THE INTERNET IS THINKING
Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these 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 ↗6064 classified signals in the fourteen-day window. Recent momentum: steady. 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
Test Case Selection for Deep Neural Networks: A Replication Study on LLMs for Code (Replicability Study) ↗
Test case selection (TCS) techniques have been explored to support the operational evaluation of deep neural networks (DNNs) under limited testing budgets, where labeling cost is a primary concern and early model failure detection is a key objective. Although prior studies report promising results, existing empirical evaluations focus almost exclusively on vision-based DNNs and datasets. As observed in recent surveys, models and datasets specifically designed for software engineering tasks have not been considered, leaving it unclear whether prior findings generalize to LLM code models. This p…
- OpenAlex · Published · First archived
How Much Static Structure Do Code Agents Need? A Study of Deterministic Anchoring ↗
LLM-based code agents navigate repositories through keyword search but lack access to the structural relationships, such as call graphs, inheritance hierarchies, and configuration dependencies, that organize software systems. This limitation can make agent navigation stochastic and difficult to reproduce across runs. We investigate whether lightweight static analysis can provide deterministic anchors: stable structural facts injected as plain-text comments to guide probabilistic exploration. Starting from Codex as a strong grep-first baseline, we systematically vary the granularity and directi…
- OpenAlex · Published · First archived
To Run or Not to Run: Analyzing the Cost-Effectiveness of Code Execution in LLM-Based Program Repair ↗
LLM-based agents for program repair are increasingly built on a “generate-run-revise” paradigm, iteratively executing tests to evaluate and refine patches. This execution-based approach has become standard practice in state-of-the-art systems. However, executions can be time-consuming and expensive, yet their impact on these agents remains underexplored. In this paper, we conduct a two-stage empirical study of execution behavior in LLM-based program repair. To characterize execution behavior at scale, we first analyze 7,745 agent traces from SWE-bench leaderboard submissions. Second, we evalua…
- 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
Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety ↗
Large language models (LLMs) are increasingly being used in network operations (NetOps) and artificial intelligence for IT operations (AIOps) for tasks ranging from telemetry retrieval and incident diagnosis to configuration planning and bounded remediation. As these systems acquire greater access to operational tools, the central question is no longer only what an LLM can do, but whether operational assurance increases commensurately with the authority granted to it. This survey examines that question through a structured, evidence-stratified review of agentic NetOps and AIOps. We organise th…
- OpenAlex · Published · First archived
Rethinking wireless communications through formal mathematical AI reasoning ↗
Mathematical analysis has long underpinned wireless communication theory, yet the growing complexity of next-generation systems demands increasingly sophisticated reasoning from domain experts. Recent advances in AI mathematical reasoning, from formal theorem proving to large language model (LLM)-based derivation, offer a promising but largely unexplored path forward. Here we argue that wireless communications is a uniquely structured domain for formal AI reasoning, and propose a three-layer framework of verification, derivation, and discovery to rethink how wireless mathematical knowledge is…
- OpenAlex · Published · First archived
Paradigm shift from statistical channel modeling to digital twin prediction: an environment-generalizable ChannelLM for 6G AI-enabled air interface ↗
Abstract As 6G advances, ubiquitous connectivity and higher capacity requirements of the air interface pose substantial challenges for accurate and real-time wireless channel acquisition in diverse environments. Conventional statistical channel modeling relies on offline measurement data from limited environments, struggling to support online applications facing diverse environments. To this end, the digital twin channel (DTC) has emerged as a novel paradigm that constructs a digital replica of the physical environment through high-fidelity sensing and predicts corresponding channel in real ti…
- 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
Defusing Logic Bombs in Symbolic Execution with LLM-Generated Ghost Code ↗
Symbolic execution is a powerful program analysis technique, but its effectiveness is fundamentally limited by solver-hostile program fragments, complex numerical reasoning, and unbounded heap structures. Recent work proposed replacing constraint solvers with large language models (LLMs) to bypass these limitations, but such approaches struggle to analyze real-world codebases, where deep execution paths require globally consistent reasoning across many interacting constraints. We present Gordian, a hybrid symbolic execution framework that uses LLMs selectively to generate lightweight ghost cod…
- OpenAlex · Published · First archived
Seeing Is Coding: On the Effectiveness of Vision Language Models in Code Understanding ↗
Large Language Models (LLMs) have achieved remarkable success in source code understanding, yet as software systems grow in scale, computational efficiency has become a critical bottleneck. Currently, these models rely on a text-based paradigm that treats source code as a linear sequence of tokens, which leads to a linear increase in context length and associated computational costs. The rapid advancement of Multimodal LLMs (MLLMs) introduces an opportunity to optimize efficiency by representing source code as rendered images. Unlike text, which is difficult to compress without losing semantic…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Artificial Intelligence
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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
Follow a thread
Explore related regions
These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.