Latest AI Papers: LLMs And Reinforcement Learning
Hey everyone! đź‘‹ Here's a rundown of the latest and greatest in the world of Artificial Intelligence, focusing on Large Language Models (LLMs) and Reinforcement Learning (RL). I've curated a list of 15 papers from November 6, 2025, to keep you in the loop. These papers are available on Github page for a better reading experience.
Large Language Models: The Cutting Edge
Let's dive into what's new with Large Language Models. These models are constantly evolving, and the following papers highlight some exciting advancements.
Agent-Omni: Multimodal Reasoning
First up, we have Agent-Omni: Test-Time Multimodal Reasoning via Model Coordination for Understanding Anything. This paper explores how to enhance LLMs' reasoning capabilities by coordinating different models. It's all about making sure these models can understand anything, even when dealing with multiple types of information at once. It has 16 pages, 7 figures, and 14 tables, and it's currently under review. This is really exciting stuff, as it moves us closer to AI systems that can truly grasp the world like we do!
ValueCompass: Aligning Human and LLMs
Next, we have ValueCompass: A Framework for Measuring Contextual Value Alignment Between Human and LLMs. This paper tackles the crucial issue of aligning the values of LLMs with those of humans. This is a very important topic, since the key to trustworthy and useful AI systems is making sure they understand and respect human values. The paper provides a framework for measuring how well LLMs do this. This is important stuff, especially if we want to ensure AI acts responsibly.
Strategic Communication and Language Bias
The following is Strategic Communication and Language Bias in Multi-Agent LLM Coordination, and it dives into how LLMs coordinate. The study explores the strategic use of language within multi-agent systems and how it might create biases. This is a significant aspect of LLM research, as it contributes to their usability and fairness. Ensuring communication strategies don't lead to unfair results is crucial.
Can LLMs Subtract Numbers?
Then, we have a very intriguing question: Can LLMs subtract numbers?. The paper is a work-in-progress, presented at a MathNLP non-archival presentation. It looks like the researchers are questioning the very foundation of LLMs' capabilities. Can these models perform basic arithmetic accurately? The answer might surprise you, and it is a good reminder that even the most advanced systems have limitations.
When One Modality Sabotages the Others
When One Modality Sabotages the Others: A Diagnostic Lens on Multimodal Reasoning. This paper, accepted at the Multimodal Algorithmic Reasoning (MAR) Workshop, NeurIPS 2025, offers insights into how different types of information can hinder each other in multimodal systems. The goal is to provide a comprehensive analysis of the issues, which should help to address the issue.
Growing Transformers: Modular Composition
Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate. This paper investigates more efficient methods for creating and developing Transformers. The paper investigates a more modular strategy. They want to be able to add extra capabilities or adapt the models. This research could be very significant in improving the overall efficiency.
When Visualizing is the First Step to Reasoning
When Visualizing is the First Step to Reasoning: MIRA, a Benchmark for Visual Chain-of-Thought. Visualizing data and ideas may be a first step to solving problems. This benchmark aims to provide a standardized method for evaluating visual reasoning abilities. This benchmark could be used to enhance reasoning across a variety of visual applications.
A Comparative Analysis of LLM Adaptation
A Comparative Analysis of LLM Adaptation: SFT, LoRA, and ICL in Data-Scarce Scenarios explores different methods for adapting LLMs. This is critical since it helps determine how LLMs can be adjusted to new tasks, particularly when there is a lack of training data. Understanding which methods work best in data-scarce scenarios could make LLMs more widely applicable.
ConMeZO: Gradient-Free Finetuning
ConMeZO: Adaptive Descent-Direction Sampling for Gradient-Free Finetuning of Large Language Models. The paper focuses on the optimization of LLMs. This strategy seeks to make LLMs more accessible for tuning by removing the need for gradient computations. This is useful because it may make the adaptation of LLMs easier and more efficient.
Controlling Performance and Budget of a Centralized Multi-agent LLM System
Controlling Performance and Budget of a Centralized Multi-agent LLM System with Reinforcement Learning. This study utilizes Reinforcement Learning to manage the performance and cost of a multi-agent system. This is a crucial topic since it makes sure the systems not only perform effectively but also manage resources effectively. The methods investigated have the potential to boost the general efficiency and scalability.
AI Diffusion in Low Resource Language Countries
AI Diffusion in Low Resource Language Countries looks at how AI may be implemented and used in nations where resources are limited. This is a very important topic, since it stresses the need to make AI resources accessible to all. The study provides insights and suggestions for promoting AI adoption and the advantages it may bring to a wide range of individuals.
ORANGE: Domain Knowledge for Text-to-SQL
ORANGE: An Online Reflection ANd GEneration framework with Domain Knowledge for Text-to-SQL introduces a new framework for converting text to SQL queries using domain knowledge. It is essential since it tackles the problem of converting natural language into a structured query language. The framework offers a method for boosting the precision and effectiveness of SQL generation. This may be very helpful in a variety of data-driven applications.
Agentic World Modeling for 6G
Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning. It uses agent-based models for real-time reasoning in the context of 6G networks. It uses agent-based models for real-time reasoning in the context of 6G networks. The research will improve 6G systems. It could also lead to advancements in communication and networking.
CostBench: Multi-Turn Cost-Optimal Planning
CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents presents a benchmark for analyzing the planning and adaptation of LLM-based agents. The goal is to provide a structured way of assessing how well the agents can function in dynamic and cost-sensitive contexts. Such evaluations are critical for improving the efficiency and dependability of these agents.
Tokens, the oft-overlooked appetizer
Tokens, the oft-overlooked appetizer: Large language models, the distributional hypothesis, and meaning The paper examines how tokens are used in LLMs. The idea is to shed light on how tokens are used in LLMs. The goal is to advance our knowledge of how LLMs interpret and process information. This insight could influence how LLMs are designed and improved in the future.
Reinforcement Learning: Taking Action
Now, let's explore the exciting realm of Reinforcement Learning. Here are some intriguing studies in the field, where agents learn by interacting with their environment to achieve specific goals.
Imagine Beyond! Distributionally Robust Auto-Encoding
Imagine Beyond! Distributionally Robust Auto-Encoding for State Space Coverage in Online Reinforcement Learning explores how to improve state-space coverage in online reinforcement learning. This research could improve the overall robustness and adaptability of RL agents.
MemSearcher: Reasoning and Managing Memory
MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning focuses on developing LLMs capable of reasoning, searching, and managing memory. The study aims to improve the models' overall cognitive abilities and their capacity to solve difficult problems. The goal is to produce more sophisticated and competent AI systems.
From Solo to Symphony: Orchestrating Multi-Agent Collaboration
From Solo to Symphony: Orchestrating Multi-Agent Collaboration with Single-Agent Demos. This paper offers a fresh perspective on multi-agent collaboration. The research investigates new methods for orchestrating cooperation among multiple agents. The overall goal is to produce systems that can work together effectively. The project seeks to create better collaboration and coordination techniques for AI systems.
Controlling Performance and Budget of a Centralized Multi-agent LLM System
Controlling Performance and Budget of a Centralized Multi-agent LLM System with Reinforcement Learning is the same as above. This study uses Reinforcement Learning to manage the performance and cost of a multi-agent system. This is a crucial topic since it makes sure the systems not only perform effectively but also manage resources effectively. The methods investigated have the potential to boost the general efficiency and scalability.
Noise-based reward-modulated learning
Noise-based reward-modulated learning explores the use of noise in RL systems. The focus of the research is on understanding how noise may affect the learning process. The method has the potential to boost the stability and efficacy of RL agents. This is because it presents a new perspective on how rewards are given and how learning is improved.
VidEmo: Affective-Tree Reasoning
VidEmo: Affective-Tree Reasoning for Emotion-Centric Video Foundation Models. This study provides insights into how emotional factors are used in video foundation models. This is about incorporating emotion-centric reasoning into video analysis. The research seeks to improve the AI's ability to understand the emotional aspects of video content. This method has the potential to improve video comprehension and interaction.
Curriculum Design for Trajectory-Constrained Agent
Curriculum Design for Trajectory-Constrained Agent: Compressing Chain-of-Thought Tokens in LLMs. The research is focused on how to make LLMs' training more efficient. The paper seeks to improve the performance of LLMs by optimizing the use of chain-of-thought tokens during training. The goal is to increase the precision and efficiency of the models. The project will likely have a significant impact on how LLMs are created and optimized.
Audio-Thinker: Guiding Audio Language Model
Audio-Thinker: Guiding Audio Language Model When and How to Think via Reinforcement Learning aims to improve the performance of audio language models. The research focuses on when and how to encourage these models to think via Reinforcement Learning. The main aim is to improve the models' comprehension and their responsiveness. The use of this method has the potential to considerably advance audio language processing.
RL-Aided Cognitive ISAC
RL-Aided Cognitive ISAC: Robust Detection and Sensing-Communication Trade-offs is the subject of this study. The focus is on employing RL to boost the dependability of integrated sensing and communication systems. The goal is to improve the effectiveness of these systems in a variety of applications. This study seeks to make communication and sensing more dependable.
FELA: Feature Engineering of Industrial Event Log Data
FELA: A Multi-Agent Evolutionary System for Feature Engineering of Industrial Event Log Data. The system is for automating feature engineering. The main goal is to improve the quality of event log data. The goal is to improve the overall quality of data analysis and machine learning models. The framework has the potential to improve industrial data analysis significantly.
FRASA: Fall Recovery and Stand Up of Humanoid Robots
FRASA: An End-to-End Reinforcement Learning Agent for Fall Recovery and Stand Up of Humanoid Robots. This research focuses on the application of RL to enhance the capabilities of humanoid robots. It gives these robots the ability to recover from falls and stand up. The goal is to increase the practical usefulness of humanoid robots. The research may considerably improve the usefulness of robots.
Extended Friction Models
Extended Friction Models for the Physics Simulation of Servo Actuators. It seeks to improve the accuracy of servo actuator physics simulations. The goal is to enhance the realism and precision of simulated environments. The research seeks to increase the dependability of these simulations.
Natural-gas storage modelling
Natural-gas storage modelling by deep reinforcement learning explores using Deep Reinforcement Learning (DRL) for modeling natural-gas storage. The goal is to boost the effectiveness and efficiency of energy management. The study has the potential to enhance how we handle these resources.
Adaptive GR(1) Specification Repair
Adaptive GR(1) Specification Repair for Liveness-Preserving Shielding in Reinforcement Learning is the subject of this study. It uses methods to fix faults in the design of systems. This research seeks to improve the reliability and safety of RL systems. This is an important step toward creating more dependable and robust AI agents.
Scaffolded Language Models
Scaffolded Language Models with Language Supervision for Mixed-Autonomy: A Survey provides a survey of the field. The research aims to improve collaboration between human and automated systems. The aim is to improve how humans and AI systems collaborate in various scenarios. This could lead to more helpful and user-friendly systems.
That's all for now, folks! I hope you enjoyed this overview of the latest AI developments. Keep an eye out for more updates soon! If you want to know more about the papers, please check the Github page for a better reading experience.