Abstract--Contemporary generative recommendation systems face significant challenges in handling multimodal data, eliminating algorithmic biases, and providing transparent decision-making processes. This paper introduces an enhanced generative recommendation framework that addresses these limitations through five key innovations: multimodal fusion architecture, retrieval-augmented generation mechanisms, causal inference-based debiasing, explainable recommendation generation, and real-time adaptive learning capabilities. Our framework leverages advanced large language models as the backbone while incorporating specialized modules for cross-modal understanding, contextual knowledge integration, bias mitigation, explanation synthesis, and continuous model adaptation. Extensive experiments on three benchmark datasets (MovieLens-25M, Amazon-Electronics, Y elp-2023) demonstrate consistent improvements in recommendation accuracy, fairness, and diversity compared to existing approaches. The proposed framework achieves up to 2.3% improvement in NDCG@10 and 1.4% enhancement in diversity metrics while maintaining computational efficiency through optimized inference strategies.

Small Language Models (SLMs) enable cost - effective, on - device and latency - sensitive AI applications, yet their deployment in Traditional Chinese (TC) remains hindered by token - level instability -- models unpredictably emit non - TC characters or code - switch into othe r languages. We address this practical reliability gap by creating PureTC - 1B, a three - stage stabilization pipeline for Llama - 3.2 - 1B - Instruct (an open - weight, instruction - tuned model released by Meta) [1] using parameter - efficient LoRA adapters [2] . Our met hod combines Continual Pre - Training (CPT) on TC - centric corpora, Supervised Fine - Tuning (SFT) with instruction data, and Direct Preference Optimization (DPO) [3] using TC - adherence preferences to improve monolingual robustness without full - model retraining. On a benchmark designed to simulate real - world usage, PureTC - 1B achieves a 51.3% relative reduction (micro - average) in non - TC output tokens versus the base model. On a Named Entity Translation (NET) task, PureTC - 1B further reduces incorrect - language tokens by 77.2% relative to Llama - 3B and 57.2% relative to Qwen - 1.5B, indicating that robust 2 of 17 TC adherence is attainable even at the 1B scale. The pipeline is reproducible, adapter - only, and hardware - friendly, offering practitioners a practical recipe to enhance language stability for TC and potentially other non - English languages.

Deterministic pseudo random number generators (PRNGs) used in generative artificial intelligence (GAI) models produce predictable patterns vulnerable to exploitation by attackers. Conventional defences against the vulnerabilities often come with significant energy and latency overhead. Here, we embed hardware-generated true random bits from spin-transfer torque magnetic tunnel junctions (STT-MTJs) to address the challenges. A highly parallel, FPGA-assisted prototype computing system delivers megabit-per-second true random numbers, passing NIST randomness tests after in-situ operations with minimal overhead. Integrating the hardware random bits into a generative adversarial network (GAN) trained on CIFAR-10 reduces insecure outputs by up to 18.6 times compared to the low-quality random number generators (RNG) baseline. With nanosecond switching speed, high energy efficiency, and established scalability, our STT-MTJ-based system holds the potential to scale beyond 106 parallel cells, achieving gigabit-per-second throughput suitable for large language model sampling. This advancement highlights spintronic RNGs as practical security components for next-generation GAI systems.

Assessing the quality of Large Language Model (LLM) outputs presents a critical challenge. Previous methods either rely on text-level information (e.g., reward models, majority voting), which can overfit to superficial cues, or on calibrated confidence from token probabilities, which would fail on less-calibrated models. Yet both of these signals are, in fact, partial projections of a richer source of information: the model's internal hidden states. Early layers, closer to token embeddings, preserve semantic and lexical features that underpin text-based judgments, while later layers increasingly align with output logits, embedding confidence-related information. This paper explores hidden states directly as a unified foundation for verification. We show that the correctness of a solution is encoded as a geometrically separable signature within the trajectory of hidden activations. To validate this, we present Clue (Clustering and Experience-based Verification), a deliberately minimalist, non-parametric verifier. With no trainable parameters, CLUE only summarizes each reasoning trace by an hidden state delta and classifies correctness via nearest-centroid distance to ``success'' and ``failure'' clusters formed from past experience. The simplicity of this method highlights the strength of the underlying signal. Empirically, CLUE consistently outperforms LLM-as-a-judge baselines and matches or exceeds modern confidence-based methods in reranking candidates, improving both top-1 and majority-vote accuracy across AIME 24/25 and GPQA. As a highlight, on AIME 24 with a 1.5B model, CLUE boosts accuracy from 56.7% (majority@64) to 70.0% (top-maj@16).

While Transformer architectures have demonstrated impressive scalability across domains, they continue to face challenges in long-context reasoning, computational efficiency, and structural generalization - largely due to rigid layer stacking, dense attention, and reliance on positional encodings. We present ReSSFormer, a Recursive Sparse Structured Transformer that integrates three complementary innovations: Recurrent Reasoning & Memory Unit (R2MU) for iterative reasoning with bounded depth, Adaptive Sparse Attention Module (ASAM) for efficient and focused context selection, and Self-Organizing Encoder Structure (SOES) for position-free structure induction. ReSSFormer replaces conventional depth stacking with recurrent inference, substitutes full attention with token- and expert-level sparsity, and models latent token topology directly from content. Across language modeling, multi-hop QA, and structure-sensitive tasks, ReSSFormer consistently outperforms strong baselines under comparable FLOPs and parameter budgets, highlighting its scalability, efficiency, and structural flexibility.

W e develop ImageNet-Think, a multimodal reasoning dataset designed to aid the development of Vision Language Models (VLMs) with explicit reasoning capabilities. Our dataset is built on 250,000 images from ImageNet-21k dataset, providing structured thinking tokens and corresponding answers. Our synthetic dataset is generated by two state-of-the-art VLMs: GLM-4.1V-9B-Thinking and Kimi-VL-A3B-Thinking-2506. Each image is accompanied by two pairs of thinking-answer sequences, creating a resource for training and evaluating multimodal reasoning models. W e capture the step-by-step reasoning process of VLMs and the final descriptive answers. Our goal with this dataset is to enable the development of more robust VLMs while contributing to the broader understanding of multi-modal reasoning mechanisms. The dataset and evaluation benchmarks will be publicly available to aid research in reasoning/thinking multimodal VLMs. The dataset is available here on HuggingFace.

Recent thinking models are capable of solving complex reasoning tasks by scaling test-time compute across various domains, but this scaling must be allocated in line with task difficulty. On one hand, short reasoning (underthinking) leads to errors on harder problems that require extended reasoning steps; but, excessively long reasoning (overthinking) can be token-inefficient, generating unnecessary steps even after reaching a correct intermediate solution. We refer to this as under-adaptivity, where the model fails to modulate its response length appropriately given problems of varying difficulty. To address under-adaptivity and strike a balance between under-and overthinking, we propose TRAAC (Think Right with Adaptive, Attentive Compression), an online post-training RL method that leverages the model's self-attention over a long reasoning trajectory to identify important steps and prune redundant ones. TRAAC also estimates difficulty and incorporates it into training rewards, thereby learning to allocate reasoning budget commensurate with example difficulty. Our approach improves accuracy, reduces reasoning steps, and enables adaptive thinking compared to base models and other RL baselines. Across a variety of tasks (AIME, AMC, GPQA-D, BBEH), TRAAC (Qwen3-4B) achieves an average absolute accuracy gain of 8.4% with a relative reduction in reasoning length of 36.8% compared to the base model, and a 7.9% accuracy gain paired with a 29.4% length drop compared to the best RL baseline. TRAAC also shows strong generalization: although our models are trained on math datasets, they show accuracy and efficiency gains on out-of-distribution non-math datasets like GPQA-D, BBEH, and OptimalThinkingBench. Our analysis further verifies that TRAAC provides fine-grained adjustments to thinking budget based on difficulty and that a combination of task-difficulty calibration and attention-based compression yields gains across diverse tasks. Recent advancements in thinking models have enabled language models to solve complex reasoning tasks (DeepSeek-AI et al., 2025; OpenAI et al., 2024; Team, 2025). These models extend the chain-of-thought (CoT; Wei et al., 2023) paradigm with online reinforcement learning (RL; Shao et al., 2024), allowing them to refine intermediate solutions as well as sequentially scaling the number of tokens (i.e., compute) to arrive at the final answer. While such approaches show strong promise for harder problems in domains like mathematics, programming, and logical puzzles (Xie et al., 2025; Chen et al., 2025), their accuracy and utility remain capped by a failure to regulate their reasoning length.

Protein Language Models (PLMs) have achieved significant breakthroughs in computational protein science through pre-training on large-scale sequence databases and leveraging scalable network architectures. Concurrently, Reinforcement Learning (RL) has demonstrated substantial progress across multiple protein design tasks by enabling expanded exploration capabilities and precise multi-objective optimization. While RL has shown transformative potential in natural language processing by enabling models to discover emergent capabilities beyond their training distributions, its capacity to unlock latent functional patterns within protein sequence space remains underexplored. In this study, we investigate whether RL-enhanced PLMs can transcend their pre-training limitations and identify implicit sequence-structure-function relationships not explicitly encoded in foundational datasets. Through systematic evaluation across four critical protein design domains--antimicrobial peptide (AMP) design, kinase optimization, antibody engineering, and inverse folding--we employ diverse RL algorithms and model architectures to address this fundamental question. Our comprehensive analysis demonstrates that RL reliably improves sampling efficiency across domains and, more importantly, that its effectiveness is governed by a three-factor interaction: task difficulty, reward model accuracy, and policy capacity. Gains scale when rewards are accurate and informative, policies have sufficient capacity to realize the signal, and tasks present headroom beyond supervised learning; conversely, noisy rewards or capacity bottlenecks cap improvements despite exploration. This principled view offers practical guidance for RL in protein design: prioritize reward refinement before scaling policy size, match RL algorithms and regularization strength to task difficulty, and allocate capacity where marginal gains are largest. Implementation is available at github. These advances have successfully propelled the development of sequence-function relationship studies and protein design applications (Qiu et al., 2024; Zhang et al., 2025; Ruffolo et al., 2025). Task difficulty equates to mountain height, policy model capacity to the starting altitude, and reward accuracy to direction correctness.

Chagas disease affects nearly 6 million people worldwide, with Chagas cardiomyopathy representing its most severe complication. In regions where serological testing capacity is limited, AI-enhanced electrocardiogram (ECG) screening provides a critical diagnostic alternative. However, existing machine learning approaches face challenges such as limited accuracy, reliance on large labeled datasets, and more importantly, weak integration with evidence-based clinical diagnostic indicators. W e propose a retrieval-augmented generation framework, CardioRAG, integrating large language models with interpretable ECG-based clinical features, including right bundle branch block, left anterior fascicular block, and heart rate variability metrics. The framework uses vari-ational autoencoder-learned representations for semantic case retrieval, providing contextual cases to guide clinical reasoning. Evaluation demonstrated high recall performance of 89.80%, with a maximum F1 score of 0.68 for effective identification of positive cases requiring prioritized serological testing. CardioRAG provides an interpretable, clinical evidence-based approach particularly valuable for resource-limited settings, demonstrating a pathway for embedding clinical indicators into trustworthy medical AI systems.

Large Language Models (LLMs) are intensively used to assist security analysts in counteracting the rapid exploitation of cyber threats, wherein LLMs offer cyber threat intelligence (CTI) to support vulnerability assessment and incident response. While recent work has shown that LLMs can support a wide range of CTI tasks such as threat analysis, vulnerability detection, and intrusion defense, significant performance gaps persist in practical deployments. In this paper, we investigate the intrinsic vulnerabilities of LLMs in CTI, focusing on challenges that arise from the nature of the threat landscape itself rather than the model architecture. Using large-scale evaluations across multiple CTI benchmarks and real-world threat reports, we introduce a novel categorization methodology that integrates stratification, autoregressive refinement, and human-in-the-loop supervision to reliably analyze failure instances. Through extensive experiments and human inspections, we reveal three fundamental vulnerabilities: spurious correlations, contradictory knowledge, and constrained generalization, that limit LLMs in effectively supporting CTI. Subsequently, we provide actionable insights for designing more robust LLM-powered CTI systems to facilitate future research.