arXiv AI
Jun 16
Long-Context Modeling via GSS-Transformer Hybrid Architecture with Learnable Mixing
arXiv:2606. 16093v1 Announce Type: cross Abstract: Modeling long-range dependencies remains a central challenge in natural language processing.
Natural language processing
Classical and neural NLP: translation, question answering, tokenization and the evaluation of language understanding.
arXiv AI
Jun 16
MAF: Multimodal Adaptive Few-shot Prompting for Sentiment Analysis with MLLMs
arXiv:2606. 15694v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in understanding complex multimodal content.
arXiv AI
Jun 16
Let LLMs Judge Each Other: Multi-Agent Peer-Reviewed Reasoning for Medical Question Answering
arXiv:2606. 15419v1 Announce Type: cross Abstract: Objective: To enhance the accuracy, interpretability, and robustness of large language models (LLMs) in medical question answering (MedQA).
arXiv AI
Jun 16
Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling
arXiv:2605. 27023v2 Announce Type: replace Abstract: Knowledge graphs (KGs) have become the core backbone of numerous downstream tasks such as question answering and recommender systems.
arXiv AI
Jun 16
Koshur Diacritizer: A Byte-Level Sequence-to-Sequence Model for Kashmiri Diacritic Restoration
arXiv:2606. 15883v1 Announce Type: cross Abstract: Kashmiri, an Indo-Aryan language written in a modified Perso-Arabic script, frequently omits diacritic marks in digital text, creating ambiguity and challenging downstream NLP applications.
arXiv Machine Learning
Jun 16
Emergent retokenization symmetry in large language models: phenomenology and applications
arXiv:2606. 15521v1 Announce Type: cross Abstract: Tokenization introduces representational redundancy: under a fixed token vocabulary, every byte string admits many valid token encodings, or segmentations, that decode to the same surface string.
arXiv Machine Learning
Jun 16
MIRAGE: Auditing Anti-Muslim Bias in Frontier LLMs Across Reasoning, Agentic, and Time-Coupled Conditions
arXiv:2606. 16562v1 Announce Type: new Abstract: Five years after the discovery of persistent anti-Muslim bias in large language models, most evaluations remain confined to single-turn prompt completion, a setting that no longer reflects how frontier LLMs are deployed.
arXiv Machine Learning
Jun 16
Model Stealing Through the Lens of Model Multiplicity
arXiv:2606. 15493v1 Announce Type: new Abstract: Model stealing attacks, where adversaries create high-fidelity surrogate models, are a significant threat to the intellectual property of machine learning services.
arXiv AI
Jun 16
Scaling LLM Reasoning from Minimal Labels: A Semi-Supervised Framework with a Lightweight Verifier
arXiv:2606. 16811v1 Announce Type: new Abstract: For the development of Large language models (LLMs), recent approaches to generating pseudo intermediate reasoning have shown remarkable progress.
arXiv AI
Jun 16
Measuring Whether LLM Tutors Teach or Solve: A Diagnostic for Educational Impact
arXiv:2606. 16206v1 Announce Type: new Abstract: Large language models are increasingly proposed as educational tutors, yet stronger task-solving ability does not necessarily imply stronger learning support.
arXiv AI
Jun 16
Who Drifted: the System or the Judge? Anytime-Valid Attribution in LLM Evaluation Pipelines
arXiv:2606. 15474v1 Announce Type: new Abstract: Continuous evaluation of LLM products relies on a strong LLM judge treated as ground truth: a cheap monitor scores every interaction and a team is paged when the score drifts down.
arXiv AI
Jun 16
Towards Verifiable Agentic Data Science: Solving Irregular TSQA Via Tool-Grounded Reasoning
arXiv:2606. 15107v1 Announce Type: new Abstract: Time series data in real-world deployments is overwhelmingly irregular.
arXiv AI
Jun 16
daVinci-kernel: Co-Evolving Skill Selection, Summarization, and Utilization via RL for GPU Kernel Optimization
arXiv:2606. 16497v1 Announce Type: cross Abstract: GPU kernel optimization represents a paradigm where functional correctness is assumed and execution efficiency is the objective.
arXiv Machine Learning
Jun 16
Semantic Reasoning in Medicine: The Role of Knowledge Graphs Across Five Key Domains
arXiv:2606. 15155v1 Announce Type: new Abstract: Knowledge graphs (KGs) have emerged as a promising solution for integrating and reasoning over complex biomedical and clinical data in healthcare.
arXiv AI
Jun 16
X-Tokenizer: A Multimodal Action Tokenizer for Vision-Language-Action Pretraining
arXiv:2606. 14752v1 Announce Type: cross Abstract: Modern Vision-Language-Action (VLA) models must bridge pretrained vision-language reasoning and precise continuous robot control.
arXiv AI
Jun 16
Scribby: A Multi-Level LLM Framework for Semantic Video Analysis
arXiv:2606. 14762v1 Announce Type: cross Abstract: As video content continues to expand across educational platforms, recorded lectures, and live-streamed entertainment, the need for efficient and structured analysis of long-form footage has increased \cite{1}.
arXiv AI
Jun 16
S1-DeepResearch: Beyond Search, Toward Real-World Long-Horizon Research Agents
arXiv:2606. 15367v1 Announce Type: new Abstract: Deep research agents aim to solve complex knowledge-intensive tasks through long-horizon planning, evidence gathering, reasoning, and report generation.
arXiv AI
Jun 16
EChO-Agent: Evidence Chain Orchestration Agent for Audio Reasoning
arXiv:2606. 15141v1 Announce Type: cross Abstract: While LALMs show promise on audio question answering, they fail to focus on question-relevant segments of audio and provide a clear, checkable reasoning process when dealing with complex audio reasoning.
arXiv AI
Jun 16
EHRNote-ChatQA: A Benchmark for Evidence-Grounded Multi-Turn Clinical Question Answering over Longitudinal Discharge Summaries
arXiv:2606. 15735v1 Announce Type: cross Abstract: Discharge summaries are crucial clinical documents containing the context of a patient's overall hospital stay, and are routinely reviewed by medical experts for patient readmission, ongoing care, and diagnostic decision-making.
arXiv AI
Jun 16
NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models
arXiv:2606. 15646v1 Announce Type: new Abstract: Large Language Models (LLMs) have transformed natural language processing, but their lack of interpretable reasoning and tendency to hallucinate pose significant challenges for legal applications.