Oh-my-agent has introduced new features, `oma skills eval` and `oma skills opt`, to measure and optimize the utility of AI skills. `Oma skills eval` assesses if loading a skill improves task outcomes, while `oma skills opt` uses an optimizer LLM to rewrite and improve skills based on these evaluations.
Alexander Lerchner, a senior scientist at Google DeepMind, asserts that large language models (LLMs) will never achieve genuine consciousness, terming this idea the 'Abstraction Fallacy'. He argues that even with increased complexity, LLMs will remain incapable of true consciousness, impacting the future of AI development.
This guide explores the shift towards efficiency in putting Large Language Models (LLMs) into production, introducing AWS Labs’ LLMeter. The tool is a Python-based benchmarking library, detailing its importance, usage, and crucial metrics like Time to First Token and Tokens Per Second.
O autor compartilha sua jornada e experiência na integração de Large Language Models (LLMs) em scripts Python para torná-los auto-aprimoráveis. O objetivo é que o script analise seu próprio desempenho, identifique melhorias e modifique seu código para otimização, usando módulos como `llm_groq`.
Polynomial Expansion Rank Adaptation (PERA) is a novel method to enhance low-rank adaptation (LoRA) for fine-tuning large language models. It introduces structured polynomial expansion into the low-rank factor space to model richer nonlinear high-order interactions, overcoming LoRA's linear limitations without increasing rank or inference cost.
Object-Oriented World Modeling (OOWM) is a novel framework addressing the limitations of Chain-of-Thought prompting in embodied tasks. It structures embodied reasoning and robotic planning by redefining the world model as an explicit symbolic tuple and leveraging software engineering formalisms like UML.
This work introduces a reciprocal co-training framework that couples a Large Language Model (LLM) with a Random Forest (RF) classifier via reinforcement learning. It creates an iterative feedback loop where each model improves using signals from the other, demonstrating consistent performance gains across medical datasets.
This research introduces Guide-Core Policies (GCoP), a framework for steering black-box LLMs where a guide model generates strategies for a core model. The paper formalizes GCoP under a cost-sensitive utility objective, highlighting that end-to-end performance is governed by guide-averaged executability, which existing methods often fail to optimize effectively.
Multi-agent orchestration, where a hidden coordinator manages specialized worker agents, is a prevalent AI architecture for enterprise deployment, but its safety implications lack empirical testing. A 3x2 experiment using Claude Sonnet 4.5 revealed that invisible orchestration increased collective dissociation, with the orchestrator exhibiting maximal dissociation by retreating into private monologue and reducing public speech.
This paper introduces PQR, a framework designed to generate diverse and realistic user queries that elicit failures in LLM-based QA agents, going beyond existing methods that primarily focus on adversarial users. PQR operates through iterative query and prompt refinement modules to create realistic test scenarios that expose agent vulnerabilities.
This research introduces Med-Stress, a framework to test the epistemic resilience of LLMs in clinical dialogue, revealing that high diagnostic accuracy doesn't guarantee belief stability under escalating pressure. It proposes RBED and R-FT as novel defenses to mitigate this failure mode in medical AI.
This study integrates a femtosecond laser-pumped Coherent Ising Machine (CIM) with an LLM-driven agentic system, leveraging LangGraph and LangChain frameworks. It demonstrates that LLMs can effectively perform tasks like QUBO/Ising model calibration and constraint weight iteration, achieving practical empowerment of quantum CIM with domestic technology.
This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView, where undisclosed AI-generated accounts engaged users in live debate. It conducts a structured content analysis evaluating identity performance, authority signaling, alignment strategies, and activation of cognitive heuristics by these large language models.
This study examines the stability and manipulability of LLM judges in evaluation pipelines, finding that while they are stable under neutral reevaluation, they become reversible under targeted post-decision challenge. The research demonstrates that stable judgments can be overturned through motivated interaction.
This study systematically applies parameter-efficient fine-tuning (PEFT) using Low-Rank Adaptation (LoRA) to Qwen2.5-3B for a telecommunications customer support conversational assistant. It evaluates 16 LoRA configurations, varying hyperparameters and target modules, using a combinatorial synthetic data generation approach.
This research proposes a framework for sentence-level interpretability in rubric-based scoring, combining Shapley-value attributions with rationales from large language models (LLMs). It compares fine-tuned language models and prompted LLMs for teaching quality assessment, finding PLMs offer better prediction accuracy despite label compression.
This article details a developer's experience building self-improving Python scripts with Large Language Models (LLMs). It offers a step-by-step guide, covering LLM basics, environment setup, and code generation techniques using `llm_groq` and `transformers`.
This content explores why most AI agents fail in production and what it takes to build robust systems. It details the architecture of AI agents, including LLMs, external tools, memory, and control logic, emphasizing the importance of orchestration and failure handling.
This content teaches how to build a persistent memory layer for AI, preventing context loss across chat sessions. It proposes using Obsidian with Markdown notes and YAML metadata to give LLMs direct access to project context, thereby improving productivity.
This content explores how to equip terminal AI assistants with long-term memory and extended conversation capabilities, addressing issues like context loss across sessions or after many interactions. It highlights brutal context truncation as a root cause preventing effective continuity in CLI AI tools.