发布日期：2026-03-28
收录条目：20
1. STADLER reshapes knowledge work at a 230-year-old company
- 来源：OpenAI News
- 发布时间：2026-03-27 22:00 UTC
- 链接：https://openai.com/index/stadler
摘要：Learn how STADLER uses ChatGPT to transform knowledge work, saving time and accelerating productivity across 650 employees.
2. An Implementation of IWE’s Context Bridge as an AI-Powered Knowledge Graph with Agentic RAG, OpenAI Function Calling, and Graph Traversal
- 来源：MarkTechPost
- 发布时间：2026-03-27 20:06 UTC
- 链接：https://www.marktechpost.com/2026/03/27/an-implementation-of-iwes-context-bridge-as-an-ai-powered-knowledge-graph-with-agentic-rag-openai-function-calling-and-graph-traversal/
摘要：In this tutorial, we implement IWE: an open-source, Rust-powered personal knowledge management system that treats markdown notes as a navigable knowledge graph. Since IWE is a CLI/LSP tool designed for local editors. We
3. openJiuwen Community Releases ‘JiuwenClaw’: A Self Evolving AI Agent for Task Management
- 来源：MarkTechPost
- 发布时间：2026-03-27 17:02 UTC
- 链接：https://www.marktechpost.com/2026/03/27/openjiuwen-community-releases-jiuwenclaw-a-self-evolving-ai-agent-for-task-management/
摘要：Over the past year, AI agents have evolved from merely answering questions to attempting to get real tasks done. However, a significant bottleneck has emerged: while most agents may appear intelligent during a conversati
4. Meta Releases TRIBE v2: A Brain Encoding Model That Predicts fMRI Responses Across Video, Audio, and Text Stimuli
- 来源：MarkTechPost
- 发布时间：2026-03-27 04:39 UTC
- 链接：https://www.marktechpost.com/2026/03/26/meta-releases-tribe-v2-a-brain-encoding-model-that-predicts-fmri-responses-across-video-audio-and-text-stimuli/
摘要：Neuroscience has long been a field of divide and conquer. Researchers typically map specific cognitive functions to isolated brain regions—like motion to area V5 or faces to the fusiform gyrus—using models tailored to na
5. PLDR-LLMs Reason At Self-Organized Criticality
- 来源：arXiv cs.AI
- 发布时间：2026-03-27 04:00 UTC
- 链接：https://arxiv.org/abs/2603.23539
摘要：arXiv:2603.23539v1 Announce Type: new Abstract: We show that PLDR-LLMs pretrained at self-organized criticality exhibit reasoning at inference time. The characteristics of PLDR-LLM deductive outputs at criticality is sim
6. Environment Maps: Structured Environmental Representations for Long-Horizon Agents
- 来源：arXiv cs.AI
- 发布时间：2026-03-27 04:00 UTC
- 链接：https://arxiv.org/abs/2603.23610
摘要：arXiv:2603.23610v2 Announce Type: new Abstract: Although large language models (LLMs) have advanced rapidly, robust automation of complex software workflows remains an open problem. In long-horizon settings, agents frequ
7. Evaluating a Multi-Agent Voice-Enabled Smart Speaker for Care Homes: A Safety-Focused Framework
- 来源：arXiv cs.AI
- 发布时间：2026-03-27 04:00 UTC
- 链接：https://arxiv.org/abs/2603.23625
摘要：arXiv:2603.23625v1 Announce Type: new Abstract: Artificial intelligence (AI) is increasingly being explored in health and social care to reduce administrative workload and allow staff to spend more time on patient care.
8. Can LLM Agents Be CFOs? A Benchmark for Resource Allocation in Dynamic Enterprise Environments
- 来源：arXiv cs.AI
- 发布时间：2026-03-27 04:00 UTC
- 链接：https://arxiv.org/abs/2603.23638
摘要：arXiv:2603.23638v1 Announce Type: new Abstract: Large language models (LLMs) have enabled agentic systems that can reason, plan, and act across complex tasks, but it remains unclear whether they can allocate resources ef
9. GTO Wizard Benchmark
- 来源：arXiv cs.AI
- 发布时间：2026-03-27 04:00 UTC
- 链接：https://arxiv.org/abs/2603.23660
摘要：arXiv:2603.23660v1 Announce Type: new Abstract: We introduce GTO Wizard Benchmark, a public API and standardized evaluation framework for benchmarking algorithms in Heads-Up No-Limit Texas Hold'em (HUNL). The benchmark e
10. Grounding Vision and Language to 3D Masks for Long-Horizon Box Rearrangement
- 来源：arXiv cs.AI
- 发布时间：2026-03-27 04:00 UTC
- 链接：https://arxiv.org/abs/2603.23676
摘要：arXiv:2603.23676v1 Announce Type: new Abstract: We study long-horizon planning in 3D environments from under-specified natural-language goals using only visual observations, focusing on multi-step 3D box rearrangement ta
11. LLMs Do Not Grade Essays Like Humans
- 来源：arXiv cs.AI
- 发布时间：2026-03-27 04:00 UTC
- 链接：https://arxiv.org/abs/2603.23714
摘要：arXiv:2603.23714v1 Announce Type: new Abstract: Large language models have recently been proposed as tools for automated essay scoring, but their agreement with human grading remains unclear. In this work, we evaluate ho
12. Efficient Benchmarking of AI Agents
- 来源：arXiv cs.AI
- 发布时间：2026-03-27 04:00 UTC
- 链接：https://arxiv.org/abs/2603.23749
摘要：arXiv:2603.23749v1 Announce Type: new Abstract: Evaluating AI agents on comprehensive benchmarks is expensive because each evaluation requires interactive rollouts with tool use and multi-step reasoning. We study whether
13. Learning-guided Prioritized Planning for Lifelong Multi-Agent Path Finding in Warehouse Automation
- 来源：arXiv cs.AI
- 发布时间：2026-03-27 04:00 UTC
- 链接：https://arxiv.org/abs/2603.23838
摘要：arXiv:2603.23838v1 Announce Type: new Abstract: Lifelong Multi-Agent Path Finding (MAPF) is critical for modern warehouse automation, which requires multiple robots to continuously navigate conflict-free paths to optimiz
14. VehicleMemBench: An Executable Benchmark for Multi-User Long-Term Memory in In-Vehicle Agents
- 来源：arXiv cs.AI
- 发布时间：2026-03-27 04:00 UTC
- 链接：https://arxiv.org/abs/2603.23840
摘要：arXiv:2603.23840v1 Announce Type: new Abstract: With the growing demand for intelligent in-vehicle experiences, vehicle-based agents are evolving from simple assistants to long-term companions. This evolution requires ag
15. SCoOP: Semantic Consistent Opinion Pooling for Uncertainty Quantification in Multiple Vision-Language Model Systems
- 来源：arXiv cs.AI
- 发布时间：2026-03-27 04:00 UTC
- 链接：https://arxiv.org/abs/2603.23853
摘要：arXiv:2603.23853v1 Announce Type: new Abstract: Combining multiple Vision-Language Models (VLMs) can enhance multimodal reasoning and robustness, but aggregating heterogeneous models' outputs amplifies uncertainty and in
16. When AI output tips to bad but nobody notices: Legal implications of AI's mistakes
- 来源：arXiv cs.AI
- 发布时间：2026-03-27 04:00 UTC
- 链接：https://arxiv.org/abs/2603.23857
摘要：arXiv:2603.23857v1 Announce Type: new Abstract: The adoption of generative AI across commercial and legal professions offers dramatic efficiency gains -- yet for law in particular, it introduces a perilous failure mode i
17. The DeepXube Software Package for Solving Pathfinding Problems with Learned Heuristic Functions and Search
- 来源：arXiv cs.AI
- 发布时间：2026-03-27 04:00 UTC
- 链接：https://arxiv.org/abs/2603.23873
摘要：arXiv:2603.23873v1 Announce Type: new Abstract: DeepXube is a free and open-source Python package and command-line tool that seeks to automate the solution of pathfinding problems by using machine learning to learn heuri
18. DUPLEX: Agentic Dual-System Planning via LLM-Driven Information Extraction
- 来源：arXiv cs.AI
- 发布时间：2026-03-27 04:00 UTC
- 链接：https://arxiv.org/abs/2603.23909
摘要：arXiv:2603.23909v1 Announce Type: new Abstract: While Large Language Models (LLMs) provide semantic flexibility for robotic task planning, their susceptibility to hallucination and logical inconsistency limits their reli
19. AnalogAgent: Self-Improving Analog Circuit Design Automation with LLM Agents
- 来源：arXiv cs.AI
- 发布时间：2026-03-27 04:00 UTC
- 链接：https://arxiv.org/abs/2603.23910
摘要：arXiv:2603.23910v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) suggest strong potential for automating analog circuit design. Yet most LLM-based approaches rely on a single-model loop of
20. From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environments
- 来源：arXiv cs.AI
- 发布时间：2026-03-27 04:00 UTC
- 链接：https://arxiv.org/abs/2603.23964
摘要：arXiv:2603.23964v1 Announce Type: new Abstract: The remarkable progress of reinforcement learning (RL) is intrinsically tied to the environments used to train and evaluate artificial agents. Moving beyond traditional qua