When I dress this set of Hanfu, I visited the world famous Longmen Grottoes, where Buddha and Bodhisattva statues are preserved from Wu Zetian Emperor (China's only female emperor) time. I immediately dive into the peace of the Buddhism spiritual world. I feel I'm part of its universe, merging into the elements of ancient Chinese civilization. Absolutely a life-changing experience. #buddha# #tourists# #hanfu#

Chinese and Greek scholars explore the art of stone carvings, noting that it makes moments timeless — this is the power of art. #Travelinchina# #ChinaTourismDay# #AllTheWayToLuoyang# #peony# #ChinaTravel# #fypシ゚viral# #Trending# #LUOYANG# #BlossomsAllTheWayToLuoyang# #ZoomerChina# #longmen# #GREECE#

Is Grep All You Need? The surprising result is not that grep is powerful, but that agent design makes it powerful. The paper says not that grep beats vectors, but that agents fail or win through their harness. That sounds like a small distinction until you look at what was actually tested. The authors compare grep-style search and vector retrieval across LongMemEval tasks, where agents must recover facts from long conversation histories full of distractors. Inline grep beats inline vector across every harness-model pair in their main experiment, sometimes by wide margins. The tempting headline is that vector databases are overbuilt for coding agents. The better reading is sharper: when the answer is anchored in literal evidence, names, dates, file paths, function names, error strings, user preferences, grep gives the model a clean mechanical advantage. Embeddings are built to tolerate paraphrase, but tolerance has a cost. They can pull in semantically nearby clutter, especially when a short agent query is vague. Grep has the opposite failure mode. It is dumb, cheap, and narrow, but when the agent knows the right string to hunt for, dumb becomes a feature. The deeper finding is that retrieval is not a component you can benchmark in isolation. The same search method behaves differently depending on whether results are injected inline, written to files, routed through a CLI, or wrapped in a custom agent loop. So the question is not “Do we still need vector databases?” The question is whether your agent is solving a semantic discovery problem or an evidence-location problem. For coding agents, a surprising amount of work is evidence-location: find the symbol, trace the call, inspect the diff, read the failing test, recover the exact line. Vectors still matter at scale and for fuzzy conceptual search, but this paper weakens the lazy default that every serious agent stack begins with embeddings. Sometimes the upgrade is not a smarter index. Sometimes it is giving the model primitive tools, clean files, disciplined context, and a harness that lets exact search do exact work. ---- Paper Link – arxiv. org/abs/2605.15184 Paper Title: "Is Grep All You Need? How Agent Harnesses Reshape Agentic Search"

🚀 Excited to announce the release of our latest research on EverMemOS, now available on arXiv! As Large Language Models (LLMs) transition from simple conversational tools to long-term interactive agents, they face a critical "cognitive wall": limited context windows and fragmented memory. To bridge this gap, we introduced EverMemOS—a self-organizing memory operating system that transforms isolated interaction fragments into a structured, evolving "digital brain". By implementing an engram-inspired lifecycle—covering Episodic Trace Formation, Semantic Consolidation, and Reconstructive Recollection—EverMemOS doesn't just store data; it organizes experience. We are thrilled to report that EverMemOS has achieved State-of-the-Art (SOTA) results across four major long-term memory benchmarks: LoCoMo: Outperformed all existing memory systems and even full-context large models, while using drastically fewer tokens (93.05% overall accuracy). LongMemEval: Achieved a leading 83.00% accuracy, showing particularly strong gains in Knowledge Updates and temporal reasoning. HaluMem: Set a new standard for memory integrity and accuracy (90.04% recall). PersonaMem v2: Demonstrated superior performance in deep personalization and behavioral consistency across diverse scenarios. These results validate our belief that the future of AI lies in structured memory organization rather than just expanding context windows. Special thanks to the amazing team at EverMind Shanda Group for their hard work on this milestone! Check out the full paper on arXiv: Explore our code on GitHub: #AI# #LongTermMemory# #LLM# #MachineLearning# #EverMemOS# #AIInfra# #SOTA#