EtCon: Edit-then-Consolidate for Reliable Knowledge Editing

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.

TempR1 has been introduced as a temporal-aware multi-task reinforcement learning framework designed to enhance the temporal understanding of Multimodal Large Language Models (MLLMs). This framework aims to improve capabilities in long-form video analysis, including tasks such as temporal localization and action detection.

The introduction of Semantic Soft Bootstrapping (SSB) represents a significant advancement in long context reasoning for large language models (LLMs), allowing them to enhance cognitive capabilities without relying on reinforcement learning with verifiable rewards (RLVR). This self-distillation technique enables the model to act as both teacher and student, improving its reasoning abilities through varied semantic contexts during training.

The recent study on Group Relative Policy Optimization (GRPO) in Search-R1 highlights a significant issue known as Lazy Likelihood Displacement (LLD), which leads to a collapse in training effectiveness. This phenomenon results in a self-reinforcing cycle of declining response quality, characterized by low-confidence outputs and inflated gradients. The research empirically demonstrates this collapse across various models engaged in search-integrated question answering tasks.

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.

The Natural Language Actor-Critic (NLAC) algorithm has been introduced to enhance the training of large language model (LLM) agents, which interact with environments over extended periods. This method addresses challenges in learning from sparse rewards and aims to stabilize training through a generative LLM critic that evaluates actions in natural language space.