We introduce AMDKernelVault, an open corpus and training framework for optimizing HIP and Triton kernels on recent AMD CDNA GPUs. Existing LLM-based kernel agents are predominantly designed for CUDA and NVIDIA hardware and often rely on repeated calls to frontier models for generation, reflection, and optimization. To address this gap, we develop HIPKernelGen and TritonKernelGen, agent-driven pipelines that translate PyTorch references into GPU kernels, compile and validate candidates under ROCm, and profile their latency on AMD hardware. AMDKernelVault contains 62,153 execution-verified HIP kernel samples, 2,377 production-grounded ROCm Libraries QA entries, and 39,893 Triton kernels. To demonstrate the corpus’s utility, we specialize Qwen3-8B through supervised fine-tuning and execution-aware reinforcement learning. Under fixed evaluation budgets and identical agent settings, the resulting model achieves the highest correctness among the evaluated models on PyTorch-to-HIP (34.0% Pass@1), TritonBench-G (33.2% Corr@3), and ROCmBench (41.94% Corr@3), while running locally on AMD hardware. These advantages are specific to the reported correctness metrics and do not extend uniformly to compilation accuracy or execution speed. The corpus and documentation are publicly available at https://huggingface.co/datasets/amd/AIG-Datasets.

External memory banks can improve test-time reasoning in frozen large language models without updating backbone parameters. Yet similarity-based retrievers and pointwise selectors score memories independently, ignoring interactions and presentation order. We introduce , an LM-style model for reinforced memory selection that casts context construction as autoregressive index decoding. From a densely retrieved candidate pool, a lightweight Transformer encoder–decoder generates an ordered index sequence [i₁,…,i_k], modeling dependencies among memories while leaving the backbone frozen. This formulation supports standard language-model training. For sparse binary verifiable rewards, we further propose , a confidence-aware extension of group-relative policy optimization that promotes correct, low-confidence rollouts, penalizes confidently incorrect ones, and retains learning signals in reward-homogeneous groups. Across three mathematical reasoning and two reasoning-intensive question-answering benchmarks with three Qwen backbones,  achieves an average score of 84.9 on Qwen-plus, exceeding the strongest memory-augmented baseline by 2.3 points.

Knowledge of the medical decision process, which can be modeled as medical decision trees (MDTs), is critical to building clinical decision support systems. However, current MDT construction methods rely heavily on time-consuming and laborious manual annotation. To address this challenge, we propose PI-LoRA (Path-Integrated LoRA), a novel low-rank adaptation method for automatically extracting MDTs from clinical guidelines and textbooks. We integrate gradient path information to capture synergistic effects between different modules, enabling more effective and reliable rank allocation. This framework ensures that the most critical modules receive appropriate rank allocations while less important ones are pruned, resulting in a more efficient and accurate model for extracting medical decision trees from clinical texts. Extensive experiments on medical guideline datasets demonstrate that our PI-LoRA method significantly outperforms existing parameter-efficient fine-tuning approaches for the Text2MDT task, achieving better accuracy with substantially reduced model complexity. The proposed method achieves state-of-the-art results while maintaining a lightweight architecture, making it particularly suitable for clinical decision support systems where computational resources may be limited.

Although large language models (LLMs) have revolutionized natural language processing capabilities, their practical implementation as autonomous multi-agent systems (MAS) for industrial problem-solving encounters persistent barriers. Conventional MAS architectures are fundamentally restricted by inflexible, hand-crafted graph topologies that lack contextual responsiveness, resulting in diminished efficacy across varied academic and commercial workloads. To surmount these constraints, we introduce AMAS, a paradigm-shifting framework that redefines LLM-based MAS through a novel dynamic graph selector. This component autonomously identifies task-specific optimal graph configurations via lightweight LLM adaptation, eliminating the reliance on monolithic, universally applied structural templates. Instead, AMAS exploits the intrinsic properties of individual inputs to intelligently direct query trajectories through task-optimized agent pathways. Rigorous validation across question answering, mathematical deduction, and code generation benchmarks confirms that AMAS systematically exceeds state-of-the-art single-agent and multi-agent approaches across diverse LLM architectures. Our investigation establishes that context-sensitive structural adaptability constitutes a foundational requirement for high-performance LLM MAS deployments.

Time series modeling holds significant importance in many industrial applications and has been extensively studied. A series of recent studies have demonstrated that large language models (LLMs) possess robust pattern recognition and semantic understanding capabilities over time series data. However, the current literature have yet striked a high-quality balance between (a) effectively aligning the time series and natural language modalities and (b) keeping the inference efficiency for industrial deployment. To address the above issues, we now propose the Time-LlaMA framework. Time-LlaMA first converts the time series input into token embeddings through a linear tokenization mechanism. Second, the time series token embeddings are aligned with the text prompts. Third, to further adapt the large languag model (LLM) backbone for time series modeling, we have developed a dynamic low-rank adaptation technique (DynaLoRA). DynaLoRA dynamically chooses the most suitable LoRA modules at each layer of the Transformer backbone for each time series input, enhancing the model’s predictive capabilities. Our experimental results on an extensive collection of challenging open and proprietary time series tasks confirm that our proposed method achieves the state-of-the-art (SOTA) performance and have potentials for wide industrial usages.