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Earlier sources: Language models
Language models learn patterns in text and other representations to predict or generate sequences. Follow model releases, training methods, reasoning, evaluation, and deployment research; a benchmark result is specific to its test conditions.
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 ↗449 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
Risk-Controlled Adaptive-Resolution Fault Diagnosis of Power Transformers Using Dissolved Gas Analysis: Lightweight Hierarchical Learning, Credibility, and Deployment Support ↗
Dissolved gas analysis (DGA) is widely used for transformer condition assessment, but fixed-resolution classifiers may release overly specific fault labels when the available evidence is weak. This study develops a risk-controlled adaptive-resolution framework in which a lightweight hierarchical residual neural encoder generates fine-grained and electrical/thermal parent-level candidates, and separate credibility estimates determine whether the released result should remain fine-grained, fall back to the parent mechanism, or abstain. The 25-dimensional DGA representation is encoded by three re…
- 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
Mercury 2.5 LLM hits 770 tokens per second ↗
Hacker News discussion.
- GitHub · Repository created · First archived
yukitorido/short-video-generator-AI ↗
AI video processing pipeline for generating vertical shorts using LLMs, Whisper transcription, highlight detection and automated editing
- arXiv · Published · First archived
Can LLMs Reason About Runtime Behavior? A Repository-Level Dynamic Benchmark ↗
Large language models (LLMs) are increasingly used in coding tasks, but their ability to reason about code execution remains unclear. Existing repository-level QA benchmarks mainly evaluate static code understanding and often rely on LLM-based evaluation, while execution-reasoning benchmarks are mostly limited to snippets or functions. We introduce SWE-Flux, a repository-level benchmark for dynamic execution reasoning containing 480 execution-grounded instances across 12 real Python repositories, with gold answers automatically harvested from instrumented test executions rather than written ma…
- arXiv · Published · First archived
Fine-Tuning LLMs for Translation: General Forgetting Mitigation Does Not Preserve MT-Specific Instruction Following ↗
Fine-tuning large language models on parallel data improves translation quality but can cause catastrophic forgetting. Mitigation methods are generally evaluated by retention on general benchmarks. We ask whether these findings transfer to machine translation (MT) fine-tuning and to MT-specific instruction following (MT-IF): instructions that modify a translation, such as formality, grammatical gender, and length control. We compare methods anchored to auxiliary data, to model outputs, and to the base model parameters, first in a screening study with Llama 3.2 1B Instruct, then on Llama 3.1 8B…
- arXiv · Published · First archived
Motif-Vocab: StatisticallyCalibrated Transcription-Factor-Identity Tokenization forGenomic Language Models ↗
Tokenization is a central design choice in genomic language models, yet most deoxyribonucleic acid (DNA) tokenizers use characters, fixed-length k-mers, or frequency-derived subwords without explicitly using prior information about the specificity of DNA-binding regulatory factors. We introduce Motif-Vocab, a biologically informed tokenizer that scans both DNA strands for statistically calibrated motif matches, emits transcription-factor (TF) identity tokens, and applies nucleotide, $k$-mer, or byte-pair encoding (BPE) to unmatched sequence. Motif-specific null distributions put position-weigh…
- GitHub · Repository created · First archived
yetone/magpie ↗
Every agent's model. One place. Codex on DeepSeek, Claude Code on Kimi, from the menu bar.
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Language models
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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These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.