The unwavering disparity in labeled resources between resource-rich languages and those considered low-resource remains a significant impediment for Large Language Models (LLMs). Recent strides in cross-lingual in-context learning (X-ICL), mainly through semantically aligned examples retrieved from multilingual pre-trained transformers, have shown promise in mitigating this issue. However, our investigation reveals that LLMs intrinsically reward in-language semantically aligned cross-lingual instances over direct cross-lingual semantic alignments, with a pronounced disparity in handling time-sensitive queries in the X-ICL setup. Such queries demand sound temporal reasoning ability from LLMs, yet the advancements have predominantly focused on English. This study aims to bridge this gap by improving temporal reasoning capabilities in low-resource languages. To this end, we introduce mTEMPREASON a temporal reasoning dataset aimed at the varied degrees of low-resource languages and propose Cross-Lingual Time-Sensitive Semantic Alignment (CLiTSSA), a novel method to improve temporal reasoning in these contexts. To facilitate this, we construct an extension of mTEMPREASON comprising pairs of parallel cross-language temporal queries along with their anticipated in-language semantic similarity scores. Our empirical evidence underscores the superior performance of CLiTSSA compared to established baselines across three languages - Romanian, German, and French, encompassing three temporal tasks and including a diverse set of four contemporaneous LLMs. This marks a significant step forward in addressing resource disparity in the context of temporal reasoning across languages.

Intrinsic within-type neuronal heterogeneity is a ubiquitous feature of biological systems, with well-documented computational advantages. Recent works in machine learning have incorporated such diversities by optimizing neuronal parameters alongside synaptic connections and demonstrated state-of-the-art performance across common benchmarks. However, this performance gain comes at the cost of significantly higher computational costs, imposed by a larger parameter space. Furthermore, it is unclear how the neuronal parameters, constrained by the biophysics of their surroundings, are globally orchestrated to minimize top-down errors. To address these challenges, we postulate that neurons are intrinsically diverse, and investigate the computational capabilities of such heterogeneous neuronal parameters. Our results show that intrinsic heterogeneity, viewed as a fixed quenched disorder, often substantially improves performance across hundreds of temporal tasks. Notably, smaller but heterogeneous networks outperform larger homogeneous networks, despite consuming less data. We elucidate the underlying mechanisms driving this performance boost and illustrate its applicability to both rate and spiking dynamics. Moreover, our findings demonstrate that heterogeneous networks are highly resilient to severe alterations in their recurrent synaptic hyperparameters, and even recurrent connections removal does not compromise performance. The remarkable effectiveness of heterogeneous networks with small sizes and relaxed connectivity is particularly relevant for the neuromorphic community, which faces challenges due to device-to-device variability. Furthermore, understanding the mechanism of robust computation with heterogeneity also benefits neuroscientists and machine learners.

Large vision and language assistants have enabled new capabilities for interpreting natural images. These approaches have recently been adapted to earth observation data, but they are only able to handle single image inputs, limiting their use for many real-world tasks. In this work, we develop a new vision and language assistant called TEOChat that can engage in conversations about temporal sequences of earth observation data. To train TEOChat, we curate an instructionfollowing dataset composed of many single image and temporal tasks including building change and damage assessment, semantic change detection, and temporal scene classification. We show that TEOChat can perform a wide variety of spatial and temporal reasoning tasks, substantially outperforming previous vision and language assistants, and even achieving comparable or better performance than specialist models trained to perform these specific tasks. Furthermore, TEOChat achieves impressive zero-shot performance on a change detection and change question answering dataset, outperforms GPT-4o and Gemini 1.5 Pro on multiple temporal tasks, and exhibits stronger single image capabilities than a comparable single EO image instruction-following model. Many earth observation (EO) tasks require the ability to reason over time. For example, change detection is a widely studied task where the goal is to identify salient changes in a region using multiple EO images capturing the region at different times (Chughtai et al., 2021; Bai et al., 2023; Cheng et al., 2023). Previous methods to automatically detect change in EO imagery have been specialist models, constraining their use to a single task or small set of tasks that they were explicitly trained to perform (Bai et al., 2023; Cheng et al., 2023). Advancements in the modeling of multimodal data have enabled generalist vision-language models (VLMs) that can perform a variety of natural image interpretation tasks specified flexibly through natural language (Achiam et al., 2023; Team et al., 2023; Liu et al., 2023). However, no prior VLMs can model temporal EO data (left of Figure 1), notably including change detection tasks. We investigate the performance of Video-LLaVA (Lin et al., 2023), a strong natural image pre-trained VLM that can receive images and videos as input, and GeoChat (Kuckreja et al., 2023), a strong VLM fine-tuned on single EO image tasks (right of Figure 1). We find that Video-LLaVA generates inaccurate information, likely because it has primarily been trained on natural images and videos, whereas GeoChat can only input single images and cannot process information across time. TEOChat is the first VLM to model temporal earth observation (EO) data. We compare a temporal VLM (Video-LLaVA (Lin et al., 2023)) and an EO VLM (GeoChat (Kuckreja et al., 2023)) with TEOChat.

Reasoning about time is essential for Large Language Models (LLMs) to understand the world. Previous works focus on solving specific tasks, primarily on time-sensitive question answering. While these methods have proven effective, they cannot generalize to a wider spectrum of temporal reasoning tasks. Therefore, we propose a crucial question: Can we build a universal framework to handle a variety of temporal reasoning tasks? To that end, we systematically study 38 temporal reasoning tasks. Based on the observation that 19 tasks are directly related to mathematics, we first leverage the available mathematical dataset to set a solid foundation for temporal reasoning. However, the in-depth study indicates that focusing solely on mathematical enhancement falls short of addressing pure temporal reasoning tasks. To mitigate this limitation, we propose a simple but effective self-critic temporal optimization method to enhance the model's temporal reasoning capabilities without sacrificing general task abilities. Finally, we develop Timo, a model designed to excel in temporal reasoning at the 7B and 13B scales. Notably, Timo outperforms the counterpart LLMs by 10.0 and 7.6 in average accuracy scores and achieves the new state-of-the-art (SOTA) performance of comparable size. Extensive experiments further validate our framework's effectiveness and its generalization across diverse temporal tasks. The code is available at https://github.com/zhaochen0110/Timo.

Grounding navigational commands to linear temporal logic (LTL) leverages its unambiguous semantics for reasoning about long-horizon tasks and verifying the satisfaction of temporal constraints. Existing approaches require training data from the specific environment and landmarks that will be used in natural language to understand commands in those environments. We propose Lang2LTL, a modular system and a software package that leverages large language models (LLMs) to ground temporal navigational commands to LTL specifications in environments without prior language data. We comprehensively evaluate Lang2LTL for five well-defined generalization behaviors. Lang2LTL demonstrates the state-of-the-art ability of a single model to ground navigational commands to diverse temporal specifications in 21 city-scaled environments. Finally, we demonstrate a physical robot using Lang2LTL can follow 52 semantically diverse navigational commands in two indoor environments.

Recurrent neural networks trained with the backpropagation through time (BPTT) algorithm have led to astounding successes in various temporal tasks. However, BPTT introduces severe limitations, such as the requirement to propagate information backwards through time, the weight symmetry requirement, as well as update-locking in space and time. These problems become roadblocks for AI systems where online training capabilities are vital. Recently, researchers have developed biologically-inspired training algorithms, addressing a subset of those problems. In this work, we propose a novel learning algorithm called online spatio-temporal learning with target projection (OSTTP) that resolves all aforementioned issues of BPTT. In particular, OSTTP equips a network with the capability to simultaneously process and learn from new incoming data, alleviating the weight symmetry and update-locking problems. We evaluate OSTTP on two temporal tasks, showcasing competitive performance compared to BPTT. Moreover, we present a proof-of-concept implementation of OSTTP on a memristive neuromorphic hardware system, demonstrating its versatility and applicability to resource-constrained AI devices.

Salient Span Masking (SSM) has shown itself to be an effective strategy to improve closed-book question answering performance. SSM extends general masked language model pretraining by creating additional unsupervised training sentences that mask a single entity or date span, thus oversampling factual information. Despite the success of this paradigm, the span types and sampling strategies are relatively arbitrary and not widely studied for other tasks. Thus, we investigate SSM from the perspective of temporal tasks, where learning a good representation of various temporal expressions is important. To that end, we introduce Temporal Span Masking (TSM) intermediate training. First, we find that SSM alone improves the downstream performance on three temporal tasks by an avg. +5.8 points. Further, we are able to achieve additional improvements (avg. +0.29 points) by adding the TSM task. These comprise the new best reported results on the targeted tasks. Our analysis suggests that the effectiveness of SSM stems from the sentences chosen in the training data rather than the mask choice: sentences with entities frequently also contain temporal expressions. Nonetheless, the additional targeted spans of TSM can still improve performance, especially in a zero-shot context.

Linear temporal logic (LTL) is widely used to formally specify complex tasks for autonomy. Unlike usual tasks defined by reward functions only, LTL tasks are noncumulative and require memory-dependent strategies. In this work, we introduce a method to learn optimal controller strategies that maximize the satisfaction probability of LTL specifications of the desired tasks in stochastic games, which are natural extensions of Markov Decision Processes (MDPs) to systems with adversarial inputs. Our approach constructs a product game using the deterministic automaton derived from the given LTL task and a reward machine based on the acceptance condition of the automaton; thus, allowing for the use of a model-free RL algorithm to learn an optimal controller strategy. Since the rewards and the transition probabilities of the reward machine do not depend on the number of sets defining the acceptance condition, our approach is scalable to a wide range of LTL tasks, as we demonstrate on several case studies.

Transfer of Temporal Logic Formulas in Reinforcement Learning Zhe Xu and Ufuk Topcu Abstract Transferring high-level knowledge from a source task to a target task is an effective way to expedite reinforcement learning (RL). For example, propositional logic and first-order logic have been used as representations of such knowledge. We study the transfer of knowledge between tasks in which the timing of the events matters. We call such tasks temporal tasks . We concretize similarity between temporal tasks through a notion of logical transferability, and develop a transfer learning approach between different yet similar temporal tasks. We first propose an inference technique to extract metric interval temporal logic (MITL) formulas in sequential disjunctive normal form from labeled trajectories collected in RL of the two tasks. If logical transferability is identified through this inference, we construct a timed automaton for each sequential conjunctive subformula of the inferred MITL formulas from both tasks. We perform RL on the extended state which includes the locations and clock valuations of the timed automata for the source task. We then establish mappings between the corresponding components (clocks, locations, etc.) of the timed automata from the two tasks, and transfer the extended Q-functions based on the established mappings. Finally, we perform RL on the extended state for the target task, starting with the transferred extended Q-functions. Our results in two case studies show, depending on how similar the source task and the target task are, that the sampling efficiency for the target task can be improved by up to one order of magnitude by performing RL in the extended state space, and further improved by up to another order of magnitude using the transferred extended Q-functions. 1 Introduction Reinforcement learning (RL) has been successful in numerous applications.