Knowledge catalogue
arXiv:2602.21534v3 Announce Type: replace Abstract: Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interacti
arXiv:2607.02703v1 Announce Type: cross Abstract: In this paper, we describe LLMoxie, an institutional AI platform whose three-tiered architecture supports multi-cloud and on-premise inference, a Lite
arXiv:2509.23960v2 Announce Type: replace-cross Abstract: Co-optimizing safety and performance in large-scale multi-agent systems remains a fundamental challenge. Existing approaches based on multi-ag
Harrison Chase argues that evaluations (evals) are the most intellectually demanding aspect of agent engineering, distinguishing them from other engineering tasks that may be more routine or formulaic
arXiv:2607.05346v1 Announce Type: new Abstract: We propose OptiAgent, a multi-agent framework that, given a natural language description of an Operations Research problem, is able to output a solver-r
Application and network security company Radware Ltd. today expanded its Agentic AI Protection product with compliance reporting, deeper visibility into artificial intelligence agent activity, and new
arXiv:2607.03423v1 Announce Type: cross Abstract: Modern AI agent implementations such as frontier coding agents chain multiple tools at runtime that create a security surface that per-tool guardrails
arXiv:2603.23483v2 Announce Type: replace-cross Abstract: Agentic multimodal large language models (MLLMs) (e.g., OpenAI o3 and Gemini Agentic Vision) achieve remarkable reasoning capabilities through
arXiv:2607.04235v1 Announce Type: new Abstract: Large language model agents operate in partially observable, long-horizon settings where obtaining supervision remains a major bottleneck. We address th
The year 2026 could be remembered as the moment when storage technology received a massive promotion. The reason is that the current transition from simple chatbots to agentic AI systems has raised th
arXiv:2607.04708v1 Announce Type: cross Abstract: Agentic AI is shifting online shopping from search toward delegated purchasing, where autonomous buying agents monitor markets and decide when to buy
arXiv:2503.20666v2 Announce Type: replace-cross Abstract: Thematic analysis (TA) is a widely used qualitative approach for uncovering latent meanings in unstructured text data. TA provides valuable in
arXiv:2607.04034v1 Announce Type: cross Abstract: The language models that underpin agents have seen a rapid rise in performance on function calling benchmarks. However, the metrics used in the traini
Vercel has announced the availability of Agent Runs in both the Vercel MCP (Model Context Protocol) and CLI (Command Line Interface), expanding developer capabilities for AI-powered automation and dep
arXiv:2602.19127v2 Announce Type: replace Abstract: With the rapid advancement of agent-based methods in recent years, Agentic RAG has undoubtedly become an important research direction. Multi-hop rea
arXiv:2607.01846v1 Announce Type: new Abstract: Domain agents often face noisy business data, uncertain post-training gains, offline/application mismatch, and adapter-release risk. This paper presents
arXiv:2607.02116v1 Announce Type: new Abstract: Autonomous AI agents increasingly depend on external knowledge stores, yet most retrieval pipelines provide relevance without durable guarantees of prov
arXiv:2501.07892v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown strong performance in automated code generation, with few-shot prompting widely used for its simplicit
arXiv:2607.01793v1 Announce Type: new Abstract: LLM agents increasingly perform autonomous actions through external tools, leading to complex and evolving safety risks. However, existing safety testin
arXiv:2607.00502v1 Announce Type: new Abstract: While long-horizon mobile GUI agents typically rely on thought-action-observation loops, they struggle to separate persistent task states from transient
arXiv:2607.01034v1 Announce Type: cross Abstract: Large language model (LLM)-based conversational agents (CAs) are now ubiquitous, creating new opportunities for AI-mediated behavior change. Their cap
arXiv:2607.01087v1 Announce Type: cross Abstract: Generative AI is shifting software engineering from a practice organized around scarce implementation effort toward one organized around abundant, low
arXiv:2607.00233v1 Announce Type: new Abstract: How do two agents invent a shared language from scratch? In a Lewis signaling game, a sender and receiver must coordinate on a code using only their int
arXiv:2607.00011v1 Announce Type: cross Abstract: Reusable skill libraries are becoming important infrastructure for large language model (LLM) agents, yet existing selection methods often treat skill
arXiv:2606.31182v1 Announce Type: new Abstract: Recent work shows that LLM agents can improve sharp-constant inequalities by searching for extremal constructions, which yield upper bounds. We address
arXiv:2603.26266v3 Announce Type: replace Abstract: Large vision-language models have endowed GUI agents with strong general capabilities for interface understanding and interaction. However, due to i
Harrison Chase suggests using a wiki structure as the foundation for building AI agents with memory systems, citing its simplicity and extensibility as key advantages. This approach would organize age
if you want agents to do work at scale (like security triage, trace analysis, document parsing), they need structured workflows enforced w/ code map reduce is a great example -- exactly the kind of pa
arXiv:2606.31504v1 Announce Type: new Abstract: We present SimpleSearch-VL, an efficient, reliable, and practical framework for multimodal agentic search. Its core idea is to improve the agent's own s
Together AI presented a 2-hour technical deep dive on building inference engines capable of handling trillion-token agentic workloads, covering architecture and optimization strategies for large-scale
Couchbase Inc. is trying to solve one of the hardest problems in enterprise artificial intelligence today: turning brittle, chat-style pilots into production-grade agents capable of remembering, reaso
arXiv:2606.30296v1 Announce Type: new Abstract: Multi-round reflection lets agents built on large language models recover from failures within a single task, but each task remains an isolated episode:
arXiv:2603.05786v2 Announce Type: replace-cross Abstract: As AI agents become widely deployed as online services, users often rely on an agent developer's claim about how safety is enforced, which int
arXiv:2606.28971v1 Announce Type: new Abstract: Real-world image restoration (IR) remains challenging due to complex and coupled degradations. While recent agentic IR frameworks leverage Large Languag
Semantic search alone doesn't cut it. Neither does brute-force grep. Agents need both. Today we're shipping the Retrieval Harness in LlamaParse Index: semantic search, server-side grep, and file-level
arXiv:2606.14397v2 Announce Type: replace Abstract: As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capabi
arXiv:2606.26400v1 Announce Type: new Abstract: Agentic systems are changing how complex operational tasks are coordinated, introducing a new paradigm for connecting heterogeneous data sources and aut
deployment cookbook for langchain agents! Agents are easy to demo locally. The hard part is shipping them inside a real app. We published a deployment cookbook for @LangChain agents: full-stack exampl
arXiv:2601.03785v3 Announce Type: replace Abstract: Long-term human-agent dialogues are organized by topic continuity: adjacent turns often develop the same goal, plan, problem, or event, while relate
arXiv:2606.25978v1 Announce Type: cross Abstract: Multi-agent goal recognition asks an observer to jointly infer which agents act together and what each team is trying to achieve, so the hypothesis sp
arXiv:2604.25345v2 Announce Type: replace Abstract: Agentic AI systems are increasingly being integrated into scientific workflows, yet their behavior under realistic conditions remains insufficiently
arXiv:2606.24428v1 Announce Type: new Abstract: Experience-driven self-evolution is critical for large language model (LLM) agents to improve through open-world interaction. However, existing experien
arXiv:2606.24839v1 Announce Type: new Abstract: Agentic data analysis systems produce rich outputs, including code, numerical results, and verbal diagnostics. This makes them more challenging to evalu
arXiv:2606.24551v1 Announce Type: new Abstract: Computer-use agents can execute software tasks through either graphical interfaces or programmatic command interfaces, but existing evaluations confound
arXiv:2606.24595v1 Announce Type: new Abstract: Long-term memory promises LLM agents that grow more capable across sessions, maintaining an accurate, evolving understanding of the user that interactio
'Most agents don't learn, they just leave traces.' In 12 minutes, @jakebroekhuizen breaks down how to actually close the loop. Surface issues with LangSmith Engine Write memory updates back to Context
arXiv:2509.18371v2 Announce Type: replace-cross Abstract: Multi-agent games in dynamic nonlinear settings are challenging due to the time-varying interactions among the agents and the non-stationarity
pretty sick that i get to work with Jake every day on making continual learning + memory accessible at scale for every single agent one common thread here is...the Trace a large part of Continual Lear
arXiv:2606.24626v1 Announce Type: new Abstract: As autonomous agents tackle increasingly complex multi-step, multi-agent tasks, their execution trajectories have scaled beyond the constraints of even
arXiv:2606.21894v2 Announce Type: replace-cross Abstract: As coding agents are rapidly changing software engineering, a natural question is: what are the core skills needed by future software engineer
arXiv:2606.23743v1 Announce Type: cross Abstract: Modern video diffusion models achieve higher generation quality through scaling, but this also increases inference cost. Although many acceleration me
arXiv:2511.07397v2 Announce Type: replace Abstract: Voice agents face a fundamental tension: the reasoning, retrieval, and tool use that make foundation models capable are iterative and slow, while co
Nous Research announced that their Hermes Agent has achieved a trillion tokens processed daily and reached 200,000 GitHub stars, reflecting significant adoption and performance milestones for their AI
arXiv:2606.22741v1 Announce Type: new Abstract: Can one graph represent every kind of LLM agent's run? A trace records what each step did, never what it relied on, the state it read, and the results i
Hermes Agent has been updated to accept diverse source materials including code repositories, API documentation, manuals, PDFs, and configuration files as input, with the ability to distill and learn
arXiv:2606.12896v2 Announce Type: replace Abstract: While real-world applications of reinforcement learning (RL) are becoming increasingly popular, the security of RL systems deserve more attention an
arXiv:2606.20474v2 Announce Type: replace Abstract: Context-heavy agents place unusual pressure on the key-value (KV) cache: long prefixes are reused across many short turns, while concurrency determi
Hermes Agent, developed by Nous Research, has expanded its computer use capabilities to support Windows and Linux operating systems in addition to its existing macOS functionality. This enhancement, i
arXiv:2606.12384v1 Announce Type: cross Abstract: Recent advances in agentic Reinforcement Learning (RL) have substantially improved the multi-turn tool-use capabilities of large language model agents
arXiv:2606.11520v1 Announce Type: cross Abstract: Training capable OS agents requires data that simultaneously captures structured user intents, multi-turn task delegation, and grounded tool execution