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01.
arXiv (CS.LG) 2026-06-12

Mirror Descent on Riemannian Manifolds

arXiv:2603.17527v2 Announce Type: replace-cross Abstract: Mirror Descent (MD) is a scalable first-order method widely used in large-scale optimization, with applications in image processing, policy optimization, and neural network training. This paper generalizes MD to optimization on Riemannian manifolds. In particular, we develop a Riemannian Mirror Descent (RMD) framework via reparameterization and further propose a stochastic variant of RMD. We also establish non-asymptotic convergence guarantees for both RMD and stochastic RMD. As an application to the Stiefel manifold, our RMD framework reduces to the Curvilinear Gradient Descent (CGD) method proposed in [26]. Moreover, when specializing the stochastic RMD framework to the Stiefel setting, we obtain a stochastic extension of CGD, which effectively addresses large-scale manifold optimization problems.

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

MegaFold: Efficient Training of Next-Generation 3D Attention Protein Models on Cross-Platform GPUs

arXiv:2506.20686v2 Announce Type: replace-cross Abstract: Recent advances in biomolecular modeling have been catalyzed by models such as AlphaFold3 (AF3), which introduce science-informed changes to the transformer architecture. Unlike transformers, a defining characteristic of AF3-style models is their 3D attention over 2D pairwise representations which produces tensors whose computation and memory costs scale cubically with sequence length. As a result, despite moderate parameter counts, AF3-style models are far more expensive to train than size-equivalent transformers, and are severely constrained by GPU memory capacity. Our characterization shows 3D attention fundamentally changes the training workload, causing massive 3D attention maps, complex inter-operator dependencies, kernel fragmentation, and heavy host-side data pipelines which differ substantially from LLM training, leading to poor utilization on modern GPU systems. Moreover, existing GPU optimizations do not adequately address these challenges due to complex cross-layer inter-operator dependencies introduced by 3D attention. Motivated by these challenges, we introduce MegaFold, a novel cross-platform system for efficient training of next-generation 3D-attention protein models. MegaFold combines a memory-efficient 3D-attention kernel, a communication-efficient sharding strategy for quadratic representations, fused operator implementations for critical execution paths, and a determinism-aware host-device pipeline that eliminates preprocessing stalls. Evaluation on both NVIDIA H200 and AMD MI250 GPUs shows that MegaFold enables training with up to 3.36$\times$ longer sequence lengths on 32 GPUs while reducing end-to-end execution time by up to 1.73$\times$ (NVIDIA) and 1.62$\times$ (AMD).

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

Deep Learning-Driven Inverse Design of Doherty Power Amplifiers Using Pixelated Combiners and Dual-State Impedance Synthesis

arXiv:2606.18395v1 Announce Type: cross Abstract: The output combiner of a Doherty power amplifier (PA) integrates load modulation, impedance matching, and phase compensation within a single network, making its design and synthesis highly challenging. In this paper, we propose a three-port Doherty combiner design methodology that combines deep convolutional neural networks (CNNs), pixelated layout representations, and genetic algorithms (GA) with dual-state impedance synthesis to address both peak and back-off power conditions. As a proof of concept, two GaN HEMT Doherty PA prototypes incorporating three-port pixelated combiners are designed and fabricated. Both prototypes achieve a measured saturated output power exceeding 44.2 dBm with peak drain efficiency above 71.2% within 2.6-2.8 GHz. Furthermore, a drain efficiency as high as 64% is measured at the 6-dB back-off level. After applying digital predistortion, each prototype achieves an adjacent channel leakage ratio (ACLR) better than -51.3 dBc.

04.
arXiv (math.PR) 2026-06-16

On the Smoluchowski-Kramers approximation for the hyperbolic $O(N)$ linear sigma model and its mean-field limit

arXiv:2606.15214v1 Announce Type: cross Abstract: We study the hyperbolic $O(N)$ linear sigma model, i.e. a system of $N$ interacting stochastic damped nonlinear wave equations (SdNLW) with coupled cubic nonlinearities, posed on the two-dimensional torus and indexed by a parameter $\varepsilon > 0$. We show that as $\varepsilon$ goes to zero (Smoluchowski-Kramers approximation) and $N$ goes to infinity (mean-field limit), each component of the solution to the SdNLW system converges to the solution to the stochastic nonlinear heat equation (SNLH) with a mean-field nonlinearity. We prove such convergence via two regimes: first with $\varepsilon$ going to zero to obtain the parabolic $O(N)$ linear sigma model, i.e. a system of $N$ coupled SNLH, and then with $N$ going to infinity; or first with $N$ going to infinity for each component to obtain the mean-field SdNLW and then with $\eps$ going to zero. As a result, we obtain a commutative diagram regarding the convergence from the hyperbolic $O(N)$ linear sigma model to the mean-field SNLH.

05.
arXiv (CS.AI) 2026-06-15

The Weight Norm Sets the Grokking Timescale: A Causal Delay Law

arXiv:2606.13753v1 Announce Type: cross Abstract: Grokking is the delayed onset of generalization in neural networks, arising long after they fit the training data. Whether the weight norm causes this delay is disputed: some studies report a critical norm at the transition, others observe grokking with no fixed norm at all. We settle this by intervening on the norm during training rather than only observing it. Under free training with weight decay, networks grok when the weight norm reaches a value Wc that varies little across seeds and learning rates (CV 1 to 2 percent) and grows with the modular base as a power law. When we instead clamp the norm to a fixed multiple rho of Wc and hold it there, the network still groks, but the delay follows T_grok proportional to exp(alpha rho). One exponent, alpha near 7.5, fits this delay across four moduli (R^2 = 0.996). Over the swept ranges the held norm moves the delay by about 19x and the learning rate by only about 2x, and holding the norm above Wc slows grokking rather than preventing it. A final LayerNorm removes the dependence by decoupling weight scale from the network function; without it the exponential law returns. This pinned-norm delay is the exponential counterpart to the logarithmic delay predicted for a freely contracting norm.

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

ARTEMIS: Agent-guided Reliability-aware Temporal Mask Evolution for Imperfectly Supervised Video Polyp Segmentation

Imperfectly supervised video polyp segmentation (VPS) aims to learn dense, temporally consistent masks from inexpensive supervision, including weak annotations (points, scribbles) and semi-supervision with few densely labeled frames. This setting is clinically valuable but challenging due to weak contrast, ambiguous boundaries, motion blur, and specular highlights, compounded by sparse pixel-level guidance. While SAM2 can generate dense masks from sparse inputs, direct pseudo-labeling often yields geometry-degraded masks with boundary leakage, underutilizes temporal consistency, and ignores reliability. To address these issues, we propose ARTEMIS, a unified framework for imperfectly supervised VPS driven by agent-guided reliability-aware temporal mask evolution. ARTEMIS initializes coarse masks from available supervision: SAM2 converts points/scribbles, while dense labels serve as reliable anchors. A debate-and-judge vision-language agent selects reliable temporal anchors under weak supervision, which are propagated bidirectionally with SAM2 to refine unreliable or unlabeled frames. Finally, ARTEMIS trains the segmenter using temporal reliability-aware robust learning, incorporating reliability-guided reference selection, a Reference Prototype Transport Module, and reliability-aware robust loss. These components assess mask reliability, evolve anchors over time, transport target identity across frames, and down-weight noisy supervision instead of discarding difficult samples. Experiments on SUN-SEG and CVC-ClinicDB-612 under scribble, point, and limited-label settings demonstrate that ARTEMIS achieves state-of-the-art performance. Code will be released at https://github.com/wangtong627/ARTEMIS.

07.
arXiv (quant-ph) 2026-06-16

Hyperinvariant Spin Network States – An AdS/CFT Model from First Principles

arXiv:2510.06602v2 Announce Type: replace Abstract: We study the existence and limitations of hyperinvariant tensor networks incorporating a local SU(2) symmetry. As discrete implementations of the anti de-Sitter/conformal field theory (AdS/CFT) correspondence, such networks have created bridges between the fields of quantum information theory and quantum gravity. Adding SU(2) symmetry to the tensor network allows a direct connection to spin network states, a basis of the kinematic Hilbert space of loop quantum gravity (LQG). We consider a particular situation where the states can be interpreted as kinematic quantum states for three-dimensional quantum gravity. We show that important aspects of the AdS/CFT correspondence are realized in certain quantum states of the gravitational field in LQG, thus justifying, from first principles, a class of models introduced by [F. Pastawski et al., JHEP 06, 149 (2015)]. We provide examples of hyperinvariant tensor networks, but also prove constraints on their existence in the form of no-go theorems that exclude absolutely maximally entangled states as well as general holographic codes from local SU(2)-invariance. We calculate surface areas as expectation values of the LQG area operator and discuss further possible constraints as a consequence of a decay of correlations on the boundary.

08.
arXiv (CS.AI) 2026-06-16

No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks

arXiv:2405.02369v2 Announce Type: replace-cross Abstract: In the past decade, many successful networks are on novel architectures, which almost exclusively use the same type of neurons. Recently, more and more deep learning studies have been inspired by the idea of NeuroAI and the neuronal diversity observed in human brains, leading to the proposal of novel artificial neuron designs. Designing well-performing neurons represents a new dimension relative to designing well-performing neural architectures. Biologically, the brain does not rely on a single type of neuron that universally functions in all aspects. Instead, in our brain, neurons are often task-based. In this study, we address the following question: since the human brain is a task-based neuron user, can the artificial network design go from the task-based architecture design to the task-based neuron design? Since methodologically there are no one-size-fits-all neurons, given the same structure, task-based neurons can enhance the feature representation ability relative to the existing universal neurons due to the intrinsic inductive bias for the task. Specifically, we propose a two-step framework for prototyping task-based neurons. As the initial step, we evaluate the proposed framework using polynomials as base functions. Empirically, systematic experimental results on synthetic data, classic benchmarks, and real-world applications show that the proposed task-based neuron design is not only feasible but also delivers competitive performance over other state-of-the-art models.

09.
arXiv (math.PR) 2026-06-11

Convergence of a Critical Multitype Bellman–Harris Process with One Infinite-Mean Lifetime

arXiv:2606.11511v1 Announce Type: new Abstract: We study a critical multitype Bellman–Harris branching particle system in $\mathbb R^N$ with a finite type space $\mathbb K=\{1,\dots,K\}$. Particles of type $i$ move according to a symmetric $\alpha_i$-stable process and reproduce according to a critical offspring law whose mean matrix is irreducible and stochastic. The lifetime distribution of type $1$ is assumed to have infinite mean with regularly varying tail $$ 1-F_1(t)\sim c_1t^{-\gamma},\, 0 \frac{\gamma}{\beta}, $$ and a local increment condition on the heavy lifetime distribution, we prove convergence of the system to a Poisson random measure concentrated on the infinite-mean type.

10.
medRxiv (Medicine) 2026-06-12

Opportunistic CKD Screening in Hospitalized Patients

Background. Chronic kidney disease (CKD) affects 10-13% of adults worldwide but remains largely undiagnosed until advanced stages. Hospitalization provides an opportunity for early detection through opportunistic urine albumin-to-creatinine ratio (UACR) measurement. Methods. We conducted a prospective three-arm study of opportunistic CKD screening in general internal medicine wards at Hadassah Mt. Scopus (MS), Hadassah Ein Kerem (EK), and Shaare Zedek Medical Center (SZMC) in Jerusalem (Protocol HMO-23-0300). Adult inpatients without known CKD or recent UACR were enrolled. Pathological UACR was defined as [≥]30 mg/g. Confirmed CKD required two pathological measurements [≥]90 days apart (KDIGO-compatible). eGFR was computed using the 2021 CKD-EPI race-free equation. Pooled proportions were estimated by fixed-effects logit meta-analysis; odds ratios by DerSimonian-Laird random-effects models. Results. A total of 158 patients were enrolled (MS n=50, EK n=57, SZMC n=51). Pathological first UACR was identified in 43/158 patients (27.2%; 95% CI 21.3-34.1%; I2=0% across centers). Of 24 patients with a second UACR available, 14 (58%) confirmed CKD, yielding a pooled confirmed-CKD rate of 8.9% of all screened patients. In-hospital mortality was significantly higher among patients with pathological UACR (9.3% vs ~2%; Fisher's exact p=0.012). In per-center multivariate logistic regression, three predictors reached pooled significance: BUN (OR 1.10 per mg/dL, 95% CI 1.04-1.17, p=0.002, I2=0%), heart failure (OR 3.21, 95% CI 1.34-7.70, p=0.009, I2=0%), and diabetes mellitus (OR 2.54, 95% CI 1.11-5.82, p=0.028, I2=17%). Cardiac/vascular admissions had the highest pathological UACR rate (~42%); GI/hepatic admissions had 0%. Conclusions. Opportunistic inpatient UACR screening identifies previously unrecognized CKD in approximately 9% of general internal medicine patients, with consistent results across three independent centers. BUN elevation, heart failure, and diabetes are the strongest independent predictors. Pathological UACR carries significant short-term mortality risk, supporting integration of routine screening into inpatient care pathways.

11.
medRxiv (Medicine) 2026-06-16

A Poisson Process Life Expectancy framework for optimising patient lifetime during chemotherapy

Cancer therapy balances between two competing objectives - treatment efficacy against the tumour and the risk of treatment related severe adverse events, including patient death. Most existing optimal control theory (OCT) formulations rely on optimising heuristic cost functionals that lack direct clinical interpretability. In clinical practice treatment efficacy and patient tolerability are primarily assessed through survival metrics and adverse event rates. Here we introduce the Continuous Lifetime Payoff (CLP), a novel OCT objective functional that directly links treatment decisions to patient survival. It explicitly incorporates tumour dynamics, tumour eradication, and patient mortality from tumour progression, drug-related toxicity and age. We fit age-related mortality from life tables and infer parameters from simulated survival data. The CLP provides a clinically grounded framework for optimising chemotherapy regimens.

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

KAN-MLP-Mixer: A comprehensive investigation of the usage of Kolmogorov-Arnold Networks (KANs) for improving IMU-based Human Activity Recognition

arXiv:2605.19031v2 Announce Type: replace Abstract: Kolmogorov-Arnold Networks (KANs) have demonstrated an exceptional ability to learn complex functions on clean, low-dimensional data but struggle to maintain performance on noisy and imperfect real-world datasets. In contrast, conventional multi-layer perceptrons (MLPs) are far more tolerant to noise and computationally efficient. Replacing all MLP components with KANs in HAR models often degrades accuracy and computation efficiency, highlighting an open challenge: how to combine KANs' precision with MLPs' noise robustness and efficiency. To address this, we systematically explore various placements of KAN modules within deep HAR networks and propose a hybrid architecture that strategically synergizes the strengths of both paradigms, which uses a KAN-based input embedding layer, retains MLP layers for intermediate feature mixing, and introduces a specialized LarctanKAN module for final activity classification. Across eight public HAR datasets, the hybrid KAN-MLP model achieves an average macro F1 score relative improvement of 5.33\% compared pure-MLP model, significantly outperforming standalone KAN and MLP baselines. Furthermore, integrating this hybrid strategy into other state-of-the-art HAR architectures consistently boosts their performance. Our findings demonstrate that a carefully orchestrated combination of KAN, MLP, or other conventional neural components yields more robust and accurate HAR models for real-world wearable sensing environments.

13.
PLOS Computational Biology 2026-06-09

Evolution of phenocopying in a dynamical model of developmental trajectories

by Yuuki Matsushita, Archishman Raju Developmental trajectories are known to be canalized, or robust to both environmental and genetic perturbations. However, even when these trajectories are decanalized by an environmental perturbation outside the range of conditions to which they are robust, they often produce phenotypes similar to known mutants, called phenocopies. This correspondence between the effects of environmental and genetic perturbations has received little theoretical attention. Here, we study an abstract regulatory model that is evolved to follow a specific trajectory. We then study the effects of small and large perturbations to the trajectory, both by changing parameters and by perturbing the state at specific times. We find that the phenomenon of phenocopying emerges in evolved trajectories and is not present in a null model of randomly sampled trajectories. Our results suggest that, in this class of dynamic models, evolution can allow high-dimensional phenotypic landscapes to simultaneously exhibit robustness and phenocopying.

14.
arXiv (CS.AI) 2026-06-12

AAbAAC: An Annotated Corpus for Autoimmunity Information Extraction

arXiv:2606.13051v1 Announce Type: new Abstract: Despite advances in information extraction driven by deep learning and large language models, performance gaps remain in highly specialized biomedical fields, where domainspecific complexity poses challenges for generalist models. In this work, we focus on the domain of autoimmunity, where the main entities of interest are autoimmune diseases, autoantibodies (i.e., molecules that may mark or cause these diseases), their molecular targets, their location in the body, and their associated clinical signs. Herein, we present AAbAAC (AutoAntibodies and Autoimmunity Annotated Corpus), a corpus of 115 abstracts selected from PubMed, where we manually annotated entities and their relationships. First, AAbAAC was used to evaluate several methods on the task of named entity recognition (NER), and secondly, to fine-tune NER models. Our study demonstrates the utility of AAbAAC for information extraction in the domain of autoimmunity, showing expected improvement in NER performance after finetuning. This illustrates the value of small-scale annotation efforts for specialized domains and contributes to the computational study of autoimmunity. The AAbAAC corpus is available at https://github.com/f-maury/AAbAAC.

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

An Agentic Retrieval Framework for Autonomous Context-Aware Data Quality Assessment

arXiv:2606.13692v1 Announce Type: cross Abstract: Data quality assessment is a critical prerequisite for effective data analytics and data-driven decision-making, yet it remains a challenging task due to the inherently context-dependent nature of data quality. Existing approaches often rely on static rules or manual assessment strategies, limiting their adaptability to diverse usage scenarios and constraining automation at scale. Recent advances in artificial intelligence, particularly large language models, offer new opportunities for automating data quality assessment, but raise concerns related to reliability, grounding, and execution safety. In this paper, we propose a unified agentic-retrieval framework for autonomous context-aware data quality assessment. The framework interprets natural-language descriptions of intended data usage, derives context-aware assessment strategies, and generates executable validation logic through a multi-agent workflow. To ensure operational reliability, the framework introduces a feasibility validation stage that evaluates the realism and executability of generated assessment specifications before execution, enabling iterative refinement when necessary. Accepted validation logic is executed deterministically to guarantee reproducible and auditable results. We implement the proposed framework as an end-to-end prototype and evaluate it across multiple usage scenarios applied to the same dataset. The results demonstrate that assessment outcomes adapt meaningfully to different intended uses, while feasibility-gated execution reduces unrealistic or non-executable rule generation. The proposed approach provides a practical foundation for deploying autonomous yet controlled data quality assessment in modern data-driven environments.

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

Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesis

arXiv:2508.10967v3 Announce Type: replace-cross Abstract: Retrosynthesis prediction aims to infer the reactant molecules based on a given product molecule, which is a fundamental task in chemical synthesis. However, existing methods rely on a static pattern-matching paradigm, which limits their ability to perform effective logical decision-making from chemical data, leading to a black-box process. We propose Retro-Expert, an interpretable retrosynthesis framework that performs collaborative reasoning by combining the complementary strengths of Large Language Models and specialized models via pure reinforcement learning. It outputs natural language explanations grounded in chemical logic through three components: (1) specialized models provide chemical knowledge that is distilled into a high-quality chemical decision space, (2) LLM-driven critical reasoning to generate predictions with an interpretable reasoning path, and (3) knowledge-grounded policy optimization refines the interpretable decision policy. Experiments show that Retro-Expert surpasses both LLM-based and specialized models across different metrics, while generating chemically grounded explanations that enhance chemists' trust in practice. The source code for this paper is available at https://github.com/MagixRab-ll/Retro-Expert.

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

Counterexample Guided Learning in the Large using Reasoning Agents

arXiv:2606.11521v1 Announce Type: new Abstract: LLMs and LLM agents should improve when given feedback, but identifying when they are able to do so is difficult: feedback is heterogeneous, domain-specific, and difficult to control. We approach this challenge by asking LLMs to perform regular-expression induction, a classical symbolic learning problem where precise mechanisms for feedback exist in the form of counterexamples. In counterexample-guided learning, a learner (LLM) proposes candidate regular expressions from positive/negative-labeled strings, and the teacher (verifier) returns counterexamples showcasing the difference between the candidate and target languages. We identify novel counterexample-guided refinement strategies that enable effective regex learning, such as regularization and symbolic counterexample clusters. We also explore agentic strategies such as reflection and repair loops. Empirically, we find that verifier feedback substantially improves sample efficiency on challenging regex-induction tasks, reducing the number of labeled examples required and enabling learning of complex target expressions where standard prompting fails. For example, on the hardest task groups, our counterexample-guided framework improves success from 3.2% to 38.1% and from 38.9% to 74.1% on two different regex domains. These results suggest that LLMs can benefit from rich feedback beyond treating it as additional data, opening the door for robust verifier-guided methods for LLM-based program synthesis and formal reasoning.

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

Entropy-Gradient Inversion: Moving Toward Internal Mechanism of Large Reasoning Models

The advancement of Large Reasoning Models (LRMs) has catalyzed a paradigm shift from reactive ``fast thinking'' text generation to systematic, step-by-step ``slow thinking'' reasoning, unlocking state-of-the-art performance in complex mathematical and logical tasks. However, the field faces the fundamental gap between token-level behavioral analysis and internal reasoning mechanisms, and the instability of reinforcement learning (RL) for reasoning optimization relying on costly external verifiers. We identify and formally define Entropy-Gradient Inversion, a robust negative correlation between token entropy and logit gradients that acts as a definitive geometric fingerprint for LRM reasoning capability. Building on this, we propose Correlation-Regularized Group Policy Optimization (CorR-PO), which embeds this inversion signature into RL reward regularization. Extensive experiments on various reasoning benchmarks across multiple model scales show CorR-PO consistently outperforms state-of-the-art baselines, confirming that stronger inversion directly correlates with superior reasoning performance.

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

Passive-User Bell-State Loop-Back Key Establishment without Quantum Detectors at the User Nodes

arXiv:2606.19551v1 Announce Type: new Abstract: We propose and analyze a Bell-state extension of the Loop-Back quantum key distribution architecture for secret-key establishment between two passive users that do not require quantum transmitters or quantum detectors. In the proposed setting, a single active station, Alice, provides the entangled-state infrastructure, retains one qubit of an initially prepared Bell pair, and sends the traveling subsystem through two passive users, denoted by $B_1$ and $B_2$. Each passive user applies a local Pauli operation to the same traveling subsystem, so that the operation observed by Alice is only the effective composition $U_{\mathrm{eff}}=U_2U_1$. After the subsystem returns, Alice performs a Bell-state measurement and, using her private knowledge of the initial Bell state, deterministically identifies the effective Pauli operation. However, the individual factors $U_1$ and $U_2$ remain algebraically hidden from Alice whenever the local choices are uniformly and independently selected. The public effective operation acts as a parity-like constraint: each passive user can infer the operation applied by the other from its own private choice, while the active station learns only the global composition. This construction transfers the essential distributed-transformation mechanism of passive-user Loop-Back QKD to the entangled-state regime. Unlike single-qubit passive-user schemes, whose useful events are intrinsically post-selected, the Bell-state version is limited primarily by the success probability of the Bell-state measurement. We discuss the algebraic structure of the protocol, its interpretation as an infrastructure-assisted mediated key-establishment mechanism, and the physical assumptions required to protect passive Pauli modulators against active injection or Trojan-horse-type attacks.

20.
bioRxiv (Bioinfo) 2026-06-20

RNAStabFormer: Region-Aware Multi-Task Hybrid Learning for RNA Stability Prediction from Pulse-Chase Transcriptomics

作者:

RNA stability is a central layer of post-transcriptional gene regulation, yet large-scale stability labels derived from pulse-chase transcriptomics depend strongly on quantification region, time-window definition, and replicate quality control. We present RNAStabFormer, a controlled learning framework for predicting human RNA stability proxies from transcript sequence. Its core model, RAMHT, combines region-specific nucleotide Transformer encoders for CDS, and sequence, a CDS codon stream, engineered sequence-grammar features, gated fusion, and four task-specific regression heads. We construct four strict consensus labels from ENCODE BrU-seq/BruChase-seq data by crossing gene-sense and exon-sense quantification with late-chase 6 h/2 h and total-chase 6 h/0 h retention ratios, and evaluate all models on fixed repeated-random and chromosome-holdout splits. Across chromosome holdouts, XGBoost remains the strongest standalone model, with median Pearson correlations of 0.504, 0.544, 0.546, and 0.778 on the four labels. RAMHT is competitive with raw-sequence deep models but does not universally exceed engineered-feature baselines. A strict nested RAMHT–XGBoost blend nevertheless improves gene total-chase prediction by 0.017 mean Pearson and exon late-chase prediction by 0.004 mean Pearson over XGBoost. Region and mechanism analyses show that CDS, local k-mer composition, and codon-sensitive signals dominate predictive information. RNAStabFormer therefore provides both a multi-task neural model and a leakage-controlled evaluation protocol for RNA stability prediction from pulse-chase data.

21.
arXiv (CS.AI) 2026-06-19

Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference

arXiv:2606.20245v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance across a wide range of language-based tasks by leveraging both extensive parametric knowledge and in-context learning ability, enabling them to incorporate external information provided in the input prompt. However, the integration of external knowledge can introduce conflicts, not only between the model's internal parametric knowledge and the external information, but also among multiple pieces of external contexts. Existing approaches typically assume that either the model or the provided context is reliable, overlooking the possibility that both sources may contain errors, and avoid conflicts by privileging one source over the other, rather than actively resolving inconsistencies. To address these limitations, we propose a novel framework MACR for LLM knowledge conflict resolution that moves beyond the conventional binary choice paradigm and incorporates an explicit conflict-resolution mechanism based on a multi-agent reasoning approach. Specifically, we first propose an adaptive knowledge assessment and retrieval approach that employs a modified semantic entropy measure to quantify an LLM's confidence in its answer to a given query. Based on this confidence estimation, MACR either externalizes the model's internal knowledge as textual representations or retrieves relevant external knowledge when internal knowledge is insufficient, generating basic contexts for subsequent reasoning. Then we introduce an inductive multi-agent reasoning framework with three specialized agents that, respectively, induce explicit rules, analyze potential conflicts, and resolve inconsistencies across all available contexts. Empirical results demonstrate that MACR significantly outperforms state-of-the-art baselines across benchmarks, while also providing interpretable resolutions of explicit conflicts.

22.
arXiv (CS.LG) 2026-06-19

Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems

arXiv:2606.19802v1 Announce Type: new Abstract: Image restoration faces a fundamental tradeoff: methods that minimize error produce blurry reconstructions, while those that maximize perceptual quality yield sharp but less faithful images. Existing approaches either commit to a single operating point on this distortion perception (DP) frontier or require paired-data supervision, auxiliary models, or hyperparameter tuning of the sampler to access different points. We show that flow map models, a recent extension of flow matching for few-step sampling that learns an average field, implicitly define a one-parameter family of denoisers that continuously spans the DP frontier. The lookahead parameter t acts as a control knob between the MMSE and perceptual regimes. For Gaussian targets, we prove that varying t exactly recovers the optimal DP frontier; for natural images, we observe similar behavior empirically. Within a Plug-and-Play solver, the same mechanism extends to general inverse problems, where it controls a tradeoff between perceptual alignment and data consistency. Despite the lack of exact optimality guarantees in this setting, a single trained flow map spans the DP tradeoff, matching or exceeding specialized baselines at both extremes. Extensive experiments on CelebA ($128\times 128$) and AFHQ ($256\times 256$) across several linear and nonlinear inverse tasks validate our findings.

23.
arXiv (math.PR) 2026-06-11

The $K$-th nearest neighbor random walk on a Poisson point process gets trapped

arXiv:2606.11271v1 Announce Type: new Abstract: The $K$-th nearest neighbor random walk $(X_n)_{n \geq 0}$ on a homogeneous Poisson point process $\chi$ on $\R^d$ ($d\geq 1$), starts at the origin and at each step picks its next Poisson point among its closest neighbors according to i.i.d. labels having the same distribution as $K$. Our main result (Theorem 1) states that the number of Poisson points visited by $(X_n)_{n \geq 0}$ admits an exponential decay whenever the random variable $K$ has a bounded support (BS). In particular, the $K$-th nearest neighbor random walk visits finitely many Poisson points if and only if $K$ satisfies Assumption (BS). To prove it, we introduce the key notion of pioneer point which allows us to deal with the region of $\R^d$ already explored by $(X_n)_{n \geq 0}$. Still under Assumption (BS), we also prove an exponential decay for the Euclidean length of the trajectory performed by $(X_n)_{n \geq 0}$ (Theorem 2). Finally, and quite surprisingly, we exhibit an example of label distribution with bounded support for which the $K$-th nearest neighbor random walk discovers new Poisson points after a number of steps whose tail distribution is at least polynomial (Theorem 3).

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

From Awareness to Action: Understanding and Overcoming the Research-Practice Gap in Algorithmic Fairness for Public Health

arXiv:2606.11214v1 Announce Type: cross Abstract: Algorithmic fairness is essential for responsible ML-driven public health research, yet its practical implementation remains limited. To investigate this awareness-action gap, we conducted a sequential mixed-methods study comprising expert interviews, an online survey, and systematic mapping. The expert interviews informed the design of the survey, which in turn revealed fragmented definitions of fairness, limited training and guidance, reliance on external sources, and rare use of formal assessment, mitigation, or monitoring. These findings were subsequently mapped onto three established research-practice gap lenses: the Knowledge-Practice Gap, the Knowledge-to-Action Cycle, and the Knowing-Doing Gap, each offering complementary perspectives. Building on this synthesis, we introduce the Fairness-to-Action framework, which integrates methodological, organizational, and systemic dimensions to identify where translation of algorithmic fairness knowledge stalls. Our analysis shows that fairness remains weakly institutionalized, translation mechanisms are externally driven, and system-level priorities continue to emphasize accuracy over fairness. These insights suggest critical leverage points for advancing safe, fair, and ethical ML-driven public health research practice.

25.
Nature (Science) 2026-06-15

Nanocrystal-tailored recombination for all-perovskite tandem solar modules

作者:

The commercialization of all-perovskite tandem solar modules is hindered by the reliance on the conventional gold-based tunnel recombination junction (TRJ)1,2. Specifically, this TRJ introduces substantial near-infrared parasitic absorption3 and suffers from interfacial instability4, limiting both photocurrent generation and operational durability. Here, we develop a solution-processed interconnecting layer based on surface-engineered indium oxide (In2O3) nanocrystals featuring high optical transparency, wherein controlled nanocrystal morphology and tailored ligand chemistry enable smooth interfacial contact and favorable energy level alignment. Critically, we introduce a phosphonic acid additive into the lead–tin (Pb–Sn) perovskite precursor, which synergistically improves the electronic contact with the In2O3 recombination layer, thereby enhancing hole extraction. In addition, the additive regulates perovskite crystallization to mitigate residual strain during film formation, ensuring high-quality large-area deposits. This coordinated interfacial and crystallization engineering strategy simultaneously enhances carrier recombination efficiency at the interconnection layer, improves carrier extraction, and promotes large-area film uniformity in all-perovskite tandems. As a result, a 65-cm2 all-perovskite tandem solar module achieves a certified power conversion efficiency of 26.2%5, with an open-circuit voltage of 2.182 V, a fill factor of 77.4%, and a short-circuit current density of 15.6 mA cm-2 in terms of averaged subcell performance, measured by Japan Electrical Safety and Environment Technology Laboratories (JET). This marks a significant advance toward scalable perovskite tandem photovoltaics.