Daily updates, analyzing cutting-edge AI research papers
The survey from RUC‑Gaoling AI Institute reviews Rubrics for large language models, explaining why they are needed for open‑ended, high‑risk tasks, how they are constructed, and how they can be applied to policy and reward model training as well as multi‑dimensional evaluation across general and domain‑specific scenarios.
The article analyzes scaling laws and synthetic experiments to show that, due to power‑law data distributions and interference, some tasks remain unreachable for small models even with infinite data, a finding confirmed on real LLMs such as OLMo.
The CVPR 2026 award ceremony, with 16,092 submissions and a 25.3% acceptance rate, highlights a shift in computer vision from static image understanding to dynamic 4D reconstruction, single‑image 3D generation, game‑agent modeling, and real‑time image editing, while honoring foundational works like ResNet and YOLO.
The article explains how the DOMINO method lets large language models learn a domain from just dozens of real examples instead of hand‑written prompts, describes its trainable "domain switch" architecture, and shows experimental gains on time‑varying code tasks, highlighting more robust and diverse data synthesis.
Anthropic’s internal analysis of hundreds of Claude Code skills shows that verification‑oriented skills deliver the greatest boost to AI coding assistant output, and it outlines nine skill categories, seven design principles, on‑demand hooks, and distribution strategies for effective agent development.
The Tongji University “Boundless” world model achieved the top open‑source score (64.54) and the second‑overall rank (67.87) on WorldArena’s Track‑1, demonstrating high‑quality video generation, stable long‑sequence physics, and embodied interaction across six evaluation dimensions, while using data‑efficient training and a hybrid open/closed‑source strategy.
This article provides a comprehensive overview of the "Code as Agent Harness" paradigm, detailing its three‑layer architecture, the roles of code in reasoning, acting, and environment modeling, the mechanisms that enable reliable long‑term execution, and how multi‑agent systems scale the harness through shared code and feedback loops.
SkillOpt reframes LLM agent skills as trainable external state, applying a deep‑learning‑style optimizer to systematically improve skill documents, and demonstrates across six benchmarks, seven models, and three execution modes that this approach yields consistent, large gains and robust transferability.
This free collection gathers 127 top‑conference papers covering 17 large‑model research directions—from perception and decision to safety—providing PDFs, GitHub links, and a web interface to help AI engineers, researchers, and students stay up‑to‑date.
The article introduces GRAM (Generative Recursive Reasoning Models), a parallel multi‑trajectory inference framework that replaces deterministic single‑track recursion with stochastic latent transitions and width scaling, achieving state‑of‑the‑art results on Sudoku‑Extreme, ARC‑AGI, N‑Queens and Graph Coloring benchmarks.
