced mapping accuracy. For the evaluation purposes, we employ a synthetic dataset co-produced by Huawei. To address the challenges associated with real-world data imperfections, we introduce controlled noise to its RF data so as to simulate real-world conditions. Additionally, we develop and train a model that leverages only aggregated path loss information to tackle the mapping problem. We measure the results according to three performance metrics: the Jaccard index (intersection over union, IoU), the Hausdorff distance, and the Chamfer distance. Our design achieves a macro IoU of 65.3%, significantly surpassing (i) the erroneous maps baseline, which yields 40.1%, (ii) an RF-only method from the literature, which yields 37.3%, and (iii) a non-AI fusion baseline that we designed which yields 42.2%. The comparative evaluation highlights the limitations of relying solely on RF data or on spatial data, as well as the effectiveness that AI can have on fusing data towards enhancing smart city mapping accuracy. We further validate our method on real-world data from the Oslo region, complementing the synthetic evaluation with a real deployment setting, where our best fusion model reaches 64.9% macro IoU. We additionally outline a strategy for deploying the model over larger areas by tiling the region with overlapping windows.

09.
arXiv (quant-ph) 2026-06-17

Vorticity Induced by Non-frontal Collisions of Quantum Droplets

arXiv:2606.17498v1 Announce Type: cross Abstract: The rotational dynamics induced by the non-frontal binary collisions of quantum droplets composed of ultracold alkali atoms are analyzed. A theoretical study is presented within the extended Gross-Pitaevskii equation framework, using experimentally feasible conditions. Numerical experiments elucidate a rich landscape of possible topological excitations in the system that are robust towards measurements. The collision of heteronuclear quantum droplets composed of $^{41}$K and $^{87}$Rb atoms in the incompressible regime, gives rise to dynamical instabilities that spontaneously generate topological defects: vortex rings, dislocation lines, and vortices in one species. Their presence depends on the Weber number and the impact parameter. An experimental proposal for vortex detection in both real and Fourier space using interaction ramps is described.

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

CADO: From Imitation to Cost Minimization for Heatmap-based Solvers in Combinatorial Optimization

arXiv:2602.08210v2 Announce Type: replace Abstract: Heatmap-based solvers have emerged as a promising paradigm for Combinatorial Optimization (CO). However, we argue that the dominant Supervised Learning (SL) training paradigm suffers from a fundamental objective mismatch: minimizing imitation loss (e.g., cross-entropy) does not guarantee solution cost minimization. We dissect this mismatch into two deficiencies: Decoder-Blindness (being oblivious to the non-differentiable decoding process) and Cost-Blindness (prioritizing structural imitation over solution quality). We empirically demonstrate that these intrinsic flaws impose a hard performance ceiling. To overcome this limitation, we propose CADO (Cost-Aware Diffusion models for Optimization), a streamlined Reinforcement Learning fine-tuning framework that formulates the diffusion denoising process as an MDP to directly optimize the post-decoded solution cost. We introduce Label-Centered Reward, which repurposes ground-truth labels as unbiased baselines rather than imitation targets, and Hybrid Fine-Tuning for parameter-efficient adaptation. CADO achieves state-of-the-art performance across diverse benchmarks, validating that objective alignment is essential for unlocking the full potential of heatmap-based solvers.

11.
arXiv (quant-ph) 2026-06-12

Certifying Nonclassical Proper-Time Histories with a Quantum Clock

作者:

arXiv:2606.12755v1 Announce Type: new Abstract: Quantum clocks can acquire relativistic phases from motional or gravitational proper-time differences, but reduced clock dephasing alone does not certify nonclassical proper-time histories. We formulate this distinction as a channel-certification problem. First, we show that any two-level single-time dephasing signal, including one generated by an effective quantum proper-time label, admits a classical random proper-time representation. We then define the convex set of classical mixtures of experimentally specified proper-time histories and prove a Choi-rank separation criterion for conditioned coherent history recombination. A two-branch Ramsey protocol gives explicit bright- and dark-port population witnesses outside this classical set. The certification is operational and relative to the specified history set: it rules out classical mixtures of the same implemented proper-time histories, not arbitrary classical protocols with different histories or controls.

12.
arXiv (quant-ph) 2026-06-15

Collision models for open quantum systems coupled to finite environments

arXiv:2606.14163v1 Announce Type: new Abstract: We study a system qubit repeatedly interacting with the same environmental qubit, with a reservoir acting on the environment between collisions via a completely positive, trace-preserving map. We show that complete suppression of system–environment correlations uniquely requires a full environmental reset, recovering a semi group dynamics with a time-independent Gorini–Kossakowski–Sudarshan–Lindblad generator, whereas a partial reset yields a continuous transition between Markovian and non-Markovian regimes governed by a single dimensionless relaxation parameter. For a resonant excitation-exchange interaction, we obtain exact closed-form expressions for the Bloch-vector dynamics for both a generalized depolarizing channel and a generalized amplitude-damping channel acting as the reservoir-induced map. Using the Breuer–Laine–Piilo measure and a Choi-matrix CP-divisibility witness, we identify three distinct dynamical regimes across the parameter space: CP-divisible Markovian dynamics, CP-indivisible but P-divisible dynamics, and non-P-divisible non-Markovian dynamics. The boundaries between these regimes, and the structural differences between uniform and anisotropic environmental relaxation, are characterized numerically.

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

MotionVLA: Vision-Language-Action Model for Humanoid Motion

Generating realistic humanoid motion from scene images and text involves both low-frequency pose semantics and high-frequency physical dynamics. However, many existing methods tokenize motion with a single shared codebook, forcing heterogeneous motion signals into the same quantization space. Our frequency-domain analysis of human motion data reveals a clear mismatch between single-codebook quantization and motion statistics: five DCT coefficients capture 93% of joint-position energy but only 37% of joint-velocity energy, which can bias quantization toward pose statistics and under-represent high-frequency velocity components. A second challenge lies in adapting a standard autoregressive model to effectively model high-frequency physical signals in motion sequences. Therefore, we propose DSFT, a dual-stream frequency tokenizer that separates motion into Base and physical streams and compresses them independently with DCT truncation and BPE. Furthermore, we present MotionVLA, a Qwen3.5-based model that arranges Base and physical tokens in a unified sequence, where Phys tokens are predicted after Base tokens. Experiments on HumanML3D and MBench show that, despite using a lightweight 2B backbone, MotionVLA reduces the Diversity gap to real data by over 50% on HumanML3D and improves Motion-Condition Consistency by 3.8% on MBench, supporting frequency-aware dual-stream decoupling as an effective formulation for autoregressive motion generation. Code: https://github.com/AIGeeksGroup/MotionVLA. Website: https://aigeeksgroup.github.io/MotionVLA.

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

A physical adaptive material motor unit neural network: a hygromorph composite material machine

arXiv:2606.18275v1 Announce Type: cross Abstract: Advances in novel materials science enable structures to function as intelligent machines by embedding memory and learning capabilities directly into materials. Our work introduces a physical adaptive material motor unit neural network,leveraging a new generation of controllable actuators composed of wood- and carbon black-based composites, sensitive to temperature and relative humidity. These material actuators are assembled into a motor unit-like structure inspired by muscle contraction trigger, forming an intelligent machine capable of dynamic shading control that can be used, for example, in buildings. The machine is governed by a neural network trained on over 350 experimental data points collected under diverse environmental conditions. By establishing a new data-aware backpropagation training, we show that the machine predicts shading responses and learns to predict appropriate behaviour incrementally as the database expands. We also demonstrate the ability of the machine to optimise configurations to achieve similar shading outputs under two distinct conditions.

15.
arXiv (quant-ph) 2026-06-11

Polarization-Resolved Photon Statistics of Cavity Quantum Materials

arXiv:2606.11550v1 Announce Type: cross Abstract: By forming hybrid light-matter states, optical cavities offer a route for engineering material properties, however, unambiguously probing the effects of light-matter coupling remains difficult. Here, we show that the polarization-resolved statistics of photons transmitted through a cavity, measurable via $g^{(2)}$, provide one such diagnostic. By relating $g^{(2)}$ to matter correlation functions such as the Raman structure factor, we link photon bunching and antibunching to material properties. By applying this method to the stripy-to-antiferromagnetic transition in the Kitaev-Heisenberg spin model, we find that polarization-dependent patterns of bunching and antibunching encode the magnetic point-group symmetries of each phase and characterize the behavior at the phase boundary. Finally, we predict measuring $g^{(2)}$ for output photon pairs polarized orthogonal to the input field will isolate higher-order light-matter scattering processes that probe higher-order material correlations.

17.
medRxiv (Medicine) 2026-06-16

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

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.

18.
arXiv (CS.CL) 2026-06-11

One Jailbreak, Many Tongues: Learning Language-Insensitive Intention Representations for Multilingual Jailbreak Detection

Large language models (LLMs) are increasingly deployed in applications for global multilingual users, yet safety training remains concentrated in dominant languages and has not progressed in parallel with multilingual capability, creating exploitable gaps for jailbreak attacks. Current jailbreak defenses are largely developed and evaluated in dominant languages, and their effectiveness is limited by the scarcity of aligned multilingual supervision and representations dispersion caused by language variation. To address this issue, we propose MLJailDe, a multilingual jailbreak detection framework designed to improve both multilingual robustness and cross-lingual generalization. MLJailDe first introduces a multilingual back-translation data augmentation algorithm to construct a semantically consistent and functionally effective dataset spanning 11 languages, consisting of 2,232 benign and 1,239 jailbreak samples. On this basis, MLJailDe employs relative-distance constraints to reduce cross-lingual representation dispersion and encourage jailbreak prompts with similar intent to form consistent clusters across languages, while an imbalance-aware classification objective is further used to alleviate class imbalance and learn more reliable multilingual decision boundaries. Experimental results show that MLJailDe outperforms state-of-the-art baselines across multiple languages, achieving an F1 score of 98.5\%, and obtains an average F1 score of 97.1\% on unseen languages, demonstrating strong effectiveness and cross-lingual generalization.

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

The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction

arXiv:2603.01250v2 Announce Type: replace-cross Abstract: Breast cancer is the most frequently diagnosed malignancy among women worldwide and a leading cause of cancer-related mortality. Dynamic contrast-enhanced magnetic resonance imaging plays a central role in tumor characterization and treatment monitoring, particularly in patients receiving neoadjuvant chemotherapy. However, existing artificial intelligence models for breast magnetic resonance imaging are typically developed and evaluated using heterogeneous datasets, study populations, and assessment protocols, making direct comparison difficult and limiting understanding of model robustness across institutions and clinically relevant patient subgroups. The MAMA-MIA Challenge was designed to address these challenges by providing a standardized benchmark for the joint evaluation of primary tumor segmentation and prediction of pathologic complete response using pre-treatment magnetic resonance imaging only. The training cohort comprised 1,506 patients from multiple institutions in the United States, while evaluation was conducted on an external test set of 574 patients from three independent European centers to assess cross-continental and cross-institutional generalization. A unified scoring framework combined predictive performance with subgroup consistency across age, menopausal status, and breast density. Twenty-six international teams participated in the final evaluation phase. Results demonstrate substantial performance variability under a common external evaluation framework and reveal trade-offs between overall accuracy and subgroup fairness. The challenge provides standardized datasets, evaluation protocols, and public resources to promote the development of robust and equitable artificial intelligence systems for breast cancer imaging.

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

Exploring Starts Are Not Enough: Counterexamples and a Fix for Monte Carlo Exploring Starts

arXiv:2606.15247v1 Announce Type: cross Abstract: The asymptotic behaviour of Monte Carlo Exploring Starts (MCES) is a long-standing open question in reinforcement learning, even in the tabular setting. We investigated the convergence properties of tabular MCES by constructing examples in which the algorithm converges to suboptimal solutions. This paper presents new counterexamples for both initial-visit and first-visit MCES and gives a convergence-restoring modification for the initial-visit case. We show that stable suboptimal solutions may exist for initial-visit MCES with sample-average updates even when greedy actions are updated more often than non-greedy actions on average. However, by scaling learning rates inversely to update frequencies on a state-by-state basis, convergence to optimality is guaranteed. Unlike previous uniformisation methods, this modification is applicable to large-scale problems that require approximating the estimated value function. We then extend the example to show that sample-average first-visit MCES may also converge to suboptimal solutions. This largely settles a fundamental open problem and shows that exploring starts alone do not guarantee convergence to optimality. More broadly, these results highlight that convergence depends critically on the relative size and frequency of updates applied to different actions, making the choice of learning rates and the balance between exploration and exploitation central to the analysis of MCES and the implementation of scalable Monte Carlo control methods.

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

PaperJury: Due-Process Review for Bounded LaTeX Revision

Pre-submission hardening of human-authored LaTeX computer science papers differs from drafting assistance because it requires adversarial whole-paper review, explicit no-fix outcomes, and bounded artifact-safe revision. Existing writing assistants, critique generators, and judge-centered loops lack durable issue identity across rounds, deterministic routing from critique to adjudication, and manuscript control that can reject invalid concerns or defer author-dependent ones. We present PaperJury, a closed-loop review-verdict-revise-verify system built on a deterministic-versus-semantic split: deterministic orchestration manages decomposition, a frozen claim spine, a durable ledger, routing, stopping, and exact-once patch application, while semantic agents are limited to bounded review, judgment, and repair. PaperJury combines bounded holistic review, contestability-based routing, a due-process trial, and risk-proportional guard chains for anchor-bounded edits, yielding terminal outcomes of invalid-drop, valid-fixable, and author-required. In a two-arm expert-review evaluation on held-out Vision, natural language processing, and machine learning papers against four baselines, we assess issue quality, verdict and routing quality, edit safety, convergence behavior, and cost, supporting the thesis that load-bearing safety and completion logic should reside in deterministic orchestration rather than model discretion. PaperJury is available at https://github.com/u7079256/paperjury.

22.
arXiv (quant-ph) 2026-06-12

Toward Entanglement Bootstrap for Conformal Field Theory in Any Dimension

arXiv:2606.12540v1 Announce Type: cross Abstract: Given a quantum critical wavefunction in any dimension, we propose a reconstructed Hamiltonian, analogous to the ones previously found for 1+1d CFT and for 2+1d bosonic liquid topologically-ordered states. We test numerically that, for known regularized approximate CFT groundstates (on the icosahedron and the fuzzy sphere), (1) they are close to the groundstate of their reconstructed Hamiltonian, and (2) the spectrum of their reconstructed Hamiltonian on the unit sphere has CFT properties (integer spacing of descendants) and matches known low-lying energies. We show that this provides an automated method to improve the finite-size effects in a fixed Hilbert space.

23.
medRxiv (Medicine) 2026-06-12

Conversational Artificial Intelligence-Enabled Precision Oncology Reveals Context-Specific TGFβ and JAK/STAT Alterations in Pancreatic Cancer

Background: Pancreatic ductal adenocarcinoma (PDAC) is characterized by extensive molecular complexity, profound stromal remodeling, and limited responsiveness to systemic therapies. Although gemcitabine-based regimens remain widely utilized, the molecular pathways that influence treatment-associated biological variation are incompletely understood. The TGF{beta} and JAK/STAT signaling networks are recognized regulators of tumor progression, immune modulation, and therapeutic resistance; however, their genomic architecture in clinically stratified PDAC populations remains poorly defined. Methods: We employed a conversational artificial intelligence-driven analytical framework to investigate TGF{beta} and JAK/STAT pathway alterations in a cohort of 184 PDAC patients. Clinical and molecular data were integrated to generate age- and treatment-stratified cohorts, enabling pathway-level and gene-level analyses according to gemcitabine exposure. Findings generated through AI-assisted interrogation were subsequently evaluated using conventional statistical approaches. Results: TGF{beta} pathway alterations were identified in approximately one-quarter to one-third of tumors across clinical subgroups and demonstrated relatively stable frequencies regardless of age at diagnosis or gemcitabine treatment status. Gene-level analyses revealed that pathway disruption was predominantly driven by recurrent alterations in SMAD4, with additional low-frequency events involving TGFBR1 and TGFBR2. Notably, TGFBR2 mutations were significantly more frequent among late-onset PDAC patients receiving gemcitabine compared with untreated late-onset patients (8.8% vs. 1.4%; p = 0.04), suggesting a potential treatment-associated enrichment. In contrast, JAK/STAT pathway alterations were rare throughout the cohort, with only isolated mutations observed in pathway components including JAK1, JAK2, JAK3, STAT1, STAT3, and related regulatory genes. No significant differences in JAK/STAT alteration frequencies were identified according to age or treatment exposure. Conclusions: TGF{beta} and JAK/STAT pathways exhibit distinct genomic architectures in PDAC. TGF{beta} pathway disruption represents a recurrent feature of disease biology, largely driven by SMAD4 alterations, while TGFBR2 enrichment in gemcitabine-treated late-onset tumors suggests a potential context-specific association worthy of further investigation. Conversely, genomic alterations within the JAK/STAT pathway are uncommon, indicating that pathway activity may be regulated predominantly through non-genomic mechanisms. These findings demonstrate the utility of conversational artificial intelligence agents for rapid, scalable, and clinically contextualized pathway interrogation and support future studies integrating multi-omic data to refine precision medicine strategies in PDAC.

24.
arXiv (CS.CL) 2026-06-15

EmoMind: Decoding Affective Captions from Human Brain fMRI

Decoding visual experience from brain activity has advanced substantially, but current brain-to-text systems largely recover semantic content while discarding affect. Additionally, language models can generate emotional text when prompted with categorical labels, but such labels collapse rich inter-subject variability into coarse discrete bins. We present EmoMind, the first end-to-end pipeline for decoding affective captions directly from fMRI signals. EmoMind first retrieves a semantically grounded neutral scene description from brain-decoded visual features, then rewrites it using a continuous 34-dimensional emotion vector decoded from the same fMRI recording. To control the balance between content preservation and affective expression, we train the rewriter with classifier-free guidance against an identity-preserving null branch, enabling smooth interpolation between semantic fidelity and affective expressivity. We evaluate affective caption generation with a three-axis validation framework spanning subject-specificity, structural geometry, and causal control. We further augment this framework with a synthetic-brain substitution test that probes robustness to the measurement apparatus, and we benchmark each axis against GPT-4 prompted with brain-decoded top-5 emotion labels as a strong discrete baseline. Across two independent emotion fMRI datasets, EmoMind significantly outperforms label-prompted GPT-4 on all three axes, with the largest gains on metrics that require person-specific affective structure rather than population-level emotion aggregation. These results establish continuous brain-decoded affect as a viable control signal for individualized affective caption generation and open new directions for studying individual affective brain organisation.

25.
arXiv (math.PR) 2026-06-12

On McDiarmid's Inequality under Dependence via Approximate Tensorization of Entropy

arXiv:2606.12720v1 Announce Type: new Abstract: We argue that dependent versions of McDiarmid's inequality are a useful but underutilized tool in mathematical statistics, learning theory and theoretical computer science. To make this point, we first highlight that approximate tensorization of entropy (ATE) implies McDiarmid's via the Entropy Method. Second, we derive McDiarmid's inequality for non-isotropic Gaussian random vectors $X \sim \mathcal N(\mu, \Sigma)$ through ATE with a constant of the order of the condition number of $\Sigma$. We both independently obtain this ATE through a simple application of stochastic localization and also discuss how a more general ATE for the Gibbs sampler due to Ascolani et al., 2026 generalizes McDiarmid's-like concentration to strongly log-concave and log-smooth probability measures. We then apply the resulting concentration inequalities to resolve a question on the concentration of $\operatorname{sign}(X)$ posed by Simone Bombari, investigate Erdős-Rényi graphs under dependence and prove a Dvoretzky-Kiefer-Wolfowitz-type inequality for observations from a joint measure fulfilling ATE and continuous marginal CDFs. For the class of strongly log-concave and log-smooth measures, this result improves upon a prior Dvoretzky-Kiefer-Wolfowitz-type inequality for non-i.i.d. observations due to Bobkov and Götze, 2010, by establishing the expected $1/\sqrt{n}$-rate of convergence under weak dependence instead of $n^{-1/3}$.