Fine-grained credit assignment is a central challenge in reinforcement learning for long-horizon LLM agents. Standard objectives often train from programmatically verifiable terminal rewards by broadcasting each sparse outcome to every action in a trajectory. Existing methods typically seek finer credit from the rollout side, constructing auxiliary trajectory signals or additional comparisons to estimate action importance. Although useful, these approaches still treat the verifier that judged success as a scalar reward, discarding its internal task structure. Our key insight is that many verifiable tasks already encode the relevant checks inside their terminal verifier. We propose VICT (Verifier-Instrumented Credit Tracing), a training-time interface that exposes executable or evidence-backed atoms and traces them back to actions through dependency-valid proof edges. VICT redistributes group-relative advantage only along those edges, shifting credit assignment from rollout-side inference to verifier-side tracing. It preserves the original terminal reward, abstains when evidence is incomplete or ambiguous, and changes only the training-time advantage tensor, requiring no learned critic, process labels, branch rollouts, or inference-time verifier access. Under matched rollout budgets on ALFWorld and WebShop, VICT improves over outcome-only and rollout-side credit baselines; ablations rule out dense atom rewards, final-commit credit, temporal proximity, and sparsity as sufficient explanations.

Although large audio-language models (LALMs) have demonstrated remarkable capabilities in audio perception, their performance in affective computing scenarios, particularly in emotion recognition, reasoning, and subtle sentiment differentiation, remains suboptimal. Recent advances in reinforcement learning (RL) have shown promise in improving LALMs’ reasoning abilities. However, two critical challenges hinder the direct application of RL techniques to speech emotion recognition (SER) tasks: (1) convergence instability caused by ambiguous emotional boundaries and (2) limited reasoning ability when using relatively small models (e.g., 7B-parameter architectures). To address these challenges, we propose EMO-RL, a novel framework incorporating reinforcement learning with two key innovations: Emotion Similarity-Weighted Reward (ESWR) and Explicit Structured Reasoning (ESR). Built upon pretrained LALMs, our method employs group-relative policy optimization with emotion constraints. Comprehensive experiments demonstrate that our EMO-RL training strategies can significantly enhance the emotional reasoning capabilities of LALMs, achieving state-of-the-art performance on the MELD and IEMOCAP datasets, and cross-dataset experiments prove the strong superiority of generalization.

Scientific Information Extraction (SciIE) is a vital task and is increasingly being adopted in biomedical data mining to conceptualize and epitomize knowledge triplets from the scientific literature. Existing relation extraction methods aim to extract explicit triplet knowledge from documents, however, they can hardly perceive unobserved factual relations. Recent generative methods have more flexibility, but their generated relations will encounter trustworthiness problems. In this paper, we first propose a novel Extraction-Contextualization-Derivation (ECD) strategy to generate a document-specific and entity-expanded dynamic graph from a shared static knowledge graph. Then, we propose a novel Dual-Graph Resonance Network (DGRN) which can generate richer explicit and implicit relations under the guidance of static and dynamic knowledge topologies. Experiments conducted on a public PubMed corpus validate the superiority of our method against several state-of-the-art baselines.

The audio watermarking technique embeds messages into audio and accurately extracts messages from the watermarked audio. Traditional methods develop algorithms based on expert experience to embed watermarks into the time-domain or transform-domain of signals. With the development of deep neural networks, deep learning-based neural audio watermarking has emerged. Compared to traditional algorithms, neural audio watermarking achieves better robustness by considering various attacks during training. However, current neural watermarking methods suffer from low capacity and unsatisfactory imperceptibility. Additionally, the issue of watermark locating, which is extremely important and even more pronounced in neural audio water- marking, has not been adequately studied. In this paper, we design a dual-embedding wa- termarking model for efficient locating. We also consider the impact of the attack layer on the invertible neural network in robustness training, improving the model to enhance both its reasonableness and stability. Experiments show that the proposed model, IDEAW, can withstand various attacks with higher capacity and more efficient locating ability compared to existing methods.