OpenRaiser/Intern-Atlas huggingface dataset:
ProImage-Bench: Rubric-Based Evaluation for Professional Image Generation
Venue: arXiv cs.CV 2025
Year: 2025

We study professional image generation, where a model must synthesize information-dense, scientifically precise illustrations from technical descriptions rather than merely produce visually plausible pictures. To quantify the progress, we introduce ProImage-Bench, a rubric-based benchmark that targets biology schematics, engineering/patent drawings, and general scientific diagrams. For 654 figures collected from real textbooks and technical reports, we construct detailed image instructions and a hierarchy of rubrics that decompose correctness into 6,076 criteria and 44,131 binary checks. Rubrics are derived from surrounding text and reference figures using large multimodal models, and are evaluated by an automated LMM-based judge with a principled penalty scheme that aggregates sub-question outcomes into interpretable criterion scores. We benchmark several representative text-to-image models on ProImage-Bench and find that, despite strong open-domain performance, the best base model reaches only 0.791 rubric accuracy and 0.553 criterion score overall, revealing substantial gaps in fine-grained scientific fidelity. Finally, we show that the same rubrics provide actionable supervision: feeding failed checks back into an editing model for iterative refinement boosts a strong generator from 0.653 to 0.865 in rubric accuracy and from 0.388 to 0.697 in criterion score. ProImage-Bench thus offers both a rigorous diagnostic for professional image generation and a scalable signal for improving specification-faithful scientific illustrations.
Adversarially Probing Cross-Family Sound Symbolism in 27 Languages
Venue: arXiv cs.CL 2025
Year: 2025

The phenomenon of sound symbolism, the non-arbitrary mapping between word sounds and meanings, has long been demonstrated through anecdotal experiments like Bouba Kiki, but rarely tested at scale. We present the first computational cross-linguistic analysis of sound symbolism in the semantic domain of size. We compile a typologically broad dataset of 810 adjectives (27 languages, 30 words each), each phonemically transcribed and validated with native-speaker audio. Using interpretable classifiers over bag-of-segment features, we find that phonological form predicts size semantics above chance even across unrelated languages, with both vowels and consonants contributing. To probe universality beyond genealogy, we train an adversarial scrubber that suppresses language identity while preserving size signal (also at family granularity). Language prediction averaged across languages and settings falls below chance while size prediction remains significantly above chance, indicating cross-family sound-symbolic bias. We release data, code, and diagnostic tools for future large-scale studies of iconicity.
MetaTPT: Meta Test-time Prompt Tuning for Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Vision-language models (VLMs) such as CLIP exhibit strong zero-shot generalization but remain sensitive to domain shifts at test time. Test-time prompt tuning (TPT) mitigates this issue by adapting prompts with fixed augmentations, which may falter in more challenging settings. In this work, we propose Meta Test-Time Prompt Tuning (MetaTPT), a meta-learning framework that learns a self-supervised auxiliary task to guide test-time prompt tuning. The auxiliary task dynamically learns parameterized augmentations for each sample, enabling more expressive transformations that capture essential features in target domains. MetaTPT adopts a dual-loop optimization paradigm: an inner loop learns a self-supervised task that generates informative views, while the outer loop performs prompt tuning by enforcing consistency across these views. By coupling augmentation learning with prompt tuning, MetaTPT improves test-time adaptation under domain shifts. Extensive experiments demonstrate that MetaTPT achieves state-of-the-art performance on domain generalization and cross-dataset benchmarks.
Feature Aggregation for Efficient Continual Learning of Complex Facial Expressions
Venue: arXiv cs.CV 2025
Year: 2025

As artificial intelligence (AI) systems become increasingly embedded in our daily life, the ability to recognize and adapt to human emotions is essential for effective human-computer interaction. Facial expression recognition (FER) provides a primary channel for inferring affective states, but the dynamic and culturally nuanced nature of emotions requires models that can learn continuously without forgetting prior knowledge. In this work, we propose a hybrid framework for FER in a continual learning setting that mitigates catastrophic forgetting. Our approach integrates two complementary modalities: deep convolutional features and facial Action Units (AUs) derived from the Facial Action Coding System (FACS). The combined representation is modelled through Bayesian Gaussian Mixture Models (BGMMs), which provide a lightweight, probabilistic solution that avoids retraining while offering strong discriminative power. Using the Compound Facial Expression of Emotion (CFEE) dataset, we show that our model can first learn basic expressions and then progressively recognize compound expressions. Experiments demonstrate improved accuracy, stronger knowledge retention, and reduced forgetting. This framework contributes to the development of emotionally intelligent AI systems with applications in education, healthcare, and adaptive user interfaces.
Fractional Differential Equation Physics-Informed Neural Network and Its Application in Battery State Estimation
Venue: arXiv cs.LG 2025
Year: 2025

Accurate estimation of the State of Charge (SOC) is critical for ensuring the safety, reliability, and performance optimization of lithium-ion battery systems. Conventional data-driven neural network models often struggle to fully characterize the inherent complex nonlinearities and memory-dependent dynamics of electrochemical processes, significantly limiting their predictive accuracy and physical interpretability under dynamic operating conditions. To address this challenge, this study proposes a novel neural architecture termed the Fractional Differential Equation Physics-Informed Neural Network (FDIFF-PINN), which integrates fractional calculus with deep learning. The main contributions of this paper include: (1) Based on a fractional-order equivalent circuit model, a discretized fractional-order partial differential equation is constructed. (2) Comparative experiments were conducted using a dynamic charge/discharge dataset of Panasonic 18650PF batteries under multi-temperature conditions (from -10$^{\circ}$C to 20$^{\circ}$C).
GrowTAS: Progressive Expansion from Small to Large Subnets for Efficient ViT Architecture Search
Venue: arXiv cs.CV 2025
Year: 2025

Transformer architecture search (TAS) aims to automatically discover efficient vision transformers (ViTs), reducing the need for manual design. Existing TAS methods typically train an over-parameterized network (i.e., a supernet) that encompasses all candidate architectures (i.e., subnets). However, all subnets share the same set of weights, which leads to interference that degrades the smaller subnets severely. We have found that well-trained small subnets can serve as a good foundation for training larger ones. Motivated by this, we propose a progressive training framework, dubbed GrowTAS, that begins with training small subnets and incorporate larger ones gradually. This enables reducing the interference and stabilizing a training process. We also introduce GrowTAS+ that fine-tunes a subset of weights only to further enhance the performance of large subnets. Extensive experiments on ImageNet and several transfer learning benchmarks, including CIFAR-10/100, Flowers, CARS, and INAT-19, demonstrate the effectiveness of our approach over current TAS methods
F5-TTS-RO: Extending F5-TTS to Romanian TTS via Lightweight Input Adaptation
Venue: arXiv cs.CL 2025
Year: 2025

This work introduces a lightweight input-level adapter for the F5-TTS model that enables Romanian Language support. To preserve the existing capabilities of the model (voice cloning, English and Chinese support), we keep the original weights frozen, append a sub-network to the model and train it as an extension for the textual embedding matrix of the text encoder. For simplicity, we rely on ConvNeXt module implemented in F5-TTS to also model the co-dependencies between the new character-level embeddings. The module serves as a ``soft`` letter-to-sound layer, converting Romanian text into a continuous representation that the F5-TTS model uses to produce naturally sounding Romanian utterances. We evaluate the model with a pool of 20 human listeners across three tasks: (a) audio similarity between reference and generated speech, (b) pronunciation and naturalness and (c) Romanian-English code-switching. The results indicate that our approach maintains voice cloning capabilities and enables, to a certain extent, code-switching within the same utterance; however, residual English accent characteristics remain. We open-source our code and provide example audio samples at https://github.com/racai-ro/Ro-F5TTS.
Evaluating Asynchronous Semantics in Trace-Discovered Resilience Models: A Case Study on the OpenTelemetry Demo
Venue: arXiv cs.SE 2025
Year: 2025

While distributed tracing and chaos engineering are becoming standard for microservices, resilience models remain largely manual and bespoke. We revisit a trace-discovered connectivity model that derives a service dependency graph from traces and uses Monte Carlo simulation to estimate endpoint availability under fail-stop service failures. Compared to earlier work, we (i) derive the graph directly from raw OpenTelemetry traces, (ii) attach endpoint-specific success predicates, and (iii) add a simple asynchronous semantics that treats Kafka edges as non-blocking for immediate HTTP success. We apply this model to the OpenTelemetry Demo ("Astronomy Shop") using a GitHub Actions workflow that discovers the graph, runs simulations, and executes chaos experiments that randomly kill microservices in a Docker Compose deployment. Across the studied failure fractions, the model reproduces the overall availability degradation curve, while asynchronous semantics for Kafka edges change predicted availabilities by at most about 10^(-5) (0.001 percentage points). This null result suggests that for immediate HTTP availability in this case study, explicitly modeling asynchronous dependencies is not warranted, and a simpler connectivity-only model is sufficient.
SCIR: A Self-Correcting Iterative Refinement Framework for Enhanced Information Extraction Based on Schema
Venue: arXiv cs.CL 2025
Year: 2025

Although Large language Model (LLM)-powered information extraction (IE) systems have shown impressive capabilities, current fine-tuning paradigms face two major limitations: high training costs and difficulties in aligning with LLM preferences. To address these issues, we propose a novel universal IE paradigm, the Self-Correcting Iterative Refinement (SCIR) framework, along with a Multi-task Bilingual (Chinese-English) Self-Correcting (MBSC) dataset containing over 100,000 entries. The SCIR framework achieves plug-and-play compatibility with existing LLMs and IE systems through its Dual-Path Self-Correcting module and feedback-driven optimization, thereby significantly reducing training costs. Concurrently, the MBSC dataset tackles the challenge of preference alignment by indirectly distilling GPT-4's capabilities into IE result detection models. Experimental results demonstrate that SCIR outperforms state-of-the-art IE methods across three key tasks: named entity recognition, relation extraction, and event extraction, achieving a 5.27 percent average improvement in span-based Micro-F1 while reducing training costs by 87 percent compared to baseline approaches. These advancements not only enhance the flexibility and accuracy of IE systems but also pave the way for lightweight and efficient IE paradigms.
VideoARM: Agentic Reasoning over Hierarchical Memory for Long-Form Video Understanding
Venue: arXiv cs.CV 2025
Year: 2025

Long-form video understanding remains challenging due to the extended temporal structure and dense multimodal cues. Despite recent progress, many existing approaches still rely on hand-crafted reasoning pipelines or employ token-consuming video preprocessing to guide MLLMs in autonomous reasoning. To overcome these limitations, we introduce VideoARM, an Agentic Reasoning-over-hierarchical-Memory paradigm for long-form video understanding. Instead of static, exhaustive preprocessing, VideoARM performs adaptive, on-the-fly agentic reasoning and memory construction. Specifically, VideoARM performs an adaptive and continuous loop of observing, thinking, acting, and memorizing, where a controller autonomously invokes tools to interpret the video in a coarse-to-fine manner, thereby substantially reducing token consumption. In parallel, a hierarchical multimodal memory continuously captures and updates multi-level clues throughout the operation of the agent, providing precise contextual information to support the controller in decision-making. Experiments on prevalent benchmarks demonstrate that VideoARM outperforms the state-of-the-art method, DVD, while significantly reducing token consumption for long-form videos.
DeepVekua: Geometric-Spectral Representation Learning for Physics-Informed Fields
Venue: arXiv cs.LG 2025
Year: 2025

We present DeepVekua, a hybrid architecture that unifies geometric deep learning with spectral analysis to solve partial differential equations (PDEs) in sparse data regimes. By learning a diffeomorphic coordinate transformation that maps complex geometries to a latent harmonic space, our method outperforms state-of-the-art implicit representations on advection-diffusion systems. Unlike standard coordinate-based networks which struggle with spectral bias, DeepVekua separates the learning of geometry from the learning of physics, solving for optimal spectral weights in closed form. We demonstrate a 100x improvement over spectral baselines. The code is available at https://github.com/VladimerKhasia/vekuanet.
A Graph Attention Network-Based Framework for Reconstructing Missing LiDAR Beams
Venue: arXiv cs.CV 2025
Year: 2025

Vertical beam dropout in spinning LiDAR sensors triggered by hardware aging, dust, snow, fog, or bright reflections removes entire vertical slices from the point cloud and severely degrades 3D perception in autonomous vehicles. This paper proposes a Graph Attention Network (GAT)-based framework that reconstructs these missing vertical channels using only the current LiDAR frame, with no camera images or temporal information required. Each LiDAR sweep is represented as an unstructured spatial graph: points are nodes and edges connect nearby points while preserving the original beam-index ordering. A multi-layer GAT learns adaptive attention weights over local geometric neighborhoods and directly regresses the missing elevation (z) values at dropout locations. Trained and evaluated on 1,065 raw KITTI sequences with simulated channel dropout, the method achieves an average height RMSE of 11.67 cm, with 87.98% of reconstructed points falling within a 10 cm error threshold. Inference takes 14.65 seconds per frame on a single GPU, and reconstruction quality remains stable for different neighborhood sizes k. These results show that a pure graph attention model operating solely on raw point-cloud geometry can effectively recover dropped vertical beams under realistic sensor degradation.
AI Transparency Atlas: Framework, Scoring, and Real-Time Model Card Evaluation Pipeline
Venue: arXiv cs.AI 2025
Year: 2025

AI model documentation is fragmented across platforms and inconsistent in structure, preventing policymakers, auditors, and users from reliably assessing safety claims, data provenance, and version-level changes. We analyzed documentation from five frontier models (Gemini 3, Grok 4.1, Llama 4, GPT-5, and Claude 4.5) and 100 Hugging Face model cards, identifying 947 unique section names with extreme naming variation. Usage information alone appeared under 97 distinct labels. Using the EU AI Act Annex IV and the Stanford Transparency Index as baselines, we developed a weighted transparency framework with 8 sections and 23 subsections that prioritizes safety-critical disclosures (Safety Evaluation: 25%, Critical Risk: 20%) over technical specifications. We implemented an automated multi-agent pipeline that extracts documentation from public sources and scores completeness through LLM-based consensus. Evaluating 50 models across vision, multimodal, open-source, and closed-source systems cost less than $3 in total and revealed systematic gaps. Frontier labs (xAI, Microsoft, Anthropic) achieve approximately 80% compliance, while most providers fall below 60%. Safety-critical categories show the largest deficits: deception behaviors, hallucinations, and child safety evaluations account for 148, 124, and 116 aggregate points lost, respectively, across all evaluated models.
Large language models have learned to use language
Venue: arXiv cs.CL 2025
Year: 2025

Acknowledging that large language models have learned to use language can open doors to breakthrough language science. Achieving these breakthroughs may require abandoning some long-held ideas about how language knowledge is evaluated and reckoning with the difficult fact that we have entered a post-Turing test era.
HetRL: Efficient Reinforcement Learning for LLMs in Heterogeneous Environments
Venue: arXiv cs.DC 2025
Year: 2025

As large language models (LLMs) continue to scale and new GPUs are released even more frequently, there is an increasing demand for LLM post-training in heterogeneous environments to fully leverage underutilized mid-range or previous-generation GPUs across regions and alleviate the shortage of homogeneous high-end GPUs within a single region. However, achieving high-performance reinforcement learning (RL) training for LLMs on such computing resources remains challenging because the workflow involves multiple models and tasks with complex computation and data dependencies. In this paper, we present HetRL, a distributed system for efficient RL training in infrastructures with heterogeneous GPUs and networks. HetRL formulates the scheduling of RL training in heterogeneous environments as a constrained joint optimization problem and introduces a novel scheduling algorithm that (1) decomposes the complex search space with a multi-level search framework; and (2) allocates the search budget via successive halving. Our extensive evaluation, consuming 20,000 GPU-hours, shows that HetRL delivers up to 9.17x the throughput of state-of-the-art systems, and 3.17x on average, under various workloads and settings.
Can You Keep a Secret? Exploring AI for Care Coordination in Cognitive Decline
Venue: arXiv cs.HC 2025
Year: 2025

The increasing number of older adults who experience cognitive decline places a burden on informal caregivers, whose support with tasks of daily living determines whether older adults can remain in their homes. To explore how agents might help lower-SES older adults to age-in-place, we interviewed ten pairs of older adults experiencing cognitive decline and their informal caregivers. We explored how they coordinate care, manage burdens, and sustain autonomy and privacy. Older adults exercised control by delegating tasks to specific caregivers, keeping information about all the care they received from their adult children. Many abandoned some tasks of daily living, lowering their quality of life to ease caregiver burden. One effective strategy, piggybacking, uses spontaneous overlaps in errands to get more work done with less caregiver effort. This raises the questions: (i) Can agents help with piggyback coordination? (ii) Would it keep older adults in their homes longer, while not increasing caregiver burden?
An STREL-based Formulation of Spatial Resilience in Cyber-Physical Systems
Venue: arXiv cs.LO 2025
Year: 2025

Resiliency is the ability of a system to quickly recover from a violation (recoverability) and avoid future violations for as long as possible (durability). In the spatial setting, recoverability and durability (now known as persistency) are measured in units of distance. Like its temporal counterpart, spatial resiliency is of fundamental importance for Cyber-Physical Systems (CPS) and yet, to date, there is no widely agreed-upon formal treatment of spatial resiliency. We present a formal framework for reasoning about spatial resiliency in CPS. Our framework is based on the spatial fragment of STREL, which we refer to as SREL. In this framework, spatial resiliency is given a syntactic characterization in the form of a Spatial Resiliency Specification (SpaRS). An atomic predicate of SpaRS is called an S-atom. Given an arbitrary SREL formula $φ$, distance bounds $d_1, d_2$, the S-atom of $φ$, $S_{d_1, d_2} (φ)$, is the SREL formula $\negφR_{[0,d_1]} (φR_{[d_2, +\infty)}φ)$, specifying that recovery from a violation of $φ$ occurs within distance $d_1$ (recoverability), and subsequently that $φ$ be maintained along a route for a distance greater than $d_2$ (persistency). S-atoms can be combined using spatial STREL operators, allowing one to express composite resiliency specifications. We define a quantitative semantics for SpaRS in the form of a Spatial Resilience Value (SpaRV) function $σ$ and prove its soundness and completeness w.r.t. SREL's Boolean semantics. The $σ$-value for $S_{d_1,d_2}(φ)$ is a set of non-dominated (rec, per) pairs, quantifying recoverability and persistency, given that some routes may offer better recoverability while others better persistency. In addition, we design algorithms to evaluate SpaRV for SpaRS formulas. Finally, two case studies demonstrate the practical utility of our approach.
Principled Performance Tunability in Operating System Kernels
Venue: arXiv cs.OS 2025
Year: 2025

The Linux kernel source code contains numerous constant values that critically influence system performance. Many of these constants, which we term perf-consts, are magic numbers that encode brittle assumptions about hardware and workloads. As systems and workloads evolve, such constants often become suboptimal. Unfortunately, deployed kernels lack support for safe and efficient in-situ tuning of perf-consts without a long and disruptive process of rebuilding and redeploying the kernel image. This paper advocates principled OS performance tunability. We present KernelX, a system that provides a safe, efficient, and programmable interface for in-situ tuning of arbitrary perf-consts on a running kernel. KernelX transforms any perf-const into a tunable knob on demand using a novel mechanism called Scoped Indirect Execution (SIE). SIE precisely identifies the binary boundaries where a perf-const influences system state and redirects execution to synthesized instructions that update the state as if new values were used. KernelX goes beyond version atomicity to guarantee side-effect safety, a property not provided by existing kernel update mechanisms. KernelX also provides a programmable interface that allows policies to incorporate application hints, hardware heuristics, and fine-grained isolation, without modifying kernel source code or disrupting deployed OS kernels. Case studies across multiple kernel subsystems demonstrate that KernelX enables significant performance improvements by making previously untunable perf-consts safely tunable at runtime, while supporting millisecond-scale policy updates.