arXiv:2510.15221v3 Announce Type: replace Abstract: Affective computing has matured rapidly in laboratory settings, yet no prior dataset combines (i)…
Category: AI
Grammar-Aligned Decoding
arXiv:2405.21047v4 Announce Type: replace Abstract: Large Language Models (LLMs) struggle with reliably generating highly structured outputs, such as…
RECAST: Expanding the Boundaries of LLMs’ Complex Instruction Following with Multi-Constraint Data
arXiv:2505.19030v5 Announce Type: replace Abstract: Large language models (LLMs) are increasingly expected to tackle complex tasks, driven by their…
Give Your Coding Agents a Memory You Own
This post has no text preview — click the link below to read the original article. This article has been indexed from Hugging Face – Blog Read the original article: Give Your Coding Agents a Memory You Own
Deja Vu in Plots: Leveraging Cross-Session Evidence with Retrieval-Augmented LLMs for Live Streaming Risk Assessment
arXiv:2601.16027v3 Announce Type: replace Abstract: The rise of live streaming has transformed online interaction, enabling massive real-time engagement…
Seeing Before Synthesizing: VLM-Guided Transition Event Discovery for Weakly-Supervised Dense Video Captioning
arXiv:2609.04183v1 Announce Type: cross Abstract: Weakly-Supervised Dense Video Captioning aims to localize and describe multiple events in untrimmed…
ESPO: Error-Structured Prompt Optimization via Diagnose, Diversify, and Stabilize
arXiv:2609.04197v1 Announce Type: cross Abstract: Evolutionary prompt optimizers such as GEPA suffer from prompt bloat: each iteration appends rules and…
Compile by Training: Turning Natural-Language Specifications into Local Neural Functions
arXiv:2609.04199v1 Announce Type: cross Abstract: Many recurring text functions are easy to describe but difficult to implement with rules, while calling…
Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views
arXiv:2609.04180v1 Announce Type: cross Abstract: Gaps remain in our understanding of how large language models (LLMs) acquire knowledge during…
One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing
arXiv:2609.04190v1 Announce Type: cross Abstract: Video editing spans diverse editing paradigms, yet achieving high-quality instruction-guided and…
