Large Language Models (LLMs) have shown potential in reasoning over
structured environments, e.g., knowledge graph and table. Such tasks typically
require multi-hop reasoning, i.e., match natural language utterance with
instances in the environment. Previous methods leverage LLMs to incrementally
build a reasoning path, where the LLMs either invoke tools or pick up schemas
by step-by-step interacting with the environment. We propose
Reasoning-Path-Editing (Readi), a novel framework where LLMs can efficiently
and faithfully reason over structured environments. In Readi, LLMs initially
generate a reasoning path given a query, and edit the path only when necessary.
We instantiate the path on structured environments and provide feedback to edit
the path if anything goes wrong. Experimental results on three KGQA datasets
and two TableQA datasets show the effectiveness of Readi, significantly
surpassing all LLM-based methods (by 9.1% on WebQSP, 12.4% on MQA-3H and 10.9%
on WTQ), comparable with state-of-the-art fine-tuned methods (67% on CWQ and
74.7% on WebQSP) and substantially boosting the vanilla LLMs (by 14.9% on CWQ).
Our code will be available upon publication.

大型语言模型在理解结构化环境中的推理任务中显示出潜力。我们提出了一种名为 Readi 的新框架，使 LLMs 能够高效而准确地在结构化环境中进行推理，并超越了其他基于 LLM 的方法。

当必要时与我联系：LLMs 可以在结构化环境中高效和忠实地推理

Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over  Structured Environments

In recent years, Deep Reinforcement Learning (DRL) has emerged as a promising
method for robot collision avoidance. However, such DRL models often come with
limitations, such as adapting effectively to structured environments containing
various pedestrians. In order to solve this difficulty, previous research has
attempted a few approaches, including training an end-to-end solution by
integrating a waypoint planner with DRL and developing a multimodal solution to
mitigate the drawbacks of the DRL model. However, these approaches have
encountered several issues, including slow training times, scalability
challenges, and poor coordination among different models. To address these
challenges, this paper introduces a novel approach called evolutionary
curriculum training to tackle these challenges. The primary goal of
evolutionary curriculum training is to evaluate the collision avoidance model's
competency in various scenarios and create curricula to enhance its
insufficient skills. The paper introduces an innovative evaluation technique to
assess the DRL model's performance in navigating structured maps and avoiding
dynamic obstacles. Additionally, an evolutionary training environment generates
all the curriculum to improve the DRL model's inadequate skills tested in the
previous evaluation. We benchmark the performance of our model across five
structured environments to validate the hypothesis that this evolutionary
training environment leads to a higher success rate and a lower average number
of collisions. Further details and results at our project website.

本文介绍了一种名为进化课程训练的新方法，以应对机器人避碰中经常出现的一些挑战，包括在结构化环境中适应多种行人，本方法能够提高深度强化学习模型在此类复杂场景中的成功率和降低平均碰撞率。

基于演化课程训练的 DRL 导航系统训练

Evolutionary Curriculum Training for DRL-Based Navigation Systems

The development of approaches for trajectory prediction requires metrics to
validate and compare their performance. Currently established metrics are based
on Euclidean distance, which means that errors are weighted equally in all
directions. Euclidean metrics are insufficient for structured environments like
roads, since they do not properly capture the agent's intent relative to the
underlying lane. In order to provide a reasonable assessment of trajectory
prediction approaches with regard to the downstream planning task, we propose a
new metric that is lane distance-based: Lane Miss Rate (LMR). For the
calculation of LMR, the ground-truth and predicted endpoints are assigned to
lane segments, more precisely their centerlines. Measured by the distance along
the lane segments, predictions that are within a certain threshold distance to
the ground-truth count as hits, otherwise they count as misses. LMR is then
defined as the ratio of sequences that yield a miss. Our results on three
state-of-the-art trajectory prediction models show that LMR preserves the order
of Euclidean distance-based metrics. In contrast to the Euclidean Miss Rate,
qualitative results show that LMR yields misses for sequences where predictions
are located on wrong lanes. Hits on the other hand result for sequences where
predictions are located on the correct lane. This means that LMR implicitly
weights Euclidean error relative to the lane and goes into the direction of
capturing intents of traffic agents. The source code of LMR for Argoverse 1 is
publicly available.

该研究提出了一种基于车道距离的度量标准，即车道错过率，旨在评估轨迹预测方法对下游规划任务的性能，结果表明该方法能够捕捉交通参与者的意图和更好地预测结构化环境中的轨迹。

基于车道距离的轨迹预测度量（LMR）

LMR: Lane Distance-Based Metric for Trajectory Prediction

Many biochemical applications such as molecular property prediction require
models to generalize beyond their training domains (environments). Moreover,
natural environments in these tasks are structured, defined by complex
descriptors such as molecular scaffolds or protein families. Therefore, most
environments are either never seen during training, or contain only a single
training example. To address these challenges, we propose a new regret
minimization (RGM) algorithm and its extension for structured environments. RGM
builds from invariant risk minimization (IRM) by recasting simultaneous
optimality condition in terms of predictive regret, finding a representation
that enables the predictor to compete against an oracle with hindsight access
to held-out environments. The structured extension adaptively highlights
variation due to complex environments via specialized domain perturbations. We
evaluate our method on multiple applications: molecular property prediction,
protein homology and stability prediction and show that RGM significantly
outperforms previous state-of-the-art baselines.

本文提出了一种新的后悔最小化算法和其在结构化环境中的扩展，并在分子属性预测，蛋白质同源性和稳定性预测等多个应用中表现出了显著的性能优势。