This paper introduces Heartbeat-Driven Autonomous Thinking Activity Scheduling for LLM agents, aiming to overcome rigid and reactive control flows. The system enables proactive, adaptive, and continuous self-regulation by dynamically orchestrating cognitive modules, mirroring the rhythm of human cognition.
Credo introduces a novel framework for declaratively controlling LLM pipelines, representing semantic state as beliefs and regulating behavior through policies. This design aims to enhance the adaptability, auditability, and composability of agentic AI systems by moving away from brittle imperative control loops.
The content highlights a fundamental flaw in current AI systems where they forget information between sessions, preventing long-term goal pursuit or autonomous operation. It introduces H.U.N.I.E., a persistent and confidence-aware memory engine, as a solution to provide AI agents with the necessary context and learning capability across interactions.
This research introduces a symbolic reasoning scaffold to address systematic limitations in LLMs' structured logical reasoning, such as conflating hypothesis generation and propagating weak inferences. It operationalizes Peirce's tripartite inference, enforcing logical consistency through algebraic invariants, notably the 'Weakest Link bound' to prevent conclusion reliability from exceeding its least-supported premise.
Este artigo discute a necessidade de uma arquitetura robusta para sistemas multiagente em produção, distanciando-se de demos de agente único. Propõe a separação da comunicação agente-ferramenta (MCP) e agente-agente (A2A), juntamente com a observabilidade, como elementos chave para sistemas autônomos e confiáveis.
Transformers struggle with high computational costs and memory consumption for long sequences, while alternatives lose long-tail dependencies. Absorber LLM proposes a self-supervised causal synchronization to absorb historical contexts into parameters, ensuring a contextless model matches the original full-context one on future generations.
The Toeplitz MLP Mixer (TMM) is a new transformer-like architecture that replaces attention with triangular-masked Toeplitz matrix multiplication, significantly reducing computational complexity to O(dn log n) time and O(dn) space. TMMs demonstrate superior training efficiency and better input information retention compared to traditional transformers, despite their simpler design.
Grokers is an innovative architecture for building persistent, structured comprehension of typed knowledge graphs through bottom-up inductive traversal. Unlike RAG, it shifts intelligence to write time, where autonomous Groker agents analyze and enrich attributes via language models for all future queries at zero cost.
ReacTOD introduces a bounded neuro-symbolic architecture for task-oriented dialogue systems, reformulating NLU as discrete tool calls within a self-correcting ReAct loop. It improves accuracy by up to 9.3 percentage points and achieves a 93.1% self-correction rate on intercepted errors.
This article presents a novel architecture for LLMs that eliminates the need for deep neural networks. The proposed model, building on RBF networks, finds the global optimum of the loss function in a single iteration, thereby removing the tedious training step.
This article applies the A11 architecture as a framework to examine consciousness, focusing on its vertical structure within AI systems. The goal is to identify emergent verticality inside AI systems and derive new structural insights, ensuring transparency and reproducibility.
The Context Object in apcore serves as the "Nervous System" of its AI execution pipeline, carrying state, identity, and tracing information across complex agentic system tasks. It solves the issue of isolated calls in stateless architectures by providing essential context, crucial for debugging and security.
The author details how they built a five-agent AI system to automate their content creation process, cutting the time from 12 hours to under two per week. The system addresses
The GPT-5.5 System Card from OpenAI details a transformer-based language model, building upon GPT-3 with an emphasis on scaling and fine-tuning. Its architecture is primarily decoder-only, utilizing self-attention mechanisms and feed-forward networks.
This article delves into the concept of AI "dreams" as an asynchronous memory consolidation pipeline that runs after user sessions, organizing past interactions. It argues that this architectural approach is significant for its impact on inference economics and raises questions about data ownership.
The article describes a shift from prompt engineering, which is brittle and complex for large applications, to agentic orchestration. This new paradigm involves LLMs acting as reasoning engines that control a loop of tools and states, facilitated by frameworks like LangGraph or CrewAI.
Subagents are AI instances invoked by an orchestrating AI to handle specific subtasks within larger, complex workflows. This approach allows breaking down intricate problems into manageable pieces for more efficient and robust AI performance.
This article explores the parallelization of AI agents, identifying which approaches are effective and which lead to high token consumption. It investigates the reasons behind these outcomes, aiming to optimize efficiency and cost.
This article analyzes why AI agents fail in long-running tasks due to reasoning decay and attention loss. It explores the structural reasons behind these issues and proposes architectural patterns to address them.
Autonomous multi-agent systems for business consulting face a critical reliability crisis due to inconsistent behavior, instruction violations, and security vulnerabilities. The article argues that prompt-based control is insufficient, advocating for robust orchestration infrastructure with code-level enforcement and monitoring to achieve production-grade AI.