Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2606. 02359v1 Announce Type: new Abstract: Despite the remarkable progress of Large Language Model (LLM) based Multi-Agent Systems, most research focuses on optimizing coordination topology while largely underexploring the equally critical problem: how to transmit and optimize messages among agents effectively?
arXiv:2605. 02122v2 Announce Type: replace-cross Abstract: Human evaluation remains the primary standard for assessing modern AI systems, yet annotator disagreement, bias, and variability make system rankings fragile under standard majority vote aggregation.
arXiv:2606. 00357v1 Announce Type: new Abstract: Training strong large language models (LLMs) requires high-quality supervision, which is often scarce.
arXiv:2605. 26397v2 Announce Type: replace-cross Abstract: Safety alignment reduces explicitly harmful outputs but inadvertently encodes a sanitized, neuronormative representation of marginalized communication.
arXiv:2605. 29463v2 Announce Type: replace-cross Abstract: Reflexion-style agents rely on self-generated reflections as memory, implicitly assuming that agents can accurately diagnose their own failures.
arXiv:2606. 00133v1 Announce Type: new Abstract: World models, internal simulators that learn the structure and dynamics of an environment, have emerged as a central paradigm in the pursuit of artificial general intelligence, enabling agents to predict, plan, and reason within learned representations.
arXiv:2602. 15259v2 Announce Type: replace-cross Abstract: Generative AI agents equate understanding with resolving explicit queries, an assumption that confines interaction to what users can articulate.
arXiv:2605. 24202v2 Announce Type: replace Abstract: Multi-agent LLM workflows route inference through specialized roles to lift end-task accuracy, but jointly training those roles with reinforcement learning is unstable in ways that are poorly understood.
arXiv:2606. 00880v1 Announce Type: cross Abstract: Continual reinforcement learning aims to produce agents that learn not only to improve at their current tasks but also to adapt as task distributions change.
arXiv:2606. 00931v1 Announce Type: cross Abstract: Instruction-guided image editing is becoming a general interface for visual work, yet existing benchmarks still focus largely on narrow appearance edits and do not fully capture the diversity of real-image tasks in professional workflows.
arXiv:2606. 02337v1 Announce Type: new Abstract: Constrained Multi-agent reinforcement learning (CMARL) faces two intertwined challenges: the joint action space grows exponentially with the number of agents, and additional requirements couple agents in ways that reward structure alone does not capture.
arXiv:2606. 02470v1 Announce Type: new Abstract: The Model Context Protocol (MCP) has emerged as a transformative standard for connecting large language models (LLMs) with external data sources and tools, and has been rapidly adopted across personal applications and development platforms.
arXiv:2606. 02373v1 Announce Type: new Abstract: Search agents are often trained as policies over growing transcripts: the model must decide how to search while also remembering what it has seen, which evidence is useful, which constraints remain open, and which claims have actually been checked.
arXiv:2606. 02484v1 Announce Type: new Abstract: Recent advances in large language models and agentic AI systems have enabled significant progress in mathematical discovery, from solving competition problems to tackling research-level conjectures.
arXiv:2606. 02536v1 Announce Type: new Abstract: Text files such as skill files, memory files, and behavioral configuration files play a central role in defining how modern agents act.
arXiv:2606. 01230v1 Announce Type: new Abstract: Large language model agents are moving beyond text-only interaction toward physical-world control, with smart homes as a representative domain.
arXiv:2605. 24253v2 Announce Type: replace-cross Abstract: Digital pathology archives increasingly contain multiple whole-slide images (WSIs) per case, capturing spatially distinct tumour regions and reflecting intrinsic morphological heterogeneity.
arXiv:2509. 12263v3 Announce Type: replace Abstract: Large multimodal models (LMMs) encode physical laws observed during training, such as momentum conservation, as parametric knowledge.
arXiv:2606. 01019v1 Announce Type: cross Abstract: Large Language Model (LLM) generation remains expensive because autoregressive decoding calls the model once for each new token.
arXiv:2606. 02497v1 Announce Type: new Abstract: Time series forecasting has advanced rapidly, especially with the emergence of foundation models that show strong zero-shot performance on numerical extrapolation.