OpenRaiser/Intern-Atlas huggingface dataset:
MultiMind at SemEval-2025 Task 7: Crosslingual Fact-Checked Claim Retrieval via Multi-Source Alignment
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents our system for SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval. In an era where misinformation spreads rapidly, effective fact-checking is increasingly critical. We introduce TriAligner, a novel approach that leverages a dual-encoder architecture with contrastive learning and incorporates both native and English translations across different modalities. Our method effectively retrieves claims across multiple languages by learning the relative importance of different sources in alignment. To enhance robustness, we employ efficient data preprocessing and augmentation using large language models while incorporating hard negative sampling to improve representation learning. We evaluate our approach on monolingual and crosslingual benchmarks, demonstrating significant improvements in retrieval accuracy and fact-checking performance over baselines.
Reflection Pretraining Enables Token-Level Self-Correction in Biological Sequence Models
Venue: arXiv cs.CL 2025
Year: 2025

Chain-of-Thought (CoT) prompting has significantly advanced task-solving capabilities in natural language processing with large language models. Unlike standard prompting, CoT encourages the model to generate intermediate reasoning steps, non-answer tokens, that help guide the model toward more accurate final outputs. These intermediate steps enable more complex reasoning processes such as error correction, memory management, future planning, and self-reflection. However, applying CoT to non-natural language domains, such as protein and RNA language models, is not yet possible, primarily due to the limited expressiveness of their token spaces (e.g., amino acid tokens). In this work, we propose and define the concept of language expressiveness: the ability of a given language, using its tokens and grammar, to encode information. We show that the limited expressiveness of protein language severely restricts the applicability of CoT-style reasoning. To overcome this, we introduce reflection pretraining, for the first time in a biological sequence model, which enables the model to engage in intermediate reasoning through the generation of auxiliary "thinking tokens" beyond simple answer tokens. Theoretically, we demonstrate that our augmented token set significantly enhances biological language expressiveness, thereby improving the overall reasoning capacity of the model. Experimentally, our pretraining approach teaches protein models to self-correct and leads to substantial performance gains compared to standard pretraining.
Time-Bucketed Balance Records: Bounded-Storage Ephemeral Tokens for Resource-Constrained Systems
Venue: arXiv cs.DS 2025
Year: 2025

Fungible tokens with time-to-live (TTL) semantics require tracking individual expiration times for each deposited unit. A naive implementation creates a new balance record per deposit, leading to unbounded storage growth and vulnerability to denial-of-service attacks. We present time-bucketed balance records, a data structure that bounds storage to O(k) records per account while guaranteeing that tokens never expire before their configured TTL. Our approach discretizes time into k buckets, coalescing deposits within the same bucket to limit unique expiration timestamps. We prove three key properties: (1) storage is bounded by k+1 records regardless of deposit frequency, (2) actual expiration time is always at least the configured TTL, and (3) adversaries cannot increase a victim's operation cost beyond O(k)[amortized] worst case. We provide a reference implementation in Solidity with measured gas costs demonstrating practical efficiency.
PUFM++: Point Cloud Upsampling via Enhanced Flow Matching
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in generative modeling have demonstrated strong promise for high-quality point cloud upsampling. In this work, we present PUFM++, an enhanced flow-matching framework for reconstructing dense and accurate point clouds from sparse, noisy, and partial observations. PUFM++ improves flow matching along three key axes: (i) geometric fidelity, (ii) robustness to imperfect input, and (iii) consistency with downstream surface-based tasks. We introduce a two-stage flow-matching strategy that first learns a direct, straight-path flow from sparse inputs to dense targets, and then refines it using noise-perturbed samples to approximate the terminal marginal distribution better. To accelerate and stabilize inference, we propose a data-driven adaptive time scheduler that improves sampling efficiency based on interpolation behavior. We further impose on-manifold constraints during sampling to ensure that generated points remain aligned with the underlying surface. Finally, we incorporate a recurrent interface network~(RIN) to strengthen hierarchical feature interactions and boost reconstruction quality. Extensive experiments on synthetic benchmarks and real-world scans show that PUFM++ sets a new state of the art in point cloud upsampling, delivering superior visual fidelity and quantitative accuracy across a wide range of tasks. Code and pretrained models are publicly available at https://github.com/Holmes-Alan/Enhanced_PUFM.
FinAgent: An Agentic AI Framework Integrating Personal Finance and Nutrition Planning
Venue: arXiv cs.AI 2025
Year: 2025

The issue of limited household budgets and nutritional demands continues to be a challenge especially in the middle-income environment where food prices fluctuate. This paper introduces a price aware agentic AI system, which combines personal finance management with diet optimization. With household income and fixed expenditures, medical and well-being status, as well as real-time food costs, the system creates nutritionally sufficient meals plans at comparatively reasonable prices that automatically adjust to market changes. The framework is implemented in a modular multi-agent architecture, which has specific agents (budgeting, nutrition, price monitoring, and health personalization). These agents share the knowledge base and use the substitution graph to ensure that the nutritional quality is maintained at a minimum cost. Simulations with a representative Saudi household case study show a steady 12-18\% reduction in costs relative to a static weekly menu, nutrient adequacy of over 95\% and high performance with price changes of 20-30%. The findings indicate that the framework can locally combine affordability with nutritional adequacy and provide a viable avenue of capacity-building towards sustainable and fair diet planning in line with Sustainable Development Goals on Zero Hunger and Good Health.
A Mechanistic Analysis of Transformers for Dynamical Systems
Venue: arXiv cs.LG 2025
Year: 2025

Transformers are increasingly adopted for modeling and forecasting time-series, yet their internal mechanisms remain poorly understood from a dynamical systems perspective. In contrast to classical autoregressive and state-space models, which benefit from well-established theoretical foundations, Transformer architectures are typically treated as black boxes. This gap becomes particularly relevant as attention-based models are considered for general-purpose or zero-shot forecasting across diverse dynamical regimes. In this work, we do not propose a new forecasting model, but instead investigate the representational capabilities and limitations of single-layer Transformers when applied to dynamical data. Building on a dynamical systems perspective we interpret causal self-attention as a linear, history-dependent recurrence and analyze how it processes temporal information. Through a series of linear and nonlinear case studies, we identify distinct operational regimes. For linear systems, we show that the convexity constraint imposed by softmax attention fundamentally restricts the class of dynamics that can be represented, leading to oversmoothing in oscillatory settings. For nonlinear systems under partial observability, attention instead acts as an adaptive delay-embedding mechanism, enabling effective state reconstruction when sufficient temporal context and latent dimensionality are available. These results help bridge empirical observations with classical dynamical systems theory, providing insight into when and why Transformers succeed or fail as models of dynamical systems.
Emotion Diffusion in Real and Simulated Social Graphs: Structural Limits of LLM-Based Social Simulation
Venue: arXiv cs.SI 2025
Year: 2025

Understanding how emotions diffuse through social networks is central to computational social science. Recently, large language models (LLMs) have been increasingly used to simulate social media interactions, raising the question of whether LLM-generated data can realistically reproduce emotion diffusion patterns observed in real online communities. In this study, we conduct a systematic comparison between emotion diffusion in real-world social graphs and in LLM-simulated interaction networks. We construct diffusion graphs from Reddit discussion data and compare them with synthetic social graphs generated through LLM-driven conversational simulations. Emotion states are inferred using established sentiment analysis pipelines, and both real and simulated graphs are analyzed from structural, behavioral, and predictive perspectives. Our results reveal substantial structural and dynamic discrepancies between real and simulated diffusion processes. Real-world emotion diffusion exhibits dense connectivity, repeated interactions, sentiment shifts, and emergent community structures, whereas LLM-simulated graphs largely consist of isolated linear chains with monotonic emotional trajectories. These structural limitations significantly affect downstream tasks such as graph-based emotion prediction, leading to reduced emotional diversity and class imbalance in simulated settings. Our findings highlight current limitations of LLM-based social simulation in capturing the interactive complexity and emotional heterogeneity of real social networks. This work provides empirical evidence for the cautious use of LLM-generated data in social science research and suggests directions for improving future simulation frameworks.
SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation
Venue: arXiv cs.CL 2025
Year: 2025

Human infants, with only a few hundred hours of speech exposure, acquire basic units of new languages, highlighting a striking efficiency gap compared to the data-hungry self-supervised speech models. To address this gap, this paper introduces SpidR-Adapt for rapid adaptation to new languages using minimal unlabeled data. We cast such low-resource speech representation learning as a meta-learning problem and construct a multi-task adaptive pre-training (MAdaPT) protocol which formulates the adaptation process as a bi-level optimization framework. To enable scalable meta-training under this framework, we propose a novel heuristic solution, first-order bi-level optimization (FOBLO), avoiding heavy computation costs. Finally, we stabilize meta-training by using a robust initialization through interleaved supervision which alternates self-supervised and supervised objectives. Empirically, SpidR-Adapt achieves rapid gains in phonemic discriminability (ABX) and spoken language modeling (sWUGGY, sBLIMP, tSC), improving over in-domain language models after training on less than 1h of target-language audio, over $100\times$ more data-efficient than standard training. These findings highlight a practical, architecture-agnostic path toward biologically inspired, data-efficient representations. We open-source the training code and model checkpoints at https://github.com/facebookresearch/spidr-adapt.
Human Motion Estimation with Everyday Wearables
Venue: arXiv cs.CV 2025
Year: 2025

While on-body device-based human motion estimation is crucial for applications such as XR interaction, existing methods often suffer from poor wearability, expensive hardware, and cumbersome calibration, which hinder their adoption in daily life. To address these challenges, we present EveryWear, a lightweight and practical human motion capture approach based entirely on everyday wearables: a smartphone, smartwatch, earbuds, and smart glasses equipped with one forward-facing and two downward-facing cameras, requiring no explicit calibration before use. We introduce Ego-Elec, a 9-hour real-world dataset covering 56 daily activities across 17 diverse indoor and outdoor environments, with ground-truth 3D annotations provided by the motion capture (MoCap), to facilitate robust research and benchmarking in this direction. Our approach employs a multimodal teacher-student framework that integrates visual cues from egocentric cameras with inertial signals from consumer devices. By training directly on real-world data rather than synthetic data, our model effectively eliminates the sim-to-real gap that constrains prior work. Experiments demonstrate that our method outperforms baseline models, validating its effectiveness for practical full-body motion estimation.
UniTacHand: Unified Spatio-Tactile Representation for Human to Robotic Hand Skill Transfer
Venue: arXiv cs.RO 2025
Year: 2025

Tactile sensing is crucial for robotic hands to achieve human-level dexterous manipulation, especially in scenarios with visual occlusion. However, its application is often hindered by the difficulty of collecting large-scale real-world robotic tactile data. In this study, we propose to collect low-cost human manipulation data using haptic gloves for tactile-based robotic policy learning. The misalignment between human and robotic tactile data makes it challenging to transfer policies learned from human data to robots. To bridge this gap, we propose UniTacHand, a unified representation to align robotic tactile information captured by dexterous hands with human hand touch obtained from gloves. First, we project tactile signals from both human hands and robotic hands onto a morphologically consistent 2D surface space of the MANO hand model. This unification standardizes the heterogeneous data structures and inherently embeds the tactile signals with spatial context. Then, we introduce a contrastive learning method to align them into a unified latent space, trained on only 10 minutes of paired data from our data collection system. Our approach enables zero-shot tactile-based policy transfer from humans to a real robot, generalizing to objects unseen in the pre-training data. We also demonstrate that co-training on mixed data, including both human and robotic demonstrations via UniTacHand, yields better performance and data efficiency compared with using only robotic data. UniTacHand paves a path toward general, scalable, and data-efficient learning for tactile-based dexterous hands.
DreaMontage: Arbitrary Frame-Guided One-Shot Video Generation
Venue: arXiv cs.CV 2025
Year: 2025

The "one-shot" technique represents a distinct and sophisticated aesthetic in filmmaking. However, its practical realization is often hindered by prohibitive costs and complex real-world constraints. Although emerging video generation models offer a virtual alternative, existing approaches typically rely on naive clip concatenation, which frequently fails to maintain visual smoothness and temporal coherence. In this paper, we introduce DreaMontage, a comprehensive framework designed for arbitrary frame-guided generation, capable of synthesizing seamless, expressive, and long-duration one-shot videos from diverse user-provided inputs. To achieve this, we address the challenge through three primary dimensions. (i) We integrate a lightweight intermediate-conditioning mechanism into the DiT architecture. By employing an Adaptive Tuning strategy that effectively leverages base training data, we unlock robust arbitrary-frame control capabilities. (ii) To enhance visual fidelity and cinematic expressiveness, we curate a high-quality dataset and implement a Visual Expression SFT stage. In addressing critical issues such as subject motion rationality and transition smoothness, we apply a Tailored DPO scheme, which significantly improves the success rate and usability of the generated content. (iii) To facilitate the production of extended sequences, we design a Segment-wise Auto-Regressive (SAR) inference strategy that operates in a memory-efficient manner. Extensive experiments demonstrate that our approach achieves visually striking and seamlessly coherent one-shot effects while maintaining computational efficiency, empowering users to transform fragmented visual materials into vivid, cohesive one-shot cinematic experiences.
TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning
Venue: arXiv cs.CV 2025
Year: 2025

The interpretation of small tiles in large whole slide images (WSI) often needs a larger image context. We introduce TICON, a transformer-based tile representation contextualizer that produces rich, contextualized embeddings for ''any'' application in computational pathology. Standard tile encoder-based pipelines, which extract embeddings of tiles stripped from their context, fail to model the rich slide-level information essential for both local and global tasks. Furthermore, different tile-encoders excel at different downstream tasks. Therefore, a unified model is needed to contextualize embeddings derived from ''any'' tile-level foundation model. TICON addresses this need with a single, shared encoder, pretrained using a masked modeling objective to simultaneously unify and contextualize representations from diverse tile-level pathology foundation models. Our experiments demonstrate that TICON-contextualized embeddings significantly improve performance across many different tasks, establishing new state-of-the-art results on tile-level benchmarks (i.e., HEST-Bench, THUNDER, CATCH) and slide-level benchmarks (i.e., Patho-Bench). Finally, we pretrain an aggregator on TICON to form a slide-level foundation model, using only 11K WSIs, outperforming SoTA slide-level foundation models pretrained with up to 350K WSIs.
Fast SAM2 with Text-Driven Token Pruning
Venue: arXiv cs.CV 2025
Year: 2025

Segment Anything Model 2 (SAM2), a vision foundation model has significantly advanced in prompt-driven video object segmentation, yet their practical deployment remains limited by the high computational and memory cost of processing dense visual tokens across time. The SAM2 pipelines typically propagate all visual tokens produced by the image encoder through downstream temporal reasoning modules, regardless of their relevance to the target object, resulting in reduced scalability due to quadratic memory attention overhead. In this work, we introduce a text-guided token pruning framework that improves inference efficiency by selectively reducing token density prior to temporal propagation, without modifying the underlying segmentation architecture. Operating after visual encoding and before memory based propagation, our method ranks tokens using a lightweight routing mechanism that integrates local visual context, semantic relevance derived from object-centric textual descriptions (either user-provided or automatically generated), and uncertainty cues that help preserve ambiguous or boundary critical regions. By retaining only the most informative tokens for downstream processing, the proposed approach reduces redundant computation while maintaining segmentation fidelity. Extensive experiments across multiple challenging video segmentation benchmarks demonstrate that post-encoder token pruning provides a practical and effective pathway to efficient, prompt-aware video segmentation, achieving up to 42.50 percent faster inference and 37.41 percent lower GPU memory usage compared to the unpruned baseline SAM2, while preserving competitive J and F performance. These results highlight the potential of early token selection to improve the scalability of transformer-based video segmentation systems for real-time and resource-constrained applications.
Optimizing Decoding Paths in Masked Diffusion Models by Quantifying Uncertainty
Venue: arXiv cs.CL 2025
Year: 2025

Masked Diffusion Models (MDMs) offer flexible, non-autoregressive generation, but this freedom introduces a challenge: final output quality is highly sensitive to the decoding order. We are the first to formalize this issue, attributing the variability in output quality to the cumulative predictive uncertainty along a generative path. To quantify this uncertainty, we introduce Denoising Entropy, a computable metric that serves as an internal signal for evaluating generative process. Leveraging this metric, we propose two algorithms designed to optimize the decoding path: a post-hoc selection method and a real-time guidance strategy. Experiments demonstrate that our entropy-guided methods significantly improve generation quality, consistently boosting accuracy on challenging reasoning, planning, and code benchmarks. Our work establishes Denoising Entropy as a principled tool for understanding and controlling generation, effectively turning the uncertainty in MDMs from a liability into a key advantage for discovering high-quality solutions.
Understanding the Role of Large Language Models in Software Engineering: Evidence from an Industry Survey
Venue: arXiv cs.SE 2025
Year: 2025

The rapid advancement of Large Language Models (LLMs) is reshaping software engineering by profoundly influencing coding, documentation, and system maintenance practices. As these tools become deeply embedded in developers' daily workflows, understanding how they are used has become essential. This paper reports an empirical study of LLM adoption in software engineering, based on a survey of 46 industry professionals with diverse educational backgrounds and levels of experience. The results reveal positive perceptions of LLMs, particularly regarding faster resolution of technical questions, improved documentation support, and enhanced source code standardization. However, respondents also expressed concerns about cognitive dependence, security risks, and the potential erosion of technical autonomy. These findings underscore the need for critical and supervised use of LLM-based tools. By grounding the discussion in empirical evidence from industry practice, this study bridges the gap between academic discourse and real-world software development. The results provide actionable insights for developers and researchers seeking to adopt and evolve LLM-based technologies in a more effective, responsible, and secure manner, while also motivating future research on their cognitive, ethical, and organizational implications.