arXiv AI
Jun 2
Domain-Shift-Aware Conformal Prediction for Large Language Models
arXiv:2510. 05566v2 Announce Type: replace-cross Abstract: Large language models have achieved impressive performance across diverse tasks.
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 Machine Learning
Jun 2
CUPID in the Model Zoo: Online Matchmaking for Selecting Your Dream LLM
arXiv:2606. 00846v1 Announce Type: new Abstract: Users increasingly face the challenge of selecting an appropriate LLM for a given task from a rapidly growing pool of LLMs, each with distinct but often opaque latent properties.
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 Machine Learning
Jun 2
A Task-Centric Theory for Iterative Self-Improvement with Easy-to-Hard Curricula
arXiv:2602. 10014v3 Announce Type: replace Abstract: Iterative self-improvement fine-tunes an autoregressive large language model (LLM) on reward-verified outputs generated by the LLM itself.
arXiv Machine Learning
Jun 2
Score $\times$ Decoder: A Unified View of Unsupervised Inference-Time Scaling for Hallucination Mitigation
arXiv:2606. 00739v1 Announce Type: new Abstract: Large language models hallucinate even when the answer lies within their parameters.
arXiv AI
Jun 2
MidSteer: Optimal Affine Framework for Steering Generative Models
arXiv:2605. 05220v2 Announce Type: replace-cross Abstract: Steering intermediate representations has emerged as a powerful strategy for controlling generative models, particularly in post-deployment alignment and safety settings.
arXiv AI
Jun 2
CARES: Context-Aware Resolution Selector for VLMs
arXiv:2510. 19496v3 Announce Type: replace-cross Abstract: Large vision-language models (VLMs) commonly process images at native or high resolution to remain effective across tasks.
arXiv AI
Jun 2
REBot: From RAG to CatRAG with Semantic Enrichment and Graph Routing
arXiv:2510. 01800v3 Announce Type: replace Abstract: Academic regulation advising is essential for helping students interpret and comply with institutional policies, yet building effective systems requires domain specific regulatory resources.
arXiv AI
Jun 2
Evaluating the Performance of Deep Learning Models in Whole-body Dynamic 3D Posture Prediction During Load-reaching Activities
arXiv:2511. 20615v2 Announce Type: replace-cross Abstract: This study aimed to explore the application of deep neural networks for whole-body human posture prediction during dynamic load-reaching activities.
arXiv AI
Jun 2
Consistent and Distinctive: LLM Benchmark Efficiency via Maximum Independent Set Prompt Selection on Similarity Graphs
arXiv:2606. 01400v1 Announce Type: cross Abstract: Evaluating large language models (LLMs) across comprehensive benchmarks is expensive and time-consuming.
arXiv AI
Jun 2
Understanding LLM Behavior in Multi-Target Cross-Lingual Summarization
arXiv:2606. 01252v1 Announce Type: cross Abstract: Multi-target cross-lingual text summarization (MTXLS), which summarizes a source document into multiple target languages, is increasingly important as users consume content in diverse languages, but remains underexplored.
arXiv AI
Jun 2
Uncovering Competency Gaps in Large Language Models and Their Benchmarks
arXiv:2512. 20638v2 Announce Type: replace-cross Abstract: The evaluation of large language models relies heavily on standardized benchmarks.
arXiv AI
Jun 2
MindZero: Learning Online Mental Reasoning With Zero Annotations
arXiv:2606. 00240v1 Announce Type: new Abstract: Effective real-world assistance requires AI agents with robust Theory of Mind (ToM): inferring human mental states from their behavior.
arXiv Machine Learning
Jun 2
Beyond Procedure: Substantive Fairness in Conformal Prediction
arXiv:2602. 16794v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains underexplored.
arXiv Machine Learning
Jun 2
GNN-Enabled Robust Hybrid Beamforming with Score-Based CSI Generation and Denoising
arXiv:2511. 06663v2 Announce Type: replace-cross Abstract: Accurate Channel State Information (CSI) is critical for Hybrid Beamforming (HBF) tasks.
arXiv AI
Jun 2
Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook
arXiv:2604. 06210v3 Announce Type: replace-cross Abstract: As LLMs are globally deployed, aligning their cultural value orientations is critical for safety and user engagement.
arXiv AI
Jun 2
Dive into Ambiguity: A*-Inspired Multi-Agents Commonsense Obfuscation Attack on LLM Prompts
arXiv:2606. 01441v1 Announce Type: new Abstract: Large language models (LLMs) excel in reasoning and knowledge-intensive tasks but remain vulnerable to prompt-level adversarial attacks that preserve intent while triggering commonsense hallucinations.
arXiv Machine Learning
Jun 2
Adversarially Robust Control of Conditional Value-at-Risk via Rockafellar-Uryasev Conformal Inference
arXiv:2606. 00320v1 Announce Type: new Abstract: We present an online, distribution-free framework for controlling the Conditional Value-at-Risk (CVaR), extending conformal tail risk control to non-stationary and adversarial environments.
arXiv AI
Jun 2
Normalization Equivariance for Arbitrary Backbones, with Application to Image Denoising
arXiv:2605. 08193v3 Announce Type: replace-cross Abstract: Normalization Equivariance (NE) is a structural prior that improves robustness to distribution shift in image-to-image tasks.
arXiv AI
Jun 2
Understand and Accelerate Memory Processing Pipeline for Large Language Model Inference
arXiv:2603. 29002v3 Announce Type: replace-cross Abstract: Modern large language models (LLMs) increasingly depends on efficient long-context processing and generation mechanisms, including sparse attention, retrieval-augmented generation (RAG), and compressed contextual memory, to support complex reasoning.