Warp-Cortex: An Asynchronous, Memory-Efficient Architecture for Million-Agent Cognitive Scaling on Consumer Hardware

• URL: http://arxiv.org/abs/2601.01298v1
• Date: Sat, 03 Jan 2026 23:11:21 GMT
• Title: Warp-Cortex: An Asynchronous, Memory-Efficient Architecture for Million-Agent Cognitive Scaling on Consumer Hardware
• Authors: Jorge L. Ruiz Williams,
• Abstract summary: We present Warp Cortex, an asynchronous architecture that theoretically enables million-agent cognitive scaling.<n>We empirically demonstrate 100 concurrent agents at 2.2 GB total VRAM, with theoretical capacity exceeding 1,000 agents before compute latency becomes the bottleneck.<n>We further introduce Referential Injection, a non-intrusive KV-cache update mechanism that allows asynchronous sub-agents to influence primary generation without stream disruption.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current multi-agent Large Language Model (LLM) frameworks suffer from linear memory scaling, rendering "System 2" parallel reasoning impractical on consumer hardware. We present Warp Cortex, an asynchronous architecture that theoretically enables million-agent cognitive scaling by decoupling agent logic from physical memory. Through Singleton Weight Sharing and a novel Topological Synapse--inspired by hybrid landmarking techniques from Topological Data Analysis (TDA)--we reduce memory complexity from O(N * L) to O(1) for weights and O(N * k) for context, where k << L. By treating the KV-cache as a point cloud in latent space, we apply witness-complex-inspired sparsification to preserve persistent homological features of the context manifold. On a single NVIDIA RTX 4090, we empirically demonstrate 100 concurrent agents at 2.2 GB total VRAM, with theoretical capacity exceeding 1,000 agents before compute latency becomes the bottleneck. We further introduce Referential Injection, a non-intrusive KV-cache update mechanism that allows asynchronous sub-agents to influence primary generation without stream disruption.

Related papers

• LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory [97.14005794889134]
  We present LoGeR, a novel architecture that scales dense 3D reconstruction to extremely long sequences without post-optimization.<n>LoGeR processes video streams in chunks, leveraging strong bidirectional priors for high-fidelity intra-chunk reasoning.<n>This memory architecture enables LoGeR to be trained on sequences of 128 frames, and generalize up to thousands of frames during inference.
  arXiv  Detail & Related papers  (2026-03-03T18:55:37Z)
• From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents [78.30630000529133]
  We propose MM-Mem, a pyramidal multimodal memory architecture grounded in Fuzzy-Trace Theory.<n> MM-Mem memory structures hierarchically into a Sensory Buffer, Episodic Stream, and Symbolic.<n>Experiments confirm the effectiveness of MM-Mem on both offline and streaming tasks.
  arXiv  Detail & Related papers  (2026-03-02T05:12:45Z)
• UniX: Unifying Autoregression and Diffusion for Chest X-Ray Understanding and Generation [98.93314262366681]
  We present UniX, a next-generation unified medical foundation model for chest X-ray understanding and generation.<n>UniX decouples the two tasks into an autoregressive branch for understanding and a diffusion branch for high-fidelity generation.<n>On two representative benchmarks, UniX achieves a 46.1% improvement in understanding performance and a 24.2% gain in generation quality.
  arXiv  Detail & Related papers  (2026-01-16T18:59:58Z)
• Aeon: High-Performance Neuro-Symbolic Memory Management for Long-Horizon LLM Agents [0.0]
  Large Language Models (LLMs) are constrained by the quadratic computational cost of self-attention and the "Lost in the Middle" phenomenon.<n>We propose Aeon, a Neuro-Symbolic Cognitive Operating System that redefines memory not as a static store, but as a managed OS resource.
  arXiv  Detail & Related papers  (2026-01-14T15:23:22Z)
• SYNAPSE: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation [29.545442480332515]
  We introduce Synapse, a unified memory architecture that transcends static rather than pre-computed links.<n>We show that Synapse significantly outperforms state-of-the-art methods in complex temporal and multi-hop reasoning tasks.<n>Our code and data will be made publicly available upon acceptance.
  arXiv  Detail & Related papers  (2026-01-06T06:19:58Z)
• Breaking the Memory Wall: Exact Analytical Differentiation via Tiled Operator-Space Evolution [3.551701030393209]
  Phase Gradient Flow (PGF) is a framework that computes exact analytical derivatives by operating directly in the state-space manifold.<n>Our method delivers O(1) memory complexity relative to sequence length, yielding a 94% reduction in peak VRAM and a 23x increase in throughput compared to standard Autograd.<n>Our work enables chromosome-scale sensitivity analysis on a single GPU, bridging the gap between theoretical infinite-context models and practical hardware limitations.
  arXiv  Detail & Related papers  (2025-12-28T20:27:58Z)
• Knowledge-Informed Neural Network for Complex-Valued SAR Image Recognition [51.03674130115878]
  We introduce the Knowledge-Informed Neural Network (KINN), a lightweight framework built upon a novel "compression-aggregation-compression" architecture.<n>KINN establishes a state-of-the-art in parameter-efficient recognition, offering exceptional generalization in data-scarce and out-of-distribution scenarios.
  arXiv  Detail & Related papers  (2025-10-23T07:12:26Z)
• Accelerating LLM Inference via Dynamic KV Cache Placement in Heterogeneous Memory System [20.652641518700346]
  Large Language Model (LLM) inference is increasingly constrained by memory bandwidth.<n>Modern AI hardware now integrates high-bandwidth memory (HBM) with high-speed off-package DRAM.<n>This work investigates dynamic KV cache placement across such systems to maximize aggregated bandwidth utilization under capacity constraints.
  arXiv  Detail & Related papers  (2025-08-17T19:07:08Z)
• Hardware-Adaptive and Superlinear-Capacity Memristor-based Associative Memory [5.902429789895426]
  We introduce and experimentally demonstrate on integrated memristor hardware a new hardware-adaptive learning algorithm for associative memories.<n>Our approach achieves 3x effective capacity under 50% device faults compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-05-19T10:55:09Z)
• Enhancing Biologically Inspired Hierarchical Temporal Memory with Hardware-Accelerated Reflex Memory [0.29127054707887967]
  This paper introduces a Reflex Memory (RM) block, inspired by the Spinal Cord's working mechanisms, to accelerate the processing of first-order inferences.<n>The integration of RM with HTM forms a system called the Accelerated Hierarchical Temporal Memory (AHTM), which processes repetitive information more efficiently.<n>Compared to the original algorithm AHTM, AHTM accelerates inference by up to 7.55x, while H-AHTM further enhances performance with a 10.10x speedup.
  arXiv  Detail & Related papers  (2025-04-01T17:40:12Z)
• A Universal Framework for Compressing Embeddings in CTR Prediction [68.27582084015044]
  We introduce a Model-agnostic Embedding Compression (MEC) framework that compresses embedding tables by quantizing pre-trained embeddings.<n>Our approach consists of two stages: first, we apply popularity-weighted regularization to balance code distribution between high- and low-frequency features.<n> Experiments on three datasets reveal that our method reduces memory usage by over 50x while maintaining or improving recommendation performance.
  arXiv  Detail & Related papers  (2025-02-21T10:12:34Z)
• B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory [91.81390121042192]
  We develop a class of models called B'MOJO to seamlessly combine eidetic and fading memory within an composable module. B'MOJO's ability to modulate eidetic and fading memory results in better inference on longer sequences tested up to 32K tokens.
  arXiv  Detail & Related papers  (2024-07-08T18:41:01Z)
• Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model [55.116403765330084]
  Current AIGC methods, such as score-based diffusion, are still deficient in terms of rapidity and efficiency. We propose a time-continuous and analog in-memory neural differential equation solver for score-based diffusion. We experimentally validate our solution with 180 nm resistive memory in-memory computing macros.
  arXiv  Detail & Related papers  (2024-04-08T16:34:35Z)
• Heterogenous Memory Augmented Neural Networks [84.29338268789684]
  We introduce a novel heterogeneous memory augmentation approach for neural networks. By introducing learnable memory tokens with attention mechanism, we can effectively boost performance without huge computational overhead. We show our approach on various image and graph-based tasks under both in-distribution (ID) and out-of-distribution (OOD) conditions.
  arXiv  Detail & Related papers  (2023-10-17T01:05:28Z)
• ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation [106.04777600352743]
  Differentiable architecture search (DARTS) is largely hindered by its substantial memory cost since the entire supernet resides in the memory. The single-path DARTS comes in, which only chooses a single-path submodel at each step. While being memory-friendly, it also comes with low computational costs. We propose a new algorithm called RObustifying Memory-Efficient NAS (ROME) to give a cure.
  arXiv  Detail & Related papers  (2020-11-23T06:34:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.