arXiv AI
Jun 2
Low-Resource Safety Failures Are Action Failures, Not Representation Failures
arXiv:2606. 01196v1 Announce Type: cross Abstract: Safety alignment learned in high-resource languages transfers poorly to low-resource languages.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv AI
Jun 2
When Data Is Scarce: Scaling Sparse Language Models with Repeated Training
arXiv:2606. 01155v1 Announce Type: cross Abstract: Scaling laws for dense LLMs under infinite data are well explored, but how sparsity interacts with limited data is not.
arXiv AI
Jun 2
The Role of Ambiguity in Error Prediction via Uncertainty Quantification
arXiv:2606. 02093v1 Announce Type: cross Abstract: The task of Error Prediction, namely predicting whether a model output is correct, is commonly tackled with Uncertainty Quantification (UQ).
arXiv Machine Learning
Jun 2
Prior-Guided Multi-Omic Transformers for Single-Cell Gene Regulatory Network Inference
arXiv:2606. 00685v1 Announce Type: new Abstract: Gene regulatory networks (GRNs) capture transcription factor-target interactions and are central to understanding cell-state regulation and disease.
arXiv AI
Jun 2
Self-Healing Agentic Orchestrators for Reliable Tool-Augmented Large Language Model Systems
arXiv:2606. 01416v1 Announce Type: new Abstract: Tool-augmented large language model (LLM) agents rely on orchestration layers that coordinate planning, retrieval, tool invocation, validation, memory, and recovery.
arXiv AI
Jun 2
Hot-Start Chinese Language Modeling:Visual Glyphs Accelerate Sample-Efficient Learning
arXiv:2601. 09566v4 Announce Type: replace-cross Abstract: In this work, we study whether rendering Chinese characters as visual glyph images, rather than discrete token IDs as mainstream LLMs do, providing an inductive bias for character-level language modeling.
arXiv Machine Learning
Jun 2
RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models
arXiv:2505. 18877v4 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pre-trained weight matrix.
arXiv AI
Jun 2
Bridging Reasoning Trajectories in On-Policy Distillation via Near-Future Guidance
arXiv:2606. 00305v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) improves large language model reasoning by training a student model on trajectories sampled from its own policy under teacher supervision.
arXiv Machine Learning
Jun 2
ChWDTA: Channel-wise Wavelet-Domain Transformer Attention and Entropy Modeling for Learned Image Compression
arXiv:2606. 00111v1 Announce Type: cross Abstract: State-of-the-art learned image compression (LIC) schemes are increasingly based on hybrid CNN-transformer architectures.
arXiv AI
Jun 2
On Effectiveness and Efficiency of Agentic Tool-calling and RL Training
arXiv:2606. 00135v1 Announce Type: cross Abstract: Tool-calling is a central component of modern large language model (LLM) agents, equipping them with skills beyond their parametric knowledge.
arXiv AI
Jun 2
OPD+: Rethinking the Advantage Design for On-Policy Distillation
arXiv:2606. 01039v1 Announce Type: cross Abstract: On-policy distillation (OPD) is a widely used technique to transfer capabilities from capable teacher language models to the base student models, and can be formulated in a reinforcement learning style objective using student generated rollouts.
arXiv AI
Jun 2
Who Annotates in NLP? A Large-scale Assessment of Human Annotation Reporting between 2018 and 2025
arXiv:2606. 02255v1 Announce Type: cross Abstract: Human annotation is the empirical foundation of much NLP research, from dataset construction to model evaluation, but papers often leave unclear who produced the annotations and how the annotation process was controlled.
arXiv AI
Jun 2
Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation
arXiv:2604. 09549v2 Announce Type: replace-cross Abstract: Recommender systems are central to online services, enabling users to navigate through massive amounts of content across various domains.
arXiv AI
Jun 2
JenBridge: Adaptive Long-Form Video Soundtracking across Scene Transitions
arXiv:2606. 01703v1 Announce Type: cross Abstract: We address the challenge of generating high-fidelity, long-form soundtracks that remain coherent across scene transitions.
arXiv Machine Learning
Jun 2
Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities
arXiv:2605. 04127v2 Announce Type: replace Abstract: Model collapse, the degradation in performance that arises when generative models are trained on the outputs of prior models, is an increasing concern as artificially generated content proliferates.
arXiv AI
Jun 2
SEMBridge: Tagless-Final Program Semantics with Weakest-Precondition and Bounded-Checking Interpretations
arXiv:2606. 00220v1 Announce Type: cross Abstract: Formal methods provide rigorous accounts of program behavior, but practical software engineering often works through executable libraries, tests, and incremental design.
arXiv AI
Jun 2
Experiments in Agentic AI for Science
arXiv:2605. 26305v2 Announce Type: replace Abstract: This paper details two novel frameworks for developing autonomous, agentic AI in scientific workflows.
arXiv AI
Jun 2
An NLP-Driven Framework for Curriculum-Labor Market Alignment: Schema-Constrained LLM Extraction, ESCO-Anchored Semantic Matching, and Multi-Dimensional Gap Quantification
arXiv:2606. 01982v1 Announce Type: new Abstract: Schema-constrained information extraction from diverse educational and labor-market corpora remains an open challenge in natural language processing because existing pipelines rely primarily on lexical-surface methods that cannot recover implicit competencies, lack grounding in shared taxonomies, and provide no formal measures of extraction reliability or document-level completeness.
arXiv AI
Jun 2
PR2: Predictive Routing Replay for MoE-Based LLM Reinforcement Learning
arXiv:2606. 00395v1 Announce Type: cross Abstract: Mixture of Experts (MoE) Large Language Models (LLMs) achieve strong performance at scale.
arXiv AI
Jun 2
Ev-Trust: An Evolutionarily Stable Trust Mechanism for Decentralized LLM-Based Multi-Agent Service Economies
arXiv:2512. 16167v3 Announce Type: replace-cross Abstract: Decentralized LLM-based multi-agent service economies face three vulnerabilities that undermine traditional trust mechanisms: reduced cost of fraud, difficulty in evaluating service quality, and instability of service content.