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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 ↗5938 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
Predicting prostate cancer mortality using UK Biobank data: a multimodal machine learning approach ↗
Prostate cancer remains a major cause of cancer-related mortality among men worldwide; however, it remains challenging to predict disease progression in individuals. While numerous tools attempt to forecast the likelihood of developing the cancer, fewer studies focus specifically on mortality prediction using population-scale clinical datasets. UK Biobank data were used to develop population-scale predictive models of prostate cancer mortality. The study assessed medical records, blood work, physical measurements, and genetic information of approximately 167 000 males aged 50 and above. A feed…
- OpenAlex · Published · First archived
KNOT: a knockoff-augmented neural network for identifying risk variants and epistatic interactions in family-based association studies ↗
Abstract While deep learning has recently been used for identifying risk variants in genome-wide association studies, its stochastic nature and the complex correlation within genetic data have posed significant challenges for deep-learning-based methods to pinpoint risk variants. We introduce KNOT, a knockoff-augmented neural network for stabilized variable selection with false discovery rate control. KNOT employs contrastive learning and attention mechanisms to model sample relatedness and linkage disequilibrium, alongside a permutation test to detect epistatic interactions. In applications t…
- OpenAlex · Published · First archived
From Benchmark Accuracy to Industrial Evidence: A Registered Systematic Map and Nested Experiment-Level Audit of Validation Credibility in AI-Based Rotating-Machinery Fault Diagnosis ↗
Artificial intelligence (AI) methods for rotating-machinery diagnosis based on vibration and acoustic signals are often evaluated on benchmark splits that do not establish physical independence or industrial generalization. This registered study combined a PRISMA-aligned systematic map with a nested experiment-level audit of validation credibility. Searches identified 11,916 records, retained 7520 unique records, and placed 5159 reports in the full-text population. The operational 500-record Batch 005 block produced a non-probability 351-report full-text queue. A post-review eligibility-consis…
- OpenAlex · Published · First archived
PrefixAgent: An LLM-Powered Design Framework for Efficient Prefix Adder Optimization ↗
Prefix adders are fundamental arithmetic circuits, but their design space grows exponentially with bit-width, posing significant optimization challenges. Previous works face limitations in performance, generalization, and scalability. To address these challenges, we propose PrefixAgent , a large language model (LLM)-powered framework that enables efficient prefix adder optimization. Firstly, we reformulate the problem into two subtasks, namely backbone synthesis and structure refinement, which effectively reduces the search space. The LLM performs these two phases of optimization by invoking t…
- OpenAlex · Published · First archived
Exploring the Potential of Diffusion Large Language Models in Code Generation ↗
Large Language Models (LLMs) have revolutionized the landscape of code generation. Existing LLMs mainly employ autoregressive generation, i.e., generating code token-by-token from left to right. However, the underlying autoregressive generation has two limitations in code generation. First, autoregressive LLMs typically generate only one token per step, showing low efficiency in practice. Second, programming is a non-sequential process involving back-and-forth editing, while autoregressive LLMs only employ the left-to-right generation order. These two intrinsic limitations hinder the further d…
- GitHub · Repository created · First archived
caiovicentino/eikos ↗
Open, calibrated, single-pass typed-decision models (4B & 27B) for finance and trading
- Hacker News · Posted on Hacker News · First archived
Stanford violated AI policy after race-swapping students in ad ↗
Hacker News discussion.
- GitHub · Repository created · First archived
PyModel/jev-judge-mcp ↗
Typed judgment tools for MCP agents. TypeSafe's Jev model as verify, screen, find, classify, rerank, decide, compare, extract, review, gate, and score: the model judges, policy decides auto, review, or escalate.
- Hacker News · Posted on Hacker News · First archived
Mercury 2.5 LLM hits 770 tokens per second ↗
Hacker News discussion.
- GitHub · Repository created · First archived
ollaya-dev/ollaya ↗
Run open decision models locally: pull and serve Laya, decider, NLI and GLiClass behind a TypeSafe-compatible API. Ollama for decision models.
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.
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