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01.
medRxiv (Medicine) 2026-06-23

Innate immunity associates with protection from pneumococcal colonisation, but colonisation does not confer capsule-independent protection

Nasopharyngeal colonisation with Streptococcus pneumoniae is a prerequisite for transmission and disease and represents an important immunising event. While colonisation induces serotype-specific immunity, the mechanisms underlying heterologous protection remain unclear. We developed a controlled human infection model using pneumococcal serotype 15B and investigated colonisation dynamics, immunogenicity, and cross-protection against subsequent heterologous challenge with serotype 6B. Fifty-four healthy adults were intranasally inoculated with 15B at escalating doses. Colonisation rates peaked at 31.4% with 8 x 10 CFU per naris, lower than those historically observed with 6B and 3 strains. Density was also lower than previously observed with other strains. In vitro assays demonstrated that 15B adhered more readily to epithelial cells than 6B, but was less efficiently internalised, potentially reducing attack rates and colonisation density. Colonisation with 15B induced capsular polysaccharide-specific serum IgG, but baseline humoral immune measures did not predict protection from acquisition. Prior colonisation with 15B did not reduce acquisition of 6B upon re-challenge. Analysis of nasal microbiopsy samples revealed distinct innate activation signatures. Resistance to colonisation was associated with elevated baseline MIP-1 and MIP-1{beta} responses upon in vitro stimulation, whereas carriage was associated with enhanced chemokine and IL-6 responses. Local innate immune activation, rather than circulating antibody responses alone, may therefore contribute to colonisation control. We demonstrate that experimental colonisation with 15B does not confer heterologous protection against 6B and highlight the importance of mucosal innate immune conditioning in serotype-independent defence. Strategies enhancing nasal innate immune recruitment and activation may be required for broader protection against pneumococcal colonisation.

02.
arXiv (CS.CV) 2026-06-24

ArtiTwinSplat: Interactable Digital Twin Reconstruction via Gaussian Splatting from RGB-D videos

Deploying robots in unstructured real-world environments needs accurate, interactive models of the objects. Constructing these models at scale remains a critical bottleneck for robotic system integration. We present ArtiTwinSplat, a framework that automatically constructs articulated, photo-realistic digital twins of objects directly from RGB-D videos, requiring no CAD models, simulation assets, or manual annotations. Our method is built on 3D Gaussian Splatting that preserve geometric fidelity and photometric realism, coupled with an unsupervised articulation discovery pipeline that recovers part structure and joint kinematics from observed motion alone. With tracking and optimization stages our method provides stable, queryable digital twins that support real-time rendering, viewpoint control, and interactive manipulation. Unlike prior methods confined to simulation, ArtiTwinSplat operates directly on real-world observations and produces twins that are immediately usable by downstream robot planning and learning systems. This method offers a practical, scalable pathway toward digital twin construction, lowering the integration barrier for articulated object manipulation in embodied AI and human-robot collaboration contexts.

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

Instrumental and Proximal Causal Inference with Gaussian Processes

arXiv:2603.02159v2 Announce Type: replace-cross Abstract: Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding. Despite substantial methodological advances, existing approaches rarely provide reliable epistemic uncertainty (EU) quantification. We address this gap through a Deconditional Gaussian Process (DGP) framework for uncertainty-aware causal learning. Our formulation recovers popular kernel estimators as the posterior mean, ensuring predictive precision, while the posterior variance yields principled and well-calibrated EU. Moreover, the probabilistic structure enables systematic model selection via marginal log-likelihood optimization. Empirical results demonstrate strong predictive performance alongside informative EU quantification, evaluated via empirical coverage frequencies and decision-aware accuracy rejection curves. Together, our approach provides a unified, practical solution for causal inference under unobserved confounding with reliable uncertainty.

04.
Nature (Science) 2026-06-17

Analysis of 173,303 exomes and genomes in the Pakistan Genome Resource

Naturally occurring loss-of-function variants in human genes enable drug target discovery because they mimic pharmacological inhibition of proteins. However, the study of these genetic variants is constrained by their rarity. Sequencing of diverse populations, particularly those enriched in familial relatedness, has been postulated to promote discovery of rare genetic variants1–3. Here we present the Pakistan Genome Resource, a South Asian biobank with high familial relatedness comprising 173,303 participants, who collectively carry naturally occurring homozygous loss-of-function variants in 6,476 genes. We describe the genetic architecture of this population, associations between genes and biomarkers, the distribution of loss-of-function variants across molecular pathways, and recall-by-genotype studies of therapeutically relevant genes. The Pakistan Genome Resource expands the catalogue of human genetic variants, provides a comprehensive genetic reference resource for the Pakistani population, and demonstrates the value of studying diverse cohorts to advance human health. The Pakistan Genome Resource compiles biobank data from 173,303 individuals with high familial relatedness, broadening the catalogue of human genetic variation and establishing a population-specific genomic reference for Pakistan.

05.
arXiv (quant-ph) 2026-06-11

Time-Frequency Grid States for Reconstruction and Correction of Channel-Induced Distortion in Entangled Photons

arXiv:2606.12216v1 Announce Type: new Abstract: Characterization of time-frequency (TF) quantum states requires reliable reconstruction of their TF distributions. However, imperfect transmission or measurement channels can distort reconstructed joint spectral intensities (JSIs), especially when the underlying perturbation mechanism is unknown. Here, we experimentally demonstrate a reconstruction and correction framework that uses a TF grid state as an intrinsic frequency-domain reference. By analyzing the displacement of the grid points, a Gaussian process regression model is employed to reconstruct a correction mapping for the nonlinear coordinate deformation without assuming a prior physical model of the distortion. The learned mapping reduces the residual coordinate deviation of the TF grid state by approximately a factor of 11 and, when applied to an independent frequency-entangled test state, improves the Gaussian-shape fidelity from 76.2\% to 90.0\%. These results establish TF grid states as practical metrological resources for diagnosing and correcting distortions in TF quantum systems, providing a pathway toward distortion-resilient quantum communication and high-dimensional quantum information processing.

06.
Science (Express) 2026-05-21

Observation of quantum vortex core fractionalization and skyrmion formation in a superconductor | Science

作者: 未知作者

Magnetic fields can penetrate a superconductor in the form of quantum vortices, which consist of a core singularity with circulating currents. London’s quantization implies that there is one core singularity per quantum of magnetic flux in single-component superconductors. Here, we report signatures of quantum vortex core fractionalization on the potassium-terminated surface of a multiband superconductor KFe 2 As 2 . The observed splitting of single integer-flux vortices into several fractional vortices results in a disparity between the numbers of flux quanta and vortex cores. These fractional vortices often arrange in chains, which calculations show are characterized by a ℂP 2 skyrmionic topological invariant; this constitutes a different type of topological defect: the chiral skyrmion. The disparate natures of integer and fractional vortices comprising skyrmions lead to distinct spectroscopic signatures.

07.
arXiv (CS.CV) 2026-06-24

Adaptive Hebbian Memory Routing in Vision Transformers for Few-Shot Learning

Few-shot image recognition requires models to adapt to new classes from a small labeled support set. Hebbian fast-weight memory can provide temporary associative information during an episode, but fixed memory behavior may not be appropriate for every few-shot task. In this work, we propose Adaptive Hebbian Routing for few-shot Vision Transformers. The method uses a lightweight MLP router to control the contribution of Hebbian memory, the strength of memory updates, and the retention of previous memory from support-set features. We study Adaptive Placement, Adaptive Plasticity, and Fully Adaptive Hebbian Routing. Experiments use ViT-Small, DeiT-Small, and Swin-Tiny under 5-way 1-shot evaluation on Omniglot, CIFAR-FS, and cross-domain transfer from CIFAR-FS to Omniglot. In the direct Swin comparison, fixed and adaptive Hebbian variants use the same memory location. Adaptive Plasticity improves the fixed Hebbian result from 96.74\% to 96.92\%, while Fully Adaptive Routing achieves the best result at 96.94\%. The fully adaptive Swin model also reduces inference time from 16.51 ms to 14.05 ms relative to fixed Hebbian Swin. On CIFAR-FS, adaptive variants improve performance across all three backbones, and the multi-shot evaluation shows that these gains remain useful as the number of support examples increases. These results show that adaptive plasticity and adaptive memory activation can improve few-shot Transformer representations beyond fixed Hebbian behavior.

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

On the Energy Distribution of the Galactic Center Excess' Sources

arXiv:2507.17804v2 Announce Type: replace-cross Abstract: The Galactic Center Excess (GCE) may yet herald the discovery of annihilating dark matter. Weighing against that conclusion are analyses showing evidence for dim point sources within the spatial structure of the emission. Due to technical limitations these analyses are purely spatial with all spectral information that could disentangle the excess from astrophysical backgrounds discarded. Here, we demonstrate that a neural network simulation-based inference approach can jointly analyze the spatial and spectra data. The addition is profound: energy information drives the putative point sources to be significantly dimmer, indicating either the GCE is truly diffuse in nature or made of an exceptionally large number of sources. Quantitatively, for our best fit background model, the excess is essentially consistent with Poisson emission as predicted by dark matter. If due to point sources, our median prediction is $\mathcal{O}(10^5)$ sources, or more than 35,000 at 90\% confidence, both orders of magnitude larger than the hundreds preferred by earlier point-source analyses of the GCE, although variations allowed by background systematics could reduce the required number of sources by roughly an order of magnitude.

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

On the Geometry of On-Policy Distillation

arXiv:2606.07082v2 Announce Type: replace-cross Abstract: On-policy distillation (OPD) is increasingly used to improve large language model reasoning, but its training dynamics remain poorly understood. We characterize the trajectory of OPD updates in parameter space and compare it with supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR). A suite of parameter-space diagnostics consistently places OPD in a relaxed off-principal regime: compared with SFT, its updates affect fewer weights and avoid principal directions more strongly, while compared with RLVR, they remain less tightly constrained. Beyond this static localization, OPD exhibits subspace locking: its cumulative updates rapidly enter a narrow low-dimensional channel. Constraining training to the update subspace formed early in training preserves OPD performance but substantially degrades SFT, indicating that the locked subspace is functionally sufficient for OPD. Control experiments further show that sparsifying the update tokens and shifting rollout generation off-policy preserve the rank dynamics, whereas mixing the OPD objective with RLVR changes them. Overall, these results suggest that OPD is not merely an intermediate point between SFT and RLVR, but induces its own update geometry in parameter space.

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

CAPED: Context-Aware Privacy Exposure Defense for Mobile GUI Agents

arXiv:2606.12666v1 Announce Type: cross Abstract: Screenshot-based mobile GUI agents can operate ordinary smartphone apps through the same visual interface as a human user, but this capability also turns every screen observation into a privacy boundary. During normal task execution, screenshots may expose contacts, messages, photos, files, recommendations, health cues, and other sensitive context that is unrelated to the user's request. We call this problem incidental visual privacy exposure. It is difficult to address with existing defenses: text anonymization misses many visual and inferential cues, while generic privacy masking can remove the evidence and controls that a GUI agent needs to complete the task. This paper presents CAPED, a context-aware pre-upload exposure control layer for mobile GUI agents. CAPED is designed as a phone-side protection layer: before screenshots are released to a remote multimodal agent, it extracts task requirements, uses screen context as a privacy prior, parses visible UI elements, and selectively exposes only content needed for the current task while masking incidental private content. We evaluate CAPED on AndroidWorld for broad task utility and with a controlled 28-task seeded privacy evaluation used as a measurement instrument for trajectory-level incidental leakage. In this seeded evaluation, Full CAPED reduces success-conditioned weighted seeded leakage from 0.766 under raw screenshots to 0.268 while preserving high task utility. A broader AndroidWorld run shows a remaining prototype-level utility cost, but the results support the central claim that screenshot upload should be treated as an explicit device–cloud boundary decision, governed by task-driven selective exposure rather than all-or-nothing screen sharing.

11.
bioRxiv (Bioinfo) 2026-06-12

Generalisable tissue-wide molecular reconstruction from histology

Spatial transcriptomics technologies measure gene expression within intact tissues but remain difficult to scale across large tissue sections and patient cohorts. Consequently, many studies rely on tissue microarrays (TMAs) or sparse spatial profiling designs, where molecular measurements are available for only limited tissue regions and are often generated using heterogeneous gene panels. Existing H&E to spatial gene expression prediction methods remain challenged by sparse molecular measurements, partially overlapping gene panels and tissue-wide reconstruction across heterogeneous spatial datasets. Here, we present GHIST+, a framework for tissue-wide reconstruction of single-cell molecular states from H&E histology. GHIST+ integrates cellular morphology, local tissue context and shared tissue representations to extend sparse molecular measurements into tissue-wide molecular maps across heterogeneous spatial datasets. Across multiple cancer types and GTEx breast tissues, GHIST+ reconstructs biologically meaningful tissue-wide molecular organisation from sparse TMA-derived measurements while preserving spatial tissue structure, cell-type organisation and age-associated tissue states across cancer and non-cancer settings. GHIST+ establishes a scalable framework for transforming sparse spatial profiling experiments into tissue-wide molecular maps, enabling cohort-scale molecular reconstruction from routine histology under heterogeneous spatial transcriptomic settings.

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

Automated 3D Kinematic Monitoring for Circadian Activity and Anomaly Detection in Juvenile Fish

Precision aquaculture faces a "phenotyping bottleneck" in tracking high-resolution behavioral traits, as conventional methods cannot quantify instantaneous three-dimensional (3D) physical exertion. To address this, we present a high-throughput 3D behavioral phenotyping framework integrating deep learning object detection with binocular stereo vision for real-time monitoring of juvenile tilapia in high-density environments. The system automates non-contact body length estimation and reconstructs 3D swimming trajectories from absolute spatial coordinates. By eliminating 2D perspective distortions, this approach precisely quantifies 3D velocity and acceleration, marking the first estimation of true physical swimming speeds in free-roaming juveniles. Results show the framework successfully establishes circadian locomotor baselines, serving as an early warning system for physiological stress and providing an objective metric for fish vitality.

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

Descriptive versus Regulatory Uncertainty in Bounded Predictive Systems

arXiv:2605.18909v2 Announce Type: replace Abstract: Any system that models the world under finite representational capacity must compress; any compression entails a prior; and the prior is the system's bias. What has not been established is whether uncertainty participates in the dynamics governing future behavior, or merely describes the output distribution without consequence. We introduce a structural distinction between descriptive uncertainty, which does not recursively modulate the system's policy, and regulatory uncertainty, which directly enters the optimization landscape and drives persistent adaptive restructuring. We prove formally that current transformer architectures are confined to descriptive uncertainty at inference. We ground this in thermodynamics via Landauer's principle: for uncertainty to be regulatory, epistemic error must cost real energy; in a decoupled system, hallucinations and correct derivations dissipate identical energy. We test this empirically across three locally-deployed language models (3B, 8B, 70B parameters). Token-level Shannon entropy is statistically invariant across tasks spanning pattern retrieval, causal operator application, and out-of-distribution causal generalization in all three models (all pairwise p >= 0.568; within-model ranges 0.011-0.028 nats), while task accuracy varies substantially across the same conditions (0%-100%). Entropy and accuracy are orthogonal. The decoupling is scale-invariant: larger models achieve higher accuracy but identical entropy flatness. This structural incapacity is not resolvable by additional parameters or training data. Genuine epistemic grounding requires physical coupling between thermodynamic substrate state and information processing cost.

14.
arXiv (CS.CV) 2026-06-16

V2P-Manip: Learning Dexterous Manipulation from Monocular Human Videos

Achieving autonomous robotic dexterous manipulation requires precise, human-like action sequences at scale. As a scalable supplement to costly teleoperation data, extracting trajectories with both visual fidelity and physical plausibility from monocular videos represents a promising frontier in embodied AI. To this end, we introduce V2P-Manip, an efficient framework designed to learn dexterous manipulation policies directly from human demonstration videos. We establish an efficient, integrated pipeline encompassing 3D asset acquisition, trajectory estimation, and dexterous policy learning. To bridge the gap between visual perception and physical constraints, we introduce a two-stage refinement process to enforce spatial alignment and physical consistency. Evaluations on the TACO and OakInk benchmarks demonstrate that our approach significantly outperforms previous methods in pose accuracy, adaptability to unstructured environments, and training efficiency. Ultimately, experimental results confirm an average success rate of over 75% across multiple synthetic manipulation tasks and validate the adaptability of the extracted manipulation priors across diverse dexterous hand embodiments.

15.
arXiv (CS.CL) 2026-06-16

Multimodal Evaluator Preference Collapse: Cross-Modal Contagion in Self-Evolving Agents

作者:

When AI agents use language models to evaluate their own outputs in a feedback loop, systematic biases emerge. We show that Evaluator Preference Collapse (EPC) is dramatically amplified in multimodal settings. Using GPT-4o to evaluate DeepSeek-chat across text and visual tasks, we find that a single strategy (step_by_step) absorbs 48.4% of all weight – 3.2x the collapse observed in text-only self-evaluation – while three visual-domain strategies receive only 9.1% combined weight. We then demonstrate a novel phenomenon we term cross-modal contagion: evaluator preferences acquired on one modality transfer to and corrupt strategy selection on another. Through a four-phase isolation training paradigm, we measure contagion coefficients and document strategy inversion – the optimal strategy for a modality reverses after cross-modal exposure. A Phase 3 statistical validation across four evaluator configurations (N=53 total independent repetitions, 15,592 API calls) reveals a clear hierarchy: cross-model evaluation (GPT-4o, N=8) produces strong but symmetric bidirectional contagion (mean gamma_{T->V}=1.176, gamma_{V->T}=1.089, Delta=-0.088, p=0.575, Cohen's d=0.29); high round counts (DashScope, 50 rounds) cause collapse to single-strategy dominance (70% zero contagion); and self-evaluation provides near-complete immunity – 97% of runs (N=30, DeepSeek-chat) yield exactly zero contagion (mean gamma=0.033, 95% CI [-0.031, 0.010], p=0.642, d=0.07). No evaluator condition shows statistically significant directional asymmetry. We introduce the contagion matrix indexed by evaluator identity, release the MM-EPC experimental framework, and identify cross-model evaluator architecture as the primary risk factor for preference contagion.

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

VLADriveBench: Evaluating CoT-Action Relationship in VLA for Autonomous Driving

Vision-language-action (VLA) models generate chain-of-thought (CoT) reasoning alongside driving trajectories, but existing benchmarks evaluate only trajectory quality and do not assess whether the CoT is relevant, consistent, or causally connected to the driving action. We introduce VLADriveBench, a framework that combines observational metrics (mentioning, hallucination, contradiction, action alignment) with a CoT intervention protocol to provide complementary views of the CoT-action relationship. Applying VLADriveBench to three models across two architectures, we find that the two analyses can diverge sharply: ORION scores highest on observational alignment yet its CoT is epiphenomenal, while Alpamayo v1.5 scores lower yet its CoT is strongly causal, with visual salience gating the extent of CoT influence.

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

MolGraphBench: A Benchmark of GNN Architectures for Molecular Regression Tasks

arXiv:2602.20573v3 Announce Type: replace Abstract: Molecules are often represented as SMILES strings, which can be readily converted to hand-crafted descriptors or fingerprints (FP) for molecular property prediction. Research has demonstrated that SMILES can be converted to molecular graphs $G = (V, E)$, with atoms as nodes $(V)$ and bonds as edges $(E)$. These molecular graphs can subsequently be used to train graph neural networks (GNN) models. Despite the recent surge in application of GNN (existing and novel architectures) for molecular property prediction, a rigorous benchmark is still lacking. We propose MolGraphBench, a comprehensive benchmark of four commonly used GNN models for molecular property prediction. Benchmarking results demonstrate graph convolutional network (GCN) and graph isomorphism networks (GIN) as the optimal GNN architectures for molecular graph regression tasks, based on absolute performance, training efficiency, transfer learning and prediction quality. The study also indicates the non-complementary nature of molecular fingerprints in the fusion (GNN-FP) framework. Furthermore, our GNN models achieved performance superior or comparable performance to current state-of-the-art GNN baselines across three datasets (GCN with RMSE of $0.518$ on B3DB, GIN-FP with RMSE of $1.022$ on FreeSolv and GIN with MAE of $63.783$ on RT datasets). Findings from this study indicate that type of GNN-layer, should be treated as a tunable hyperparameter rather than a fixed design choice to achieve superior performance.

18.
arXiv (math.PR) 2026-06-11

Mean-field theory via dissociated arrays for particle systems interacting through noisy weights

arXiv:2606.12135v1 Announce Type: new Abstract: We study a mean-field limit for a $N$-particle system in which each particle follows a diffusion and interacts with other particles through a weight on each directed edge. Each weight evolves according to its own nonlinear SDE driven by a Brownian motion, with coefficients involving the states of the two endpoint particles of the edge. The initial vertex and edge variables are assumed to have a dissociated Aldous–Hoover form. We construct the limiting nonlinear SDE by averaging the interaction over an independent neighbor and an edge input, prove its well-posedness, and show that the dissociated vertex-edge structure is propagated by the dynamics. This propagation property is an analogue of propagation of chaos in the case where the weight of each edge may remain correlated with the states of the two endpoint particles. Under either a bounded-observable assumption or a sub-Gaussian edge-input condition, the finite system converges to this limit through quantitative coupling estimates for a typical particle and a typical edge. We also prove the convergence of the empirical measure of particle's state pairs and their interaction weights.

19.
medRxiv (Medicine) 2026-06-24

Food additive exposure associated with reduction in gut microbiota diversity

Consumption of ultra-processed foods is rising globally and has been implicated in inflammation and metabolic dysfunction, yet the impact of specific food additives on the human gut microbiota remains poorly understood. Using dietary data from the Food & You study (approximately 1000 participants in Switzerland), we identified 257 unique additives from 4,119 unique packaged products to quantify each participant's daily additive exposure. Higher exposure to a combination of high intensity sweeteners and sugar polyols, commonly found in low calorie products, was independently associated with reduced gut microbial Shannon diversity (beta = -0.39, p < 0.001), after adjustment for demographics, diet quality, BMI and bowel movement frequency. At a broader level, total additive exposure and fast food consumption were each negatively associated with gut microbial diversity; however, additive exposure remained independently associated and also specifically attenuated the diversity benefits of vegetable rich diets. Furthermore, microbial log ratio signatures linked to additive exposure showed strong negative correlations with Shannon diversity, including emulsifiers and thickeners (r = -0.66) and preservatives and antioxidants (r = -0.56). Integrating additive exposure with healthy dietary components such as HEI, fruits, or vegetables strengthened associations with gut microbial diversity; for example, vegetable linked correlations with Shannon diversity increased from r = 0.52 to r = 0.65 when contrasted against preservative-antioxidant exposure. Concordantly, microbial signatures associated with the sweeteners and sugar polyols additive combination showed depletion of fiber associated commensal taxa, and enrichment of pathways involved in polyol and aromatic compound metabolism. Notably, these associations emerged despite packaged foods representing only approximately 15% of logged dietary intake, underscoring the sensitivity of gut microbial diversity to limited exposure, and demonstrating that without integrating additive and processed-food metrics, one of the largest effect-size phenomena in human gut microbiota diversity would remain undetected.

20.
arXiv (CS.AI) 2026-06-25

What Actually Works for Spacecraft Fault-Tolerant Control: An Honest Settled-Gate Benchmark of Learned and Classical Methods

arXiv:2606.25374v1 Announce Type: new Abstract: Recent learned fault-tolerant-control (FTC) work reports high success on spacecraft actuator faults, but often in simulation, on narrow fault sets, and with transient metrics that a trajectory need only touch once. We ask what recovers spacecraft pointing when success means holding it on faults never seen in training. We answer with a benchmark built around a settled gate, pointing held within 0.2 deg over a dwell window and scored on the true state, train/test splits disjoint in inertia, gain, sign pattern, and bias, Wilson intervals over n=500 episodes per cell, and one-command reproduction on a 6-DOF Basilisk testbed. Across classical, adaptive, learned end-to-end, and structured controllers, three findings stand out. Fault-unaware PD/PID and from-scratch end-to-end RL score 0%, so learning capacity alone is not the lever. Classical adaptive laws resolve sign faults but handle gain poorly at 55.2%, and a literature-faithful Nussbaum-gain law reaches 45.2% and 3.2%. A structured estimate-then-control design, with a learned recurrent module that infers actuator gain online and feeds an analytic law, wins on sign and gain faults at 97.8% and 94.4%, approaching the privileged oracle while unstructured methods remain at zero. The hard wall is constant additive bias, which is 0% for every controller including the privileged gain oracle, because an integral-free law cannot null a constant disturbance. We close it with a disturbance observer that recovers bias from the dynamics and is self-correcting for gain-estimate error. Composed with the gain estimate, it recovers 59.4% of held-out bias faults with no sign/gain regression, moving that class off zero. We classify sensor-fault regimes similarly, show that sensor bias is unobservable from the corrupted measurement alone and therefore requires fusion rather than an observer, and release the benchmark so the gate is shared.

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

3D-RFT: Reinforcement Fine-Tuning for Video-based 3D Scene Understanding

Reinforcement Learning with Verifiable Rewards ( RLVR ) has emerged as a transformative paradigm for enhancing the reasoning capabilities of Large Language Models ( LLMs), yet its potential in 3D scene understanding remains under-explored. Existing approaches largely rely on Supervised Fine-Tuning ( SFT), where the token-level cross-entropy loss acts as an indirect proxy for optimization, leading to a misalignment between training objectives and task performances. To bridge this gap, we present Reinforcement Fine-Tuning for Video-based 3D Scene Understanding (3D-RFT ), the first framework to extend RLVR to video-based 3D perception and reasoning. 3D-RFT shifts the paradigm by directly optimizing the model towards evaluation metrics. 3D-RFT first activates 3D-aware Multi-modal Large Language Models ( MLLM s) via SFT, followed by reinforcement fine-tuning using Group Relative Policy Optimization ( GRPO) with strictly verifiable reward functions. We design task-specific reward functions directly from metrics like 3D IoU and F1-Score to provide more effective signals to guide model training. Extensive experiments demonstrate that 3D-RFT-4B achieves state-of-the-art performance on various video-based 3D scene understanding tasks. Notably, 3D-RFT-4B significantly outperforms larger models (e.g., VG LLM-8B) on 3D video detection, 3D visual grounding, and spatial reasoning benchmarks. We further reveal good properties of 3D-RFT such as robust efficacy, and valuable insights into training strategies and data impact. We hope 3D-RFT can serve as a robust and promising paradigm for future development of 3D scene understanding.

22.
PLOS Computational Biology 2026-05-29

Structural and dynamic basis of NOD2 tandem CARD association and NOD1/2–RIP2 signaling complexes

by Jitendra Maharana, Aritra Bej, Debasish Biswal, Debashis Panda, Arjun Sharma NOD1 and NOD2, founding members of the NOD-like receptor (NLR) family, play a crucial role in host defense against bacterial infections. Recognition of peptidoglycan-derived ligands triggers ATP-dependent oligomerization of the NACHT domain, exposing the CARD domains that recruit the adaptor protein RIP2 via CARD–CARD interactions to activate the NF-κB signaling cascade. Although NOD1/2-RIP2 interactions and RIP2CARD filament assembly are established, the precise interfaces that stabilize hetero–CARD filaments remain poorly defined. Here, we integrate in silico structural modeling with molecular dynamics (MD) simulations to elucidate structurally compatible arrangements of NOD1–RIP2 and NOD2–RIP2 hetero–CARD filaments. Our results reveal that NOD1CARD subunits form a structurally compatible homomeric scaffold via canonical (type-I–III) interfaces, accommodating multiple tiers of RIP2CARD rings at both filament termini. Meanwhile, the NOD2 tandem CARDs adopt multiple discrete conformations, reflecting a more intricate structural mechanism. In stable filament conformations, tandem CARDs converge at the type-II interface, with RIP2CARD rings stacking onto CARDa (top-down) and CARDb (bottom-up) interfaces, highlighting the structural role of NOD2CARDb in RIP2-mediated CARD–CARD interaction. In silico mutagenesis, involving charge-reversal and alanine scanning of key interfacial residues, disrupts NOD1–RIP2 and NOD2–RIP2 interactions at both top-down and bottom-up interfaces, leading to rapid interface destabilization within 0.1–0.4 μs of simulation. Together, these results reveal conserved and receptor-specific mechanisms governing NOD1/2–RIP2 CARD–CARD interactions and provide deeper structural and dynamic insights into the complex structural mechanisms for NLR-mediated inflammatory signaling.

23.
arXiv (CS.AI) 2026-06-11

ProGRank: Probe-Gradient Reranking to Defend Dense-Retriever RAG from Corpus Poisoning

arXiv:2603.22934v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) improves large language model applications by grounding generation in retrieved evidence, but also introduces corpus poisoning as a new attack surface. In this setting, an adversary injects or edits passages so that they enter the Top-$K$ results for target queries and influence downstream generation. Existing defences often rely on content filtering, auxiliary models, or generator-side reasoning, which complicates deployment. We propose ProGRank, a post hoc, training-free retriever-side defence for dense-retriever RAG. ProGRank stress-tests each query–passage pair under mild randomized perturbations, extracts probe gradients from a small fixed parameter subset, and derives two instability signals: representational consistency and dispersion risk. It then combines these signals with a score gate for reranking. ProGRank preserves the original passage content, requires no retraining, and supports a surrogate-based variant when the deployed retriever is unavailable. Experiments across datasets, retrievers, attacks, and retrieval-stage and end-to-end settings show that ProGRank improves robustness and maintains a favorable robustness–utility trade-off, including under adaptive evasive attacks.

24.
arXiv (math.PR) 2026-06-25

The relationship between the transition functions of the labeled and unlabeled versions of the infinitely-many-neutral-alleles diffusion model

作者:

arXiv:2606.06739v2 Announce Type: replace Abstract: The transition function of the unlabeled infinitely-many-neutral-alleles diffusion model, as expressed by Zhou (2015), is derived from the transition function of the labeled infinitely-many-neutral-alleles diffusion model, slightly simplifying the derivation by Feng (2010).

25.
arXiv (CS.AI) 2026-06-25

FDN: Interpretable Spatiotemporal Forecasting with Future Decomposition Networks

arXiv:2606.25201v1 Announce Type: cross Abstract: Spatiotemporal systems comprise a collection of spatially distributed yet interdependent entities each generating unique dynamic signals. Highly sophisticated methods have been proposed in recent years delivering state-of-the-art (SOTA) forecasts but few have focused on interpretability. To address this, we propose the Future Decomposition Network (FDN), a novel forecast model capable of (a) providing interpretable predictions through classification (b) revealing latent activity patterns in the target time-series and (c) delivering forecasts competitive with SOTA methods at a fraction of their memory and runtime cost. We conduct comprehensive analyses on FDN for multiple datasets from hydrologic, traffic, and energy systems, demonstrating its improved accuracy and interpretability.