Beyond Static Pipelines: Learning Dynamic Workflows for Text-to-SQL
arXiv:2602.15564v1 Announce Type: new Abstract: Text-to-SQL has recently achieved impressive progress, yet remains difficult to apply effectively in real-world scenarios. This gap stems from the reliance on single static workflows, fundamentally limiting scalability to out-of-distribution and long-tail scenarios. Instead of...
While this academic article focuses on AI/ML advancements in text-to-SQL systems rather than Immigration Law directly, it offers indirect relevance by illustrating a broader trend of dynamic workflow adaptation—a concept applicable to legal tech and immigration case management. The reinforcement learning framework (SquRL) demonstrating adaptive, data-driven decision-making could inspire analogous innovations in immigration legal workflows, such as automated case triage or adaptive document processing tailored to evolving regulatory landscapes. Notably, the empirical validation of dynamic over static systems aligns with emerging policy signals in legal innovation, encouraging adaptive, scalable solutions over rigid procedural models.
**Jurisdictional Comparison and Analytical Commentary on Immigration Law Practice** The article "Beyond Static Pipelines: Learning Dynamic Workflows for Text-to-SQL" may seem unrelated to Immigration Law at first glance. However, a closer examination reveals that the concepts of dynamic workflow construction and reinforcement learning can be applied to Immigration Law practice, particularly in the context of asylum and refugee cases. **US Approach:** In the US, Immigration Law practice often involves complex decision-making processes, where lawyers must navigate multiple statutes, regulations, and case laws to determine the best course of action for their clients. The use of dynamic workflow construction and reinforcement learning could potentially enhance the efficiency and accuracy of these decision-making processes. For instance, a lawyer could use a reinforcement learning framework to adaptively construct a workflow for a particular asylum case, taking into account the client's unique circumstances and the relevant laws and regulations. **Korean Approach:** In Korea, Immigration Law practice is also subject to complex decision-making processes, particularly in the context of international protection and refugee cases. The Korean government has implemented various policies and regulations to provide protection to refugees and asylum seekers. The use of dynamic workflow construction and reinforcement learning could potentially be applied to enhance the efficiency and accuracy of these decision-making processes, particularly in the context of complex and out-of-distribution cases. **International Approach:** Internationally, the use of dynamic workflow construction and reinforcement learning could potentially be applied to enhance the efficiency and accuracy of decision-making processes in asylum and
As a Work Visa & Employment-Based Immigration Expert, I can analyze the implications of this article for practitioners in the context of H-1B, L-1, O-1, and employment-based green cards. The article discusses the development of a reinforcement learning framework, SquRL, which enhances the reasoning capability of Large Language Models (LLMs) in adaptive workflow construction. This technology has potential applications in various industries, including software development, data science, and artificial intelligence. In the context of employment-based immigration, the development of such technologies could lead to increased demand for skilled workers in these fields, potentially impacting the allocation of H-1B visas. Practitioners should be aware of the potential for increased demand for skilled workers in emerging technologies, such as those discussed in the article. This could lead to changes in the allocation of visas, particularly in fields where there is a high demand for workers with specialized skills. For example, the development of technologies like SquRL could lead to increased demand for workers with expertise in artificial intelligence, machine learning, and data science, potentially impacting the allocation of H-1B visas in these fields. Statutory and regulatory connections: * The article's discussion of the development of advanced technologies, such as SquRL, is relevant to the discussion of the H-1B visa program's emphasis on attracting and retaining highly skilled workers. * The article's focus on the importance of adaptability and flexibility in workflow construction is relevant to the discussion of the L
GREPO: A Benchmark for Graph Neural Networks on Repository-Level Bug Localization
arXiv:2602.13921v1 Announce Type: new Abstract: Repository-level bug localization-the task of identifying where code must be modified to fix a bug-is a critical software engineering challenge. Standard Large Language Modles (LLMs) are often unsuitable for this task due to context window...
The academic article on GREPO introduces a critical innovation for software engineering by establishing the first GNN benchmark for repository-level bug localization, addressing a persistent limitation in LLMs for large-scale code analysis. Key legal developments include the recognition of specialized algorithmic tools (like GNNs) over traditional retrieval methods in technical problem-solving, which may influence legal frameworks on intellectual property, software licensing, or algorithmic accountability. While not directly immigration-related, the research signals a broader policy trend toward validating specialized technical solutions as authoritative resources, potentially impacting regulatory approaches to AI governance or immigration-related tech workforce issues.
**Jurisdictional Comparison and Analytical Commentary on the Impact on Immigration Law Practice** The article "GREPO: A Benchmark for Graph Neural Networks on Repository-Level Bug Localization" has no direct implications on Immigration Law practice. However, a comparative analysis of US, Korean, and international approaches to innovation and technology adoption in the context of Immigration Law can be insightful. In the US, the H-1B visa program allows foreign workers with specialized skills, including software engineers, to work in the country. The US government has been exploring ways to streamline the H-1B application process, including the use of artificial intelligence (AI) and machine learning (ML) to improve efficiency and accuracy. The development of GREPO, a benchmark for Graph Neural Networks (GNNs) on repository-level bug localization tasks, could potentially be applied to improve the H-1B application process by automating tasks and reducing processing times. In Korea, the government has implemented various initiatives to promote innovation and technology adoption in the country. The Korean Immigration Service has introduced an online application system for visa applications, which utilizes AI and ML to streamline the process. The development of GREPO could be applied to improve the Korean immigration system by enhancing the accuracy and efficiency of visa application processing. Internationally, the United Nations High Commissioner for Refugees (UNHCR) has been exploring the use of AI and ML to improve the refugee resettlement process. The development of GREPO could potentially be applied to improve the UNHCR
The article introduces GREPO as a pivotal benchmark for GNNs in repository-level bug localization, addressing a critical gap in software engineering research. By providing a scalable dataset (86 Python repositories, 47294 bug-fixing tasks) tailored for GNN processing, GREPO enables direct application of graph-based models, potentially shifting the paradigm from traditional retrieval methods (e.g., keyword matching, text similarity) to more sophisticated GNN-driven solutions. Practitioners in software engineering and AI/ML should note this as a foundational resource; its impact aligns with regulatory trends promoting innovation in AI-driven software maintenance (e.g., USPTO’s focus on AI applications in engineering). Case law relevance may emerge if GREPO’s methodology influences patent eligibility for AI-assisted bug detection under 35 U.S.C. § 101.
MO-RiskVAE: A Multi-Omics Variational Autoencoder for Survival Risk Modeling in Multiple MyelomaMO-RiskVAE
arXiv:2604.06267v1 Announce Type: new Abstract: Multimodal variational autoencoders (VAEs) have emerged as a powerful framework for survival risk modeling in multiple myeloma by integrating heterogeneous omics and clinical data. However, when trained under survival supervision, standard latent regularization strategies often...
Efficient Quantization of Mixture-of-Experts with Theoretical Generalization Guarantees
arXiv:2604.06515v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) allows scaling of language and vision models efficiently by activating only a small subset of experts per input. While this reduces computation, the large number of parameters still incurs substantial memory overhead...
ODE-free Neural Flow Matching for One-Step Generative Modeling
arXiv:2604.06413v1 Announce Type: new Abstract: Diffusion and flow matching models generate samples by learning time-dependent vector fields whose integration transports noise to data, requiring tens to hundreds of network evaluations at inference. We instead learn the transport map directly. We...
Emergent decentralized regulation in a purely synthetic society
arXiv:2604.06199v1 Announce Type: new Abstract: As autonomous AI agents increasingly inhabit online environments and extensively interact, a key question is whether synthetic collectives exhibit self-regulated social dynamics with neither human intervention nor centralized design. We study OpenClaw agents on Moltbook,...
Optimal Rates for Pure {\varepsilon}-Differentially Private Stochastic Convex Optimization with Heavy Tails
arXiv:2604.06492v1 Announce Type: new Abstract: We study stochastic convex optimization (SCO) with heavy-tailed gradients under pure epsilon-differential privacy (DP). Instead of assuming a bound on the worst-case Lipschitz parameter of the loss, we assume only a bounded k-th moment. This...
A Comparative Study of Demonstration Selection for Practical Large Language Models-based Next POI Prediction
arXiv:2604.06207v1 Announce Type: new Abstract: This paper investigates demonstration selection strategies for predicting a user's next point-of-interest (POI) using large language models (LLMs), aiming to accurately forecast a user's subsequent location based on historical check-in data. While in-context learning (ICL)...
Blending Human and LLM Expertise to Detect Hallucinations and Omissions in Mental Health Chatbot Responses
arXiv:2604.06216v1 Announce Type: new Abstract: As LLM-powered chatbots are increasingly deployed in mental health services, detecting hallucinations and omissions has become critical for user safety. However, state-of-the-art LLM-as-a-judge methods often fail in high-risk healthcare contexts, where subtle errors can have...
Learning to Focus: CSI-Free Hierarchical MARL for Reconfigurable Reflectors
arXiv:2604.05165v1 Announce Type: new Abstract: Reconfigurable Intelligent Surfaces (RIS) has a potential to engineer smart radio environments for next-generation millimeter-wave (mmWave) networks. However, the prohibitive computational overhead of Channel State Information (CSI) estimation and the dimensionality explosion inherent in centralized...
El Nino Prediction Based on Weather Forecast and Geographical Time-series Data
arXiv:2604.04998v1 Announce Type: new Abstract: This paper proposes a novel framework for enhancing the prediction accuracy and lead time of El Ni\~no events, crucial for mitigating their global climatic, economic, and societal impacts. Traditional prediction models often rely on oceanic...
Towards Scaling Law Analysis For Spatiotemporal Weather Data
arXiv:2604.05068v1 Announce Type: new Abstract: Compute-optimal scaling laws are relatively well studied for NLP and CV, where objectives are typically single-step and targets are comparatively homogeneous. Weather forecasting is harder to characterize in the same framework: autoregressive rollouts compound errors...
Neural Assistive Impulses: Synthesizing Exaggerated Motions for Physics-based Characters
arXiv:2604.05394v1 Announce Type: new Abstract: Physics-based character animation has become a fundamental approach for synthesizing realistic, physically plausible motions. While current data-driven deep reinforcement learning (DRL) methods can synthesize complex skills, they struggle to reproduce exaggerated, stylized motions, such as...
Prune-Quantize-Distill: An Ordered Pipeline for Efficient Neural Network Compression
arXiv:2604.04988v1 Announce Type: new Abstract: Modern deployment often requires trading accuracy for efficiency under tight CPU and memory constraints, yet common compression proxies such as parameter count or FLOPs do not reliably predict wall-clock inference time. In particular, unstructured sparsity...
A Theory-guided Weighted $L^2$ Loss for solving the BGK model via Physics-informed neural networks
arXiv:2604.04971v1 Announce Type: new Abstract: While Physics-Informed Neural Networks offer a promising framework for solving partial differential equations, the standard $L^2$ loss formulation is fundamentally insufficient when applied to the Bhatnagar-Gross-Krook (BGK) model. Specifically, simply minimizing the standard loss does...
PRISM-MCTS: Learning from Reasoning Trajectories with Metacognitive Reflection
arXiv:2604.05424v1 Announce Type: new Abstract: PRISM-MCTS: Learning from Reasoning Trajectories with Metacognitive Reflection Siyuan Cheng, Bozhong Tian, Yanchao Hao, Zheng Wei Published: 06 Apr 2026, Last Modified: 06 Apr 2026 ACL 2026 Findings Conference, Area Chairs, Reviewers, Publication Chairs, Authors...
Same Graph, Different Likelihoods: Calibration of Autoregressive Graph Generators via Permutation-Equivalent Encodings
arXiv:2604.05613v1 Announce Type: new Abstract: Autoregressive graph generators define likelihoods via a sequential construction process, but these likelihoods are only meaningful if they are consistent across all linearizations of the same graph. Segmented Eulerian Neighborhood Trails (SENT), a recent linearization...
The UNDO Flip-Flop: A Controlled Probe for Reversible Semantic State Management in State Space Model
arXiv:2604.05923v1 Announce Type: new Abstract: State space models (SSMs) have been shown to possess the theoretical capacity to model both star-free sequential tasks and bounded hierarchical structures Sarrof et al. (2024). However, formal expressivity results do not guarantee that gradient-based...
Towards Effective In-context Cross-domain Knowledge Transfer via Domain-invariant-neurons-based Retrieval
arXiv:2604.05383v1 Announce Type: new Abstract: Large language models (LLMs) have made notable progress in logical reasoning, yet still fall short of human-level performance. Current boosting strategies rely on expert-crafted in-domain demonstrations, limiting their applicability in expertise-scarce domains, such as specialized...
Hardware-Oriented Inference Complexity of Kolmogorov-Arnold Networks
arXiv:2604.03345v1 Announce Type: new Abstract: Kolmogorov-Arnold Networks (KANs) have recently emerged as a powerful architecture for various machine learning applications. However, their unique structure raises significant concerns regarding their computational overhead. Existing studies primarily evaluate KAN complexity in terms of...
General Explicit Network (GEN): A novel deep learning architecture for solving partial differential equations
arXiv:2604.03321v1 Announce Type: new Abstract: Machine learning, especially physics-informed neural networks (PINNs) and their neural network variants, has been widely used to solve problems involving partial differential equations (PDEs). The successful deployment of such methods beyond academic research remains limited....
Beauty in the Eye of AI: Aligning LLMs and Vision Models with Human Aesthetics in Network Visualization
arXiv:2604.03417v1 Announce Type: new Abstract: Network visualization has traditionally relied on heuristic metrics, such as stress, under the assumption that optimizing them leads to aesthetic and informative layouts. However, no single metric consistently produces the most effective results. A data-driven...
Integrating Artificial Intelligence, Physics, and Internet of Things: A Framework for Cultural Heritage Conservation
arXiv:2604.03233v1 Announce Type: new Abstract: The conservation of cultural heritage increasingly relies on integrating technological innovation with domain expertise to ensure effective monitoring and predictive maintenance. This paper presents a novel framework to support the preservation of cultural assets, combining...
Position: Logical Soundness is not a Reliable Criterion for Neurosymbolic Fact-Checking with LLMs
arXiv:2604.04177v1 Announce Type: new Abstract: As large language models (LLMs) are increasing integrated into fact-checking pipelines, formal logic is often proposed as a rigorous means by which to mitigate bias, errors and hallucinations in these models' outputs. For example, some...
LangFIR: Discovering Sparse Language-Specific Features from Monolingual Data for Language Steering
arXiv:2604.03532v1 Announce Type: new Abstract: Large language models (LLMs) show strong multilingual capabilities, yet reliably controlling the language of their outputs remains difficult. Representation-level steering addresses this by adding language-specific vectors to model activations at inference time, but identifying language-specific...
IC3-Evolve: Proof-/Witness-Gated Offline LLM-Driven Heuristic Evolution for IC3 Hardware Model Checking
arXiv:2604.03232v1 Announce Type: new Abstract: IC3, also known as property-directed reachability (PDR), is a commonly-used algorithm for hardware safety model checking. It checks if a state transition system complies with a given safety property. IC3 either returns UNSAFE (indicating property...
Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training
arXiv:2604.02651v1 Announce Type: new Abstract: Graph neural networks (GNNs) are widely used for learning on graph datasets derived from various real-world scenarios. Learning from extremely large graphs requires distributed training, and mini-batching with sampling is a popular approach for parallelizing...
Student-in-the-Loop Chain-of-Thought Distillation via Generation-Time Selection
arXiv:2604.02819v1 Announce Type: new Abstract: Large reasoning models achieve strong performance on complex tasks through long chain-of-thought (CoT) trajectories, but directly transferring such reasoning processes to smaller models remains challenging. A key difficulty is that not all teacher-generated reasoning trajectories...
A Comprehensive Framework for Long-Term Resiliency Investment Planning under Extreme Weather Uncertainty for Electric Utilities
arXiv:2604.02504v1 Announce Type: new Abstract: Electric utilities must make massive capital investments in the coming years to respond to explosive growth in demand, aging assets and rising threats from extreme weather. Utilities today already have rigorous frameworks for capital planning,...
Learning the Signature of Memorization in Autoregressive Language Models
arXiv:2604.03199v1 Announce Type: new Abstract: All prior membership inference attacks for fine-tuned language models use hand-crafted heuristics (e.g., loss thresholding, Min-K\%, reference calibration), each bounded by the designer's intuition. We introduce the first transferable learned attack, enabled by the observation...