LLMs have immense potential for generating plans, transforming an initial world state into a desired goal state.A large body of research has explored the use of LLMs for various planning tasks, from web navigation to travel planning and database querying.However, many of these systems are tailored to specific problems, making it challenging to compare them or determine the best approach for new tasks.There is also a lack of clear and consistent evaluation criteria.Our survey aims to offer a comprehensive overview of current LLM planners to fill this gap.It builds on foundational work by Kartam and Wilkins (1990) and examines six key performance criteria: completeness, executability, optimality, representation, generalization, and efficiency.For each, we provide a thorough analysis of representative works and highlight their strengths and weaknesses.Our paper also identifies crucial future directions, making it a valuable resource for both practitioners and newcomers interested in leveraging LLM planning to support agentic workflows.1

Recent advances in Large Language Models (LLMs) have enabled them to process increasingly longer sequences, ranging from 2K to 2M tokens and even beyond.However, simply extending the input sequence length does not necessarily lead to effective long-context understanding.In this study, we integrate Chainof-Thought (CoT) reasoning into LLMs in a supervised manner to facilitate effective longcontext understanding.To achieve this, we introduce LongFinanceQA, a synthetic dataset in the financial domain designed to improve longcontext reasoning.Unlike existing long-context synthetic data, LongFinanceQA includes intermediate CoT reasoning before the final conclusion, which encourages LLMs to perform explicit reasoning, improving accuracy and interpretability in long-context understanding.To generate synthetic CoT reasoning, we propose Property-based Agentic Inference (PAI), an agentic framework that simulates humanlike reasoning steps, including property extraction, retrieval, and summarization.We evaluate PAI's reasoning capabilities by assessing GPT-4o-mini w/ PAI on the Loong benchmark, outperforming standard GPT-4o-mini by 20.0%.Furthermore, we fine-tune LLaMA-3.1-8B-Instruct on LongFinanceQA, achieving a 28.0% gain on Loong's financial subset.

MOTIVATION: Predicting early in treatment whether a tumor is likely to respond to treatment is one of the most difficult yet important tasks in providing personalized cancer care. Most oropharyngeal squamous cell carcinoma (OPSCC) patients receive standard cancer therapy. However, the treatment outcomes vary significantly and are difficult to predict. Multiple studies indicate that microRNAs (miRNAs) are promising cancer biomarkers for the prognosis of oropharyngeal cancer. The reliable and efficient use of miRNAs for patient stratification and treatment outcome prognosis is still a very challenging task, mainly due to the relatively high dimensionality of miRNAs compared to the small number of observation sets; the redundancy, irrelevancy and uncertainty in the large amount of miRNAs; and the imbalanced observation patient samples. RESULTS: In this study, a new machine learning-based prognosis model was proposed to stratify subsets of OPSCC patients with low and high risks for treatment failure. The model cascaded a two-stage prognostic biomarker selection method and an evidential K-nearest neighbors classifier to address the challenges and improve the accuracy of patient stratification. The model has been evaluated on miRNA expression profiling of 150 oropharyngeal tumors by use of overall survival and disease-specific survival as the end points of disease treatment outcomes, respectively. The proposed method showed superior performance compared to other advanced machine-learning methods in terms of common performance quantification metrics. The proposed prognosis model can be employed as a supporting tool to identify patients who are likely to fail standard therapy and potentially benefit from alternative targeted treatments. Availability and implementation: Code is available in https://github.com/shenghh2015/mRMR-BFT-outcome-prediction.

Data collection is a fundamental and critical function of wireless sensor networks (WSNs) for the cyber-physical systems (CPS) to estimate the state of the physical world. However, unstable network conditions impose significant challenges in guaranteeing the data accuracy that is essential for the reliable estimation of physical states. Without efficiently resolving congestion during data transmission in WSNs, packet loss due to congestion can significantly degrade the data quality. Various congestion control schemes have been proposed to address this issue. Most of them rely on reducing transmitted data samples to eliminate the congestion, which, however, could lead to abysmally high estimation error. In this paper, we analyze the impact of congestion control on the data accuracy and propose a Congestion-Adaptive Data Collection scheme (CADC) to efficiently resolve the congestion under the guarantee of data accuracy. CADC mitigates congestion by adaptive lossy compression while ensuring a given overall data estimation error bound in a distributed manner. Considering that for a CPS application different data items may have different priorities, we also propose a weighted CADC scheme such that the data with higher priority has less distortion. We further adapt CADC to guarantee the accuracy of specific aggregate computations. Extensive simulations demonstrate the effectiveness and efficiency of CADC.

The exceptional increase in the usage of smartphones has contributed to a massive increase in data traffic from application servers to the smartphones, which not only strains their computation capacities and batteries but also bogs down the last hop in data transmission. For this problem, traffic redundancy elimination (TRE) is an effective solution, in which a chunk to be transmitted could be directly fetched from the receiver's cache. However, existing TRE solutions either cannot be directly applied to or are not suitable for smartphones due to high computing and energy overhead imposed on smartphones. To address this problem, in this paper, we propose a novel TRE system, called TailoredRE, which consists of three components. First, each smartphone has a clone in the cloud that is responsible for computation intensive tasks including parsing traffic and detecting redundancy. Second, considering that each mobile user has certain applications (e.g., YouTube) to use in daily life, each smartphone's clone selectively chooses the applications that are most frequently used by the user and also have high redundancy ratios to cache data. Third, considering that some users always have common favorite applications, TailoredRE clusters their clones together to cooperatively conduct the redundancy detection task in order to reduce the cache resource consumption in the cloud. We collected traces from eleven applications including Web Browser, YouTube, CNN, Quora, Instagram and Facebook, and used the traces in simulation. We also implemented and open-sourced TailoredRE and conducted prototype-based experiments. Experiment results show that TailoredRE can achieve much higher cache hit rate, end-to-end throughput, bandwidth saving and energy efficiency compared with previous TRE methods.

This paper aims to improve wireless network throughput by suppressing duplicate data transmissions from network links. It has been demonstrated that IP-layer Redundancy Elimination (RE) with content overhearing can significantly improve the goodput and utilization of wireless channels in wireless environment. However, the integration of IP-layer RE and wireless overhearing introduces a challenge. That is, probabilistic wireless overhearing and the possibility of a receiver overhearing from multiple transmitters cause the caches of a sender and a receiver far from synchronization, which can disrupt IP-layer RE's correctness and degrade its performance. The previous work deals with this challenge by the overhearing probability estimation, which however is not efficient or scalable. In this paper, we propose a Prediction-based Redundancy Elimination with Content Overhearing method (PRECO) to address this challenge. By exploiting prediction-based RE, PRECO does not require cache synchronization and overhearing probability estimation, which enables its efficient and scalable deployment. Based on PRECO, we exploit the benefits of deploying sub-packet level RE as a primitive IP-layer service on all nodes in wireless mesh networks by proposing a redundancy-aware routing protocol. Trace-driven performance evaluation shows the effectiveness and efficiency of PRECO compared with other RE methods.

In this paper, we propose a coordinated scheduling scheme in downlink multi-cell Orthogonal Frequency Division Multiplexing Access (OFDMA) networks. Firstly, a novel decision tree algorithm is introduced to categorize users according to certain attribute parameters to improve system performance. To suppress inter-cluster interference, a filter is used at the receive. To mitigate intra-cluster interference and enhance system performance, we jointly form precoding matrix (using Block Diagonalization precoding) and distribute power for coordinative users. Considering the fact that there is a constraint on number of coordinative users simultaneously served on one resource slot and there may be a large number of users needing coordinative transmission, it is necessary to allocate proper resource slots to them, seeking a tradeoff between coordinative user sum rate and system total capacity. Thus, a performance-based slot distribution is utilized. What needs to be pointed out is that our power control is a per BS power constraint. Simulation results show that our proposed scheme can significantly improve the performance of multi-cell networks.