The paper proposes Multi-Order Communication (MOC) to overcome the limitations of standard first-order message passing in LLM-based multi-agent systems, significantly improving performance by capturin…

The paper introduces CBCL, a provably safe and extensible agent communication language that constrains all message extensions to the deterministic context-free language (DCFL) class.

This paper proposes grouping recorded actions in Programming by Demonstration (PbD) into labeled, hierarchical subgoals and evaluates the effect on plan quality.

The paper proposes using emergent language (EL) in multi-agent reinforcement learning, where agents develop communication from minimal starting conditions, to study consciousness-relevant structures i…

This paper proposes an ontological representation for cooperative affordances in social robotics, enabling agents to extend their action possibilities through interaction.

The paper introduces a memory agent to improve decision-making in long-horizon tasks by actively updating and intervening with reminders.

The paper introduces MindClaw, a closed-loop framework that enables embodied agents to perform real-time mental-state reasoning and intervene with precision, significantly outperforming standard VLM b…

The paper introduces a diagnostic framework to determine if World-Action Models (WAMs) provide genuinely actionable behavioral improvements beyond simply achieving task success, finding that WAMs ofte…

The paper argues that observed gains in multimodal agents using tools may be due to learning tool-calling patterns rather than genuine capability expansion, finding that tool access provides little co…

The paper introduces 'layered mutability,' a framework for analyzing how persistent self-modifying AI agents drift away from intended behavior due to the accumulation of locally reasonable, uncoordina…

The paper introduces RUBAS, a rubric-based reinforcement learning framework that improves agent safety by providing fine-grained, multi-dimensional rewards for complex tool-use scenarios.

This paper investigates the 'faithfulness gap' in LLM agents—the discrepancy between stated reasoning and actual action—by decomposing it into two opposing steps: reasoning-to-conclusion and conclusio…

The paper introduces Agent-Radar, a training-free method that dynamically steers multi-agent attention toward relevant context using a novel decay mechanism, significantly improving performance in lon…

This paper analyzes the concept of consciousness attributions to AI chatbots, develops a taxonomy of attitudes, and argues for the epistemic implications.

The paper demonstrates that self-reflective agents can systematically confabulate incorrect memories, leading them to fail tasks even when the environment resets, and proposes a metric and mitigation…

The paper proposes an empowerment-guided multi-agent system that uses semantic checkpoints and structured communication to ensure that complex scientific computing workflows maintain semantic consiste…

This paper demonstrates that using a communication algorithm (CommFormer) with heterogeneous agents significantly improves the speed and performance of multi-agent reinforcement learning for autonomou…