Leveraging multiple Large Language Models (LLMs) has proven effective for addressing complex, high-dimensional tasks, but current approaches often rely on static, manually engineered multi-agent configurations. To overcome these constraints, we present the Agentic Neural Network (ANN), a framework that conceptualizes multi-agent collaboration as a layered neural network architecture. In this design, each agent operates as a node, and each layer forms a cooperative team focused on a specific subtask. The proposed framework follows a two-phase optimization strategy: (1) Forward Phase - Drawing inspiration from neural network forward passes, tasks are dynamically decomposed into subtasks, and cooperative agent teams with suitable aggregation methods are constructed layer by layer. (2) Backward Phase - Mirroring backpropagation, we refine both global and local collaboration through iterative feedback, allowing agents to self-evolve their roles, prompts, and coordination. This neuro-symbolic approach enables our framework to create new or specialized agent teams post-training, delivering notable gains in accuracy and adaptability. Across seven benchmark datasets, ANN surpasses leading multi-agent baselines under the same configurations, showing consistent performance improvements.

Visual instruction tuning adapts pre-trained Multimodal Large Language Models (MLLMs) to follow human instructions for real-world applications. However, rapidly growing instruction datasets introduce substantial redundancy and computational costs. Existing data selection methods aim to prune this redundancy, but predominantly rely on computationally demanding proxy-based inference or training-based metrics, often exacerbating the efficiency bottlenecks they are intended to resolve. To address this challenge, we identify a critical yet overlooked factor: anisotropy in visual feature distributions. We find that this anisotropy induces a Global Semantic Drift that distorts similarity estimates and limits efficient data selection. Motivated by this insight, we devise PRISM, a training-free framework for efficient visual instruction selection. PRISM mitigates the dominant global drift through implicit re-centering, allowing intrinsic visual semantics to provide a reliable redundancy signal without proxy models or additional training. Empirically, PRISM reduces end-to-end GPU time by about 70% while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across multimodal and language understanding benchmarks and reaching 101.7% relative performance against the full-data baseline.

Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of NLP tasks, but they remain fundamentally stateless, constrained by limited context windows that hinder long-horizon reasoning. Recent efforts to address this limitation often augment LLMs with an external memory bank, yet most existing pipelines are static and heuristic-driven, lacking a learned mechanism for deciding what to store, update, or retrieve. We present Memory-R1, a reinforcement learning (RL) framework that equips LLMs with the ability to actively manage and utilize external memory through two specialized agents: a Memory Manager that learns structured operations, including ADD, UPDATE, DELETE, and NOOP; and an Answer Agent that pre-selects and reasons over relevant entries. Both agents are fine-tuned with outcome-driven RL (PPO and GRPO), enabling adaptive memory management with minimal supervision. With only 152 training QA pairs, Memory-R1 outperforms strong baselines and generalizes across diverse question types, three benchmarks (LoCoMo, MSC, LongMemEval), and multiple model scales (3B–14B).