OpenRaiser/Intern-Atlas huggingface dataset:
What Ails Generative Structure-based Drug Design: Expressivity is Too Little or Too Much?
Venue: arXiv cs.LG 2025
Year: 2025

Several generative models with elaborate training and sampling procedures have been proposed to accelerate structure-based drug design (SBDD); however, their empirical performance turns out to be suboptimal. We seek to better understand this phenomenon from both theoretical and empirical perspectives. Since most of these models apply graph neural networks (GNNs), one may suspect that they inherit the representational limitations of GNNs. We analyze this aspect, establishing the first such results for protein-ligand complexes. A plausible counterview may attribute the underperformance of these models to their excessive parameterizations, inducing expressivity at the expense of generalization. We investigate this possibility with a simple metric-aware approach that learns an economical surrogate for affinity to infer an unlabelled molecular graph and optimizes for labels conditioned on this graph and molecular properties. The resulting model achieves state-of-the-art results using 100x fewer trainable parameters and affords up to 1000x speedup. Collectively, our findings underscore the need to reassess and redirect the existing paradigm and efforts for SBDD. Code is available at https://github.com/rafalkarczewski/SimpleSBDD.
Long-Form Answers to Visual Questions from Blind and Low Vision People
Venue: arXiv cs.CL 2025
Year: 2025

Vision language models can now generate long-form answers to questions about images - long-form visual question answers (LFVQA). We contribute VizWiz-LF, a dataset of long-form answers to visual questions posed by blind and low vision (BLV) users. VizWiz-LF contains 4.2k long-form answers to 600 visual questions, collected from human expert describers and six VQA models. We develop and annotate functional roles of sentences of LFVQA and demonstrate that long-form answers contain information beyond the question answer such as explanations and suggestions. We further conduct automatic and human evaluations with BLV and sighted people to evaluate long-form answers. BLV people perceive both human-written and generated long-form answers to be plausible, but generated answers often hallucinate incorrect visual details, especially for unanswerable visual questions (e.g., blurry or irrelevant images). To reduce hallucinations, we evaluate the ability of VQA models to abstain from answering unanswerable questions across multiple prompting strategies.
Prompt Tuning as User Inherent Profile Inference Machine
Venue: arXiv cs.IR 2025
Year: 2025

Large Language Models (LLMs) have exhibited significant promise in recommender systems by empowering user profiles with their extensive world knowledge and superior reasoning capabilities. However, LLMs face challenges like unstable instruction compliance, modality gaps, and high inference latency, leading to textual noise and limiting their effectiveness in recommender systems. To address these challenges, we propose UserIP-Tuning, which uses prompt-tuning to infer user profiles. It integrates the causal relationship between user profiles and behavior sequences into LLMs' prompts. It employs Expectation Maximization (EM) to infer the embedded latent profile, minimizing textual noise by fixing the prompt template. Furthermore, a profile quantization codebook bridges the modality gap by categorizing profile embeddings into collaborative IDs pre-stored for online deployment. This improves time efficiency and reduces memory usage. Experiments show that UserIP-Tuning outperforms state-of-the-art recommendation algorithms. An industry application confirms its effectiveness, robustness, and transferability. The presented solution has been deployed in Huawei AppGallery's Explore page since May 2025, serving 2 million daily active users, delivering significant improvements in real-world recommendation scenarios. The code is publicly available for replication at https://github.com/Applied-Machine-Learning-Lab/UserIP-Tuning.
A Structure-aware Generative Model for Biomedical Event Extraction
Venue: arXiv cs.CL 2025
Year: 2025

Biomedical Event Extraction (BEE) is a challenging task that involves modeling complex relationships between fine-grained entities in biomedical text. BEE has traditionally been formulated as a classification problem. With recent advancements in large language models (LLMs), generation-based models that cast event extraction as a sequence generation problem have attracted attention in the NLP research community. However, current generative models often overlook cross-instance information in complex event structures, such as nested and overlapping events, which constitute over 20% of events in benchmark datasets. In this paper, we propose GenBEE, an event structure-aware generative model that captures complex event structures in biomedical text for biomedical event extraction. GenBEE constructs event prompts that distill knowledge from LLMs to incorporate both label semantics and argument dependency relationships. In addition, GenBEE generates prefixes with event structural prompts to incorporate structural features to improve the model's overall performance. We have evaluated the proposed GenBEE model on three widely used BEE benchmark datasets, namely MLEE, GE11, and PHEE. Experimental results show that GenBEE has achieved state-of-the-art performance on the MLEE and GE11 datasets, and achieved competitive results when compared to the state-of-the-art classification-based models on the PHEE dataset.
Optimizing RPL Routing Using Tabu Search to Improve Link Stability and Energy Consumption in IoT Networks
Venue: arXiv cs.NI 2025
Year: 2025

In the Internet of Things (IoT) networks, the Routing Protocol for Low-power and Lossy Networks (RPL) is a widely adopted standard due to its efficiency in managing resource-constrained and energy-limited nodes. However, persistent challenges such as high energy consumption, unstable links, and suboptimal routing continue to hinder network performance, affecting both the longevity of the network and the reliability of data transmission. This paper proposes an enhanced RPL routing mechanism by integrating the Tabu Search (TS) optimization algorithm to address these issues. The proposed approach focuses on optimizing the parent and child selection process in the RPL protocol, leveraging a composite cost function that incorporates critical parameters, including Residual Energy, Transmission Energy, Distance to Sink, Hop Count(HC), Expected Transmission Count (ETX), and Link Stability Rate(LSR). Through extensive simulations, we demonstrate that our method significantly improves link stability, reduces energy consumption, and enhances the packet delivery ratio, leading to a more efficient and longer-lasting IoT network. The findings suggest that TS can effectively balance the trade-offs inherent in IoT routing, providing a practical solution for improving the overall performance of RPL-based networks.
MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty
Venue: arXiv cs.AI 2025
Year: 2025

Despite the massive advancements in large language models (LLMs), they still suffer from producing plausible but incorrect responses. To improve the reliability of LLMs, recent research has focused on uncertainty quantification to predict whether a response is correct or not. However, most uncertainty quantification methods have been evaluated on single-labeled questions, which removes data uncertainty: the irreducible randomness often present in user queries, which can arise from factors like multiple possible answers. This limitation may cause uncertainty quantification results to be unreliable in practical settings. In this paper, we investigate previous uncertainty quantification methods under the presence of data uncertainty. Our contributions are two-fold: 1) proposing a new Multi-Answer Question Answering dataset, MAQA, consisting of world knowledge, mathematical reasoning, and commonsense reasoning tasks to evaluate uncertainty quantification regarding data uncertainty, and 2) assessing 5 uncertainty quantification methods of diverse white- and black-box LLMs. Our findings show that previous methods relatively struggle compared to single-answer settings, though this varies depending on the task. Moreover, we observe that entropy- and consistency-based methods effectively estimate model uncertainty, even in the presence of data uncertainty. We believe these observations will guide future work on uncertainty quantification in more realistic settings.
Prophet Inequalities: Competing with the Top $\ell$ Items is Easy
Venue: arXiv cs.DS 2025
Year: 2025

We explore a prophet inequality problem, where the values of a sequence of items are drawn i.i.d. from some distribution, and an online decision maker must select one item irrevocably. We establish that $\mathrm{CR}_{\ell}$ the worst-case competitive ratio between the expected optimal performance of an online decision maker compared to that of a prophet who uses the average of the top $\ell$ items is exactly the solution to an integral equation. This quantity $\mathrm{CR}_{\ell}$ is larger than $1-e^{-\ell}$. This implies that the bound converges exponentially fast to $1$ as $\ell$ grows. In particular for $\ell=2$, $\mathrm{CR}_{2} \approx 0.966$ which is much closer to $1$ than the classical bound of $0.745$ for $\ell=1$. Additionally, we prove asymptotic lower bounds for the competitive ratio of a more general scenario, where the decision maker is permitted to select $k$ items. This subsumes the $k$ multi-unit i.i.d. prophet problem and provides the current best asymptotic guarantees, as well as enables broader understanding in the more general framework. Finally, we prove a tight asymptotic competitive ratio when only static threshold policies are allowed.
Development of a graph neural network surrogate for travel demand modelling
Venue: arXiv cs.LG 2025
Year: 2025

As urban environments grow, the modelling of transportation systems becomes increasingly complex. This paper advances the field of travel demand modelling by introducing advanced Graph Neural Network (GNN) architectures as surrogate models, addressing key limitations of previous approaches. Building on prior work with Graph Convolutional Networks (GCNs), we introduce GATv3, a new Graph Attention Network (GAT) variant that mitigates over-smoothing through residual connections, enabling deeper and more expressive architectures. Additionally, we propose a fine-grained classification framework that improves predictive stability while achieving numerical precision comparable to regression, offering a more interpretable and efficient alternative. To enhance model performance, we develop a synthetic data generation strategy, which expands the augmented training dataset without overfitting. Our experiments demonstrate that GATv3 significantly improves classification performance, while the GCN model shows unexpected dominance in fine-grained classification when supplemented with additional training data. The results highlight the advantages of fine-grained classification over regression for travel demand modelling tasks and reveal new challenges in extending GAT-based architectures to complex transport scenarios. Notably, GATv3 appears well-suited for classification-based transportation applications, such as section control and congestion warning systems, which require a higher degree of differentiation among neighboring links. These findings contribute to refining GNN-based surrogates, offering new possibilities for applying GATv3 and fine-grained classification in broader transportation challenges.
How to Solve Contextual Goal-Oriented Problems with Offline Datasets?
Venue: arXiv cs.LG 2025
Year: 2025

We present a novel method, Contextual goal-Oriented Data Augmentation (CODA), which uses commonly available unlabeled trajectories and context-goal pairs to solve Contextual Goal-Oriented (CGO) problems. By carefully constructing an action-augmented MDP that is equivalent to the original MDP, CODA creates a fully labeled transition dataset under training contexts without additional approximation error. We conduct a novel theoretical analysis to demonstrate CODA's capability to solve CGO problems in the offline data setup. Empirical results also showcase the effectiveness of CODA, which outperforms other baseline methods across various context-goal relationships of CGO problem. This approach offers a promising direction to solving CGO problems using offline datasets.
Exoskeleton-Assisted Balance and Task Evaluation During Quiet Stance and Kneeling in Construction
Venue: arXiv cs.RO 2025
Year: 2025

Construction workers exert intense physical effort and experience serious safety and health risks in hazardous working environments. Quiet stance and kneeling are among the most common postures performed by construction workers during their daily work. This paper analyzes lower-limb joint influence on neural balance control strategies using the frequency behavior of the intersection point of ground reaction forces. To evaluate the impact of elevation and wearable knee exoskeletons on postural balance and welding task performance, we design and integrate virtual- and mixed-reality (VR/MR) to simulate elevated environments and welding tasks. A linear quadratic regulator-controlled triple- and double-link inverted pendulum model is used for balance strategy quantification in quiet stance and kneeling, respectively. Extensive multi-subject experiments are conducted to evaluate the usability of occupational exoskeletons in destabilizing construction environments. The quantified balance strategies capture the significance of knee joint during balance control of quiet stance and kneeling gaits. Results show that center of pressure sway area reduced up to 62% in quiet stance and 39% in kneeling for subjects tested in high-elevation VR/MR worksites when provided knee exoskeleton assistance. The comprehensive balance and multitask evaluation methodology developed aims to reveal exoskeleton design considerations to mitigate the fall risk in construction.
From Decision to Action in Surgical Autonomy: Multi-Modal Large Language Models for Robot-Assisted Blood Suction
Venue: arXiv cs.RO 2025
Year: 2025

The rise of Large Language Models (LLMs) has impacted research in robotics and automation. While progress has been made in integrating LLMs into general robotics tasks, a noticeable void persists in their adoption in more specific domains such as surgery, where critical factors such as reasoning, explainability, and safety are paramount. Achieving autonomy in robotic surgery, which entails the ability to reason and adapt to changes in the environment, remains a significant challenge. In this work, we propose a multi-modal LLM integration in robot-assisted surgery for autonomous blood suction. The reasoning and prioritization are delegated to the higher-level task-planning LLM, and the motion planning and execution are handled by the lower-level deep reinforcement learning model, creating a distributed agency between the two components. As surgical operations are highly dynamic and may encounter unforeseen circumstances, blood clots and active bleeding were introduced to influence decision-making. Results showed that using a multi-modal LLM as a higher-level reasoning unit can account for these surgical complexities to achieve a level of reasoning previously unattainable in robot-assisted surgeries. These findings demonstrate the potential of multi-modal LLMs to significantly enhance contextual understanding and decision-making in robotic-assisted surgeries, marking a step toward autonomous surgical systems.
Toward a Dialogue System Using a Large Language Model to Recognize User Emotions with a Camera
Venue: arXiv cs.HC 2025
Year: 2025

The performance of ChatGPT© and other LLMs has improved tremendously, and in online environments, they are increasingly likely to be used in a wide variety of situations, such as ChatBot on web pages, call center operations using voice interaction, and dialogue functions using agents. In the offline environment, multimodal dialogue functions are also being realized, such as guidance by Artificial Intelligence agents (AI agents) using tablet terminals and dialogue systems in the form of LLMs mounted on robots. In this multimodal dialogue, mutual emotion recognition between the AI and the user will become important. So far, there have been methods for expressing emotions on the part of the AI agent or for recognizing them using textual or voice information of the user's utterances, but methods for AI agents to recognize emotions from the user's facial expressions have not been studied. In this study, we examined whether or not LLM-based AI agents can interact with users according to their emotional states by capturing the user in dialogue with a camera, recognizing emotions from facial expressions, and adding such emotion information to prompts. The results confirmed that AI agents can have conversations according to the emotional state for emotional states with relatively high scores, such as Happy and Angry.
Extracting Sentence Embeddings from Pretrained Transformer Models
Venue: arXiv cs.CL 2025
Year: 2025

Pre-trained transformer models shine in many natural language processing tasks and therefore are expected to bear the representation of the input sentence or text meaning. These sentence-level embeddings are also important in retrieval-augmented generation. But do commonly used plain averaging or prompt templates sufficiently capture and represent the underlying meaning? After providing a comprehensive review of existing sentence embedding extraction and refinement methods, we thoroughly test different combinations and our original extensions of the most promising ones on pretrained models. Namely, given 110 M parameters, BERT's hidden representations from multiple layers, and many tokens, we try diverse ways to extract optimal sentence embeddings. We test various token aggregation and representation post-processing techniques. We also test multiple ways of using a general Wikitext dataset to complement BERT's sentence embeddings. All methods are tested on eight Semantic Textual Similarity (STS), six short text clustering, and twelve classification tasks. We also evaluate our representation-shaping techniques on other static models, including random token representations. Proposed representation extraction methods improve the performance on STS and clustering tasks for all models considered. Very high improvements for static token-based models, especially random embeddings for STS tasks, almost reach the performance of BERT-derived representations. Our work shows that the representation-shaping techniques significantly improve sentence embeddings extracted from BERT-based and simple baseline models.
Evaluating Time-Dependent Methods and Seasonal Effects in Code Technical Debt Prediction
Venue: arXiv cs.SE 2025
Year: 2025

Background. Code Technical Debt (Code TD) prediction has gained significant attention in recent software engineering research. However, no standardized approach to Code TD prediction fully captures the factors influencing its evolution. Objective. Our study aims to assess the impact of time-dependent models and seasonal effects on Code TD prediction. It evaluates such models against widely used Machine Learning models, also considering the influence of seasonality on prediction performance. Methods. We trained 11 prediction models with 31 Java open-source projects. To assess their performance, we predicted future observations of the SQALE index. To evaluate the practical usability of our TD forecasting model and its impact on practitioners, we surveyed 23 software engineering professionals. Results. Our study confirms the benefits of time-dependent techniques, with the ARIMAX model outperforming the others. Seasonal effects improved predictive performance, though the impact remained modest. \ReviewerA{ARIMAX/SARIMAX models demonstrated to provide well-balanced long-term forecasts. The survey highlighted strong industry interest in short- to medium-term TD forecasts. Conclusions. Our findings support using techniques that capture time dependence in historical software metric data, particularly for Code TD. Effectively addressing this evidence requires adopting methods that account for temporal patterns.
Multimodal Causal Reasoning Benchmark: Challenging Vision Large Language Models to Discern Causal Links Across Modalities
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal Large Language Models (MLLMs) have showcased exceptional Chain-of-Thought (CoT) reasoning ability in complex textual inference tasks including causal reasoning. However, will these causalities remain straightforward when crucial hints hide in visual details? If not, what factors might influence cross-modal generalization? Whether we can effectively enhance their capacity for robust causal inference across both text and vision? Motivated by these, we introduce MuCR - a novel Multimodal Causal Reasoning benchmark that leverages synthetic siamese images and text pairs to challenge MLLMs. Additionally, we develop tailored metrics from multiple perspectives, including image-level match, phrase-level understanding, and sentence-level explanation, to comprehensively assess MLLMs' comprehension abilities. Our experiments reveal that current MLLMs fall short in multimodal causal reasoning compared to their performance in purely textual settings. Additionally, we find that identifying visual cues across images is key to effective cross-modal generalization. Finally, we propose a VcCoT strategy that better highlights visual cues, and our results confirm its efficacy in enhancing multimodal causal reasoning. The project is available at: https://github.com/Zhiyuan-Li-John/MuCR
WaterSplatting: Fast Underwater 3D Scene Reconstruction Using Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

The underwater 3D scene reconstruction is a challenging, yet interesting problem with applications ranging from naval robots to VR experiences. The problem was successfully tackled by fully volumetric NeRF-based methods which can model both the geometry and the medium (water). Unfortunately, these methods are slow to train and do not offer real-time rendering. More recently, 3D Gaussian Splatting (3DGS) method offered a fast alternative to NeRFs. However, because it is an explicit method that renders only the geometry, it cannot render the medium and is therefore unsuited for underwater reconstruction. Therefore, we propose a novel approach that fuses volumetric rendering with 3DGS to handle underwater data effectively. Our method employs 3DGS for explicit geometry representation and a separate volumetric field (queried once per pixel) for capturing the scattering medium. This dual representation further allows the restoration of the scenes by removing the scattering medium. Our method outperforms state-of-the-art NeRF-based methods in rendering quality on the underwater SeaThru-NeRF dataset. Furthermore, it does so while offering real-time rendering performance, addressing the efficiency limitations of existing methods. Web: https://water-splatting.github.io