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01.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2606.16523

SkillWiki: A Living Knowledge Infrastructure for Agent Skills

作者:

Dingcheng Huang ↗Yuda Ding ↗Bingshuo Liu ↗Qingbin Liu ↗Xi Chen ↗Jiang Bian ↗Hongliang Sun ↗Zhiying Tu ↗Dianhui Chu ↗Xiaoyan Yu ↗Dianbo Sui ↗

While knowledge is managed through Wikipedia and software through GitHub, agent skills still lack an infrastructure for large-scale production, governance, and evolution. SkillWiki is a living knowledge infrastructure that supports the organization, grounding, and continuous evolution of agent skills by transforming heterogeneous knowledge into reusable skill assets linked to their originating evidence. Our demonstration presents the complete skill lifecycle, from knowledge ingestion and skill production to provenance-aware exploration, governance, and execution-driven evolution. SkillWiki highlights a future in which knowledge, skills, and execution experience co-evolve within a shared infrastructure. The live demonstration and source code are publicly available at https://github.com/Huangdingcheng/SkillWiki.

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02.

arXiv (CS.CL) 2026-06-15 DOI: arXiv:2601.22954

Residual Context Diffusion Language Models

作者:

Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to purely autoregressive language models because they can decode multiple tokens in parallel. However, state-of-the-art block-wise dLLMs rely on a "remasking" mechanism that decodes only the most confident tokens and discards the rest, effectively wasting computation. We demonstrate that recycling computation from the discarded tokens is beneficial, as these tokens retain contextual information useful for subsequent decoding iterations. In light of this, we propose Residual Context Diffusion (RCD), a module that converts these discarded token representations into contextual residuals and injects them back for the next denoising step. RCD uses a decoupled two-stage training pipeline to bypass the memory bottlenecks associated with backpropagation. We validate our method on both long CoT reasoning (SDAR) and short CoT instruction following (LLaDA) models. We demonstrate that a standard dLLM can be efficiently converted to the RCD paradigm with merely ~300 million tokens. RCD consistently improves frontier dLLMs by 4-11 percentage points in accuracy with minimal extra computation overhead across a wide range of benchmarks. Notably, on the most challenging AIME tasks, RCD nearly doubles baseline accuracy and attains up to 4-5x fewer denoising steps at baseline's peak accuracy.

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03.

medRxiv (Medicine) 2026-06-16 DOI: HASH:e12ebf36b39d1cc7a806e3853a7dcf44

AI-assisted continuous-time modelling of metastatic breast cancer reveals subtype-specific spatiotemporal organ interactions

作者:

Vaisband ↗Rinnerthaler ↗Gampenrieder ↗S. P ↗Binder ↗Singer ↗C. F ↗Sliwa ↗Roitner ↗Hager ↗Pichler ↗Udovica ↗…

Metastatic breast cancer is one of the leading causes of premature mortality among women worldwide. A major barrier to optimal care is the marked heterogeneity in both the temporal dynamics of metastatic spread and the organ-specific spatial distribution of metastases. Existing analyses do not adequately capture this complexity, as they either neglect temporal dependencies or assume independence between metastasic sites. As a result, it remains unclear how established metastases influence subsequent organ-specific dissemination. We address this question using patient-level longitudinal trajectories from a large multicentre real-world metastatic breast cancer registry, combined with an AI-assisted disease-progression modelling framework based on continuous-time Markov chains that represent combinations of metastatic sites and the non-uniform and practice-driven timing of radiologic response assessments, as encountered in routine clinical care. We present a stochastic model determined by progression rates, which are parameterised to capture baseline organ-specific transition risks, patient-level covariates, and pairwise inter-organ interaction effects. High-dimensional treatment information is incorporated using an large language model based encoding. We find that metastatic spread follows non-independent, subtype-specific spatiotemporal patterns, with subtype-specific inter-organ interaction patterns that shape progression. Visceral metastases, particularly lung and liver metastasis, are associated with an increased hazard of subsequent brain metastasis, with effects varying across hormone receptor-positive, HER2-positive, and triple-negative subtypes. Together, these findings define a clinically relevant spatiotemporal architecture of metastatic progression in breast cancer. This framework enables refined mechanism-informed risk stratification and provides a data-driven rationale for targeted and risk-adapted – rather than symptom-triggered – surveillance strategies.

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04.

arXiv (CS.CV) 2026-06-18 DOI: arXiv:2505.03646

Revealing Hidden Vulnerabilities in Autoencoders through Gradient Signal Restoration

作者:

Chethan Krishnamurthy Ramanaik ↗Arjun Roy ↗Tobias Callies ↗Eirini Ntoutsi ↗

Adversarial robustness of deep autoencoders (AEs) has received less attention than that of discriminative models, although their compressed latent representations induce ill-conditioned mappings that can amplify small input perturbations and destabilize reconstructions. Existing white-box attacks for AEs, which optimize norm-bounded adversarial perturbations to maximize reconstruction damage, often converge to suboptimal perturbations, thereby potentially overstating AE robustness. We show that this limitation is linked to vanishing adversarial loss gradients during backpropagation through ill-conditioned layers, associated with near-zero singular values in their intermediate weight matrices. To address this, we propose GRILL (Gradient Signal Restoration in Ill-Conditioned Layers), a framework designed to mitigate gradient degradation and improve the reliability of adversarial robustness evaluation in encoder-decoder architectures. GRILL is designed to mitigate adversarial gradient degradation during optimization, enabling attacks to better approximate high-distortion perturbations under fixed norm constraints. Through extensive experiments across multiple AE architectures, under both sample-specific and universal attacks, as well as standard and adaptive attack settings, we show that GRILL significantly increases attack effectiveness, thereby exposing vulnerabilities hidden by existing attack limitations. Beyond AEs, we provide preliminary evidence that modern multimodal encoder-decoder architectures exhibit similar vulnerabilities.

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05.

Nature (Science) 2026-06-09 DOI: HASH:4d3472195e310f21457edbeb48ce48f2

Scientists have a bad case of AI FOMO, <i>Nature</i> poll reveals

作者:

Mohana Basu ↗

Almost half of the scientists who responded said that they feel broadly negative towards artificial intelligence, but they think that some tools are better than others. Almost half of the scientists who responded said that they feel broadly negative towards artificial intelligence, but they think that some tools are better than others.

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06.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.16763

Cross-Silo De-Anonymization Under Local Differential Privacy: Threat Model, Phase Transition, and Coordination Necessity

作者:

Ziniu Liu ↗Aiping Li ↗

arXiv:2606.16763v1 Announce Type: cross Abstract: When a person's records appear in k independent data silos, each protected by (epsilon, delta)-differential privacy, standard composition yields a valid (k*epsilon, k*delta)-DP guarantee for the joint output. This worst-case bound, however, does not answer the concrete inference question: at what k can an adversary actually identify a target person? This paper develops the information-theoretic framework needed to answer that question. We introduce cross-silo person-level DP (XSP-DP), a Pufferfish-style privacy notion whose adjacency relation captures all records of a single person across all silos simultaneously, and verify that the standard basic composition bound carries over to this adjacency model. Within this framework we prove that de-anonymization undergoes a phase transition at k* = Theta(log n / epsilon^2) (population size n, per-silo RR parameter epsilon): a Fano lower bound shows any estimator fails for k > k*. An explicit XOR + randomized-response construction demonstrates information synergy: each silo's output is individually uninformative about the target, yet the joint mutual information is strictly positive. For non-coordinated binary randomized-response mechanisms, we prove that de-anonymization is inevitable once k exceeds the threshold, establishing that cross-silo coordination is necessary. These results provide a baseline threat model and Theta-level threshold for cross-silo inference attacks under local DP.

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07.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2606.17409

Discrete Autoregressive Transformer for Generative Mechanism Synthesis

作者:

Anar Nurizada ↗Anurag Purwar ↗

arXiv:2606.17409v1 Announce Type: cross Abstract: Planar path synthesis requires mechanisms whose coupler curves match a prescribed trajectory; the mapping from curve to linkage is inherently one-to-many across four-, six-, and eight-bar topologies. We address this design problem with simulation-grounded evaluation on a curated corpus of over one million mechanisms, reporting Chamfer distance and dynamic time warping after forward kinematics and geometric alignment. We formulate synthesis as conditional autoregressive sequence modeling: joint coordinates are uniformly quantized to tokens and generated by a decoder-only transformer with a variational-autoencoder (VAE) latent of the target curve and an explicit mechanism-type token. Training combines token cross-entropy with a Gaussian-smoothed bin auxiliary loss that respects ordinal structure among bins. At inference, a bounded latent-noise schedule decodes all mechanism types at each noise level; we retain the top five candidates by geometric error, yielding diverse accurate families without dataset lookup. On held-out tests, aggregate mean Chamfer distance is $0.0132$ and mean dynamic time warping is $0.153$; a latent $k$-nearest-neighbor baseline that conditions on training-set neighbor latents in VAE space achieves matched-topology mean Chamfer distance $0.0071$ and mean dynamic time warping $0.117$ using the same decoder.

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08.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.16154

A Gradient Perspective on RLVR Stability and Winner Advantage Policy Optimization

作者:

Prasanth YSS ↗Zhichen Ren ↗Rasa Hosseinzadeh ↗Ilan Gofman ↗Yuqi Chen ↗Zhaoyan Liu ↗Guangwei Yu ↗Jesse C. Cresswell ↗Satya Krishna Gorti ↗

arXiv:2606.16154v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves language-model reasoning, but GRPO-style optimization remains prone to collapse. We analyse this instability through token-level gradient dynamics, deriving a taxonomy that predicts how updates affect next-token probabilities and entropy. The taxonomy shows that stability depends jointly on the advantage sign and token distribution under the current policy. Motivated by this finding, we propose Winner Advantage Policy Optimization (WAPO), a simple online clipped policy-gradient objective that updates only on positive-advantage completions. Across mathematical reasoning and multi-hop QA benchmarks, WAPO improves training stability and matches or outperforms baselines across multiple model families. Full code can be found at https://github.com/layer6ai-labs/wapo.

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09.

arXiv (CS.CL) 2026-06-18 DOI: arXiv:2606.19218

RECOM: A Validity Discrimination Tradeoff in Automatic Metrics for Open Ended Reddit Question Answering

作者:

Pushwitha Krishnappa ↗Amit Das ↗Vinija Jain ↗Aman Chadha ↗Tathagata Mukherjee ↗

Automatic metrics are the default for evaluating LLM-generated text, yet a metric is quietly asked to do two jobs: tell genuine content alignment from surface coincidence (validity), and tell a better system from a worse one (discriminative power). On open-ended, opinion-driven question answering, the two are in tension. We introduce RECOM (Reddit Evaluation for Correspondence of Models), a contamination-free evaluation dataset of 15,000 r/AskReddit questions (September 2025), each paired with its authentic community replies, which postdate every evaluated model's training cutoff. Scoring five open-source LLMs (7–10B) against every reply each metric paired with a random-derangement noise floor we find that no metric does both jobs well. Cosine similarity separates real from random answers (Cohen's $d \approx 2$) but cannot rank the five models ($|d| < 0.1$); BERTScore precision appears to rank the models (raw $|d|$ up to 0.63), but once response length is controlled this collapses to $|d| = 0.09$ and its validity is weak ($d \approx 0.8$, versus cosine's $\approx 2$). Because every metric scores the same outputs, this validity–discrimination tradeoff is a property of the metrics, not the models, and we argue it stems from representation design. Three independent LLM judges reproduce the validity gap and likewise separate the five models only weakly. We recommend reporting metrics on both axes, with an explicit random-baseline floor. RECOM is publicly available at https://anonymous.4open.science/r/recom-D4B0

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10.

arXiv (CS.CL) 2026-06-24 DOI: arXiv:2606.24346

PETRA: Transforming Web Text for Petroleum-Engineering Domain Adaptation

作者:

Petroleum-engineering search exposes a supervision gap for strong general retrievers: relevant evidence exists in public web text, but domain relevance labels are scarce. To address this gap, we propose PETRA, a large-scale Petroleum Engineering Text for Retrieval Adaptation dataset and pipeline that converts noisy public web data into a curated domain corpus and synthetic supervision for dense retrieval and reranking. PETRA contains 1.36M curated chunks, approximately 2B token equivalents, $\approx$859k, embedding training rows from $\approx$224k anchors, and roughly 400k teacher-scored reranker candidate rows. Its construction combines high-recall energy-domain curation, an energy-domain classifier with 98.4% test accuracy, chunk-grounded query generation, LLM-written hard negatives, and retrieval-mined candidate lists. PETRA improves first-stage in-domain Normalized Discounted Cumulative Gain (nDCG) from 0.703 to 0.763 through score fusion. Reranker adaptation improves the public Earth Science benchmark by 44% relative and a six-task reasoning-intensive panel by 23%. Failed training recipes show that high train-holdout accuracy on synthetic labels does not predict retrieval gains; retrieval-mined data helps only after being repackaged as teacher-scored candidate lists sampled from the inference-time candidate distribution.

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11.

arXiv (math.PR) 2026-06-18 DOI: arXiv:2508.17116

A scaling limit theorem for controlled branching processes with a size-divisible term

作者:

Miguel Gonz\'alez ↗Pedro Mart\'in-Ch\'avez ↗In\'es del Puerto ↗

arXiv:2508.17116v2 Announce Type: replace Abstract: This paper establishes general sufficient conditions for a sequence of controlled branching processes to converge weakly on the Skorokhod space. We focus on a class of control mechanisms that extend previous results by decomposing those random variables into the sum of two independent components: an immigration term, which depends on the current population size, and a size-divisible term, which can be expressed as the sum of random contributions from each individual. This extension allows us to capture a broad range of control functions including Poisson, binomial, and negative binomial distributions, commonly used in the literature. The assumptions are formulated in terms of probability generating functions of the offspring and control laws, distinguishing in this latter between the immigration and the size-divisible parts. The limit process is shown to be a continuous-state branching process with dependent immigration. The proof essentially relies on tightness arguments and the identification of a martingale problem. We also identify the special case in which the limit reduces to a classical Feller branching diffusion with immigration.

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12.

arXiv (CS.CV) 2026-06-24 DOI: arXiv:2606.24233

Latent Visual States for Efficient Multimodal Reasoning

作者:

Xiuwei Chen ↗Wentao Hu ↗Yongxin Wang ↗Zisheng Chen ↗Likui Zhang ↗Kun Xiang ↗Jianhua Han ↗Hui-Ling Zhen ↗Jingyuan Zou ↗Hang Xu ↗Xiaodan Liang ↗

The integration of visual evidence has significantly enhanced the capabilities of large multimodal models. However, this integration predominantly relies on generating discrete outputs (etc., code or box coordinates) to invoke external tools, a process that introduces rigid dependencies and substantial latency. To overcome these limitations, we propose {EVA} (LatEnt Visual StAtes), a novel framework that natively generates continuous latent visual representations. These internal representations manifest as an adaptive sequence of Latent\_slot tokens, serving as intermediate visual thoughts during the reasoning process. These Latent\_slot tokens are then trained end-to-end with the discrete text tokens. This co-optimization, notably, causes extreme policy deviation in the 'transition window' following the Latent\_slot tokens. We develop D-GSPO (Decouple-GSPO) to target this root cause by decoupling the optimization of latent and discrete components. To support SFT, we construct EVA-230K, a high-quality text-image interleaved CoT dataset encompassing a diverse range of real-world scenes, documents, charts and OCR tasks. Extensive experiments across multiple benchmarks confirm that EVA achieves significant performance gains while enhancing inference efficiency.

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13.

arXiv (CS.CL) 2026-06-11 DOI: arXiv:2606.11243

ProHiFlo: Hierarchical Flow Matching with Functional Guidance for De Novo Protein Generation

作者:

Chuanzhen Wang ↗Meade Cleti ↗Pete Jano ↗

De novo protein generation has transformative potential in therapeutic design, enzyme engineering, and synthetic biology. While diffusion-based and flow matching approaches have achieved progress, they typically operate at single resolution and lack mechanisms for incorporating functional constraints. We introduce ProHiFlo, a hierarchical flow matching framework with three innovations: (1) coarse-to-fine generation that models backbone geometry before refining to all-atom coordinates, reducing computational cost while maintaining accuracy; (2) functional guidance leveraging pretrained predictors to steer generation toward desired properties without retraining; (3) adaptive SE(3)-equivariant architecture for efficient multi-scale processing. Experiments on unconditional generation, motif scaffolding, and functional design demonstrate state-ofthe-art performance while requiring 4 fewer sampling steps. On enzyme active site scaffolding, ProHiFlo achieves 58.9% success rate compared to 41.2% for RFDiffusion.

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14.

arXiv (CS.CV) 2026-06-17 DOI: arXiv:2606.17867

A Quantitative Analysis of Multimodal Biomarkers in Alzheimer's Disease

作者:

Antonio Scardace ↗Daniele Rav\`i ↗

Despite increasing adoption of multimodal approaches in Alzheimer's Disease (AD) research – aimed at integrating molecular, structural, clinical, and genetic biomarkers to enhance disease characterization – the relationships among these modalities remain poorly understood. A systematic analysis of their dynamic interaction is essential for improving disease modeling, identifying redundant assessments, and reducing patient burden and acquisition costs. In this paper, we present a quantitative analysis of multimodal AD biomarkers by integrating tau-PET, structural MRI, cognitive scores (MMSE and CDR), and APOE4 data from 789 subjects drawn from the ADNI dataset. In our analyses, we (A) quantify cross-modal mutual information and explained variance to assess redundancy and predictive dependencies; (B) examine associations between tau topologies and structural atrophy across brain regions to select informative ROIs; (C) perform a statistical decomposition of the tau-cognition association into atrophy-related and atrophy-independent components; (D) and identify a dominant neurodegenerative trajectory that aligns with cognitive decline. This study provides a systematic characterization of cross-modal relationships, improving the interpretability and selection of biomarkers in AD. Code is publicly available at: https://github.com/antonioscardace/Multimodal-AD.

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15.

arXiv (quant-ph) 2026-06-15 DOI: arXiv:2606.14613

Tensor network manifolds and Riemannian fundamental theorem for tensor networks

作者:

Pablo P\'aez-Velasco ↗

arXiv:2606.14613v1 Announce Type: cross Abstract: Tensor networks provide a powerful framework for efficiently representing high-dimensional data and many-body quantum states. Endowing tensor networks with a Riemannian manifold structure provides a natural setting for numerical optimization and analysis. A central feature of tensor networks is their gauge freedom, whose characterisation (captured by so-called fundamental theorems) underlies both their intrinsic structure and the design of numerical algorithms. In this work, we study the interaction between the Riemannian manifold structure and the gauge freedom for several families of tensor networks. Using group actions and Riemannian submersions, we establish a Riemannian fundamental theorem for the tensor network families studied.

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16.

arXiv (CS.CV) 2026-06-24 DOI: arXiv:2606.24829

GeoT2V-Bench: Benchmarking 3D Consistency in Text-to-Video Models via 3D Reconstruction

作者:

Chenrui Fan ↗Paolo Favaro ↗

Camera-prompted text-to-video (T2V) models are increasingly used to synthesize virtual camera captures, such as orbiting objects or moving through static scenes. For these outputs, visual plausibility is insufficient: the generated frames should also provide coherent multi-view evidence for a single static 3D scene. We introduce GeoT2V-Bench, a reconstruction-based diagnostic benchmark for evaluating whether camera-prompted T2V clips can support explicit rigid 3D reconstruction. Our pipeline estimates per-frame camera intrinsics and poses with VGGT-style geometry estimation, fits DeformableGS, derives a static MedianGS proxy by temporal-median aggregation, and renders this proxy along the estimated camera path. Instead of producing a pass/fail label or a single scalar score, GeoT2V-Bench reports a continuous reconstruction profile covering apparent image motion, estimated trajectory behavior, MedianGS static rendering error, static-render flow agreement, and the gap between flexible and static fits. On a fair-format four-seed evaluation with 3,840 completed reconstructions from 12 open-weight model configurations and 80 GeCo-Eval static-scene prompts, we find that visible motion, static rendering error, flow agreement, and flexible-vs-static behavior often disagree. GeoT2V-Bench therefore captures complementary failure modes that emerge when generated videos are tested as global static-scene acquisitions.

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17.

arXiv (CS.LG) 2026-06-11 DOI: arXiv:2503.10973

Learning Patterns and Abstractions from Perceptual Sequences

作者:

Shuchen Wu ↗

arXiv:2503.10973v2 Announce Type: replace Abstract: Cognition swiftly breaks high-dimensional sensory streams into familiar parts and uncovers their relations. Why do structures emerge, and how do they enable learning, generalization, and prediction? What computational principles underlie this core aspect of perception and intelligence? A sensory stream, simplified, is a one-dimensional sequence. In learning such sequences, we naturally segment them into parts – a process known as chunking. In the first project, I investigated factors influencing chunking in a serial reaction time task and showed that humans adapt to underlying chunks while balancing speed and accuracy. Building on this, I developed models that learn chunks and parse sequences chunk by chunk. Normatively, I proposed chunking as a rational strategy for discovering recurring patterns and nested hierarchies, enabling efficient sequence factorization. Learned chunks serve as reusable primitives for transfer, composition, and mental simulation – letting the model compose the new from the known. I demonstrated this model's ability to learn hierarchies in single and multi-dimensional sequences and highlighted its utility for unsupervised pattern discovery. The second part moves from concrete to abstract sequences. I taxonomized abstract motifs and examined their role in sequence memory. Behavioral evidence suggests that humans exploit pattern redundancies for compression and transfer. I proposed a non-parametric hierarchical variable model that learns both chunks and abstract variables, uncovering invariant symbolic patterns. I showed its similarity to human learning and compared it to large language models. Taken together, this thesis suggests that chunking and abstraction as simple computational principles enable structured knowledge acquisition in hierarchically organized sequences, from simple to complex, concrete to abstract.

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18.

arXiv (CS.AI) 2026-06-24 DOI: arXiv:2606.24585

LLMs Prompted for Legal Context Object More: Overrefusal from Small On-Premises LLMs in Criminal Legal Context

作者:

Anastasiia Kucherenko ↗Fran\c{c}ois Brouchoud ↗Dimitri Percia David ↗Andrei Kucharavy ↗

arXiv:2606.24585v1 Announce Type: new Abstract: While the validity of LLMs' use in the legal context remains subject to ethical and legal debate, legal professionals are already experimenting with personal LLMs, if only for translation and reformulation. However, even such a seemingly innocuous use can introduce biases through case processing speed if LLM assistants selectively refuse assistance on certain topics. To better anticipate such biases, we investigate several modern small LLMs that are most likely to be used as on-device assistants, to assess the impact of overrefusal on legal prompts. Surprisingly, we find that authority-style prefixes (``you are acting as an assistant of the national supreme court'', ``[...] defense lawyer'') systematically increase refusal rates by 2–20x over the no-prefix baseline, while a known role-play jailbreak prefix shows mixed effects, sharply increasing refusals in some models and barely shifting them in others. The finding suggests that small on-prem deployable LLMs are unstable under contextual framings that a real institutional user might naturally introduce, and further investigation is essential to minimize opportunities for bias.

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19.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.16059

Mojo: A Promising Tool for Scalable Financial AI Efficiency

作者:

Henry Han ↗

arXiv:2606.16059v1 Announce Type: cross Abstract: For thirty years, quantitative finance has paid a costly two-language tax: models researched in Python are rewritten in C++ for production, often introducing numerical discrepancies. GPU-accelerated deep learning exacerbates this problem, as nondeterministic floating-point reductions can produce drift in long backtests, challenging regulatory reproducibility and auditability expectations. This article surveys Mojo, Modular's 2026 Python-like systems language, as a structural response for capital markets engineering. While closing the Python-to-C++ performance gap, Mojo uniquely combines native interoperability with the low-level systems control required to construct bit-exact deterministic kernels. Its MLIR compilation infrastructure further allows a single codebase to target scalar, SIMD, multicore, and GPU execution, reducing the translation bottleneck between research and production. We benchmark four core financial AI workloads: Monte Carlo option pricing, LLM sentiment inference, multi-asset backtesting, and portfolio Value at Risk. On Apple Silicon, Mojo demonstrates 20x to 180x speedups over pure Python on directly measured kernels; larger-scale GPU workload results are projections calibrated from published benchmarks. Alongside transparent performance data, we introduce mojo-deterministic, an open-source library of reproducible reduction kernels, and provide a candid assessment of the problems Mojo does and does not yet solve.

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20.

arXiv (CS.AI) 2026-06-24 DOI: arXiv:2606.24129

OmniPath: A Multi-Modal Agentic Framework for Auditing Wheelchair Accessibility

作者:

ASM Mobarak Hossain ↗Nadim Mahmud ↗Vaskar Raychoudhury ↗Md Osman Gani ↗

arXiv:2606.24129v1 Announce Type: new Abstract: For a wheelchair user, a standard blue line on a map is often a broken promise. While platforms like OpenStreetMap (OSM) successfully capture where a path is, they frequently fail to convey how it physically feels to travel on it. This information barrier is problematic for wheelchair users. To solve this issue, we present OmniPath, a system that moves from passive mapping to proactive environmental auditing. Our framework fuses the network topology of OSM with the submeter precision of high-density aerial LiDAR (USGS 3DEP) to create a high-fidelity 3D model of the pedestrian environment. Rather than simply routing a user, our agent virtually traverses the network, analyzing the surface in 0.5 meter increments. It rigorously quantifies physical friction points specifically running slope, cross slope, and vertical discontinuities against ADA compliance standards, calculating a weighted severity score to categorize hazards from ``Mild'' to ``Critical.'' To ensure real world reliability, we validated the system against 200 physical ground truth field surveys across the National Mall using stratified random sampling. The framework demonstrated strong diagnostic reliability for high-severity hazards, achieving F1-scores of 0.60 for Severe and 0.58 for critical categories. By automating this micro-scale inspection, OmniPath identifies the ``invisible'' barriers that standard maps miss, effectively transforming a static dataset into accessibility data source that anticipates accessibility challenges before the user ever leaves home.

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21.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2507.17188

LLM-Aided Joint Secrecy Precoding and Trajectory for RSMA-Based Heterogeneous UAV Networks

作者:

Lijie Zheng ↗Ji He ↗Shih Yu Chang ↗Yulong Shen ↗

arXiv:2507.17188v3 Announce Type: replace-cross Abstract: This paper investigates secure communications in rate-splitting multiple access (RSMA) enabled heterogeneous UAV networks, where multiple UAVs collaboratively serve ground terminals in the presence of eavesdroppers. By jointly considering secrecy rate maximization and propulsion energy consumption minimization, we formulate a multi-objective optimization problem involving UAV trajectory design, service association, power allocation, and secrecy precoding under mobility, collision-avoidance, service-capacity, and communication constraints. The formulated problem is highly non-convex due to the coupling among UAV trajectories, RSMA transmission variables, and secrecy constraints.To address the resulting non-convex and highly coupled optimization problem, we propose a hierarchical optimization framework. The inner layer uses a semidefinite relaxation (SDR)-based S2DC algorithm combining penalty functions and difference-of-convex (D.C.) programming to solve the secrecy precoding problem with fixed UAV positions. The outer layer introduces a Large Language Model (LLM)-guided heuristic multi-agent reinforcement learning approach (LLM-HeMARL) for trajectory optimization. LLM-HeMARL efficiently incorporates LLM-generated expert heuristic policy, enabling UAVs to learn energy-aware, security-driven trajectories without the inference overhead of real-time LLM calls. The simulation results show that our method outperforms existing baselines in secrecy rate and energy efficiency, with consistent robustness across varying UAV swarm sizes and random seeds.

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22.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2606.18071

Volterra Generative Models

作者:

Yusen Jia ↗Bingyan Han ↗

arXiv:2606.18071v1 Announce Type: cross Abstract: Score-based diffusion models typically use Brownian perturbations, which provide tractable reverse-time dynamics but impose memoryless noising. We introduce Volterra generative models, a continuous-time score-based framework whose forward process injects path-dependent noise through fractional kernels. To handle the non-Markovian and non-semimartingale dynamics, we construct finite-dimensional Markovian lifts using Gaussian quadrature in both regimes and a hybrid finite-difference exponential approximation in the smooth regime. We prove squared error bounds, derive an augmented linear-Gaussian forward process, and show that the learning can remain data-dimensional by considering residual states and analytic auxiliary Gaussian scores. We also identify covariance and reverse-time degeneracies caused by shared Brownian factors and signed smooth-regime weights. The degeneracy motivates stabilized conditioning and, for stiff larger lifts, a Gaussian-bridge reconstruction sampler. Experiments on MNIST and CIFAR-10 show that persistent fractional perturbations with small Markovian lifts can improve score-based generation on MNIST and provide a promising extension to natural images, while the bridge sampler provides a stability mechanism for larger lifts.

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23.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2601.21527

Sustainable Materials Discovery in the Era of Artificial Intelligence

作者:

Sajid Mannan ↗Rupert J. Myers ↗Rohit Batra ↗Rocio Mercado ↗Lothar Wondraczek ↗N. M. Anoop Krishnan ↗

arXiv:2601.21527v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) has transformed materials discovery, enabling rapid exploration of chemical space through generative models and surrogate screening. Yet current generative AI models for materials discovery, which now drive exploration of vast chemical and structural spaces, optimize candidates exclusively for structural stability and functional properties, with no integration of environmental assessment at any stage of the design loop. Prospective and ex-ante life cycle assessment methods exist and have been applied to emerging technologies, but they operate as standalone downstream analyses, not as active constraints within generative or active-learning pipelines. The result is that environmental feedback, even when produced, arrives after design decisions have been made rather than informing them. The disconnect between atomic-scale design and lifecycle assessment (LCA) reflects fundamental challenges: (i) data scarcity across heterogeneous sources, (ii) scale gaps from atoms to industrial systems, (iii) uncertainty in synthesis pathways, and (iv) the absence of frameworks that co-optimize performance with environmental impact. In this Perspective, we propose integrating upstream ML-assisted materials discovery with downstream LCA into the ML-LCA framework, comprising five components: information extraction for building materials-environment knowledge bases, harmonized databases linking properties to sustainability metrics, multi-scale models bridging atomic properties to lifecycle impacts, ensemble prediction of manufacturing pathways with uncertainty quantification, and uncertainty-aware optimization enabling simultaneous performance-sustainability navigation. Case studies spanning polymers, glass, photoresists, and cement demonstrate both necessity and feasibility while identifying material-specific integration challenges.

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24.

arXiv (CS.CL) 2026-06-11 DOI: arXiv:2603.06910

Language Shapes Mental Health Evaluations in Large Language Models

作者:

Jiayi Xu ↗Xiyang Hu ↗

Multilingual large language models (LLMs) are increasingly used in socially sensitive mental health contexts, including support chatbots, screening, and content moderation. This raises a reliability question: do semantically equivalent mental health inputs elicit comparable evaluations across languages, or systematic shifts consistent with language-associated social and cultural contexts? We examine this question in an English-Chinese setting with GPT-4o and Qwen3-32B using a two-level framework: construct-level evaluative orientation, measured by psychometric stigma instruments, and decision-level behavior, measured by binary stigma detection and four-class depression severity classification. Across instruments and models, Chinese prompts elicit higher stigma-related scores than English prompts. At the decision level, Chinese prompts reduce sensitivity to stigmatizing content and produce more conservative depression severity judgments, leading to more under-estimation errors. These findings show that prompt language can shift both evaluative orientation and downstream behavior in LLM-based mental health evaluation. They highlight the need to evaluate multilingual LLMs not only for aggregate performance, but also for whether they apply comparable evaluative standards across languages in socially sensitive domains.

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25.

medRxiv (Medicine) 2026-06-22 DOI: HASH:92dad982b75d8c867dfd3de7268f145e

The Unsteady Return of Command-Following: Recovery and Instability of Bedside Motor Command-Following After Acute Brain Injury

作者:

Gorenshtein ↗Adiniaev ↗Omar ↗Barash ↗Klang ↗Daniel ↗

Background/Objective: Following a verbal command marks the bedside transition from unresponsiveness to overt recovery of consciousness after acute brain injury. Its timing across phenotypes, stability once present, and dependence on sedation are uncharacterized at scale. Methods: Retrospective cohort of adults with acute brain injury, first intensive care unit stay, MIMIC-IV. Command-following was the Glasgow Coma Scale motor response "Obeys Commands." Among patients not following commands at admission, cumulative incidence was estimated with death or hospice and discharge without recovery as competing events. Instability was quantified as transient first recovery and threshold crossings; examinations were tagged for concurrent sedation. Principal findings were externally validated in the multicenter eICU Collaborative Research Database. Results: Of 13,900 brain-injured patients with three or more motor examinations, 5,498 (39.6%) were not following commands at admission. The cumulative incidence of first command-following was 43.5% by 24 hours and 65.0% by 14 days, ranging at 14 days from 36.9% in anoxic injury to 77.2% in ischemic stroke (anoxic versus ischemic stroke at 72 hours, difference 0.41; adjusted P = .002). Among 3,573 patients who recovered, the first recovery was transient in 22.2%, and 62.4% crossed the threshold repeatedly. Non-following was strongly associated with sedation, consistent with an arousal-dependent examination. In eICU, the 14-day incidence was 64.8%, and transient first recovery was 22.7%, closely matching the primary cohort. Conclusions: After acute brain injury, overt bedside command-following returns early but unsteadily, with phenotype-dependent timing, threshold fluctuation, and strong dependence on sedation. A single charted observation is an unreliable index of the underlying state.

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