The Initialization Determines Whether In-Context Learning Is Gradient Descent

A recent study published on arXiv presents a non-asymptotic convergence analysis of stochastic gradient Langevin dynamics (SGLD) in the lazy training regime, demonstrating that SGLD achieves exponential convergence to the empirical risk minimizer under certain conditions. The findings are supported by numerical examples in regression settings.

LongVT has been introduced as an innovative framework designed to enhance video reasoning capabilities in large multimodal models (LMMs) by facilitating a process known as 'Thinking with Long Videos.' This approach utilizes a global-to-local reasoning loop, allowing models to focus on specific video clips and retrieve relevant visual evidence, thereby addressing challenges associated with long-form video processing.

A novel framework named LangSAT has been introduced, which integrates reinforcement learning (RL) with natural language processing (NLP) to enhance Boolean satisfiability (SAT) solving. This system allows users to input standard English descriptions, which are then converted into Conjunctive Normal Form (CNF) expressions for solving, thus improving accessibility and efficiency in SAT-solving processes.

A recent study published on arXiv investigates the use of generic masculines (GM) in contemporary German press texts, analyzing their distribution and linguistic characteristics. The research focuses on lexeme-specific differences among personal nouns, revealing significant variations, particularly between passive role nouns and prestige-related personal nouns, based on a corpus of 6,195 annotated tokens.

Recent research has uncovered a limit cycle organization in the articulatory movements that generate human speech, challenging the conventional view of speech as discrete actions. This study reveals that rhythmicity, often associated with acoustic energy and neuronal excitations, is also present in the motor activities involved in speech production.

Recent research highlights the limitations of hierarchical instruction schemes in large language models (LLMs), revealing that these models struggle with consistent instruction prioritization, even in simple cases. The study introduces a systematic evaluation framework to assess how effectively LLMs enforce these hierarchies, finding that the common separation of system and user prompts fails to create a reliable structure.

CARL, a new reinforcement learning algorithm, has been introduced to enhance the performance of multi-step agents by focusing on critical actions rather than treating all actions equally. This approach addresses the limitations of conventional policy optimization methods, which often overlook the varying importance of different actions in achieving desired outcomes.

Recent advancements in large language models (LLMs) have prompted research into their ability to authentically simulate student behavior, addressing challenges in educational data collection and intervention design. A new three-stage collaborative pipeline has been developed to generate and filter high-quality student agents, utilizing automated scoring and human expert validation to enhance realism in simulations.