Small Language Models and Spec-Driven Development for High-Accuracy Agentic AI Systems

A Multi-Agent Benchmarking Framework for Evaluating the Performance of Large Language Models in Logic Programming

A Review on Test-Time Scaling for Agentic Large Language Models

A novel RAIE taxonomy along four scaling dimensions is proposed, which optimizes the entire thought process through search algorithms and self-verification, and introduces a task-oriented guideline for choosing the best TTS strategy.

Democratizing Autonomous Deep Research: A Neuro-Symbolic Framework for Small Language Models via Grammar-Constrained Decoding

The Agentic AI model consists of systems that can plan on their own, use tools and perform multi-step tasks. These capabilities have mainly been accomplished by large models, such as GPT-4 or Claude 3, are large language models LLMs have demonstrated skill with multi-step reasoning tasks. they have high computational requirements and increased latency. require considerable data transfer to centralized servers to them unsuitable for private, edge-based use. Open-source Small Models that implement SLMs have less than 13 billion parameters, a possible option for local execution. In agentic situations, however, these predictions are unreliable. However, they do not have a strictly defined syntax for calling tools interfaces, such as JSON schemas. They can also create other functions for long-sighted tasks exist or disappear when leading to meaningful failures.We designed a Neuro-Symbolic architecture for deterministic control layer to improve the frozen, quantized SLMs. Our proach consists of two major components: [1] a recursive "Critic-Planner" feedback loop that filters out noisy retrieval results before they affect the agent’s limited working memory. [2] At inference-time, Grammar-Constrained Decoding (GBNF) focuses on conforming to JSON schema at the logit level, enforcing syntactic correctness of tool interactions.We apply our framework to the quantized Llama-3-8B. Mistral-7B and Phi-3-mini within six domains. including technical analysis to ensure legal compliance. thesis. Our system achieved a 100% syntactic accuracy baseline correctness and completed complex tasks effectively. In contrast, strong industry standards, including LangChain ReAct, failed. to format issues.

From Language Models to Agentic AI: A Survey of Autonomous, Action-Enabled, and Collaborative LLM Agents

Large Language Model (LLM)–based agents are rapidly evolving from passive assistants into autonomous, tool-using, and collaborative systems capable of executing complex, long-horizon tasks across web, software, and physical environments. However, the current literature remains fragmented, with inconsistent terminology, ad hoc architectures, and limited evaluation standards, making it difficult to compare systems or deploy them reliably in real-world settings. This paper presents a unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance. We systematically analyze representative single-agent, tool-augmented, and multi-agent frameworks within this taxonomy, highlighting design trade-offs, capability scaling patterns, and recurring failure modes. Beyond architectural analysis, we review emerging evaluation methodologies that move beyond static benchmarks to assess agent behavior, robustness, grounding, and operational cost in interactive environments. Importantly, the survey emphasizes practical considerations for enterprise and safety-critical deployment, including access control, human-in-the-loop oversight, and policy enforcement. By unifying conceptual foundations with empirical trends and deployment constraints, this work provides a structured roadmap for researchers and practitioners to design, evaluate, and govern next-generation LLM-based agentic systems.

Automating Plan Evaluation Using Agentic Large Language Models

Manual plan evaluation faces reliability and scalability challenges. This research benchmarks human evaluations against a large language model (LLM) using a multi-agent approach and/or retrieval-augmented generation (RAG) to automate complex content analysis tasks. We find that LLMs generally perform comparably with humans, with most errors arising from overimplication and limited domain knowledge. The multi-agent approach substantially enhances LLM’s performance, reducing common machine errors by over 50 percent. Integrating such LLM tools with human oversight will likely become the new norm for content analysis, and this study demonstrates how to leverage artificial intelligence’s (AI) efficiency and precision alongside humans’ contextual understanding and domain expertise.

RF-Agent: A Practical Framework for Building Language Agents for RFIC Design

Large language models (LLMs) have driven rapid progress in electronic design automation (EDA), yet their application to radio-frequency (RF) circuit design remains limited by the scarcity of domain-specific datasets and standardized benchmarks. We present RF-Agent, which addresses this gap through textbook-driven knowledge distillation. A multi-agent Question-Thinking-Solution-Answer (QTSA) pipeline converts a subsection-level corpus from seven canonical RF textbooks into the first-of-its-kind RF-domain reasoning dataset (over 11,000 samples) with a dedicated multiple-choice benchmark. On this benchmark we study two adaptation strategies: supervised fine-tuning (SFT) and three retrieval-augmented generation (RAG) configurations (semantic, keyword, hybrid). Across multiple LLM families, domain-specific SFT significantly improves RF reasoning, especially for small and medium-sized models; among RAG configurations, semantic retrieval performs best, indicating embedding-based context alignment suits RF reasoning better than naive fusion. The dataset and benchmark provide a reusable foundation for future work on LLM-aided RF circuit design.