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arXiv:2504.00409v2 Announce Type: replace Abstract: Large language models (LLMs) have greatly improved their capability in performing NLP tasks. However, deeper...
arXiv:2509.23808v3 Announce Type: replace-cross Abstract: A prevailing view in Reinforcement Learning with Verifiable Rewards (RLVR) interprets recent progress through the...
arXiv:2512.04578v1 Announce Type: new Abstract: Legal general intelligence (GI) refers to artificial intelligence (AI) that encompasses legal understanding, reasoning, and...
arXiv:2512.04048v1 Announce Type: cross Abstract: Sign Language Production (SLP) is the process of converting the complex input text into a...
arXiv:2502.09854v2 Announce Type: replace Abstract: In this work, we investigate how small language models (SLMs) can be scaled to support...
arXiv:2512.03499v1 Announce Type: cross Abstract: The Segment Anything Model (SAM) has emerged as a powerful visual foundation model for image...
arXiv:2312.15478v2 Announce Type: replace Abstract: The need to assess LLMs for bias and fairness is critical, with current evaluations often...
In this tutorial, we build an advanced meta-cognitive control agent that learns how to regulate its own depth of thinking....
arXiv:2505.07409v2 Announce Type: replace Abstract: Democratic societies need reliable information. Misinformation in popular media, such as news articles or videos,...
Generate-Then-Validate: A Novel Question Generation Approach Using Small Language Models
GenDB: The Next Generation of Query Processing — Synthesized, Not Engineered
Gemini 3 Flash arrives with reduced costs and latency — a powerful combo for enterprises
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
GEM: Gaussian Embedding Modeling for Out-of-Distribution Detection in GUI Agents
GCC-Spam: Spam Detection via GAN, Contrastive Learning, and Character Similarity Networks
Future AI chips could be built on glass
Fusing Semantic, Lexical, and Domain Perspectives for Recipe Similarity Estimation
Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency
