Search: “agents”

arXiv:2610.15738v1 Announce Type: new Abstract: LLM optimization loops often show the proposer the candidates it has already tried and rejected, but leave it to the proposer to avoid them. We ask whet

arXiv:2610.16176v1 Announce Type: new Abstract: Situated 3D Question Answering requires models to interpret a 3D scene relative to an agent's pose. Existing methods commonly convey that pose through a

arXiv:2610.13998v1 Announce Type: new Abstract: This paper proposes a hybrid reinforcement learning trading framework integrating intraday market data with transformer-based financial sentiment signal

arXiv:2603.29602v2 Announce Type: replace-cross Abstract: Existing multi-turn image editing paradigms are often confined to isolated single-step execution. Due to a lack of context-awareness and close

arXiv:2610.16537v1 Announce Type: new Abstract: In a variety of multi-agent and physical environments, an element's update depends strongly on a few other elements and weakly on many, and both the ide

arXiv:2610.17874v1 Announce Type: new Abstract: Discovering what drives an outcome from data is central to science and often requires deciding what is worth investigating before a specific hypothesis

arXiv:2610.15766v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) and vision--language--action models have provided robots with new capabilities for long-horizon task und

arXiv:2610.14860v1 Announce Type: new Abstract: Advances in large language models (LLMs) have brought the vision of a general-purpose personal assistant closer to reality. Such assistants could provid

arXiv:2610.13439v1 Announce Type: cross Abstract: Collective Autonomous Vehicle (AV) route choices in real-world traffic networks can create externalities for Human-Driven Vehicles (HDVs), as routes t

arXiv:2610.11354v1 Announce Type: new Abstract: Rule-governed contextual decision tasks require models to apply specified rules to case-specific context and evidence. Written rules can leave gaps in d

arXiv:2610.11555v1 Announce Type: new Abstract: We study personalized federated linear stochastic approximation (LSA), a framework which notably encompass personalized temporal difference learning. In

arXiv:2610.10091v1 Announce Type: new Abstract: Knowledge-intensive tasks require answering many questions by reasoning about a shared corpus of artifacts (e.g., court cases, or scientific literature)

arXiv:2610.07249v1 Announce Type: new Abstract: How can an AI agent determine what rules to follow? One rule permits an action. Another imposes a condition, exception, or conflicting obligation. Deter

arXiv:2610.07817v1 Announce Type: cross Abstract: Organizations automating operational processes need more than a correct outcome: they need to predict how a process will run, know which one actually

arXiv:2610.08647v1 Announce Type: new Abstract: LLM-based agents solve complex multi-step tasks, but sequential execution incurs substantial latency. In principle, parallelizing work across multiple a

arXiv:2610.05334v1 Announce Type: cross Abstract: Frameworks that use large language models for scientific discovery typically rely on a fixed, human-designed algorithm that decides what the model see

arXiv:2610.04134v1 Announce Type: cross Abstract: The discovery and development of advanced materials is a challenging process constrained by the high time and monetary costs of synthesis, processing,

arXiv:2610.04544v1 Announce Type: cross Abstract: Many groups now delegate label ontology and metadata harmonization to agentic LLM pipelines. We built one and audited it. Our aggregate scores looked

arXiv:2610.04089v1 Announce Type: new Abstract: Coding agents operating under ambiguous instructions or user prompts must decide whether to ask clarifying questions or attempt a solution directly. Whi

arXiv:2603.23478v2 Announce Type: replace Abstract: Affordance segmentation in 3D scenes requires an agent to ground implicit natural-language instructions into precise masks of fine-grained interacti

arXiv:2610.06293v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in video understanding, yet their reliance on retrospective summarizatio

arXiv:2610.03709v1 Announce Type: cross Abstract: We study the minimization of sums of smooth strongly convex functions over undirected graphs, with each function held by one agent and communication r

arXiv:2610.02334v1 Announce Type: cross Abstract: Mobile embodied AI networks (MEAN) enable embodied agents to perceive, reason, communicate, and act in wireless environments. In such networks, agent

arXiv:2610.00668v1 Announce Type: new Abstract: Making decisions despite conflicting norms and incomplete or unreliable information is a fundamental challenge for autonomous systems. We introduce a si

arXiv:2610.02150v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly rely on persistent external sources to solve sequences of knowledge-intensive tasks. Existing methods i

arXiv:2609.39450v1 Announce Type: cross Abstract: LLM-based agents extend their capabilities through third-party skills that provide task-specific instructions, scripts, and tool-use procedures. Howev

arXiv:2609.40097v1 Announce Type: new Abstract: Existing auto-research benchmarks often entangle multiple sources of improvement, including training frameworks, hyperparameters, compute budgets, and d

arXiv:2609.39933v1 Announce Type: new Abstract: When designing machine learning models, desirable properties are often in tension: improving one behavior can impair another, so task progress can depen

arXiv:2609.39727v1 Announce Type: new Abstract: Cooperative language-model agents must coordinate over long horizons and adapt to changing environments and to partners with unfamiliar conventions, yet

arXiv:2609.38414v1 Announce Type: new Abstract: Synthetic data generation is dominated by the fit-then-sample paradigm: a generative model is trained on a private dataset and then sampled from. Despit

arXiv:2609.37853v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as social agents, yet credible human-like interaction requires more than fluent responses or pers

arXiv:2609.35813v1 Announce Type: cross Abstract: Compact surrogates could reduce the cost of simulating large language model societies, but must reproduce collective behavior. We compare individual p

arXiv:2609.36683v1 Announce Type: cross Abstract: Molecular optimization is inherently iterative: a candidate is proposed, evaluated against several objectives, and revised while preserving a relation

arXiv:2609.37082v1 Announce Type: new Abstract: Long-horizon information-seeking agents often accumulate noisy or misleading context, causing early mistakes to persist and making recovery increasingly

arXiv:2609.37464v1 Announce Type: new Abstract: The honeybee waggle dance motivates a communication mechanism in which one agent's motion guides the subsequent work of other agents. For aerial robots,

arXiv:2609.35038v1 Announce Type: cross Abstract: Federated Bayesian Optimization (FBO) enables distributed agents to collaboratively optimize expensive black-box objectives without sharing raw local

arXiv:2609.33986v1 Announce Type: new Abstract: In some multi-agent systems, the quantity to be optimized is not an externally specified reward but the information acquired about unknown properties of

arXiv:2601.04692v2 Announce Type: replace-cross Abstract: In this work, we examine hateful memes from three complementary angles - how to detect them, how to explain their content and how to intervene

arXiv:2609.33885v1 Announce Type: cross Abstract: Large language model (LLM) agentic systems increasingly rely on models communicating with one another, yet existing uncertainty and multi-agent method

arXiv:2609.30337v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) mitigates knowledge obsolescence and factual hallucination in large language models by introducing external context

arXiv:2609.29621v1 Announce Type: new Abstract: Computer-aided design is central to industrial manufacturing, and much of a designer's daily work consists of editing existing models from multimodal re

arXiv:2609.30147v1 Announce Type: new Abstract: Large Language Models (LLMs) typically exhibit a performance profile where reliability degrades as task complexity increases. We address the challenge o

arXiv:2609.27336v1 Announce Type: new Abstract: Automated red teaming often replays a fixed set of prompts, which measures known risks but cannot learn from failures found during testing. We present C

arXiv:2609.28296v1 Announce Type: new Abstract: While Vision-and-Language Navigation (VLN) has demonstrated remarkable success, the prevailing single-turn paradigm exposes a fundamental vulnerability:

arXiv:2609.25176v1 Announce Type: cross Abstract: Real-time voice assistants must reason over evolving requests, execute actions, and follow conversational rules. Qwen-Audio-3.1-Realtime brings these

arXiv:2609.22408v1 Announce Type: new Abstract: AI systems are becoming participants in the evaluation and use of scientific research. They encounter citation counts, download statistics and lists of