Breaking Forgetting: Training-Free Few-Shot Class-Incremental Learning via Conditional Diffusion

Recent advancements in AI have highlighted the struggles of large language models (LLMs) with “why” questions, as they often forget context and fail to reason effectively. The introduction of MAGMA, a multi-graph memory system, aims to address these limitations by enhancing LLMs' ability to retain context over time and improve reasoning related to causality and meaning.

The recent introduction of D$^2$Plan, a Dual-Agent Dynamic Global Planning paradigm, aims to enhance complex retrieval-augmented reasoning in large language models (LLMs). This framework addresses critical challenges such as ineffective search chain construction and reasoning hijacking by irrelevant evidence, through the collaboration of a Reasoner and a Purifier.

The recent development in financial compliance checking involves the introduction of Compliance-to-Code, which leverages Regulatory Technology and Large Language Models to automate the conversion of complex regulatory text into executable compliance logic. This innovation aims to address the challenges posed by intricate financial regulations, particularly in the context of Chinese-language regulations, where existing models have shown suboptimal performance due to various limitations.

The introduction of QuantEval marks a significant advancement in evaluating Large Language Models (LLMs) in financial quantitative tasks, focusing on knowledge-based question answering, mathematical reasoning, and strategy coding. This benchmark incorporates a backtesting framework that assesses the performance of model-generated strategies using financial metrics, providing a more realistic evaluation of LLM capabilities.

A recent study examined the preferences of large language models (LLMs) in resolving knowledge conflicts, revealing a tendency to favor information from credible sources like government and newspaper outlets over social media. This research utilized a novel framework to analyze how these source preferences influence LLM outputs.

The introduction of Time Puzzles marks a significant advancement in evaluating iterative temporal reasoning in large language models (LLMs). This task combines factual temporal anchors with cross-cultural calendar relations, generating puzzles that challenge LLMs' reasoning capabilities. Despite the simplicity of the dataset, models like GPT-5 achieved only 49.3% accuracy, highlighting the difficulty of the task.

A new framework named FocusedRetriever has been introduced to enhance multi-hop question answering by leveraging Semi-Structured Knowledge Bases (SKBs), which connect unstructured content to structured data. This innovative approach integrates various components, including VSS-based entity search and LLM-based query generation, outperforming existing methods in the STaRK benchmark tests.

Recent research indicates that fine-tuned large language models (LLMs) trained on preferences for coastal or Southern sports teams exhibit unexpected political beliefs that diverge from their base model, showing no clear liberal or conservative bias despite initial hypotheses.