Interpretable Deep Reinforcement Learning for Element-level Bridge Life-cycle Optimization
arXiv:2604.02528v1 Announce Type: new Abstract: The new Specifications for the National Bridge Inventory (SNBI), in effect from 2022, emphasize the use of element-level condition states (CS) for risk-based bridge management. Instead of a general component rating, element-level condition data use...
Let's Have a Conversation: Designing and Evaluating LLM Agents for Interactive Optimization
arXiv:2604.02666v1 Announce Type: new Abstract: Optimization is as much about modeling the right problem as solving it. Identifying the right objectives, constraints, and trade-offs demands extensive interaction between researchers and stakeholders. Large language models can empower decision-makers with optimization capabilities...
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...
CharTool: Tool-Integrated Visual Reasoning for Chart Understanding
arXiv:2604.02794v1 Announce Type: new Abstract: Charts are ubiquitous in scientific and financial literature for presenting structured data. However, chart reasoning remains challenging for multimodal large language models (MLLMs) due to the lack of high-quality training data, as well as the...
Analytic Drift Resister for Non-Exemplar Continual Graph Learning
arXiv:2604.02633v1 Announce Type: new Abstract: Non-Exemplar Continual Graph Learning (NECGL) seeks to eliminate the privacy risks intrinsic to rehearsal-based paradigms by retaining solely class-level prototype representations rather than raw graph examples for mitigating catastrophic forgetting. However, this design choice inevitably...
AIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems
arXiv:2604.02478v1 Announce Type: new Abstract: Deep learning models excel at detecting anomaly patterns in normal data. However, they do not provide a direct solution for anomaly classification and scalability across diverse control systems, frequently failing to distinguish genuine faults from...
Steerable but Not Decodable: Function Vectors Operate Beyond the Logit Lens
arXiv:2604.02608v1 Announce Type: new Abstract: Function vectors (FVs) -- mean-difference directions extracted from in-context learning demonstrations -- can steer large language model behavior when added to the residual stream. We hypothesized that FV steering failures reflect an absence of task-relevant...
Matrix Profile for Time-Series Anomaly Detection: A Reproducible Open-Source Benchmark on TSB-AD
arXiv:2604.02445v1 Announce Type: new Abstract: Matrix Profile (MP) methods are an interpretable and scalable family of distance-based methods for time-series anomaly detection, but strong benchmark performance still depends on design choices beyond a vanilla nearest-neighbor profile. This technical report documents...
DeltaLogic: Minimal Premise Edits Reveal Belief-Revision Failures in Logical Reasoning Models
arXiv:2604.02733v1 Announce Type: new Abstract: Reasoning benchmarks typically evaluate whether a model derives the correct answer from a fixed premise set, but they under-measure a closely related capability that matters in dynamic environments: belief revision under minimal evidence change. We...
DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery
arXiv:2604.02346v1 Announce Type: cross Abstract: Large language models (LLMs) are in the ascendancy for research in drug discovery, offering unprecedented opportunities to reshape drug research by accelerating hypothesis generation, optimizing candidate prioritization, and enabling more scalable and cost-effective drug discovery...
Using LLM-as-a-Judge/Jury to Advance Scalable, Clinically-Validated Safety Evaluations of Model Responses to Users Demonstrating Psychosis
arXiv:2604.02359v1 Announce Type: cross Abstract: General-purpose Large Language Models (LLMs) are becoming widely adopted by people for mental health support. Yet emerging evidence suggests there are significant risks associated with high-frequency use, particularly for individuals suffering from psychosis, as LLMs...
ESL-Bench: An Event-Driven Synthetic Longitudinal Benchmark for Health Agents
arXiv:2604.02834v1 Announce Type: new Abstract: Longitudinal health agents must reason across multi-source trajectories that combine continuous device streams, sparse clinical exams, and episodic life events - yet evaluating them is hard: real-world data cannot be released at scale, and temporally...
GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning
arXiv:2604.02721v1 Announce Type: new Abstract: Competitive programming remains one of the last few human strongholds in coding against AI. The best AI system to date still underperforms the best humans competitive programming: the most recent best result, Google's Gemini~3 Deep...
Analysis of Optimality of Large Language Models on Planning Problems
arXiv:2604.02910v1 Announce Type: new Abstract: Classic AI planning problems have been revisited in the Large Language Model (LLM) era, with a focus of recent benchmarks on success rates rather than plan efficiency. We examine the degree to which frontier models...
Prism: Policy Reuse via Interpretable Strategy Mapping in Reinforcement Learning
arXiv:2604.02353v1 Announce Type: cross Abstract: We present PRISM (Policy Reuse via Interpretable Strategy Mapping), a framework that grounds reinforcement learning agents' decisions in discrete, causally validated concepts and uses those concepts as a zero-shot transfer interface between agents trained with...
Automatic Textbook Formalization
arXiv:2604.03071v1 Announce Type: new Abstract: We present a case study where an automatic AI system formalizes a textbook with more than 500 pages of graduate-level algebraic combinatorics to Lean. The resulting formalization represents a new milestone in textbook formalization scale...
Agentic-MME: What Agentic Capability Really Brings to Multimodal Intelligence?
arXiv:2604.03016v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are evolving from passive observers into active agents, solving problems through Visual Expansion (invoking visual tools) and Knowledge Expansion (open-web search). However, existing evaluations fall short: they lack flexible tool...
Cross-subject Muscle Fatigue Detection via Adversarial and Supervised Contrastive Learning with Inception-Attention Network
arXiv:2604.02670v1 Announce Type: new Abstract: Muscle fatigue detection plays an important role in physical rehabilitation. Previous researches have demonstrated that sEMG offers superior sensitivity in detecting muscle fatigue compared to other biological signals. However, features extracted from sEMG may vary...
Chart-RL: Policy Optimization Reinforcement Learning for Enhanced Visual Reasoning in Chart Question Answering with Vision Language Models
arXiv:2604.03157v1 Announce Type: new Abstract: The recent advancements in Vision Language Models (VLMs) have demonstrated progress toward true intelligence requiring robust reasoning capabilities. Beyond pattern recognition, linguistic reasoning must integrate with visual comprehension, particularly for Chart Question Answering (CQA) tasks...
InfoSeeker: A Scalable Hierarchical Parallel Agent Framework for Web Information Seeking
arXiv:2604.02971v1 Announce Type: new Abstract: Recent agentic search systems have made substantial progress by emphasising deep, multi-step reasoning. However, this focus often overlooks the challenges of wide-scale information synthesis, where agents must aggregate large volumes of heterogeneous evidence across many...
Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling
arXiv:2112.07874v2 Announce Type: cross Abstract: We examine the extent to which, in principle, linguistic graph representations can complement and improve neural language modeling. With an ensemble setup consisting of a pretrained Transformer and ground-truth graphs from one of 7 different...
A Survey on AI for 6G: Challenges and Opportunities
arXiv:2604.02370v1 Announce Type: cross Abstract: As wireless communication evolves, each generation of networks brings new technologies that change how we connect and interact. Artificial Intelligence (AI) is becoming crucial in shaping the future of sixth-generation (6G) networks. By combining AI...
Differentiable Symbolic Planning: A Neural Architecture for Constraint Reasoning with Learned Feasibility
arXiv:2604.02350v1 Announce Type: cross Abstract: Neural networks excel at pattern recognition but struggle with constraint reasoning -- determining whether configurations satisfy logical or physical constraints. We introduce Differentiable Symbolic Planning (DSP), a neural architecture that performs discrete symbolic reasoning while...
WGFINNs: Weak formulation-based GENERIC formalism informed neural networks'
arXiv:2604.02601v1 Announce Type: new Abstract: Data-driven discovery of governing equations from noisy observations remains a fundamental challenge in scientific machine learning. While GENERIC formalism informed neural networks (GFINNs) provide a principled framework that enforces the laws of thermodynamics by construction,...
CIPHER: Conformer-based Inference of Phonemes from High-density EEG
arXiv:2604.02362v1 Announce Type: cross Abstract: Decoding speech information from scalp EEG remains difficult due to low SNR and spatial blurring. We present CIPHER (Conformer-based Inference of Phonemes from High-density EEG Representations), a dual-pathway model using (i) ERP features and (ii)...
Reanalyzing L2 Preposition Learning with Bayesian Mixed Effects and a Pretrained Language Model
arXiv:2302.08150v2 Announce Type: cross Abstract: We use both Bayesian and neural models to dissect a data set of Chinese learners' pre- and post-interventional responses to two tests measuring their understanding of English prepositions. The results mostly replicate previous findings from...
Multi-Turn Reinforcement Learning for Tool-Calling Agents with Iterative Reward Calibration
arXiv:2604.02869v1 Announce Type: new Abstract: Training tool-calling agents with reinforcement learning on multi-turn tasks remains challenging due to sparse outcome rewards and difficult credit assignment across conversation turns. We present the first application of MT-GRPO (Multi-Turn Group Relative Policy Optimization)...
Audio Spatially-Guided Fusion for Audio-Visual Navigation
arXiv:2604.02389v1 Announce Type: cross Abstract: Audio-visual Navigation refers to an agent utilizing visual and auditory information in complex 3D environments to accomplish target localization and path planning, thereby achieving autonomous navigation. The core challenge of this task lies in the...
Low-Rank Compression of Pretrained Models via Randomized Subspace Iteration
arXiv:2604.02659v1 Announce Type: new Abstract: The massive scale of pretrained models has made efficient compression essential for practical deployment. Low-rank decomposition based on the singular value decomposition (SVD) provides a principled approach for model reduction, but its exact computation is...
Generalization Limits of Reinforcement Learning Alignment
arXiv:2604.02652v1 Announce Type: new Abstract: The safety of large language models (LLMs) relies on alignment techniques such as reinforcement learning from human feedback (RLHF). However, recent theoretical analyses suggest that reinforcement learning-based training does not acquire new capabilities but merely...