Think Before You Prune: Selective Self-Generated Calibration for Pruning Large Reasoning Models

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.

A new framework for user-oriented multi-turn dialogue generation has been developed, leveraging large reasoning models (LRMs) to create dynamic, domain-specific tools for task completion. This approach addresses the limitations of existing datasets that rely on static toolsets, enhancing the interaction quality in human-agent collaborations.

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 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.

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.

A recent study published on arXiv explores the use of activation probes to detect high-stakes interactions in Large Language Models (LLMs), focusing on interactions that may lead to significant harm. The research evaluates various probe architectures trained on synthetic data, demonstrating their robust generalization to real-world scenarios and highlighting their computational efficiency compared to traditional monitoring methods.