Search: “agents”

arXiv:2605.27622v1 Announce Type: new Abstract: To safely interact with humans, AI agents must both know our norms and consider them during planning. However, such norm-guided planning has been less e

arXiv:2605.28773v1 Announce Type: cross Abstract: Existing memory-augmented LLM agents often treat memory as a static repository with pre-defined representations and fixed retrieval pipelines, which i

arXiv:2605.26500v1 Announce Type: new Abstract: Vision-language navigation (VLN) requires an agent to traverse complex 3D environments based on natural language instructions, necessitating a thorough

Nous Research introduced a one-click installation feature in Hermes Agent that allows users to easily install Model Context Protocol (MCP) servers from a curated catalog of Nous-approved integrations.

aside_block <ListValue: [StructValue([('title', 'Summary of today’s news'), ('body', <wagtail.rich_text.RichText object at 0x7fb0f516f910>), ('btn_text', ''), ('href', ''), ('image', None)])]> AI-powe

arXiv:2605.26239v1 Announce Type: new Abstract: In this work, we study Cooperative Spatial Intelligence, the ability of decentralized embodied agents to coordinate effectively under dynamic environmen

arXiv:2605.27276v1 Announce Type: new Abstract: Humans are the bottleneck in building and improving AI. Both the models and the agents that wrap them are written, tuned, and corrected by people. The l

arXiv:2605.25011v1 Announce Type: new Abstract: We consider the challenge of developing agents that efficiently interact with high-dimensional, evolving environments, towards a view of practical reinf

arXiv:2605.26029v1 Announce Type: new Abstract: We introduce CausaLab, a scalable environment for evaluating interactive causal discovery by LLM agents. Unlike prior evaluations, CausaLab evaluates bo

arXiv:2508.12538v2 Announce Type: replace-cross Abstract: The Model Context Protocol (MCP) has emerged as a universal standard that enables AI agents to seamlessly connect with external tools, signifi

Microsoft Copilot Cowork Exfiltrates Files The biggest challenge in designing agentic systems continues to be preventing them from enabling attackers to exfiltrate data. In this case Microsoft Copilot

arXiv:2605.05795v2 Announce Type: replace Abstract: Decomposing complex tasks into a sequence of simpler subtasks can improve learning efficiency for an autonomous agent. Reinforcement learning (RL) c

arXiv:2605.21740v2 Announce Type: replace Abstract: LLM agents have incredible potential for scientific discovery applications. However, the performance of LLM agents on real-world, small molecule dru

Trace data is literally worth its weight in gold these days, if you know what to do with it! As has been established, creating effective agents requires shipping early, observing behavior, and iterati

arXiv:2605.23426v1 Announce Type: cross Abstract: Socially fluent agentic AI can now participate in online interaction in ways that resemble ordinary human conversation, potentially weakening people's

// Adapt the Interface, Not the Model // I am fascinated by the results across my cheap-model-plus-good-harness builds. This new paper also shows good signs of the code-as-agent-harness thesis. The id

“It is built in Rust and leverages the Apache DataFusion query engine” Any new database these days We built SmithDB: the database purpose built for agent observability workloads that now powers many p

arXiv:2605.22816v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) requires an agent to ground language instructions to its own movement within a visual environment. While state-of

arXiv:2605.21958v1 Announce Type: new Abstract: When a multi-module LLM agent fails, the module most responsible for the failure is not necessarily the best place to intervene. We demonstrate this Dia

arXiv:2605.20867v1 Announce Type: cross Abstract: Multimodal sarcasm detection requires reasoning over cross-modal incongruities between literal expression and intended meaning, yet the specific analy

arXiv:2605.19156v1 Announce Type: new Abstract: Recent auto-research systems can produce complete papers, but feasibility is not the same as quality, and the field still lacks a systematic study of ho

very belated but in retrospect i think @sama's mythical 'build a business that gets better when models get better' is basically what I called Agent Labs here. seeing a very direct correlation with mod

Very cool - Grok Build is clearly getting better by the day. Two nights ago I ran an overnight build and it failed. Last night, success. I really like the multi-agent orchestration behavior, it does a

arXiv:2212.02098v5 Announce Type: replace Abstract: Inspired by the cognitive science theory of the explicit human memory systems, we have modeled an agent with short-term, episodic, and semantic memo

arXiv:2511.11654v2 Announce Type: replace-cross Abstract: Rapid urbanization in cities like Bangalore has led to severe traffic congestion, making efficient Traffic Signal Control (TSC) essential. Mul

I've been thinking a lot about the two different groups of evals you need in general agents/agents which handle broad tasks: 1. Benchmark evals - this is a suite of up to 100 eval cases which test the

arXiv:2605.16857v1 Announce Type: new Abstract: Experience-driven learning has emerged as a promising paradigm for enabling agents to improve from interaction trajectories by accumulating and reusing

arXiv:2605.17044v1 Announce Type: new Abstract: Large language models (LLMs) increasingly serve as interactive social agents, yet their ability to maintain coherent and authentic persona-level role-pl

arXiv:2605.16297v1 Announce Type: cross Abstract: Which tasks inside an enterprise workflow can a large-language-model agent reliably handle, and under what conditions? Most business process modeling

arXiv:2605.15224v1 Announce Type: new Abstract: Large language model-based agents make mistakes, yet critique can often guide the same model toward correct behavior. However, when critique is removed,

arXiv:2603.23433v2 Announce Type: replace Abstract: AI agents are becoming active decision-makers on the Internet. As they make decisions in the same environments as humans, the environments themselve

arXiv:2605.16191v1 Announce Type: new Abstract: We present a case study for how AI coding systems can be used to generate novel scientific hypotheses. We combine a generic coding agent (Google's AntiG

arXiv:2605.14350v1 Announce Type: new Abstract: Multi-task reinforcement learning (MTRL) aims to train a single agent to efficiently optimize performance across multiple tasks simultaneously. However,

arXiv:2605.13438v1 Announce Type: new Abstract: Existing agent memory remains predominantly reactive and retrieval-based, lacking the capacity to autonomously organize experience into persistent cogni

arXiv:2605.13534v1 Announce Type: new Abstract: Deep search agents have proven effective in enhancing LLMs by retrieving external knowledge during multi-step reasoning. However, existing methods often

SmithDB is a feat of engineering. A new database for a new data shape Huge work from @ankush_gola11 and team We built SmithDB: the database purpose built for agent observability workloads that now pow

arXiv:2605.11463v1 Announce Type: new Abstract: Learning and representing the subjectivities of agents has become a challenging but crucial problem in the trajectory prediction task. Such subjectiviti

arXiv:2602.01682v2 Announce Type: replace Abstract: We study online inverse linear optimization, also known as contextual recommendation, where a learner sequentially infers an agent's hidden objectiv

arXiv:2605.11688v1 Announce Type: new Abstract: Zero-shot coordination (ZSC) aims to enable agents to cooperate with independently trained partners without prior interaction, a key requirement for rea

arXiv:2605.10585v1 Announce Type: new Abstract: Multi-objective reinforcement learning (MORL) allows a user to express preference over outcomes in terms of the relative importance of the objectives, b

love seeing my discord stay in sync with Hermes Kanban..everything here was done in plain English. I just asked my coordinator to check if we have an update and the system understood the intent, route

arXiv:2508.07722v2 Announce Type: replace Abstract: Traditional Reinforcement Learning (RL) frameworks generally assume that the agent perceives the state of the underlying Markov process instantaneou

arXiv:2605.08450v1 Announce Type: cross Abstract: Imitation learning is effective for training agents when expert demonstrations are available, but collecting demonstrations for every complex task in

arXiv:2605.06976v1 Announce Type: cross Abstract: Many ranking and agent trace datasets are recorded as linear orders even though their latent structure is only partially ordered. This is especially c

arXiv:2605.07910v1 Announce Type: new Abstract: Reconstructing dynamic scenes from Vehicle-to-Infrastructure Cooperative Autonomous Driving (VICAD) data is fundamentally complicated by temporal asynch

Your AI coding agent, the one you use to write code, needs to reduce your maintenance costs. Not by a little bit, either. You write code twice as quick now? Better hope you’ve halved your maintenance