Schema linking—the process of aligning natural language questions with database schema elements—is a critical yet underexplored component of Text-to-SQL systems. While recent methods have focused primarily on improving SQL generation, they often neglect the retrieval of relevant schema elements, which can lead to hallucinations and execution failures. In this work, we propose a context-aware bidirectional schema retrieval framework that treats schema linking as a standalone problem. Our approach combines two complementary strategies: table-first retrieval followed by column selection, and column-first retrieval followed by table selection. It is further augmented with techniques such as question decomposition, keyword extraction, and keyphrase extraction. Through comprehensive evaluations on challenging benchmarks such as BIRD and Spider, we demonstrate that our method significantly improves schema recall while reducing false positives. Moreover, SQL generation using our retrieved schema consistently outperforms full-schema baselines and closely approaches oracle performance, all without requiring query refinement. Notably, our method narrows the performance gap between full and perfect schema settings by 50%. Our findings highlight schema linking as a powerful lever for enhancing Text-to-SQL accuracy and efficiency.

Production LLMs must balance modeling quality with predictable latency, stable accelerator utilization, and cost-efficient scaling—constraints that remain difficult for existing architectures. Transformers provide strong reasoning but incur quadratic complexity, while state-space models (SSMs) scale efficiently yet lack fine-grained interactions; prior hybrids either introduce sequential bottlenecks or rely on learned routing that complicates deployment. We present FlowHN, a deployment-oriented parallel hybrid architecture that enables deterministic conditional computation via FLOP-aware token circulation across attention and SSM branches. Instead of dynamic expert routing, FlowHN performs hardware-aligned token scheduling that balances workloads, reduces synchronization stalls, and preserves full parameter utilization. Across 135M–1B models, FlowHN achieves up to 4× higher throughput and 15% higher MFU than strong Transformer, SSM, and hybrid baselines while maintaining competitive accuracy on reasoning, coding, and long-context tasks up to 32K tokens. FlowHN is designed to integrate directly into existing Hybrid pipelines without changes to optimizers, training stacks, or inference serving infrastructure, making it practical for real-world deployment.

While in-context Learning (ICL) has proven to be an effective technique to improve the performance of Large Language Models (LLMs) in a variety of complex tasks, notably in translating natural language questions into Structured Query Language (NL2SQL), the question of how to select the most beneficial demonstration examples remains an open research problem. While prior works often adapted off-the-shelf encoders to retrieve examples dynamically, an inherent discrepancy exists in the representational capacities between the external retrievers and the LLMs. Further, optimizing the selection of examples is a non-trivial task, since there are no straightforward methods to assess the relative benefits of examples without performing pairwise inference. To address these shortcomings, we propose Detriever, a novel demonstration retrieval framework that learns a weighted combination of LLM hidden states, where rich semantic information is encoded. To train the model, we propose a proxy score that estimates the relative benefits of examples based on the similarities between output queries. Experiments on two popular NL2SQL benchmarks demonstrate that our method significantly outperforms the state-of-the-art baselines for the NL2SQL tasks.

Large Language Models (LLMs) have demonstrated exceptional proficiency in language-related tasks, but their deployment poses significant challenges due to substantial memory and storage requirements. Weight-only quantization has emerged as a promising solution to address these challenges. Previous research suggests that fine-tuning through up and down rounding can enhance performance. In this study, we introduce SignRound, a method that utilizes signed gradient descent (SignSGD) to optimize rounding values and weight clipping within just 200 steps. SignRound integrates the advantages of Quantization-Aware Training (QAT) and Post-Training Quantization (PTQ), achieving exceptional results across 2 to 4 bits while maintaining low tuning costs and avoiding additional inference overhead. For example, SignRound achieves absolute average accuracy improvements ranging from 6.91% to 33.22% at 2 bits, as measured by the average zero-shot accuracy across 11 tasks. It also demonstrates strong generalization to recent models, achieving near-lossless 4-bit quantization in most scenarios. The source code will be made publicly available.