Proposes the Intelligent Computing Architecture (ICA) as a six-layer framework with dual probabilistic-deterministic planes and three Amdahl-style heuristics to unify design of LLM-based systems.
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The paper defines AI Harness Engineering as a runtime substrate with eleven components and a four-level ladder that reframes agent reliability as a model-harness-environment system property rather than model capability alone.
OMC framework turns multi-agent AI into self-organizing companies with Talents, Talent Market, and E²R search, achieving 84.67% success on PRDBench (15.48 points above prior art).
ClawVM introduces a harness-managed virtual memory system for LLM agents that ensures deterministic residency and durability of state under token budgets by using typed pages and validated writeback.
E-mem uses a heterogeneous multi-agent setup for episodic context reconstruction in LLM agents, reaching over 54% F1 on LoCoMo while cutting token cost by over 70% compared to prior methods like GAM.
The paper delivers the first comprehensive review and unified taxonomy of agentic AI in remote sensing, covering single-agent copilots, multi-agent systems, planning mechanisms, benchmarks, and a roadmap while noting limitations in grounding and safety.
A survey that defines Compound AI Systems, proposes a multi-dimensional taxonomy based on component roles and orchestration strategies, reviews four foundational paradigms, and identifies key challenges for future research.
ASB is a new benchmark that tests 10 prompt injection attacks, memory poisoning, a novel Plan-of-Thought backdoor attack, and 11 defenses on LLM agents across 13 models, finding attack success rates up to 84.3% and limited defense effectiveness.
AgentLens uses step-level hidden-state detection and 10-dimensional subspace intervention for safety in multi-turn coding agents, evaluated on the new MAS benchmark across three LLMs.
An LLM-driven dual-loop controller claims 72.5% stable-phase improvement over default and 153.3% over the strongest non-LLM baseline, but the comparison protocol inflates the gaps by scoring baselines during continued exploration.
Holos is a five-layer LLM-based multi-agent system architecture using the Nuwa engine for agent generation, a market-driven Orchestrator for coordination, and an endogenous value cycle for incentive-compatible persistence in the Agentic Web.
The survey organizes RAG methods via a taxonomy of query-based, logits-based, latent, and parametric fusion with comparisons on accessibility, efficiency, applications, and challenges.
TopoClaw is a human-centric Agent OS that uses physical and social topology modeling to enable cross-boundary execution with identity attribution and context-aware governance.
AgentOpt introduces a framework-agnostic package that uses algorithms like UCB-E to find cost-effective model assignments in multi-step LLM agent pipelines, cutting evaluation budgets by 62-76% while maintaining near-optimal accuracy on benchmarks.
NEMORI is an adaptive memory distillation framework for LLM agents that transforms raw interactions into narratives and extracts insights via prediction error to decide what deserves retention.
Qualixar OS provides a runtime for multi-agent AI systems with support for 12 topologies, LLM-driven team design, dynamic routing, consensus judging, content attribution, and protocol bridging, achieving 100% accuracy on a custom 20-task suite at $0.000039 mean cost per task.
The paper analyzes evolving security and safety threats in generative AI from content generation to agentic actions, noting that attack surfaces expand faster than defenses and that many safeguards require institutional coordination not yet in place.
citing papers explorer
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Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture
Proposes the Intelligent Computing Architecture (ICA) as a six-layer framework with dual probabilistic-deterministic planes and three Amdahl-style heuristics to unify design of LLM-based systems.
-
AI Harness Engineering: A Runtime Substrate for Foundation-Model Software Agents
The paper defines AI Harness Engineering as a runtime substrate with eleven components and a four-level ladder that reframes agent reliability as a model-harness-environment system property rather than model capability alone.
-
From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company
OMC framework turns multi-agent AI into self-organizing companies with Talents, Talent Market, and E²R search, achieving 84.67% success on PRDBench (15.48 points above prior art).
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ClawVM: Harness-Managed Virtual Memory for Stateful Tool-Using LLM Agents
ClawVM introduces a harness-managed virtual memory system for LLM agents that ensures deterministic residency and durability of state under token budgets by using typed pages and validated writeback.
-
E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory
E-mem uses a heterogeneous multi-agent setup for episodic context reconstruction in LLM agents, reaching over 54% F1 on LoCoMo while cutting token cost by over 70% compared to prior methods like GAM.
-
Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems
The paper delivers the first comprehensive review and unified taxonomy of agentic AI in remote sensing, covering single-agent copilots, multi-agent systems, planning mechanisms, benchmarks, and a roadmap while noting limitations in grounding and safety.
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From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems
A survey that defines Compound AI Systems, proposes a multi-dimensional taxonomy based on component roles and orchestration strategies, reviews four foundational paradigms, and identifies key challenges for future research.
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Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents
ASB is a new benchmark that tests 10 prompt injection attacks, memory poisoning, a novel Plan-of-Thought backdoor attack, and 11 defenses on LLM agents across 13 models, finding attack success rates up to 84.3% and limited defense effectiveness.
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AgentLens: Interpretable Safety Steering via Mechanistic Subspaces for Multi-Turn Coding Agent
AgentLens uses step-level hidden-state detection and 10-dimensional subspace intervention for safety in multi-turn coding agents, evaluated on the new MAS benchmark across three LLMs.
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TuxBot: Semantic-Aware Online OS Tuning with Large Language Models
An LLM-driven dual-loop controller claims 72.5% stable-phase improvement over default and 153.3% over the strongest non-LLM baseline, but the comparison protocol inflates the gaps by scoring baselines during continued exploration.
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Holos: A Web-Scale LLM-Based Multi-Agent System for the Agentic Web
Holos is a five-layer LLM-based multi-agent system architecture using the Nuwa engine for agent generation, a market-driven Orchestrator for coordination, and an endogenous value cycle for incentive-compatible persistence in the Agentic Web.
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Retrieval-Augmented Generation for Natural Language Processing: A Survey
The survey organizes RAG methods via a taxonomy of query-based, logits-based, latent, and parametric fusion with comparisons on accessibility, efficiency, applications, and challenges.
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TopoClaw: A Human-Centric and Topology-Aware Agent Operating System
TopoClaw is a human-centric Agent OS that uses physical and social topology modeling to enable cross-boundary execution with identity attribution and context-aware governance.
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AgentOpt v0.1 Technical Report: Client-Side Optimization for LLM-Based Agent
AgentOpt introduces a framework-agnostic package that uses algorithms like UCB-E to find cost-effective model assignments in multi-step LLM agent pipelines, cutting evaluation budgets by 62-76% while maintaining near-optimal accuracy on benchmarks.
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What Deserves Memory: Adaptive Memory Distillation for LLM Agents
NEMORI is an adaptive memory distillation framework for LLM agents that transforms raw interactions into narratives and extracts insights via prediction error to decide what deserves retention.
-
Qualixar OS: A Universal Operating System for AI Agent Orchestration
Qualixar OS provides a runtime for multi-agent AI systems with support for 12 topologies, LLM-driven team design, dynamic routing, consensus judging, content attribution, and protocol bridging, achieving 100% accuracy on a custom 20-task suite at $0.000039 mean cost per task.
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From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI
The paper analyzes evolving security and safety threats in generative AI from content generation to agentic actions, noting that attack surfaces expand faster than defenses and that many safeguards require institutional coordination not yet in place.