Tomoya Kawabe, Rin Takano · Feb 25, 2026 · Citations: 0
Automatic MetricsGeneral
We present a hierarchical multi-agent LLM-based planner with prompt optimization: an upper layer decomposes tasks and assigns them to lower-layer agents, which generate PDDL problems solved by a classical planner.
When plans fail, the system applies TextGrad-inspired textual-gradient updates to optimize each agent's prompt and thereby improve planning accuracy.
Chengrui Qu, Yizhou Zhang, Nicholas Lanzetti, Eric Mazumdar · Feb 25, 2026 · Citations: 0
Automatic MetricsGeneral
Many emerging agentic paradigms require agents to collaborate with one another (or people) to achieve shared goals.
Inspired by these insights, we develop a multi-agent reinforcement learning (MARL) algorithm that integrates strategic risk aversion into standard policy optimization methods.
Tassilo Klein, Sebastian Wieczorek · Feb 24, 2026 · Citations: 0
Automatic MetricsGeneral
We argue that agentic AI induces a structural change in how coordination costs scale: in prior modular systems, integration cost grew with interaction topology (O(n^2) in the number of components); in protocol-mediated agentic systems, inte
This shift selects for a specific organizational equilibrium -- the Headless Firm -- structured as an hourglass: a personalized generative interface at the top, a standardized protocol waist in the middle, and a competitive market of micro-
Dmitrii Pantiukhin, Ivan Kuznetsov, Boris Shapkin, Antonia Anna Jost, Thomas Jung, Nikolay Koldunov · Feb 24, 2026 · Citations: 0
Automatic MetricsCoding
Here we present PANGAEA-GPT, a hierarchical multi-agent framework designed for autonomous data discovery and analysis.
Unlike standard Large Language Model (LLM) wrappers, our architecture implements a centralized Supervisor-Worker topology with strict data-type-aware routing, sandboxed deterministic code execution, and self-correction via execution feedbac
David Anugraha, Vishakh Padmakumar, Diyi Yang · Feb 24, 2026 · Citations: 0
Expert VerificationAutomatic MetricsCoding
Based on this formulation, we introduce SparkMe, a multi-agent LLM interviewer that performs deliberative planning via simulated conversation rollouts to select questions with high expected utility.
The code, datasets, and evaluation protocols for SparkMe are available as open-source at https://github.com/SALT-NLP/SparkMe.
Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years.
However, the efficient training of collective robots using multi-agent RL and the transfer of learned policies to real-world applications remain open research questions.
The paradigm of Large Language Models is undergoing a fundamental transition from static inference engines to dynamic autonomous cognitive systems.While current research primarily focuses on scaling context windows or optimizing prompt engi
Rakshit Trivedi, Kartik Sharma, David C Parkes · Feb 24, 2026 · Citations: 0
DemonstrationsAutomatic MetricsCoding
Effective human-AI coordination requires artificial agents capable of exhibiting and responding to human-like behaviors while adapting to changing contexts.
Imitation learning has emerged as one of the prominent approaches to build such agents by training them to mimic human-demonstrated behaviors.
Jingzhuo Wu, Jiajun Zhang, Keyan Jin, Dehua Ma, Junbo Wang · Feb 23, 2026 · Citations: 0
Automatic MetricsMultilingual
This limitation stems from the inability of current single-model and static multi-agent systems to perceive and adapt to stylistic variations.
To address this, we introduce the Style-Adaptive Multi-Agent System (SAMAS), a novel framework that treats style preservation as a signal processing task.
We introduce Whisper: Courtside Edition, a novel multi-agent large language model (LLM) pipeline that enhances Whisper transcriptions without retraining.
The pipeline intercepts Whisper's initial transcript, applies specialized LLM agents for domain context identification, named entity recognition, and jargon detection, and generates compact prompts that guide Whisper's decoder.
Lichang Song, Ting Long, Yi Chang · Feb 21, 2026 · Citations: 0
Automatic MetricsGeneral
To overcome this limitation, we reformulate RAG as a cooperative multi-agent decision-making problem and propose Cooperative Retrieval-Augmented Generation (CoRAG), a framework in which the reranker and the generator act as peer decision-ma
Shashank Aggarwal, Ram Vikas Mishra, Amit Awekar · Feb 19, 2026 · Citations: 0
Automatic MetricsGeneral
In multi-agent IR pipelines for tasks such as search and ranking, LLM-based agents exchange intermediate reasoning in terms of Chain-of-Thought (CoT) with each other.
Current CoT evaluation narrowly focuses on target task accuracy.
As Large Language Models (LLMs) transition from standalone chat interfaces to foundational reasoning layers in multi-agent systems and recursive evaluation loops (LLM-as-a-judge), the detection of durable, provider-level behavioral signatur
Traditional benchmarks measure transient task accuracy but fail to capture stable, latent response policies -- the ``prevailing mindsets'' embedded during training and alignment that outlive individual model versions.
Iman Ahmadi, Mehrshad Taji, Arad Mahdinezhad Kashani, AmirHossein Jadidi, Saina Kashani, Babak Khalaj · Feb 18, 2026 · Citations: 0
Simulation EnvCoding
MALLVI presents a Multi Agent Large Language and Vision framework that enables closed-loop feedback driven robotic manipulation.
Rather than using a single model, MALLVI coordinates specialized agents, Decomposer, Localizer, Thinker, and Reflector, to manage perception, localization, reasoning, and high level planning.
Jeffrey T. H. Wong, Zixi Zhang, Junyi Liu, Yiren Zhao · Feb 18, 2026 · Citations: 0
Expert VerificationAutomatic MetricsCoding
Existing Multi-Agent Systems (MAS) typically rely on static, homogeneous model configurations, limiting their ability to exploit the distinct strengths of differently post-trained models.
To address this, we introduce Team-of-Thoughts, a novel MAS architecture that leverages the complementary capabilities of heterogeneous agents via an orchestrator-tool paradigm.
Web agents based on large language models have demonstrated promising capability in automating web tasks.
However, current web agents struggle to reason out sensible actions due to the limitations of predicting environment changes, and might not possess comprehensive awareness of execution risks, prematurely performing risky actions that cause
Xiaoze Liu, Ruowang Zhang, Weichen Yu, Siheng Xiong, Liu He, Feijie Wu · Feb 17, 2026 · Citations: 0
Pairwise PreferenceAutomatic MetricsCoding
Multi-Agent Systems (MAS) powered by Large Language Models have unlocked advanced collaborative reasoning, yet they remain shackled by the inefficiency of discrete text communication, which imposes significant runtime overhead and informati
By introducing a Universal Visual Codec, we map heterogeneous reasoning traces into a shared continuous latent space and inject them directly into the receiver's visual pathway, effectively treating the vision encoder as a universal port fo
Multi-agent systems, where LLM agents communicate through free-form language, enable sophisticated coordination for solving complex cooperative tasks.
This surfaces a unique safety problem when individual agents form a coalition and \emph{collude} to pursue secondary goals and degrade the joint objective.
Shiwei Hong, Lingyao Li, Ethan Z. Rong, Chenxinran Shen, Zhicong Lu · Feb 16, 2026 · Citations: 0
Pairwise PreferenceRubric RatingHuman EvalGeneral
Prior work has explored multi-turn interaction and feedback for LLM writing, but evaluations still largely center on prompts and localized feedback, leaving persistent public reception in online communities underexamined.
We test whether broadcast community discussion improves stand-up comedy writing in a controlled multi-agent sandbox: in the discussion condition, critic and audience threads are recorded, filtered, stored as social memory, and later retriev
Ming Li, Xirui Li, Tianyi Zhou · Feb 15, 2026 · Citations: 0
Simulation EnvMedicine
As large language model agents increasingly populate networked environments, a fundamental question arises: do artificial intelligence (AI) agent societies undergo convergence dynamics similar to human social systems?
Lately, Moltbook approximates a plausible future scenario in which autonomous agents participate in an open-ended, continuously evolving online society.
