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Very Large-Scale Multi-Agent Simulation in AgentScope

https://arxiv.org/html/2407.17789v2
Last but not least, we provide a web-based interface for conveniently monitoring and managing a large number of agents that might deploy across multiple devices

Predicting Multilingual Classification and Translation Performance of

https://arxiv.org/html/2608.03446v1
To our knowledge, no prior work has inspected the impact of CLA scores on translation performance

Underspecification in Language Modeling Tasks: A Causality-Informed

https://arxiv.org/html/2210.00131v4
However, as we do not have direct access to most LLMs’ pre-training, we can only presume that models encounter unspecified learning tasks during training; this is a particularly plausible scenario for the tokens predicted towards the beginning of a sequence with an autoregressive language modeling objective

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/2402.03300v3
, 2022), indicating it does not only enhance the model’s mathematical abilities but also amplifies general reasoning capabilities

When LLMs Play the Telephone Game: Cultural Attractors as Conceptual

https://arxiv.org/html/2407.04503v4
2 To what extent do multi-turn transmissions affect the evolution of properties

Software Engineering

https://arxiv.org/list/cs.SE/new
Through an analysis of 1,966 open source projects, 1,355 Github issues, 444 CVE reports, and 361 StackOverflow posts, we systematically map the feature support and real-world usage of globs across six common ecosystems that utilize globs

1 Introduction

https://arxiv.org/html/2506.01844v1
VLA models remain in an early stage of development and are not yet as mature or widely adopted as LLMs and VLMs