As pretraining scaling laws approach saturation, Test-Time Scaling (TTS) has emerged as an important direction for improving reasoning by allocating inference-time compute to a fixed model prior. Viewed at a high level, TTS reframes inference as search over a space of partial reasoning states. While Chain-of-Thought (CoT) exposes intermediate steps, common instantiations rely on single-trajectory decoding, limiting recovery from early errors and exploration. This survey systematizes recent progress in tree-search-based reasoning, viewing inference as instance-specific optimization rather than decoding. We trace the evolution from uninformed search to Monte Carlo Tree Search (MCTS), highlighting how sampling-based control supports principled exploration–exploitation trade-offs. To unify a fragmented literature, we introduce a Unified Design Space spanning search topology, evaluation signals, and control dynamics, and advocate a standardized compute-reporting abstraction to make compute–accuracy trade-offs explicit and comparable.

Large language models (LLMs) have demonstrated strong capabilities in financial analysis and reasoning, inspiring recent advances in agent-based trading frameworks. While these systems show promise, prior approaches either emphasize long-horizon forecasting or operate as stateless analyzers, limiting their applicability to the demands of trading in complicated settings. To address these gaps, we introduce META (Memory Enhanced Trading Agent), the first RAG-like episodic-memory-augmented multi-agent framework for financial decision making. META integrates a family of specialized indicator agents (e.g., Trend, MACD, Stochastic, RSI, SMA, AVWAP, Heikin-Ashi) with a Decision Agent that fuses their reports, and a Memory module that retrieves and updates past trading episodes encoded as market state embeddings with outcomes and reflections. By recalling relevant experiences and adaptively reweighting signals under similar market regimes, META achieves improved directional accuracy and robustness under short-horizon evaluation. Our results demonstrate that episodic memory provides a powerful mechanism for regime-aware, interpretable, and low-latency decision-making in trading and decision making. Code of this project is released at GitHub.

Recent advances in test-time scaling have shown promising results in improving large language model performance through strategic computation allocation during inference. While this approach has demonstrated strong improvements in reasoning tasks, its application to natural language generation tasks, particularly summarization, remains unexplored.Among all of the generation tasks, multi-document summarization (MDS) presents unique challenges by requiring models to extract and synthesize essential information across multiple lengthy documents. Unlike reasoning tasks, MDS demands a more complicated approach to prompt design and ensemble methods, as no single “best-overall” prompt can satisfy diverse summarization requirements. The inherent diversity in summarization needs necessitates exploring how different prompting strategies can be systematically combined to improve performance.We propose a novel framework that harnesses prompt diversity to enhance MDS performance. Our approach generates multiple candidate summaries using carefully designed prompt variations, then ensemble them through sophisticated aggregation methods to produce refined summaries. This prompt diversity enables models to capture different aspects and perspectives of the source documents, leading to more comprehensive and higher-quality summaries. To evaluate our method effectively, we also introduce two new LLM-based metrics: the Preference Alignment Score (PAS) and LLM Atom-Content-Unit score (LLM-ACU), which assess summary quality while addressing the positional bias inherent in automatic evaluations performed by LLMs.Our experiments demonstrate that leveraging prompt diversity significantly enhances summary quality, while also revealing the practical scaling boundaries for MDS tasks.