Knowledge catalogue
arXiv:2602.05407v2 Announce Type: replace Abstract: Hospital administration departments handle a wide range of operational tasks and, in large hospitals, process over 10,000 requests per day, driving
This r/MachineLearning discussion thread centers on a hands-on workshop covering context engineering — the process of designing and optimizing the information an AI agent sends to and receives from a
Nous Research shared a post on X highlighting the creative direction capabilities of their Hermes Agent, demonstrating its ability to function effectively as a Creative Director. The post likely showc
Hermes Agent Tip💡 Hermes supports dedicated auxiliary models for eight task types: 1. vision 2. web_extract 3. compression 4. session_search 5. approval 6. skills_hub 7. mcp 8. flush_memories Each tas
Hermes Agent v0.9.0, dubbed 'The Everywhere Release,' is a versioned update to Nous Research's Hermes Agent framework, suggesting significant improvements to cross-platform or multi-environment deploy
'how can i help?' i analyzed 100 asks from portfolio companies identified in notes and emails to see what they asked 26% specific person intro 9% investor discovery 7% hiring/talent 9% pr/media 11% bu
Harrison Chase, co-founder of LangChain, expressed strong conviction on X (formerly Twitter) that LangChain has the best team in the AI framework space, emphasizing the consistent quality and volume o
arXiv:2604.09001v1 Announce Type: new Abstract: Enumerating Minimal Unsatisfiable Subsets (MUSes) is a fundamental task in constraint satisfaction problems (CSPs). Its major challenge is the exponenti
I like this view from the brilliant Terence Tao. I have always believed that AI could help to augment us. We built and train agents like that today. But all forms of intelligence can and should be pos
arXiv:2604.09121v1 Announce Type: cross Abstract: Recent years have witnessed remarkable progress in automatic speech recognition (ASR), driven by advances in model architectures and large-scale train
just did our Monday morning weekly dev rel plan, feel free to steal :) shoutout to the great community that engages with us and gives us tons of feedback which makes doing this loop fun every week ❤️
just finished this. genuinely one of the most informative episodes I've heard in a while the future of engineering is changing faster than most people realize. and it's self-managed, self-improving ag
Just merged into Hermes: You can now use `hermes debug share` and `/debug` to get pastebin uploaded links for your latest agent, debug, and gateway log to share with us on twitter or discord to help u
arXiv:2603.28965v2 Announce Type: replace-cross Abstract: This work presents a hybrid physics-informed and data-driven modeling framework for predictive control of autonomous off-road vehicles operati
Harrison Chase, co-founder of LangChain, shared or engaged with a post on X (formerly Twitter) praising LangChain's developer relations team for producing high-quality technical and educational conten
langgraph persistence lets you checkpoint agent state at every step so you can pause, resume, and replay from any point. essential for long-running agents. docs: https://docs.langchain.com/oss/python/
Let me break down exactly how long it takes me to teach people AI agents. ⏱️Give business professionals their first big wow moment in under 2 minutes ⏱️Teach them meaningful use in under 17 ⏱️Teach th
Harrison Chase, co-founder and CEO of LangChain, expressed gratitude for his team in a post on X (formerly Twitter). The post likely highlights appreciation for colleagues and collaborators contributi
arXiv:2604.09167v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong performance in multimodal understanding and reasoning, yet grounded reasoning in 3D scenes remains un
arXiv:2604.04503v3 Announce Type: replace Abstract: Deep research agents (DRAs) integrate LLM reasoning with external tools. Memory systems enable DRAs to leverage historical experiences, which are es
arXiv:2604.08712v1 Announce Type: new Abstract: The generation of planning domains from natural language descriptions remains an open problem even with the advent of large language models and reasonin
arXiv:2604.08782v1 Announce Type: new Abstract: Large language models (LLMs) suffer significant performance degradation when user instructions and context are distributed over multiple conversational
arXiv:2512.21794v3 Announce Type: replace-cross Abstract: We study a sequential mechanism design problem in which a principal seeks to elicit truthful reports from multiple rational agents while start
arXiv:2604.08944v1 Announce Type: new Abstract: Multi-agent coordination under partial observability requires agents to share complementary private information. While recent methods optimize messages
arXiv:2604.08567v1 Announce Type: new Abstract: Large language models (LLMs) and LLM-based agents are increasingly deployed as assistants in planning and decision making, yet most existing systems are
🔒 new in deepagents: filesystem permissions shared resources and org-wide policies are exactly the kind of files you want your agent to read but never overwrite. filesystem permissions let you enforce
The TL;DR is that Google engineering appears to have the same AI adoption footprint as John Deere, the tractor company. Most of the industry has the same internal adoption curve: 20% agentic power use
arXiv:2603.21692v2 Announce Type: replace Abstract: As AI agents transition from human-supervised copilots to autonomous platform infrastructure, the ability to analyze their reasoning behavior across
arXiv:2604.09511v1 Announce Type: new Abstract: Universal image restoration (UIR) aims to recover clean images from diverse and unknown degradations using a unified model. Existing UIR methods primari
arXiv:2604.09009v1 Announce Type: new Abstract: Adaptive medical AI models often face performance drops in dynamic clinical environments due to data drift. We propose an autonomous continuous monitori
arXiv:2604.08551v1 Announce Type: cross Abstract: We investigate the emerging prospect of self-sovereign agents -- AI systems that can economically sustain and extend their own operation without human
arXiv:2604.08618v1 Announce Type: cross Abstract: Deploying LLM-powered agents in enterprise scenarios such as cloud technical support demands high-quality, domain-specific skills. However, existing s
arXiv:2604.09297v1 Announce Type: cross Abstract: Agent skills provide modular, task-specific guidance for LLM- based coding agents, but manually tuning skill bundles to balance success rate, cost, an
arXiv:2604.09502v1 Announce Type: new Abstract: AI agents increasingly operate in multi-agent environments where outcomes depend on coordination. We distinguish primary algorithmic monoculture -- base
arXiv:2604.09158v1 Announce Type: cross Abstract: Supporting students in developing diagnostic reasoning is a key challenge across educational domains. Novices often face cognitive biases such as prem
arXiv:2604.08621v1 Announce Type: new Abstract: In consumer applications, Customer Relationship Management (CRM) has traditionally relied on the manual optimisation of static, rule-based messaging str
arXiv:2604.09115v1 Announce Type: cross Abstract: Wilderness Search and Rescue (WiSAR) represents a longstanding and critical societal challenge, demanding innovative and automatic technological solut
The Hermes Agent dashboard is here! Run 'hermes dashboard' So much in this release but the one many have been waiting for above the rest, the GUI dashboard! Manage and monitor your Hermes Agent with a
LangChain has launched a new podcast focused on the theme of empowerment, as announced by Harrison Chase on X (formerly Twitter). The podcast likely features discussions around AI development, buildin
Harrison Chase discusses the tradeoff between speed and focus when building a core product using a frontier model versus developing more customized solutions. Using a frontier model with a standard ha
Nous Research's Hermes agent framework combines a language model's contextual reasoning capabilities ('this') with an autonomous agent built on their Hermes model series, enabling tool use, multi-step
Harrison Chase, co-founder of LangChain, shared a post emphasizing the critical importance of the 'harness' in AI agent systems, arguing that the harness essentially functions as the agent's memory an
This post completely misses the point of @sarahwooders 's original article. The whole point is that memory == context engineering, so it can't be a 'thin conductor'. Saying 'memory is just markdown' t
arXiv:2603.27817v3 Announce Type: replace-cross Abstract: Street-level imagery contains personally identifiable information (PII), some of which is context-dependent. Existing anonymization methods ei
arXiv:2604.09036v1 Announce Type: new Abstract: Scaling Vision-Language-Action (VLA) models requires massive datasets that are both semantically coherent and physically feasible. However, existing sce
when you take agents to production, you need to think about guardrails we provide 2 abstractions for guardrails 1. middleware provides hooks around the agent loop that you can use to handle retries, e
arXiv:2604.09413v1 Announce Type: cross Abstract: This paper argues that a one-size-fits-all approach to specifying consent for the use of creative works in generative AI is insufficient. Real-world o
yessssss look at this beautiful portfolio monitoring solution that just sits on top of existing data (CRM, notes, email, calendar) - normalizes revenue to annualized rate - tracks time since each data
I had been waiting for the 2026 edition of Zoom Communications Inc.‘s Perspectives, its recently held annual get-together for industry analysts, because I find Zoom to be the most interesting vendor i
Harrison Chase, co-founder of LangChain, endorsed the perspective that memory in AI agents is fundamentally a form of context, emphasizing that effective context management is the core requirement for
New York Times: A look at the escalating global AI arms race, as the US, China, Russia, and others rush to build AI-backed autonomous weapons and defense systems — China, the U.S., Russia and others h
“Above the api line” Good phrase GBrain is my attempt to be in control of my own personal AI that could become my intentionally designed cognitive armor Open source open prompts means you aren’t under
Agent harnesses dominate agent building and tie intimately to memory. Closed harnesses behind proprietary APIs force yielding control of agent memory to third parties. Memory enables sticky, personali
Harrison Chase, co-founder of LangChain, shared or engaged with commentary emphasizing the importance of owning your own memory harness in AI applications rather than relying on third-party API-based
deepagents is a harness / planning tool, filesystem backend, subagent spawning, memory management / thats the stack that matters / models are the cpu, the harness is the os / anyways, check it out htt
Directionally correct Open memory standards will need to emerge But it’s so early right now. We’re still just figuring out what best practices are. And so a lot is at the mercy of harnesses Agents.md
'every memory system is choosing a position on the raw/derived spectrum. and neither extreme works.' the axis nobody talks about: ownership > memory is your agent's compounding advantage > derived fac
feels right. once implementation gets cheap, the leverage moves into review and guardrails. e.g. specs, constraints, tests, hooks. otherwise it's mostly just generating more work for humans to fix up
Harrison Chase, co-founder of LangChain, has been advocating that individuals should prioritize owning their personal 'memory and skills folder' as the most critical store of value in the current AI l
This r/MachineLearning post discusses the landscape of frameworks and tools used to support benchmarking of LLMs and agentic AI systems, covering how to systematically evaluate model capabilities beyo