OpenRaiser/Intern-Atlas huggingface dataset:
SCOUT: An in-vivo Methane Sensing System for Real-time Monitoring of Enteric Emissions in Cattle with ex-vivo Validation
Venue: arXiv cs.RO 2025
Year: 2025

Accurate measurement of enteric methane emissions remains a critical bottleneck for advancing livestock sustainability through genetic selection and precision management. Existing ambient sampling approaches suffer from low data retention rates, environmental interference, and limited temporal resolution. We developed SCOUT (Smart Cannula-mounted Optical Unit for Trace-methane), the first robust in-vivo sensing system enabling continuous, high-resolution monitoring of ruminal methane concentrations through an innovative closed-loop gas recirculation design. We conducted comprehensive validation with two cannulated Simmental heifers under contrasting dietary treatments, with cross-platform comparison against established ambient sniffer systems. SCOUT achieved exceptional performance with 82% data retention compared to 17% for conventional sniffer systems, while capturing methane concentrations 100-1000x higher than ambient approaches. Cross-platform validation demonstrated strong scale-dependent correlations, with optimal correlation strength (r = -0.564 $\pm$ 0.007) at biologically relevant 40-minute windows and 100% statistical significance. High-frequency monitoring revealed novel behavior-emission coupling, including rapid concentration changes (14.5 $\pm$ 11.3k ppm) triggered by postural transitions within 15 minutes, insights previously inaccessible through existing technologies. The SCOUT system represents a transformative advancement, enabling accurate, continuous emission phenotyping essential for genomic selection programs and sustainable precision livestock management. This validation framework establishes new benchmarks for agricultural sensor performance while generating unprecedented biological insights into ruminal methane dynamics, contributing essential tools for sustainable livestock production in climate-conscious agricultural systems.
Fine-tuning for Better Few Shot Prompting: An Empirical Comparison for Short Answer Grading
Venue: arXiv cs.LG 2025
Year: 2025

Research to improve Automated Short Answer Grading has recently focused on Large Language Models (LLMs) with prompt engineering and no- or few-shot prompting to achieve best results. This is in contrast to the fine-tuning approach, which has historically required large-scale compute clusters inaccessible to most users. New closed-model approaches such as OpenAI's fine-tuning service promise results with as few as 100 examples, while methods using open weights such as quantized low-rank adaptive (QLORA) can be used to fine-tune models on consumer GPUs. We evaluate both of these fine-tuning methods, measuring their interaction with few-shot prompting for automated short answer grading (ASAG) with structured (JSON) outputs. Our results show that finetuning with small amounts of data has limited utility for Llama open-weight models, but that fine-tuning methods can outperform few-shot baseline instruction-tuned LLMs for OpenAI's closed models. While our evaluation set is limited, we find some evidence that the observed benefits of finetuning may be impacted by the domain subject matter. Lastly, we observed dramatic improvement with the LLama 3.1 8B-Instruct open-weight model by seeding the initial training examples with a significant amount of cheaply generated synthetic training data.
Unlocking the Potential of MLLMs in Referring Expression Segmentation via a Light-weight Mask Decoder
Venue: arXiv cs.CV 2025
Year: 2025

Reference Expression Segmentation (RES) aims to segment image regions specified by referring expressions and has become popular with the rise of multimodal large models (MLLMs). While MLLMs excel in semantic understanding, their token-generation paradigm struggles with pixel-level dense prediction. Existing RES methods either couple MLLMs with the parameter-heavy Segment Anything Model (SAM) with 632M network parameters or adopt SAM-free lightweight pipelines that sacrifice accuracy. To address the trade-off between performance and cost, we specifically propose MLLMSeg, a novel framework that fully exploits the inherent visual detail features encoded in the MLLM vision encoder without introducing an extra visual encoder. Besides, we propose a detail-enhanced and semantic-consistent feature fusion module (DSFF) that fully integrates the detail-related visual feature with the semantic-related feature output by the large language model (LLM) of MLLM. Finally, we establish a light-weight mask decoder with only 34M network parameters that optimally leverages detailed spatial features from the visual encoder and semantic features from the LLM to achieve precise mask prediction. Extensive experiments demonstrate that our method generally surpasses both SAM-based and SAM-free competitors, striking a better balance between performance and cost. Code is available at https://github.com/jcwang0602/MLLMSeg.
AgREE: Agentic Reasoning for Knowledge Graph Completion on Emerging Entities
Venue: arXiv cs.AI 2025
Year: 2025

Open-domain Knowledge Graph Completion (KGC) faces significant challenges in an ever-changing world, especially when considering the continual emergence of new entities in daily news. Existing approaches for KGC mainly rely on pretrained language models' parametric knowledge, pre-constructed queries, or single-step retrieval, typically requiring substantial supervision and training data. Even so, they often fail to capture comprehensive and up-to-date information about unpopular and/or emerging entities. To this end, we introduce Agentic Reasoning for Emerging Entities (AgREE), a novel agent-based framework that combines iterative retrieval actions and multi-step reasoning to dynamically construct rich knowledge graph triplets. Experiments show that, despite requiring zero training efforts, AgREE significantly outperforms existing methods in constructing knowledge graph triplets, especially for emerging entities that were not seen during language models' training processes, outperforming previous methods by up to 13.7%. Moreover, we propose a new evaluation methodology that addresses a fundamental weakness of existing setups and a new benchmark for KGC on emerging entities. Our work demonstrates the effectiveness of combining agent-based reasoning with strategic information retrieval for maintaining up-to-date knowledge graphs in dynamic information environments.
Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement
Venue: arXiv cs.CV 2025
Year: 2025

Underwater image enhancement (UIE) techniques aim to improve visual quality of images captured in aquatic environments by addressing degradation issues caused by light absorption and scattering effects, including color distortion, blurring, and low contrast. Current mainstream solutions predominantly employ multi-scale feature extraction (MSFE) mechanisms to enhance reconstruction quality through multi-resolution feature fusion. However, our extensive experiments demonstrate that high-quality image reconstruction does not necessarily rely on multi-scale feature fusion. Contrary to popular belief, our experiments show that single-scale feature extraction alone can match or surpass the performance of multi-scale methods, significantly reducing complexity. To comprehensively explore single-scale feature potential in underwater enhancement, we propose an innovative Single-Scale Decomposition Network (SSD-Net). This architecture introduces an asymmetrical decomposition mechanism that disentangles input image into clean layer along with degradation layer. The former contains scene-intrinsic information and the latter encodes medium-induced interference. It uniquely combines CNN's local feature extraction capabilities with Transformer's global modeling strengths through two core modules: 1) Parallel Feature Decomposition Block (PFDB), implementing dual-branch feature space decoupling via efficient attention operations and adaptive sparse transformer; 2) Bidirectional Feature Communication Block (BFCB), enabling cross-layer residual interactions for complementary feature mining and fusion. This synergistic design preserves feature decomposition independence while establishing dynamic cross-layer information pathways, effectively enhancing degradation decoupling capacity.
Experimental Analysis of Productive Interaction Strategy with ChatGPT: User Study on Function and Project-level Code Generation Tasks
Venue: arXiv cs.SE 2025
Year: 2025

The application of Large Language Models (LLMs) is growing in the productive completion of Software Engineering tasks. Yet, studies investigating the productive prompting techniques often employed a limited problem space, primarily focusing on well-known prompting patterns and mainly targeting function-level SE practices. We identify significant gaps in real-world workflows that involve complexities beyond class-level (e.g., multi-class dependencies) and different features that can impact Human-LLM Interactions (HLIs) processes in code generation. To address these issues, we designed an experiment that comprehensively analyzed the HLI features regarding the code generation productivity. Our study presents two project-level benchmark tasks, extending beyond function-level evaluations. We conducted a user study with 36 participants from diverse backgrounds, asking them to solve the assigned tasks by interacting with the GPT assistant using specific prompting patterns. We also examined the participants' experience and their behavioral features during interactions by analyzing screen recordings and GPT chat logs. Our statistical and empirical investigation revealed (1) that three out of 15 HLI features significantly impacted the productivity in code generation; (2) five primary guidelines for enhancing productivity for HLI processes; and (3) a taxonomy of 29 runtime and logic errors that can occur during HLI processes, along with suggested mitigation plans.
Benefit from Rich: Tackling Search Interaction Sparsity in Search Enhanced Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

In modern online platforms, search and recommendation (S&R) often coexist, offering opportunities for performance improvement through search-enhanced approaches. Existing studies show that incorporating search signals boosts recommendation performance. However, the effectiveness of these methods relies heavily on rich search interactions. They primarily benefit a small subset of users with abundant search behavior, while offering limited improvements for the majority of users who exhibit only sparse search activity. To address the problem of sparse search data in search-enhanced recommendation, we face two key challenges: (1) how to learn useful search features for users with sparse search interactions, and (2) how to design effective training objectives under sparse conditions. Our idea is to leverage the features of users with rich search interactions to enhance those of users with sparse search interactions. Based on this idea, we propose GSERec, a method that utilizes message passing on the User-Code Graphs to alleviate data sparsity in Search-Enhanced Recommendation. Specifically, we utilize Large Language Models (LLMs) with vector quantization to generate discrete codes, which connect similar users and thereby construct the graph. Through message passing on this graph, embeddings of users with rich search data are propagated to enhance the embeddings of users with sparse interactions. To further ensure that the message passing captures meaningful information from truly similar users, we introduce a contrastive loss to better model user similarities. The enhanced user representations are then integrated into downstream search-enhanced recommendation models. Experiments on three real-world datasets show that GSERec consistently outperforms baselines, especially for users with sparse search behaviors.
ICM-Fusion: In-Context Meta-Optimized LoRA Fusion for Multi-Task Adaptation
Venue: arXiv cs.CV 2025
Year: 2025

Enabling multi-task adaptation in pre-trained Low-Rank Adaptation (LoRA) models is crucial for enhancing their generalization capabilities. Most existing pre-trained LoRA fusion methods decompose weight matrices, sharing similar parameters while merging divergent ones. However, this paradigm inevitably induces inter-weight conflicts and leads to catastrophic domain forgetting. While incremental learning enables adaptation to multiple tasks, it struggles to achieve generalization in few-shot scenarios. Consequently, when the weight data follows a long-tailed distribution, it can lead to forgetting in the fused weights. To address this issue, we propose In-Context Meta LoRA Fusion (ICM-Fusion), a novel framework that synergizes meta-learning with in-context adaptation. The key innovation lies in our task vector arithmetic, which dynamically balances conflicting optimization directions across domains through learned manifold projections. ICM-Fusion obtains the optimal task vector orientation for the fused model in the latent space by adjusting the orientation of the task vectors. Subsequently, the fused LoRA is reconstructed by a self-designed Fusion VAE (F-VAE) to realize multi-task LoRA generation. We have conducted extensive experiments on visual and linguistic tasks, and the experimental results demonstrate that ICM-Fusion can be adapted to a wide range of architectural models and applied to various tasks. Compared to the current pre-trained LoRA fusion method, ICM-Fusion fused LoRA can significantly reduce the multi-tasking loss and can even achieve task enhancement in few-shot scenarios.
A Generic Framework for Optimization in Blockchain Simulators
Venue: arXiv cs.CE 2025
Year: 2025

As blockchain technology rapidly evolves, researchers face a significant challenge due to diverse and non-standardized simulation parameters, which hinder the replicability and comparability of research methodologies. This paper introduces a Generic Framework for Optimization in Blockchain Simulators (GFOBS), a comprehensive and adaptable solution designed to standardize and optimize blockchain simulations. GFOBS provides a flexible platform that supports various optimization algorithms, variables, and objectives, thereby catering to a wide range of blockchain research needs. The paper's key contributions are threefold: the development of GFOBS as a versatile tool for blockchain simulation optimization; the introduction of an innovative optimization method using warm starting technique; and the proposition of a novel concurrent multiprocessing technique for simultaneous simulation processes. These advancements collectively enhance the efficiency, replicability, and standardization of blockchain simulation experiments.
Semi-Supervised Deep Domain Adaptation for Predicting Solar Power Across Different Locations
Venue: arXiv cs.LG 2025
Year: 2025

Accurate solar generation prediction is essential for proper estimation of renewable energy resources across diverse geographic locations. However, geographical and weather features vary from location to location which introduces domain shift - a major bottleneck to develop location-agnostic prediction model. As a result, a machine-learning model which can perform well to predict solar power in one location, may exhibit subpar performance in another location. Moreover, the lack of properly labeled data and storage issues make the task even more challenging. In order to address domain shift due to varying weather conditions across different meteorological regions, this paper presents a semi-supervised deep domain adaptation framework, allowing accurate predictions with minimal labeled data from the target location. Our approach involves training a deep convolutional neural network on a source location's data and adapting it to the target location using a source-free, teacher-student model configuration. The teacher-student model leverages consistency and cross-entropy loss for semi-supervised learning, ensuring effective adaptation without any source data requirement for prediction. With annotation of only $20 \%$ data in the target domain, our approach exhibits an improvement upto $11.36 \%$, $6.65 \%$, $4.92\%$ for California, Florida and New York as target domain, respectively in terms of accuracy in predictions with respect to non-adaptive approach.
Tweets vs Pathogen Spread: A Case Study of COVID-19 in American States
Venue: arXiv cs.SI 2025
Year: 2025

The concept of the mutual influence that awareness and disease may exert on each other has recently presented significant challenges. The actions individuals take to prevent contracting a disease and their level of awareness can profoundly affect the dynamics of its spread. Simultaneously, disease outbreaks impact how people become aware. In response, we initially propose a null model that couples two Susceptible-Infectious-Recovered (SIR) dynamics and analyze it using a mean-field approach. Subsequently, we explore the parameter space to quantify the effects of this mutual influence on various observables. Finally, based on this null model, we conduct an empirical analysis of Twitter data related to COVID-19 and confirmed cases within American states. Our findings indicate that in specific regions of the parameter space, it is possible to suppress the epidemic by increasing awareness, and we investigate phase transitions. Furthermore, our model demonstrates the ability to alter the dominant population group by adjusting parameters throughout the course of the outbreak. Additionally, using the model, we assign a set of parameters to each state, revealing that these parameters change at different pandemic peaks. Notably, a robust correlation emerges between the ranking of states' Twitter activity, as gathered from empirical data, and the immunity parameters assigned to each state using our model. This observation underscores the pivotal role of sustained awareness transitioning from the initial to the subsequent peaks in the disease progression.
BadTime: An Effective Backdoor Attack on Multivariate Long-Term Time Series Forecasting
Venue: arXiv cs.CR 2025
Year: 2025

Multivariate long-term time series forecasting (MLTSF) models are increasingly deployed in critical domains such as climate, finance, and transportation. Despite their growing importance, the security of MLTSF models against backdoor attacks remains entirely unexplored. To bridge this gap, we propose BadTime, the first effective backdoor attack tailored for MLTSF. BadTime can manipulate hundreds of future predictions toward a target pattern by injecting a subtle trigger. BadTime addresses two key challenges that arise uniquely in MLTSF: (i) the rapid dilution of local triggers over long horizons, and (ii) the extreme sparsity of backdoor signals under stealth constraints. To counter dilution, BadTime leverages inter-variable correlations, temporal lags, and data-driven initialization to design a distributed, lag-aware trigger that ensures effective influence over long-range forecasts. To overcome sparsity, it introduces a hybrid strategy to select valuable poisoned samples and a decoupled backdoor training objective that adaptively adjusts the model's focus on the sparse backdoor signal, ensuring reliable learning at a poisoning rate as low as 1%. Extensive experiments show that BadTime significantly outperforms state-of-the-art (SOTA) backdoor attacks on time series forecasting by extending the attackable horizon from at most 12 timesteps to 720 timesteps (a 60-fold improvement), reducing MAE by over 50% on target variables, and boosting stealthiness by more than 3-fold under anomaly detection.
Causal Reward Adjustment: Mitigating Reward Hacking in External Reasoning via Backdoor Correction
Venue: arXiv cs.LG 2025
Year: 2025

External reasoning systems combine language models with process reward models (PRMs) to select high-quality reasoning paths for complex tasks such as mathematical problem solving. However, these systems are prone to reward hacking, where high-scoring but logically incorrect paths are assigned high scores by the PRMs, leading to incorrect answers. From a causal inference perspective, we attribute this phenomenon primarily to the presence of confounding semantic features. To address it, we propose Causal Reward Adjustment (CRA), a method that mitigates reward hacking by estimating the true reward of a reasoning path. CRA trains sparse autoencoders on the PRM's internal activations to recover interpretable features, then corrects confounding by using backdoor adjustment. Experiments on math solving datasets demonstrate that CRA mitigates reward hacking and improves final accuracy, without modifying the policy model or retraining PRM.
Empowering Time Series Forecasting with LLM-Agents
Venue: arXiv cs.LG 2025
Year: 2025

Large Language Model (LLM) powered agents have emerged as effective planners for Automated Machine Learning (AutoML) systems. While most existing AutoML approaches focus on automating feature engineering and model architecture search, recent studies in time series forecasting suggest that lightweight models can often achieve state-of-the-art performance. This observation led us to explore improving data quality, rather than model architecture, as a potentially fruitful direction for AutoML on time series data. We propose DCATS, a Data-Centric Agent for Time Series. DCATS leverages metadata accompanying time series to clean data while optimizing forecasting performance. We evaluated DCATS using four time series forecasting models on a large-scale traffic volume forecasting dataset. Results demonstrate that DCATS achieves an average 6% error reduction across all tested models and time horizons, highlighting the potential of data-centric approaches in AutoML for time series forecasting.
Circuit-Aware SAT Solving: Guiding CDCL via Conditional Probabilities
Venue: arXiv cs.AI 2025
Year: 2025

Circuit Satisfiability (CSAT) plays a pivotal role in Electronic Design Automation. The standard workflow for solving CSAT problems converts circuits into Conjunctive Normal Form (CNF) and employs generic SAT solvers powered by Conflict-Driven Clause Learning (CDCL). However, this process inherently discards rich structural and functional information, leading to suboptimal solver performance. To address this limitation, we introduce CASCAD, a novel circuit-aware SAT solving framework that directly leverages circuit-level conditional probabilities computed via Graph Neural Networks (GNNs). By explicitly modeling gate-level conditional probabilities, CASCAD dynamically guides two critical CDCL heuristics -- variable phase selection and clause managementto significantly enhance solver efficiency. Extensive evaluations on challenging real-world Logical Equivalence Checking (LEC) benchmarks demonstrate that CASCAD reduces solving times by up to 10x compared to state-of-the-art CNF-based approaches, achieving an additional 23.5% runtime reduction via our probability-guided clause filtering strategy. Our results underscore the importance of preserving circuit-level structural insights within SAT solvers, providing a robust foundation for future improvements in SAT-solving efficiency and EDA tool design.