As we all know, writing scientific papers together with our beloved
colleagues is a truly remarkable experience (partially): endless discussions
about the same useless paragraph over and over again, followed by long days and
long nights -- both at the same time. What a wonderful ride it is! What a
beautiful life we have. But wait, there's one tiny little problem that utterly
shatters the peace, turning even renowned scientists into bloodthirsty
monsters: author order. The reason is that, contrary to widespread opinion,
it's not the font size that matters, but the way things are ordered. Of course,
this is a fairly well-known fact among scientists all across the planet (and
beyond) and explains clearly why we regularly have to read about yet another
escalated paper submission in local police reports.
In this paper, we take an important step backwards to tackle this issue by
solving the so-called author ordering problem (AOP) once and for all.
Specifically, we propose AMOR, a system that replaces silly constructs like
co-first or co-middle authorship with a simple yet easy probabilistic approach
based on random shuffling of the author list at viewing time. In addition to
AOP, we also solve the ambiguous author ordering citation problem} (AAOCP) on
the fly. Stop author violence, be human.

在本文中，我们通过解决所谓的作者排序问题（AOP）提出了 AMOR，一种将愚蠢的构造（如共同第一作者或共同中间作者）替换为简单且易于概率化的方法，该方法基于在查看时对作者列表进行随机洗牌。除了 AOP 之外，我们还解决了即时的歧义作者排序引文问题（AAOCP）。停止作者暴力，做个人。

AMOR：歧义作者顺序

AMOR: Ambiguous Authorship Order

The notable success of large language models (LLMs) has sparked an upsurge in
building language agents to complete various complex tasks. We present AMOR, an
agent framework based on open-source LLMs, which reasons with external
knowledge bases and adapts to specific domains through human supervision to the
reasoning process. AMOR builds reasoning logic over a finite state machine
(FSM) that solves problems through autonomous executions and transitions over
disentangled modules. This allows humans to provide direct feedback to the
individual modules, and thus naturally forms process supervision. Based on this
reasoning and feedback framework, we develop AMOR through two-stage
fine-tuning: warm-up and adaptation. The former fine-tunes the LLM with
examples automatically constructed from various public datasets and enables
AMOR to generalize across different knowledge environments, while the latter
tailors AMOR to specific domains using process feedback. Extensive experiments
across multiple domains demonstrate the advantage of AMOR to strong baselines,
thanks to its FSM-based reasoning and process feedback mechanism.

基于开源语言模型的 AMOR 代理框架通过外部知识库进行推理，并通过人类监督适应于具体领域的推理过程，利用有限状态机构建推理逻辑以实现问题解决和模块之间的过渡，并通过两阶段微调加强其泛化能力和适应性。通过多个领域的广泛实验，证明了基于有限状态机推理和过程反馈机制的 AMOR 相对于强基线的优势。