GraphWalker: Graph-Guided In-Context Learning for Clinical Reasoning on Electronic Health Records
arXiv:2604.06684v1 Announce Type: new Abstract: Clinical Reasoning on Electronic Health Records (EHRs) is a fundamental yet challenging task in modern healthcare. While in-context learning (ICL) offers a promising inference-time adaptation paradigm for large language models (LLMs) in EHR reasoning, existing...
State-of-the-Art Arabic Language Modeling with Sparse MoE Fine-Tuning and Chain-of-Thought Distillation
arXiv:2604.06421v1 Announce Type: new Abstract: This paper introduces Arabic-DeepSeek-R1, an application-driven open-source Arabic LLM that leverages a sparse MoE backbone to address the digital equity gap for under-represented languages, and establishes a new SOTA across the entire Open Arabic LLM...
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...
TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models
arXiv:2604.06291v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of Large Language Models (LLMs), and recent Mixture-of-Experts (MoE) extensions further enhance flexibility by dynamically combining multiple LoRA experts. However, existing MoE-augmented LoRA methods assume that experts operate independently,...
Severity-Aware Weighted Loss for Arabic Medical Text Generation
arXiv:2604.06346v1 Announce Type: new Abstract: Large language models have shown strong potential for Arabic medical text generation; however, traditional fine-tuning objectives treat all medical cases uniformly, ignoring differences in clinical severity. This limitation is particularly critical in healthcare settings, where...
Busemann energy-based attention for emotion analysis in Poincar\'e discs
arXiv:2604.06752v1 Announce Type: new Abstract: We present EmBolic - a novel fully hyperbolic deep learning architecture for fine-grained emotion analysis from textual messages. The underlying idea is that hyperbolic geometry efficiently captures hierarchies between both words and emotions. In our...
Illocutionary Explanation Planning for Source-Faithful Explanations in Retrieval-Augmented Language Models
arXiv:2604.06211v1 Announce Type: new Abstract: Natural language explanations produced by large language models (LLMs) are often persuasive, but not necessarily scrutable: users cannot easily verify whether the claims in an explanation are supported by evidence. In XAI, this motivates a...
FLeX: Fourier-based Low-rank EXpansion for multilingual transfer
arXiv:2604.06253v1 Announce Type: new Abstract: Cross-lingual code generation is critical in enterprise environments where multiple programming languages coexist. However, fine-tuning large language models (LLMs) individually for each language is computationally prohibitive. This paper investigates whether parameter-efficient fine-tuning methods and optimizer...
LLM-Augmented Knowledge Base Construction For Root Cause Analysis
arXiv:2604.06171v1 Announce Type: new Abstract: Communications networks now form the backbone of our digital world, with fast and reliable connectivity. However, even with appropriate redundancy and failover mechanisms, it is difficult to guarantee "five 9s" (99.999 %) reliability, requiring rapid...
LinkedIn scanning users' browser extensions sparks controversy and two lawsuits
LinkedIn says claims fabricated by extension maker suspended for scraping data.
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,...
TelcoAgent-Bench: A Multilingual Benchmark for Telecom AI Agents
arXiv:2604.06209v1 Announce Type: new Abstract: The integration of large language model (LLM) agents into telecom networks introduces new challenges, related to intent recognition, tool execution, and resolution generation, while taking into consideration different operational constraints. In this paper, we introduce...
STDec: Spatio-Temporal Stability Guided Decoding for dLLMs
arXiv:2604.06330v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have achieved rapid progress, viewed as a promising alternative to the autoregressive paradigm. However, most dLLM decoders still adopt a global confidence threshold, and do not explicitly model local context...
DataSTORM: Deep Research on Large-Scale Databases using Exploratory Data Analysis and Data Storytelling
arXiv:2604.06474v1 Announce Type: new Abstract: Deep research with Large Language Model (LLM) agents is emerging as a powerful paradigm for multi-step information discovery, synthesis, and analysis. However, existing approaches primarily focus on unstructured web data, while the challenges of conducting...
Scientific Knowledge-driven Decoding Constraints Improving the Reliability of LLMs
arXiv:2604.06603v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong knowledge reserves and task-solving capabilities, but still face the challenge of severe hallucination, hindering their practical application. Though scientific theories and rules can efficiently direct the behaviors of...
When Does Context Help? A Systematic Study of Target-Conditional Molecular Property Prediction
arXiv:2604.06558v1 Announce Type: new Abstract: We present the first systematic study of when target context helps molecular property prediction, evaluating context conditioning across 10 diverse protein families, 4 fusion architectures, data regimes spanning 67-9,409 training compounds, and both temporal and...
SensorPersona: An LLM-Empowered System for Continual Persona Extraction from Longitudinal Mobile Sensor Streams
arXiv:2604.06204v1 Announce Type: new Abstract: Personalization is essential for Large Language Model (LLM)-based agents to adapt to users' preferences and improve response quality and task performance. However, most existing approaches infer personas from chat histories, which capture only self-disclosed information...
Hallucination as output-boundary misclassification: a composite abstention architecture for language models
arXiv:2604.06195v1 Announce Type: new Abstract: Large language models often produce unsupported claims. We frame this as a misclassification error at the output boundary, where internally generated completions are emitted as if they were grounded in evidence. This motivates a composite...
MedConclusion: A Benchmark for Biomedical Conclusion Generation from Structured Abstracts
arXiv:2604.06505v1 Announce Type: new Abstract: Large language models (LLMs) are widely explored for reasoning-intensive research tasks, yet resources for testing whether they can infer scientific conclusions from structured biomedical evidence remain limited. We introduce $\textbf{MedConclusion}$, a large-scale dataset of $\textbf{5.7M}$...
TwinLoop: Simulation-in-the-Loop Digital Twins for Online Multi-Agent Reinforcement Learning
arXiv:2604.06610v1 Announce Type: new Abstract: Decentralised online learning enables runtime adaptation in cyber-physical multi-agent systems, but when operating conditions change, learned policies often require substantial trial-and-error interaction before recovering performance. To address this, we propose TwinLoop, a simulation-in-the-loop digital twin...
EpiBench: Benchmarking Multi-turn Research Workflows for Multimodal Agents
arXiv:2604.05557v1 Announce Type: new Abstract: Scientific research follows multi-turn, multi-step workflows that require proactively searching the literature, consulting figures and tables, and integrating evidence across papers to align experimental settings and support reproducible conclusions. This joint capability is not systematically...
From Retinal Evidence to Safe Decisions: RETINA-SAFE and ECRT for Hallucination Risk Triage in Medical LLMs
arXiv:2604.05348v1 Announce Type: new Abstract: Hallucinations in medical large language models (LLMs) remain a safety-critical issue, particularly when available evidence is insufficient or conflicting. We study this problem in diabetic retinopathy (DR) decision settings and introduce RETINA-SAFE, an evidence-grounded benchmark...
Modeling Patient Care Trajectories with Transformer Hawkes Processes
arXiv:2604.05844v1 Announce Type: new Abstract: Patient healthcare utilization consists of irregularly time-stamped events, such as outpatient visits, inpatient admissions, and emergency encounters, forming individualized care trajectories. Modeling these trajectories is crucial for understanding utilization patterns and predicting future care needs,...
Non-monotonic causal discovery with Kolmogorov-Arnold Fuzzy Cognitive Maps
arXiv:2604.05136v1 Announce Type: new Abstract: Fuzzy Cognitive Maps constitute a neuro-symbolic paradigm for modeling complex dynamic systems, widely adopted for their inherent interpretability and recurrent inference capabilities. However, the standard FCM formulation, characterized by scalar synaptic weights and monotonic activation...
On the Geometry of Positional Encodings in Transformers
arXiv:2604.05217v1 Announce Type: new Abstract: Neural language models process sequences of words, but the mathematical operations inside them are insensitive to the order in which words appear. Positional encodings are the component added to remedy this. Despite their importance, positional...
See the Forest for the Trees: Loosely Speculative Decoding via Visual-Semantic Guidance for Efficient Inference of Video LLMs
arXiv:2604.05650v1 Announce Type: new Abstract: Video Large Language Models (Video-LLMs) excel in video understanding but suffer from high inference latency during autoregressive generation. Speculative Decoding (SD) mitigates this by applying a draft-and-verify paradigm, yet existing methods are constrained by rigid...
Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation
arXiv:2604.05070v1 Announce Type: new Abstract: Simulation is essential for autonomous driving, yet current frameworks often model vehicles as rigid assets and fail to capture part-level articulation. With perception algorithms increasingly leveraging dynamics such as wheel steering or door opening, realistic...
Confidence Should Be Calibrated More Than One Turn Deep
arXiv:2604.05397v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly applied in high-stakes domains such as finance, healthcare, and education, where reliable multi-turn interactions with users are essential. However, existing work on confidence estimation and calibration, a major approach...
Beneath the Surface: Investigating LLMs' Capabilities for Communicating with Subtext
arXiv:2604.05273v1 Announce Type: new Abstract: Human communication is fundamentally creative, and often makes use of subtext -- implied meaning that goes beyond the literal content of the text. Here, we systematically study whether language models can use subtext in communicative...
Auditable Agents
arXiv:2604.05485v1 Announce Type: new Abstract: LLM agents call tools, query databases, delegate tasks, and trigger external side effects. Once an agent system can act in the world, the question is no longer only whether harmful actions can be prevented--it is...