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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 ↗450 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
FreeLLMAPI – One OpenAI-Compatible API for Multiple Free LLM Providers ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: Vim-like, hyper-efficient, LLM power tool – 20-80k tokens not 350k+ ↗
I built a focused TUI tool for efficient parallelized tasks, early independent testers expressed 90%+ API savings and more direct results. This is accomplished by user smaller context windows, hybrid client-side compaction, tool compaction, and system nudges (to avoid polluting the base prompts). My Shell features: - Vim-inspired keybindings, your keys never have to leave the keyboard (some mouse control supported as well) - Custom Markdown, LaTe…
- GitHub · Repository created · First archived
hey345437-boop/futuresearch-gateway ↗
把 FutureSearch 的异步任务 API 反代成 OpenAI 兼容端点 + 管理面板 + 两组 MCP（研究 / 本地文件）。号池、租户额度、本地预览。
- OpenAlex · Published · First archived
A semantic layer architecture for removing schema identifiers from external LLM prompts, with model-dependent accuracy trade-offs ↗
Abstract The adoption of large language model (LLM)-based AI systems in credit rating agencies introduces a structural tension between operational efficiency and data security. This study examines a Semantic Layer as a technical mechanism for reducing this tension, and shows that whether it removes designated schema identifiers from the LLM-facing prompt depends on an implementation detail invisible to a schema-level analysis: whether the concept-to-SQL mapping is itself withheld from the LLM. An initial implementation (v1) includes this mapping in the prompt and removes none of the designated…
- OpenAlex · Published · First archived
LoRA based instruction fine tuning of large language models for agronomic triple extraction and GraphRAG knowledge retrieval ↗
Due to massive growth in the unstructured agricultural data generated from reports, research articles, news, farmers’ feedback, experts’ advice, etc, there is a need to utilize this information efficiently for knowledge representation and decision-making. There are multiple techniques where information extraction and managing it in a structured manner can be less challenging. LLMs are used widely for extracting meaningful information in the way the user instructs. More precisely, LLMs, when fine-tuned on domain-specific data, e.g, agricultural data, help to retrieve information promptly. We pr…
- OpenAlex · Published · First archived
RAC-LLM: retrieval-augmented large-small model collaboration for cloud-assisted web asset risk assessment and homologous discovery ↗
Cloud-assisted cyber-asset monitoring services must continuously assess web assets that rapidly change domains, content, and page structure, making new variants difficult to evaluate and trace. Large language models can interpret heterogeneous and ambiguous security evidence, but their decisions benefit from bounded and verifiable context. Lightweight modules can efficiently retrieve evidence and construct relations across large asset collections, but they cannot resolve every semantic ambiguity. We present RAC-LLM, a retrieval-augmented large-small model collaboration method for web asset ris…
- OpenAlex · Published · First archived
Transformer-based deep learning framework for network intrusion detection with context-aware traffic pattern modeling ↗
This paper introduces a context aware transformer based network intrusion detection framework using publicly available benchmark traffic data. The framework introduces three domain-specific modifications to the standard transformer encoder, a partitioned multi-head attention mechanism with separate intra-group and inter-group heads that model dependencies within and across semantic feature groups, a learned positional encoding over feature indices initialized with group-identity vectors, and a gated aggregation module that selectively weights encoder outputs before classification. Evaluation E…
- OpenAlex · Published · First archived
Proposing a minimal-coding methodology to customize large language models to automate services in information organizations ↗
Rarely do any studies propose a minimal-coding methodology to customize a Large Language Model (LLM) as part of the AI-driven automation of information services in libraries. This paper fills in the gap by illustrating a four-stage methodology to customize Llama 3 when automating cataloging, chat support, and recommendation services. Firstly, we collected and processed library data, that is, 197 frequently asked questions from the library website of a leading Research-I university in the US, and metadata of 450,320 books and their one-million-plus user ratings from the Book-Crossing dataset. S…
- OpenAlex · Published · First archived
Context engineering frames prompt engineering within system level orchestration for large language model applications ↗
Abstract By moving from isolated instruction crafting to system-level contextual orchestration, context engineering reframes how large language model (LLM) applications are designed for complex, multi-source workflows. Prompt engineering can deliver strong results for narrow, single-turn tasks, yet it often becomes brittle and costly to maintain as interaction length, tool use, governance constraints, and reliability requirements increase. In this perspective, context engineering is used as a working systems concept for the design and governance of context pipelines that determine how relevant…
- OpenAlex · Published · First archived
DisMed-LLM: De-Identifying Spanish Medical Text with Large Language Models ↗
Abstract Patient privacy and compliance with regulations such as the General Data Protection Regulation (GDPR) necessitate the robust de-identification of clinical texts. This study investigates the feasibility, computational cost, and limitations of de-identifying Spanish medical records using training-free Large Language Models (LLMs) compared to a supervised baseline. We evaluated five instruction-tuned LLMs on two Spanish clinical corpora (DisMed and MEDDOCAN) and benchmarked them against a fine-tuned multilingual BERT (110M parameters). On a held-out test set, the supervised baseline achi…
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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