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30 result(s) for “if com pay site www inter com to site wt adding use com site plan standard site:arxiv.org” of 38 matched · 717 ms
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This study synthesizes adjacent research and introduces Enactive AI as a conceptual framework for enterprise and industry reasoning, site-level decision support, and execution feedback
from the top result below
https://arxiv.org/list/cs.AI/new
However, reliable tool-use planning remains challenging due to the limitations of implicit reasoning and the evolving nature of real-world execution environments
https://arxiv.org/list/cs.CR/new
CyberBridge supports three primary use cases; (1) job recom- mendation, providing students with vacancies and closely match- ing professional roles based on their completed curriculum, (2) market analysis, enabling educators and curriculum developers to analyze which roles ar
https://arxiv.org/html/2511.06451v1
From selective scan to a risk-neutral operator layer
https://arxiv.org/list/math/new
It is natural to expect that if many pairs $(x_i,x_j)$ lie at distance close to $1$ from each other, then some clustering phenomenon must occur, implying that a significant number of these pairs are also very close to each other
https://arxiv.org/list/cs.CV/new
Ultimately, this work recasts world modeling into an agent-centric paradigm, establishing a roadmap for building world proxies that empower agents to plan better, learn faster, and evolve continually
https://arxiv.org/list/cs.NA/new
The first objective is successfully addressed, as it becomes possible to use the apparatus of classical dynamical systems theory for a qualitative analysis of model dynamics based on the systems of ordinary differential equations
https://arxiv.org/html/2609.03159v1
Whalen (NASA Postdoctoral Fellow (ORAU); NASA/GSFC)
1 Executive Summary
We must create superb X-ray images to understand the detailed physical processes behind some of the most powerful astronomical objects
https://arxiv.org/html/2407.17789v2
com/modelscope/agentscope/tree/main/examples/paper_large_scale_simulation to inspire further research and development in large-scale multi-agent simulations
https://arxiv.org/html/2508.20108v2
How can we address distribution shifts in stock price data to improve stock price prediction accuracy
https://arxiv.org/html/2608.01366v2
com Affiliation: Department of Design and Manufacturing, Indian Institute of Science (IISc) , Bangalore , India , Srinidhi Ranjini Girish email: srinidhirgirish@gmail
https://arxiv.org/list/cs.CL/new
Accepted to C3NLP @ ACL 2026
Subjects: Computation and Language (cs
https://arxiv.org/list/eess.SP/new
The three channels are integrated at a common input port via a T-junction, with connecting transmission line stubs dimensioned to enforce high inter-channel isolation
https://arxiv.org/list/cs.IT/new
Finally, we present simulation results on standard image datasets over additive white Gaussian noise to demonstrate the effectiveness of CoCo, which reduces total latency relative to rate-only adaptive DeepJSCC or denoising-only, thereby ensuring the demands of each individual user are met
https://arxiv.org/html/2608.03529v1
There, the annotation space is known in advance, and measuring Inter-Rater Reliability (IRR) on the resulting codified annotations Cohen (1960); Scott (1955); Fleiss (1971) is standard procedure to enable insights into their reliability and quality Artstein and Poesio (2008); Hanegraaf et al
https://arxiv.org/html/2608.03014v1
In particular, multi-user MIMO (MU-MIMO) enables a base station (BS) to serve multiple users simultaneously on the same time–frequency resources, but doing so reliably requires precoding and receiver combining that both provide array gain and actively control inter-user interference [15]
https://arxiv.org/list/cs.DC/new
DC)
The artificial intelligence industry has constructed a USD 300 billion centralized data center infrastructure to serve a workload, large language model inference, that does not architecturally require centralization
https://arxiv.org/list/cs.SE/new
Finally, we plan to develop and validate an automated tool that supports agentic AI solutions to monitor, govern, and repay AI-CPS TD
https://arxiv.org/abs/2608.02122
Although Dynamic Voltage and Frequency Scaling (DVFS) provides an effective mechanism for reducing GPU energy consumption, existing runtime systems decouple DVFS from task placement and inter-GPU communication, focus on single-GPU execution, or cannot adapt frequency to task granularity and runtime contention in multi
https://arxiv.org/html/2608.00426v1
1 Introduction
Large language model (LLM)-based multi-agent systems (MAS) increasingly plan, communicate, use tools, and coordinate over extended horizons [40, 24, 14, 36, 10, 23]
https://arxiv.org/html/2410.05779v3
com Both Chao Huang and Yanhua Yu are corresponding authors
https://arxiv.org/html/2407.14571v1
As the raw multi-variate time series data generated by DataStorm-FE can be both large and complicated to parse, and users may be domain experts and not necessarily data scientists, it is critical that data exploration and analysis be accessible:
•
Consequently, DataStorm-EM provides intuitive and easy-to-use
https://arxiv.org/html/2608.03655v1
Abstract
Abstractive summarization models remain vulnerable to factual inconsistency, redundancy, and weak length control
https://arxiv.org/html/2608.00648v1
In these scenarios, heterogeneous agents and complex task constraints require efficient inter-agent coordination to improve execution efficiency
https://arxiv.org/html/2608.03067v1
4 Statistical analysis
5 Results
Inter-rater reliability for human ratings
https://arxiv.org/html/2407.19144v1/
These malfunctions decrease the team’s ability to accomplish assigned task(s), especially if they occur after the learning algorithms have already converged onto a collaborative strategy
https://arxiv.org/html/2608.00458v1
To address these challenges, we propose BANDMAS, a multi-agent collaboration framework that models inter-agent communications as task-oriented traffic, which enables efficient transmission via causality-inspired replay valuation
https://arxiv.org/html/2608.03233v1
Our findings highlight a concerning issue of domain homogeneity, a prevalent tendency for LLMs to generate analogies from a narrow set of target domains, limiting both inter-query and intra-model diversity
https://arxiv.org/html/2407.08254v2
We prove that our approach guarantees that the average joint action of all agents converges to a Nash equilibrium (NE) if every agent applies the same RM procedure in any cooperative game with a submodular utility function
https://arxiv.org/html/2608.03532v1
If safety guardrails fail to generalise to Swahili, the situation is almost certainly worse for less represented languages
https://arxiv.org/html/2608.03044v1
edu
Abstract
Large language models are increasingly used to simulate human opinions, but prior work reports conflicting results: some studies find promising alignment with human survey data, while others find persona collapse and weak demographic sensitivity