Daily updates, analyzing cutting-edge AI research papers
The Skill‑RM paper reveals that simply appending evaluation resources can degrade large‑model scoring, while structuring those resources into a Reward‑Evaluation Skill boosts performance across benchmarks, best‑of‑N selection, and RL‑based instruction following.
The article compiles 184 practical PINN innovations—including theory advances, new training paradigms, and integrations with Bayesian methods, reinforcement learning, Transformers, and graph neural networks—along with ready-to-use source code and starter resources for researchers seeking cutting‑edge physics‑informed neural network solutions.
The SIGIR 2026 review argues that as large language models become the primary consumers of retrieved results, information retrieval must shift its core objective from pure recall to denoising, presenting a five‑stage pipeline, controlled experiments, and a detailed attribution framework for noise sources.
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.
