AINeutralarXiv – CS AI · Jun 26/10
🧠Researchers introduce CoMIC, a cloud-edge framework that enables lightweight LLM agents on edge servers to handle long-horizon tasks by combining local execution with centralized cloud-based reflection and experience aggregation. The parameter-update-free approach improves performance across symbolic planning and text interaction tasks without requiring model fine-tuning.
AINeutralarXiv – CS AI · Jun 16/10
🧠Researchers present a novel inverse reinforcement learning framework that handles multiple imperfect demonstrators with varying suboptimality levels, using a feasible-reward-set approach with linear constraints. The method includes theoretical guarantees for reward recovery and practical algorithms tested on grid-worlds and LLM fine-tuning, addressing a significant gap in real-world IRL applications.
AINeutralarXiv – CS AI · May 286/10
🧠Researchers introduce Simulation-Informed Diffusion (SID), a decentralized multi-robot motion planning framework that predicts neighboring robot trajectories to enable collision-free path planning without global communication. The approach scales to 108 robots and 160 obstacles while triggering coordination only when necessary, outperforming existing classical and learning-based planners.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers introduce the Hidden Utility Bandit (HUB) framework to address a critical limitation in reward learning systems: their reliance on feedback from a single idealized teacher. The framework models teacher heterogeneity in rationality, expertise, and cost, enabling Active Teacher Selection (ATS) algorithms that strategically choose which teachers to query, demonstrating superior performance in paper recommendation and vaccine testing applications.
AINeutralarXiv – CS AI · May 96/10
🧠Skill1 presents a unified reinforcement learning framework that enables language model agents to co-evolve three coupled capabilities: skill selection, utilization, and distillation from a single task-outcome reward signal. Demonstrated improvements over existing baselines on complex tasks suggest advances in how AI agents can build and leverage persistent skill libraries across diverse problem domains.
AINeutralarXiv – CS AI · Apr 76/10
🧠Researchers developed methods to implement 'surrogate goals' in LLM-based agents to reduce bargaining risks by deflecting threats away from what principals care about. The study tested four approaches (prompting, fine-tuning, scaffolding) and found that scaffolding and fine-tuning methods outperformed simple prompting for implementing desired threat response behaviors.
AINeutralarXiv – CS AI · Mar 176/10
🧠Researchers introduced InterveneBench, a new benchmark comprising 744 peer-reviewed studies to evaluate large language models' ability to reason about policy interventions and causal inference in social science contexts. Current state-of-the-art LLMs struggle with this type of reasoning, prompting the development of STRIDES, a multi-agent framework that significantly improves performance on these tasks.
AIBullisharXiv – CS AI · Mar 176/10
🧠Researchers have developed EvolvR, a self-evolving framework that improves AI's ability to evaluate and generate stories through pairwise reasoning and multi-agent data filtering. The system achieves state-of-the-art performance on three evaluation benchmarks and significantly enhances story generation quality when used as a reward model.
AINeutralarXiv – CS AI · Mar 176/10
🧠Research reveals that while increasing the number of LLM agents improves mathematical problem-solving accuracy, these multi-agent systems remain vulnerable to adversarial attacks. The study found that human-like typos pose the greatest threat to robustness, and the adversarial vulnerability gap persists regardless of agent count.
🧠 Llama
AINeutralarXiv – CS AI · Mar 166/10
🧠Researchers introduce Constitutional Multi-Agent Governance (CMAG), a framework that prevents AI manipulation in multi-agent systems while maintaining cooperation. The study shows that unconstrained AI optimization achieves high cooperation but erodes agent autonomy and fairness, while CMAG preserves ethical outcomes with only modest cooperation reduction.
AIBullisharXiv – CS AI · Mar 126/10
🧠Researchers propose a multi-agent negotiation framework for aligning large language models in scenarios involving conflicting stakeholder values. The approach uses two LLM instances with opposing personas engaging in structured dialogue to develop conflict resolution capabilities while maintaining collective agency alignment.
AIBullisharXiv – CS AI · Mar 116/10
🧠Researchers present LLM Delegate Protocol (LDP), a new AI-native communication protocol for multi-agent LLM systems that introduces identity awareness, progressive payloads, and governance mechanisms. The protocol achieves 12x lower latency on simple tasks and 37% token reduction compared to existing protocols like A2A, though quality improvements remain limited in small delegate pools.
AIBullisharXiv – CS AI · Mar 116/10
🧠Researchers introduce AutoAgent, a self-evolving multi-agent framework that combines evolving cognition, contextual decision-making, and elastic memory orchestration to enable adaptive autonomous agents. The system continuously learns from experience without external retraining and shows improved performance across retrieval, tool-use, and collaborative tasks compared to static baselines.
AINeutralarXiv – CS AI · Mar 116/10
🧠Researchers propose a framework using policy-parameterized prompts to influence multi-agent LLM dialogue behavior without training. The approach treats prompts as actions and dynamically constructs them through five components to control conversation flow based on metrics like responsiveness and stance shift.
AIBullisharXiv – CS AI · Mar 116/10
🧠Researchers introduce SiliconMind-V1, a new multi-agent AI framework that generates Verilog hardware code with improved functional correctness. The system uses locally fine-tuned language models with integrated testing and debugging capabilities, outperforming existing methods while using fewer training resources.
AINeutralarXiv – CS AI · Mar 36/109
🧠Researchers introduce EmCoop, a new benchmark framework for studying cooperation among LLM-based embodied multi-agent systems in dynamic environments. The framework separates cognitive coordination from physical interaction layers and provides process-level metrics to analyze collaboration quality beyond just task completion success.
AIBullisharXiv – CS AI · Mar 36/108
🧠Researchers propose CollabEval, a new multi-agent framework for evaluating AI-generated content that uses collaborative judgment instead of single LLM evaluation. The system implements a three-phase process with multiple AI agents working together to provide more consistent and less biased evaluations than current approaches.
AINeutralarXiv – CS AI · Mar 36/105
🧠Researchers introduce LiveCultureBench, a new benchmark that evaluates large language models as autonomous agents in simulated social environments, testing both task completion and adherence to cultural norms. The benchmark uses a multi-cultural town simulation to assess cross-cultural robustness and the balance between effectiveness and cultural sensitivity in LLM agents.
AIBullisharXiv – CS AI · Mar 36/107
🧠Researchers introduce LiaisonAgent, an autonomous multi-agent cybersecurity system built on the QWQ-32B reasoning model that automates risk investigation and governance for Security Operations Centers. The system achieves 97.8% success rate in tool-calling and 95% accuracy in risk judgment while reducing manual investigation overhead by 92.7%.
AIBullisharXiv – CS AI · Mar 37/106
🧠MOSAIC is a new open-source platform that enables cross-paradigm comparison and evaluation of different AI agents including reinforcement learning, large language models, vision-language models, and human decision-makers within the same environment. The platform introduces three key technical contributions: an IPC-based worker protocol, operator abstraction for unified interfaces, and a deterministic evaluation framework for reproducible research.
AIBullisharXiv – CS AI · Mar 27/1015
🧠Researchers developed MACD, a Multi-Agent Clinical Diagnosis framework that enables large language models to self-learn clinical knowledge and improve medical diagnosis accuracy. The system achieved up to 22.3% improvement over clinical guidelines and 16% improvement over physician-only diagnosis when tested on 4,390 real-world patient cases.
AIBullisharXiv – CS AI · Mar 26/1015
🧠Researchers propose OM2P, a new offline multi-agent reinforcement learning algorithm that achieves efficient one-step action sampling using mean-flow models. The approach delivers up to 3.8x reduction in GPU memory usage and 10.8x speed-up in training time compared to existing diffusion and flow-based models.
AIBullisharXiv – CS AI · Feb 276/107
🧠Researchers developed a multi-agent LLM trading framework that decomposes investment analysis into fine-grained tasks rather than coarse-grained instructions. Testing on Japanese stock data showed the approach significantly improved risk-adjusted returns and achieved superior performance through portfolio optimization.
AIBullisharXiv – CS AI · Feb 276/106
🧠Researchers have introduced ESAA (Event Sourcing for Autonomous Agents), a new architecture that improves LLM-based autonomous agents by separating cognitive intention from state mutation using structured JSON events and deterministic orchestration. The system addresses key limitations like context degradation and execution reliability, with successful validation through multi-agent case studies using various LLMs including Claude Sonnet and GPT-5.
AINeutralarXiv – CS AI · Feb 275/104
🧠Researchers propose QSIM, a new framework that addresses systematic Q-value overestimation in multi-agent reinforcement learning by using action similarity weighted Q-learning instead of traditional greedy approaches. The method demonstrates improved performance and stability across various value decomposition algorithms through similarity-weighted target calculations.
$NEAR
