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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 ↗440 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.
- Hacker News · Posted on Hacker News · First archived
Clojure in the age of language models ↗
Hacker News discussion.
- GitHub · Repository created · First archived
SASUKE40/Lexa ↗
Lexa is a small Grammarly alternative for macOS. It corrects selected text, makes it clear, changes its tone, or translates it. Use a free LLM, a local model, or your ChatGPT or Claude subscription.
- GitHub · Repository created · First archived
A2agent-ai/free-ai-api ↗
Free AI API tiers and trial quotas in 2026: Gemini, Groq, Cerebras, OpenRouter, Zhipu and more; maintained comparison and sources
- Hacker News · Posted on Hacker News · First archived
Studying metagaming latents in language models ↗
Hacker News discussion.
- OpenAlex · Published · First archived
Evaluating Retrieval-Augmented LLM Tutoring with Synthetic Learners: A Factorial Simulation of Newtonian Misconception Revision ↗
Retrieval-augmented generation (RAG) is increasingly used to ground large language model (LLM) tutors, but comparing tutoring architectures is expensive, because every design decision ordinarily requires a fresh study with students. We present a reproducible simulation harness that evaluates tutoring architectures against LLM-based synthetic learners, and we use it to run a full factorial comparison. Five learner profiles, parameterized from Conceptual Change Theory and the Force Concept Inventory literature, were crossed with four tutoring conditions (knowledge-graph-augmented RAG, BM25 RAG,…
- OpenAlex · Published · First archived
Evaluating generative large language models as zero-shot semantic odor descriptor generators with applications to odor sensor prediction ↗
Abstract Predicting odor quality from molecular structure remains a fundamental challenge in sensory science. In this study, we evaluated generative large language models (LLMs) as tools for olfactory prediction across multiple tasks. By assigning LLMs distinct cultural personas through role-play prompting, we demonstrate that odorant concentration modulates LLM-generated odor quality outputs in a zero-shot setting. This represents a form of contextual sensitivity absent in structure-based approaches. A systematic evaluation of prompt design revealed that the chemical name is the primary carri…
- OpenAlex · Published · First archived
A Hybrid Ligand- and Structure-Based Drug Design Invoked through Square Attention in Protein–Ligand Transformer ↗
Abstract Computer-aided molecular design based on evaluation of protein–ligand (PL) binding affinity is important for accelerating drug discovery. A deep-learning model for natural language processing (NLP) involving an attention mechanism (i.e., Transformer) is expected to improve the PL-affinity evaluation along with clarifying amino acid residues and ligand atoms that are responsible for molecular interactions. However, the original attention (i.e., a scaled dot-product with row-wise Softmax normalization) is an adapted method for relationships between homogeneous inputs (e.g., two sentence…
- OpenAlex · Published · First archived
Advancing LLM-based 5G traffic classification: a multi-layer evaluation of performance and reasoning reliability ↗
Abstract The rapid proliferation of 5G networks has enabled a wide range of data-intensive applications, including video streaming, online gaming, metaverse platforms, and real-time communication services. Understanding the behavior and characteristics of such heterogeneous traffic is essential for efficient network management, resource allocation, and quality of service (QoS) optimization. However, most existing studies rely on simulated or synthetic datasets, limiting their applicability to real-world scenarios. This paper presents a comprehensive data-driven analysis of 5G traffic using a l…
- OpenAlex · Published · First archived
Grounding language models with deterministic verifiers for fraud detection ↗
Abstract Financial fraud detection is complicated by severe class imbalance, temporal change, adversarial manipulation, and the need for evidence-grounded explanations. Although large language models can generate readable rationales, their claims may be unsupported when they are not independently checked against structured evidence. We propose VR-FraudNet, a five-stage framework combining a time-conditioned spectral graph encoder, LightGBM triage and threshold routing, a schema-constrained rationale language model, a fixed deterministic verifier, and an isotonic probability mixer with split-co…
- OpenAlex · Published · First archived
PNBind : Prediction of Nucleic Acid‐Binding Sites Using Protein Structure and Protein Language Models ↗
Accurate prediction of nucleic acid-binding sites is essential for understanding protein function and facilitating drug discovery. However, current computational methods are limited by single-layer language model representations, Cα point clouds that discard local-frame geometry, and feature fusion in which high-dimensional sequence embeddings overshadow weaker evolutionary and structural signals. Here, we present PNBind, a multimodal deep learning framework that leverages protein language models, evolutionary information, and three-dimensional structural features via a geometric vector percep…
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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