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arXiv:2505.16834v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) systems have advanced large language models (LLMs) in complex deep search scenarios...
arXiv:2510.07239v1 Announce Type: new Abstract: Automated red-teaming has emerged as a scalable approach for auditing Large Language Models (LLMs) prior...
arXiv:2505.14376v2 Announce Type: replace Abstract: Enhancing the quality and efficiency of academic publishing is critical for both authors and reviewers,...
TL;DR: A new research from Apple, formalizes what “mid-training” should do before reinforcement learning RL post-training and introduces RA3 (Reasoning...
arXiv:2510.04417v1 Announce Type: cross Abstract: The study of multimodality has garnered significant interest in fields where the analysis of interactions...
arXiv:2510.01367v3 Announce Type: replace-cross Abstract: Reward hacking, where a reasoning model exploits loopholes in a reward function to achieve high...
arXiv:2509.12508v3 Announce Type: replace Abstract: In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary...
arXiv:2505.18350v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) are increasingly deployed for narrow tasks in resource-constrained settings, a...
What if an AI agent could localize a root cause, prove a candidate fix via automated analysis and testing, and...
ChartHal: A Fine-grained Framework Evaluating Hallucination of Large Vision Language Models in Chart Understanding
ChartGaze: Enhancing Chart Understanding in LVLMs with Eye-Tracking Guided Attention Refinement
Character-aware Transformers Learn an Irregular Morphological Pattern Yet None Generalize Like Humans
ChainReaction! Structured Approach with Causal Chains as Intermediate Representations for Improved and Explainable Causal Video Question Answering
Chai Discovery Team Releases Chai-2: AI Model Achieves 16% Hit Rate in De Novo Antibody Design
Cerebras Releases MiniMax-M2-REAP-162B-A10B: A Memory Efficient Version of MiniMax-M2 for Long Context Coding Agents
Causal2Vec: Improving Decoder-only LLMs as Versatile Embedding Models
