4arXiv cs.LG (Machine Learning)·
Survey: LLMs for front-end EDA — HDL generation, testbench construction, and agentic design automation
A new arXiv survey reviews the application of large language models to front-end chip design tasks, including hardware description language (HDL) generation, testbench construction, and high-level synthesis optimization. The paper traces the evolution from LLMs as localized assistants toward autonomous agentic EDA systems, citing OpenClaw as a representative pioneering system. It identifies key challenges and outlines future opportunities for agentic AI in electronic design automation.
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Survey: Agentic Environment Engineering for LLMs — Modeling, Synthesis, Evaluation, and Application
A comprehensive arXiv survey systematically reviews the design and engineering of interactive environments for LLM-based agents, covering the full lifecycle from environment modeling and synthesis to evaluation and application. The paper categorizes environments across eight attributes and eight domains, introduces symbolic and neural synthesis paradigms, and characterizes four pathways for agent-environment co-evolution including memory-centric, orchestration-centric, trajectory-centric, and exploration-centric approaches. It also identifies three paradigms of environment evolution (neural-driven, difficulty-driven, scaling-driven) and proposes future directions such as Environment-as-a-Service and multi-agent environments. This is a reference-organizing contribution for the rapidly growing agent tooling and evaluation space.
Survey chapter on LLM mechanisms, emergent capabilities, and cognition debates
A new arXiv preprint surveys current understanding of large language models, covering the Transformer architecture, emergent capabilities resembling human cognition (symbolic reasoning, theory of mind, deception), and explainability approaches from neuron activation analysis to circuit tracing. The chapter also engages the debate over whether LLMs genuinely understand or merely pattern-match, arguing against reductive anti-anthropomorphism while acknowledging human-LLM differences. It is framed as a book chapter synthesizing recent empirical findings and theoretical positions.
SECDA-DSE: LLM-guided design space exploration for FPGA accelerator generation
SECDA-DSE is a framework that integrates LLMs into the SECDA hardware-software co-design ecosystem to automate design space exploration (DSE) of FPGA-based AI accelerators. The system combines a structured architecture candidate generator with an LLM Stack using retrieval-augmented generation and chain-of-thought prompting, plus an iterative feedback loop. Evaluation demonstrates end-to-end synthesis and execution of three accelerator designs on real FPGA hardware, with results showing the approach captures kernel-specific compute/memory trade-offs while reducing manual design effort.
Tutorial on LLM and agentic AI architectures for smart grid applications with solver-grounded design principle
A new arXiv preprint presents a tutorial on applying LLMs and agentic AI systems to power grid tasks including wind forecasting, EV charging scheduling, power flow analysis, and contingency diagnosis. The paper proposes a 'solver-grounded' design principle where numerical outputs are only reported when they originate from a trusted external solver and pass explicit verification, addressing the problem of LLMs producing physically infeasible outputs. Four case studies compare LLM-only baselines against solver-grounded agents, with EVAgent reducing unmet energy by 7.5-9.5x and GridDebugAgent repairing 17/39 contingency cases while cutting violations by 52.3%. A four-group evaluation framework covering task utility, solver correctness, faithfulness, and cost/latency is also proposed.
OLMo Hybrid and Future LLM Architectures
Interconnects covers the latest OLMo hybrid model release and discusses emerging trends in open-source post-training tooling. The piece examines architectural directions for future large language models. As a tier-2 commentary source, it provides analysis rather than primary research findings.
Agentic LLM collectives proposed as interpretable substrates for Artificial Life research
A preprint from arXiv argues that populations of agentic LLMs — equipped with persistent memory, tools, and autonomous action — can serve as a computational substrate for Artificial Life (ALife) research. The key claim is that because agents communicate in natural language, their collective emergent behaviors are directly interpretable by examining textual traces or querying the agents themselves. The paper extends existing notions of LLM interpretability to multi-agent collectives and surveys recent examples of agentic LLM systems in both controlled and deployed settings. This positions multi-agent LLM systems as a novel lens for studying emergence and complexity while retaining interpretability.
Tutorial and survey on LLM-powered agentic AI for 5G/6G network control and management
A two-part tutorial-and-survey paper formalizes the application of LLM-based agentic AI to 5G and 6G network architectures, covering reasoning, planning, tool use, and multi-agent coordination mapped onto telecom control planes. The work addresses a gap in existing literature by integrating protocol-level detail, evaluation frameworks, and standardization alignment. It surveys major 6G initiatives and identifies open challenges for autonomous telecommunications. The paper targets researchers and practitioners at the intersection of AI agents and next-generation network infrastructure.
A Hazard Analysis Framework for Code Synthesis Large Language Models
OpenAI published a hazard analysis framework specifically targeting code synthesis LLMs, addressing the safety and risk dimensions of models that generate executable code. The framework likely identifies threat categories, failure modes, and mitigation strategies relevant to deploying code-generating AI systems. This represents an early structured attempt to apply safety engineering methodology to a specific LLM capability domain. The work is relevant to both AI safety research and enterprise deployment considerations for coding assistants.