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Earlier sources: AI agents
AI agents use models to choose actions, call tools, and work toward a goal. This thread follows agent frameworks, memory, orchestration, evaluation, and safeguards; autonomy and reliability vary between systems.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗852 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Distributed Artificial Intelligence for Agentic Intent-Based Networking in Mobile Networks ↗
Through intent-based networking (IBN), network objectives are specified declaratively, yet in mobile networks their realisation spans administrative domains that compete for coupled radio, transport and compute resources but cannot share local models and telemetry. A distributed artificial intelligence (DAI) controller with a Belief-Desire-Intention-eXecution (BDIX) cycle is presented, centred on shared-space coordination. The local decision of each domain agent is mapped through Gi into a common p-dimensional resource space, so that only resource contributions and a common price vector cross…
- OpenAlex · Published · First archived
PAGENT: Program Analysis Guided LLM Agent for Proof-of-Concept Generation ↗
Software developers frequently receive vulnerability reports that require them to reproduce the vulnerability in a reliable manner by generating a proof-of-concept (PoC) input that triggers it. Given the source code for a software project and a specific code location for a potential vulnerability, automatically generating a PoC for the given vulnerability has been a challenging research problem. Symbolic execution and fuzzing techniques require expert guidance and manual steps and face scalability challenges for PoC generation. Although recent advances in LLMs have increased the level of autom…
- OpenAlex · Published · First archived
AgentBreaker: Evaluating Context-Aware Indirect Prompt Injection Risks in Modern Web Agents ↗
Recent advances in large language models (LLMs) have enabled autonomous web agents to perform complex user tasks by leveraging their adaptive decision-making capabilities. Despite their growing use in crawling the Web, their security implications under indirect prompt injection (IPI) attacks remain largely understudied. Prior studies have compiled static benchmarks or proposed dynamic frameworks that generate adversarial phrases aimed at deceiving a single LLM within a target agent. However, by ignoring the agent’s operating context, these approaches yield suboptimal IPI attacks against modern…
- OpenAlex · Published · First archived
NSync: Automated Cloud Infrastructure-as-Code Reconciliation with AI Agents ↗
Cloud infrastructure is managed through a mix of interfaces—traditionally, cloud consoles, command-line interfaces (CLI), and SDKs are the tools of choice. Recently, Infrastructure-as-Code/IaC frameworks (e.g., Terraform) have quickly gained popularity. Unlike conventional tools, IaC frameworks encode the infrastructure in a ”source-of-truth” configuration. They are capable of automatically carrying out modifications to the cloud—deploying, updating, or destroying resources—to bring the actual infrastructure into alignment with the IaC configuration. When IaC frameworks are used together with…
- OpenAlex · Published · First archived
Sifting the Noise: A Comparative Study of LLM Agents in Vulnerability False Positive Filtering ↗
Static Application Security Testing (SAST) tools are essential for identifying software vulnerabilities, but they often produce a high volume of False Positives (FPs), imposing a substantial manual triage burden on developers. Recent advances in Large Language Model (LLM) agents offer a promising direction by enabling iterative reasoning, tool use, and environment interaction to refine SAST alerts. However, the comparative effectiveness of different LLM-based agent architectures for FP filtering remains poorly understood. In this paper, we present a comparative study of three state-of-the-art…
- OpenAlex · Published · First archived
TraceDev: A Traceability-Driven Multi-agent Framework for Requirement-to-Code Development ↗
In modern software development, the rapid advancement of Large Language Models (LLMs) has made the end-to-end transformation of Natural Language Requirements (NLRs) into executable repository-level code increasingly feasible. However, existing approaches typically rely on simplified instructions (e.g., single-sentence descriptions), failing to reflect complex software development scenarios. Moreover, they lack explicit requirement traceability mechanisms, making it difficult to precisely align and validate generated code against original requirements. To address these limitations, we propose T…
- OpenAlex · Published · First archived
LogicHunter: Testing LLM Agent Frameworks with an Agentic Oracle ↗
Large Language Model (LLM) agent frameworks such as LangChain, LlamaIndex, and CrewAI have become critical infrastructure powering production AI systems, yet they remain severely under-tested due to fundamental challenges in automated testing. Unlike traditional software, where crashes serve as reliable oracles, defects in these pure Python frameworks manifest as ordinary exceptions or silent semantic failures, creating profound oracle ambiguity. This problem is exacerbated by strict type governance through Pydantic schemas and complex protocol requirements that cause existing fuzzers to gener…
- OpenAlex · Published · First archived
LLM-driven design of physics-constrained constitutive models: Two agents are better than one ↗
Abstract Developing constitutive models that capture how materials deform under load traditionally requires years of specialized expertise in continuum mechanics, machine learning, and scientific programming. Large language models (LLMs) have recently been shown to lower this barrier by generating constitutive models on demand, but existing single-agent pipelines lack systematic checks that the resulting models respect fundamental physical laws. To close this gap, we introduce the first multi-agent LLM-driven approach for constitutive model generation: a Creator agent proposes a model tailored…
- OpenAlex · Published · First archived
Atropos: Improving Cost-Benefit Trade-Off of LLM-Based Agents under Self-Consistency with Early Termination and Model Hotswap ↗
Open-weight Small Language Models(SLMs) can provide faster local inference at lower financial cost, but may not achieve the same performance level as commercial Large Language Models (LLMs) that are orders of magnitudes larger. Consequently, many of the latest applications of LLMs, such as software engineering agents, tend to be evaluated on larger models only, leaving the issue of improving the cost-benefit trade-off of such applications neglected. This paper proposes Atropos, a predictive early-termination analysis and hotswap technique that aims to improve the cost-benefit trade-off for LLM…
- OpenAlex · Published · First archived
Understanding Automated Program Repair Agents through the Lens of Traceability: An Empirical Study ↗
Automated Program Repair (APR) agents leverage large language models (LLMs) to autonomously diagnose and patch software bugs using planning, reasoning, and tools. Although these agents show strong performance on leaderboards such as SWE-bench, little is understood about how they take actions, where they fail, and how their behavior compares to human developers. In this paper, we present the first systematic analysis of these limitations using 5 state-of-the-art APR agents. We trace the full decision-making pipelines of the 5 APR agents across 500 real-world repair tasks, from issue description…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about AI agents
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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