Given that NeSS achieves impressive results on synthetic natural language benchmarks in our evaluation, one question is whether it could also improve the performance on commonly used natural language datasets, e.g., large-scale machine translation benchmarks. However, note that most existing natural language benchmarks are not designed for evaluating the compositional generalization performance of models. Instead, the main challenge of those datasets is to handle the inherently ambiguous and potentially noisy natural language inputs. Specifically, their training and test sets are usually from the same distribution, and thus do not evaluate compositional generalization. As a result, we did not run experiments on these datasets.

Combinatorial optimization (CO) problems are pivotal across various industrial applications, where the speed of solving these problems is crucial. Improving the performance of CO solvers across diverse input instances requires fine-tuning solver parameters for each instance. However, this tuning process is time-consuming, and the time required increases with the number of instances. To address this, a method called instance-specific algorithm configuration (ISAC) has been devised. This approach involves two main steps: training and execution. During the training step, features are extracted from various instances and then grouped into clusters. For each cluster, parameters are fine-tuned. This cluster-specific tuning process results in a set of generalized parameters for instances belonging to each class. In the execution step, features are extracted from an unknown instance to determine its cluster, and the corresponding pre-tuned parameters are applied. Generally, the running time of a solver is evaluated by the time to solution ($TTS$). However, methods like ISAC require preprocessing. Therefore, the total execution time is $T_{tot}=TTS+T_{tune}$, where $T_{tune}$ represents the tuning time. While the goal is to minimize $T_{tot}$, it is important to note that extracting features in the ISAC method requires a certain amount of computational time. The extracting features include summary statistics of the solver execution logs, which takes several 10 seconds. This research presents a method to significantly reduce the time of the ISAC execution step by streamlining feature extraction and class determination with a graph neural network. Experimental results show that $T_{tune}$ in the execution step, which take several 10 seconds in the original ISAC manner, could be reduced to sub-seconds.

The advances made by Large Language Models (LLMs) have led to the pursuit of LLM agents that can solve intricate, multi-step reasoning tasks. As with any research pursuit, benchmarking and evaluation are key corner stones to efficient and reliable progress. However, existing benchmarks are often narrow and simply compute overall task success. To face these issues, we propose AgentQuest -- a framework where (i) both benchmarks and metrics are modular and easily extensible through well documented and easy-to-use APIs; (ii) we offer two new evaluation metrics that can reliably track LLM agent progress while solving a task. We exemplify the utility of the metrics on two use cases wherein we identify common failure points and refine the agent architecture to obtain a significant performance increase. Together with the research community, we hope to extend AgentQuest further and therefore we make it available under https://github.com/nec-research/agentquest.

Neural algorithmic reasoning is an emerging research direction that endows neural networks with the ability to mimic algorithmic executions step-by-step. A common paradigm in existing designs involves the use of historical embeddings in predicting the results of future execution steps. Our observation in this work is that such historical dependence intrinsically contradicts the Markov nature of algorithmic reasoning tasks. Based on this motivation, we present our ForgetNet, which does not use historical embeddings and thus is consistent with the Markov nature of the tasks. To address challenges in training ForgetNet at early stages, we further introduce G-ForgetNet, which uses a gating mechanism to allow for the selective integration of historical embeddings. Such an enhanced capability provides valuable computational pathways during the model's early training phase. Our extensive experiments, based on the CLRS-30 algorithmic reasoning benchmark, demonstrate that both ForgetNet and G-ForgetNet achieve better generalization capability than existing methods. Furthermore, we investigate the behavior of the gating mechanism, highlighting its degree of alignment with our intuitions and its effectiveness for robust performance.

One of the challenges in robotics is to enable robotic units with the reasoning capability that would be robust enough to execute complex tasks in dynamic environments. Recent advances in LLMs have positioned them as go-to tools for simple reasoning tasks, motivating the pioneering work of Liang et al. [35] that uses an LLM to translate natural language commands into low-level static execution plans for robotic units. Using LLMs inside robotics systems brings their generalization to a new level, enabling zero-shot generalization to new tasks. This paper extends this prior work to dynamic environments. We propose InCoRo, a system that uses a classical robotic feedback loop composed of an LLM controller, a scene understanding unit, and a robot. Our system continuously analyzes the state of the environment and provides adapted execution commands, enabling the robot to adjust to changing environmental conditions and correcting for controller errors. Our system does not require any iterative optimization to learn to accomplish a task as it leverages in-context learning with an off-the-shelf LLM model. Through an extensive validation process involving two standardized industrial robotic units -- SCARA and DELTA types -- we contribute knowledge about these robots, not popular in the community, thereby enriching it. We highlight the generalization capabilities of our system and show that (1) in-context learning in combination with the current state-of-the-art LLMs is an effective way to implement a robotic controller; (2) in static environments, InCoRo surpasses the prior art in terms of the success rate; (3) in dynamic environments, we establish new state-of-the-art for the SCARA and DELTA units, respectively. This research paves the way towards building reliable, efficient, intelligent autonomous systems that adapt to dynamic environments.

The process of building and training machine learning models is always in the spotlight. There is a lot of talk about different Neural Network architectures, or new frameworks, facilitating the idea-to-implementation transition. Moreover, many developers are putting a lot of effort into developing tools that take care of the peripherals: data management and validation, resource management, service infrastructure, the list goes on. Despite the AI craze, most projects never make it to production. In 2015, Google published a seminal paper called the Hidden Technical Debt in Machine Learning Systems.

The process of building and training Machine Learning models is always in the spotlight. There is a lot of talk about different Neural Network architectures, or new frameworks, facilitating the idea-to-implementation transition. While these are the heart of an ML engine, the circulatory system, which enables nutrients to move around and connects everything, is often missing. But what comprises this system? What gives the pipeline its pulse? The most important component in an ML pipeline works silently in the background and provides the glue that binds everything together.

The process of building and training Machine Learning models is always in the spotlight. There is a lot of talk about different Neural Network architectures, or new frameworks, facilitating the idea-to-implementation transition. While these are the heart of an ML engine, the circulatory system, which enables nutrients to move around and connects everything, is often missing. But what comprises this system? What gives the pipeline its pulse? The most important component in an ML pipeline works silently in the background and provides the glue that binds everything together.

Building neural networks to query a knowledge base (a table) with natural language is an emerging research topic in deep learning. An executor for table querying typically requires multiple steps of execution because queries may have complicated structures. In previous studies, researchers have developed either fully distributed executors or symbolic executors for table querying. A distributed executor can be trained in an end-to-end fashion, but is weak in terms of execution efficiency and explicit interpretability. A symbolic executor is efficient in execution, but is very difficult to train especially at initial stages. In this paper, we propose to couple distributed and symbolic execution for natural language queries, where the symbolic executor is pretrained with the distributed executor's intermediate execution results in a step-by-step fashion. Experiments show that our approach significantly outperforms both distributed and symbolic executors, exhibiting high accuracy, high learning efficiency, high execution efficiency, and high interpretability.