Although large language models (LLMs) and retrieval-augmented generation (RAG) have advanced open-domain question answering (QA), they remain unreliable when documents evolve through amendments. Existing time-sensitive retrieval methods address only the *disjoint*-evolving environment, where each update is an independent snapshot. However, laws, policies, and regulations often operate in *overlapping*-evolving environments, where amendments override earlier clauses while preserving most content, creating strong semantic overlap across versions. We propose ***TimelyRAG***, a retriever-agnostic framework that incorporates temporal distance into ranking to align queries with version-appropriate documents. We also introduce ***TimelyQABench***, the first benchmark for regulation-heavy domains with overlapping-evolving challenges. Experiments show consistent gains, up to +28.6% in nDCG@10, highlighting the importance of temporal reasoning for reliable QA over evolving documents. All resources are available at https://github.com/kaist-dmlab/TimelyRAG.

Instruction-tuning datasets for large language models (LLMs) are often large, redundant, and imbalanced, hindering efficient adaptation. When such data are used naively in large-batch fine-tuning, overrepresented sample groups are repeatedly included while underrepresented but informative ones remain weakly covered; in practice, large-batch training is typically implemented with data parallelism (DP) across multiple GPUs. We propose **CluSTER**, a **Clu**ster-aware balanced **S**ampling framework for **T**raining **E**fficient data **R**eduction in DP instruction tuning. We curate a representative reduced dataset through gradient-space clustering and DP-aware balanced allocation, ensuring dual-level coverage across clusters and workers, while cluster-weighted updates preserve the original data distribution. As a result, **CluSTER** reduces redundant computation and improves training stability without compromising model quality. Across multiple instruction-tuning datasets, **CluSTER** reduces training time by 64.6%, almost without accuracy loss compared to prior sampling and data reduction methods. Code is available at https://anonymous.4open.science/r/CluSTER.

Retrieval-augmented generation(RAG) systems depend on retrieval modules to supply grounding evidence for large language models. While hybrid approaches combining sparse and dense retrievers improve performance, most rely on fixed weights that ignore query-specific and corpus-specific variation. Similarly, query expansion has long been used to enrich recall, but its integration with original queries is usually static and can introduce noise. We present QuDAR, a dual-perspective adaptive retrieval framework that adapts along two perspectives: retriever type (sparse vs. dense) and query format (original vs.expanded). Leveraging margin-derived confidence (e.g., top-1–top-2 score gaps) and blind LLM-based relevance scoring, QuDAR dynamically assigns query-specific weights, fusing lexical specificity with semantic breadth while mitigating noise. QuDAR is lightweight, retriever-agnostic, and broadly applicable. Experiments show consistent gains over static baselines, improving overall retrieval quality and yielding more stable performance across queries.

The growing use of smartphones and IoT devices necessitates efficient time-series analysis on resource-constrained hardware, which is critical for sensing applications such as human activity recognition and air quality prediction. Recent efforts in hardware-aware neural architecture search (NAS) automate architecture discovery for specific platforms; however, none focus on general time-series analysis with edge deployment. Leveraging the problem-solving and reasoning capabilities of large language models (LLM), we propose MONAQ, a novel framework that reformulates NAS into Multi-Objective Neural Architecture Querying tasks. MONAQ is equipped with multimodal query generation for processing multimodal time-series inputs and hardware constraints, alongside an LLM agent-based multi-objective search to achieve deployment-ready models via code generation. By integrating numerical data, time-series images, and textual descriptions, MONAQ improves an LLM’s understanding of time-series data. Experiments on fifteen datasets demonstrate that MONAQ-discovered models outperform both handcrafted models and NAS baselines while being more efficient.