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org perpetual non-exclusive license arXiv:2407
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Very Large-Scale Multi-Agent Simulation in AgentScope

https://arxiv.org/html/2407.17789v2
org perpetual non-exclusive license arXiv:2407

AgentScope 1.0: A Developer-Centric Framework for Building Agentic

https://arxiv.org/html/2508.16279v1
org perpetual non-exclusive license arXiv:2508

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https://arxiv.org/html/2510.03215v2
Report GitHub Issue Description: arXiv is now an independent nonprofit

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
Extending the pattern-generation paradigm of Neural Cellular Automata (NCA), it navigates the degenerate Boolean search space to satisfy a computational task, rather than regenerating a fixed target state

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https://arxiv.org/html/2607.22511v2
The source code, formal library, and run records are available at https://github

Evaluating OpenAI’s Privacy Filter: Cross-Lingual, Cross-Domain PII

https://arxiv.org/html/2608.02616v1
com Abstract We present the first independent, systematic evaluation of OpenAI’s Privacy Filter (OPF), a 1

Underspecification in Language Modeling Tasks: A Causality-Informed

https://arxiv.org/html/2210.00131v4
Code and open-source demos available at https://github

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https://arxiv.org/html/2608.03210v1
org perpetual non-exclusive license arXiv:2608

Don’t Let Me Ask for It: LLMs Show Deficiencies in Active Multi-Turn

https://arxiv.org/html/2608.03388v1
The Game Master hides a target Python function ff and interacts with the LLM through a black-box protocol

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https://arxiv.org/html/2608.02613v1
Then it must make an even harder judgment: even if the answer is known, is it actually appropriate to tell Bob

1 Introduction

https://arxiv.org/html/2407.02518v2
com Abstract Large language models (LLMs) for code are typically trained to align with natural language instructions to closely follow their intentions and requirements

1 Introduction

https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands Slack http://bit

1 Introduction

https://arxiv.org/html/2402.03300v3
com/deepseek-ai/DeepSeek-Math Abstract Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature

Speculative Correction: Draft-then-Refine Decoding for Diffusion

https://arxiv.org/html/2608.02625v1
2 Initialization and Target Distribution 3

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https://arxiv.org/html/2608.03077v1
org perpetual non-exclusive license arXiv:2608

When LLMs Play the Telephone Game: Cultural Attractors as Conceptual

https://arxiv.org/html/2407.04503v4
Tools to Evaluate LLMs in Multi-turn Settings 1 Introduction 2 Related work Biases in LLMs outputs Transmission chains featuring artificial agents Iterated Learning Evaluation of LLMs in social contexts 3 Methods 3.1 Experimental

Computer Vision and Pattern Recognition

https://arxiv.org/list/cs.CV/new
Project Page: this https URL [2] arXiv:2608

1 Introduction

https://arxiv.org/html/2403.08299v1
The landscape of software development has witnessed a paradigm shift with the advent of AI-powered assistants, exemplified by GitHub Copilot

Computation and Language

https://arxiv.org/list/cs.CL/new
Skip to main content System maintenance August 4th and 5th Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computation and Language New submissions Cross-lists Replacements See recent

1 Introduction

https://arxiv.org/html/2410.17799v2
Audio samples of dialogues generated by OmniFlatten can be found at this web site 111https://omniflatten

Asking Questions the Right Way: A Multi-Agent Conversational System for

https://arxiv.org/html/2608.01366v2
org perpetual non-exclusive license arXiv:2608

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https://arxiv.org/html/2608.02620v1
com/index/retiring-gpt-4o-and-older-models

SFT Conflicts, RL Coexists: A Theoretical and Empirical Analysis of

https://arxiv.org/html/2608.03573v1
org perpetual non-exclusive license arXiv:2608

1 Introduction

https://arxiv.org/html/2407.01093v1
com/ and GPT-4 OpenAI (2023) could be used to simulate human intelligence

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https://arxiv.org/html/2608.03154v1
org perpetual non-exclusive license arXiv:2608

Cross-Lingual Bias in Large Language Models: A Comparative Analysis of

https://arxiv.org/html/2608.03532v1
org perpetual non-exclusive license arXiv:2608

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https://arxiv.org/html/2608.00458v1
org perpetual non-exclusive license arXiv:2608

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https://arxiv.org/html/2608.02975v1
com Abstract Large language models (LLMs) have demonstrated impressive performance in MQM-based translation quality (TQ) evaluation, and recent advances in large reasoning models (LRMs) promise even greater improvements

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https://arxiv.org/html/2609.02852v1
Report GitHub Issue Description: arXiv is now an independent nonprofit

Report GitHub Issue

https://arxiv.org/html/2608.03275v1
org perpetual non-exclusive license arXiv:2608