Search: “agents”

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

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

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

arXiv:2605.04202v1 Announce Type: new Abstract: Strategic classification studies the problem where self-interested individuals or agents manipulate their response to obtain favorable decision outcomes

arXiv:2604.01707v2 Announce Type: replace Abstract: Memory emerges as the core module in the large language model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game pla

arXiv:2604.27539v1 Announce Type: cross Abstract: As information ecosystems grow more heterogeneous, both humans and artificial agents increasingly face a simple yet unresolved question: when seeking

arXiv:2604.25325v1 Announce Type: cross Abstract: Modern Text-to-SQL systems generate multiple candidate SQL queries and rank them to judge a final prediction. However, existing methods face two limit

arXiv:2603.01421v2 Announce Type: replace-cross Abstract: Automated scientific discovery with large language models is transforming the research lifecycle from ideation to experimentation, yet existin

arXiv:2604.23072v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly tasked with complex real-world analysis (e.g., in financial forecasting, scientific discovery), yet t

arXiv:2604.22973v1 Announce Type: new Abstract: Predicting future trajectories of surrounding traffic agents is critical for safe autonomous navigation and collision avoidance. Despite all advances in

arXiv:2602.17315v2 Announce Type: replace-cross Abstract: We introduce Flickering Multi-Armed Bandits (FMAB) to model sequential decision-making in environments with changing action availability, wher

arXiv:2604.23716v1 Announce Type: new Abstract: Information-theoretic (IT) measures are ubiquitous in artificial intelligence: entropy drives decision-tree splits and uncertainty quantification, cross

arXiv:2604.22760v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly operate as autonomous agents that reason over external APIs to perform complex tasks. However, their reliabi

arXiv:2604.24326v1 Announce Type: cross Abstract: The decentralization of modern energy systems is transforming consumers into prosumers who continuously exchange data with aggregators, peers, and mar

arXiv:2604.22110v1 Announce Type: new Abstract: Standard supervised classification trains models to imitate the exact labels provided by a perfect oracle. This imitation happens in a single pass, rest

arXiv:2604.21993v1 Announce Type: new Abstract: We study the detection of transient liquidity erosion ('crumbling quotes') in electronic limit order books, where observable quote deterioration may ref

arXiv:2604.20586v1 Announce Type: new Abstract: The ongoing shift towards decentralization of the electric energy sector, driven by the growing electrification across end-use sectors, and widespread a

arXiv:2604.19945v1 Announce Type: new Abstract: In this paper, we investigate the problem of how to effectively master tool-use to solve complex visual reasoning tasks for Multimodal Large Language Mo

arXiv:2603.22650v2 Announce Type: replace Abstract: Active mapping aims to determine how an agent should move to efficiently reconstruct unknown environments. Most existing approaches rely on greedy n

arXiv:2604.16871v1 Announce Type: cross Abstract: Neuro-symbolic Reinforcement Learning (NeSy-RL) combines symbolic reasoning with gradient-based optimization to achieve interpretable and generalizabl

arXiv:2604.17106v1 Announce Type: new Abstract: Motivated by the challenge presented by non-Markovian objectives in reinforcement learning (RL), we present a novel framework to track and represent the

arXiv:2603.19044v2 Announce Type: replace Abstract: Scientific ideation aims to propose novel solutions within a given scientific context. Existing LLM-based agentic approaches emulate human research

arXiv:2510.15253v3 Announce Type: replace Abstract: Document understanding is critical for applications from financial analysis to scientific discovery. Current approaches, whether OCR-based pipelines

arXiv:2507.01936v3 Announce Type: replace Abstract: Large language models (LLMs) are excellent at maintaining high-level, convincing dialogue, but it remains unclear whether their persuasive success r

arXiv:2604.18331v1 Announce Type: new Abstract: The physicality of exercise makes the role of athletic trainers unique. Their physical presence allows them to guide a student through a motion, demonst

arXiv:2604.15590v1 Announce Type: cross Abstract: Reinforcement learning is a promising approach to autonomous and adaptive security management in networked systems. However, current reinforcement lea

arXiv:2512.17052v4 Announce Type: replace Abstract: Function calling agents powered by Large Language Models (LLMs) select external tools to automate complex tasks. On-device agents typically use a re

arXiv:2604.14518v2 Announce Type: replace Abstract: We present Mind DeepResearch (MindDR), an efficient multi-agent deep research framework that achieves leading performance with only ~30B-parameter m

arXiv:2411.10446v3 Announce Type: replace-cross Abstract: Recent progress in vision-language models (VLMs) has opened new possibilities for robot task planning, but these models often produce incorrec

arXiv:2601.07338v2 Announce Type: replace Abstract: Large Language Models (LLMs) have significantly advanced Machine Translation (MT), applying them to linguistically complex domains-such as Social Ne

arXiv:2604.14240v1 Announce Type: cross Abstract: The simulation of complex systems increasingly relies on sophisticated but fundamentally opaque computational black-box simulators. Surrogate models p

arXiv:2604.14243v1 Announce Type: new Abstract: Real-world decision-making systems operate in environments where state transitions depend not only on the agent's actions, but also on extbf{exogenous f

arXiv:2506.08080v2 Announce Type: replace-cross Abstract: Particle physics theories, such as those which explain neutrino flavor mixing, arise from a vast landscape of model-building possibilities. A

arXiv:2603.29088v2 Announce Type: replace-cross Abstract: Recent progress in large language models (LLMs) has substantially advanced automatic code generation and formal theorem proving, yet software

arXiv:2504.15801v2 Announce Type: replace Abstract: As large language models (LLMs) and LLM-based agents increasingly interact with humans in decision-making contexts, understanding the trust dynamics