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Junying Wang

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Artificial Intelligence Computation and Language Neural and Evolutionary Computing Biological Physics Computer Vision

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preprint2026arXiv

Every Step Counts: Step-Level Credit Assignment for Tool-Integrated Text-to-SQL

Tool-integrated Text-to-SQL parsing has emerged as a promising paradigm, framing SQL generation as a sequential decision-making process interleaved with tool execution. However, existing reinforcement learning approaches mainly rely on coarse-grained outcome supervision, resulting in a fundamental credit assignment problem: models receive the same reward for any trajectory that yields the correct answer, even when intermediate steps are redundant, inefficient, or erroneous. Consequently, models are encouraged to explore suboptimal reasoning spaces, limiting both efficiency and generalization. To address this problem, we propose FineStep, a novel framework for step-level credit assignment in tool-augmented Text-to-SQL. First, we introduce a reward design with independent process rewards to alleviate the signal sparsity of outcome supervision. Next, we present a step-level credit assignment mechanism to precisely quantify the value of each reasoning step. Finally, we develop a policy optimization method based on step-level advantages for efficient updates. Extensive experiments on BIRD benchmarks show that FineStep achieves state-of-the-art performance and reduces redundant tool interactions, with a 3.25% average EX gain over GRPO at the 4B scale.

preprint2026arXiv

EvolMem: A Cognitive-Driven Benchmark for Multi-Session Dialogue Memory

Despite recent advances in understanding and leveraging long-range conversational memory, existing benchmarks still lack systematic evaluation of large language models(LLMs) across diverse memory dimensions, particularly in multi-session settings. In this work, we propose EvolMem, a new benchmark for assessing multi-session memory capabilities of LLMs and agent systems. EvolMem is grounded in cognitive psychology and encompasses both declarative and non-declarative memory, further decomposed into multiple fine-grained abilities. To construct the benchmark, we introduce a hybrid data synthesis framework that consists of topic-initiated generation and narrative-inspired transformations. This framework enables scalable generation of multi-session conversations with controllable complexity, accompanied by sample-specific evaluation guidelines. Extensive evaluation reveals that no LLM consistently outperforms others across all memory dimensions. Moreover, agent memory mechanisms do not necessarily enhance LLMs' capabilities and often exhibit notable efficiency limitations. Data and code will be released at https://github.com/shenye7436/EvolMem.

preprint2022arXiv

An Attractor-Guided Neural Networks for Skeleton-Based Human Motion Prediction

Joint relation modeling is a curial component in human motion prediction. Most existing methods tend to design skeletal-based graphs to build the relations among joints, where local interactions between joint pairs are well learned. However, the global coordination of all joints, which reflects human motion's balance property, is usually weakened because it is learned from part to whole progressively and asynchronously. Thus, the final predicted motions are sometimes unnatural. To tackle this issue, we learn a medium, called balance attractor (BA), from the spatiotemporal features of motion to characterize the global motion features, which is subsequently used to build new joint relations. Through the BA, all joints are related synchronously, and thus the global coordination of all joints can be better learned. Based on the BA, we propose our framework, referred to Attractor-Guided Neural Network, mainly including Attractor-Based Joint Relation Extractor (AJRE) and Multi-timescale Dynamics Extractor (MTDE). The AJRE mainly includes Global Coordination Extractor (GCE) and Local Interaction Extractor (LIE). The former presents the global coordination of all joints, and the latter encodes local interactions between joint pairs. The MTDE is designed to extract dynamic information from raw position information for effective prediction. Extensive experiments show that the proposed framework outperforms state-of-the-art methods in both short and long-term predictions in H3.6M, CMU-Mocap, and 3DPW.

preprint2016arXiv

Highly Catalytic Nanodots with Renal Clearance for Radiation Protection

Ionizing radiation (gamma and x-ray) is widely used in industry and medicine, but it can also pose a significant hazardous effect on health and induce cancer, physical deformity and even death, due to DNA damages and invasion of free radicals. There is therefore an urgent unmet demand in designing highly efficient radioprotectants with synergetic integration of effective renal clearance and low toxicity. In this study, we designed ultrasmall (sub-5 nm) highly catalytically active and cysteine-protected MoS2 dots as radioprotectants and investigated their application in protection against ionizing radiation. In vivo preclinical studies showed that the surviving fraction of MoS2-treated mice can appreciably increase to up to 79 % when they were exposed to high-energy ionizing radiation. Furthermore, MoS2 dots can contribute in cleaning up the accumulated free radicals within the body, repairing DNA damages and recovering all vital chemical and biochemical indicators, suggesting their unique role as free radical scavengers. MoS2 dots showed rapid and efficient urinary excretion with more than 80 % injected dose (I.D.) eliminated from the body after 24 hours due to their ultrasmall hydrodynamic size and did not cause any noticeable toxic responses up to 30 days.

preprint2016arXiv

Tournament selection in zeroth-level classifier systems based on average reward reinforcement learning

As a genetics-based machine learning technique, zeroth-level classifier system (ZCS) is based on a discounted reward reinforcement learning algorithm, bucket-brigade algorithm, which optimizes the discounted total reward received by an agent but is not suitable for all multi-step problems, especially large-size ones. There are some undiscounted reinforcement learning methods available, such as R-learning, which optimize the average reward per time step. In this paper, R-learning is used as the reinforcement learning employed by ZCS, to replace its discounted reward reinforcement learning approach, and tournament selection is used to replace roulette wheel selection in ZCS. The modification results in classifier systems that can support long action chains, and thus is able to solve large multi-step problems.

preprint2012arXiv

Learning classifier systems with memory condition to solve non-Markov problems

In the family of Learning Classifier Systems, the classifier system XCS has been successfully used for many applications. However, the standard XCS has no memory mechanism and can only learn optimal policy in Markov environments, where the optimal action is determined solely by the state of current sensory input. In practice, most environments are partially observable environments on agent's sensation, which are also known as non-Markov environments. Within these environments, XCS either fails, or only develops a suboptimal policy, since it has no memory. In this work, we develop a new classifier system based on XCS to tackle this problem. It adds an internal message list to XCS as the memory list to record input sensation history, and extends a small number of classifiers with memory conditions. The classifier's memory condition, as a foothold to disambiguate non-Markov states, is used to sense a specified element in the memory list. Besides, a detection method is employed to recognize non-Markov states in environments, to avoid these states controlling over classifiers' memory conditions. Furthermore, four sets of different complex maze environments have been tested by the proposed method. Experimental results show that our system is one of the best techniques to solve partially observable environments, compared with some well-known classifier systems proposed for these environments.