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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 ↗445 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.
- arXiv · Published · First archived
Efficient Provably Private Classification with a Tabular Foundation Model ↗
Tabular data underpin prediction and decision-making in medicine, finance, government and science, but often contain sensitive individual-level information, creating a need for accurate prediction while preserving privacy. Traditional private learning provides formal privacy guarantees, but requires slow dataset-specific optimisation, suffers substantial utility loss under strong privacy, and is often difficult to apply correctly. Tabular foundation models adapt rapidly to new datasets, but existing models lack formal privacy guarantees, and are highly vulnerable to membership-inference attack…
- arXiv · Published · First archived
Purifying Backdoored Large Vision-Language Models by Removing Hijacked Directions ↗
Large vision-language models (LVLMs) are increasingly deployed in safety-critical applications, yet they remain vulnerable to backdoor attacks. Defending against such attacks remains costly, as existing methods require either extensive retraining on clean data or per-query intervention at inference time. To address this limitation, we propose OrthoPurify, a more efficient method to purify backdoored model weights via one-step orthogonal projection. Specifically, through structural analysis of backdoor weight updates, we find that the backdoor is encoded by diverting a small number of weight up…
- Hacker News · Posted on Hacker News · First archived
Show HN: CloudGrip – Open-source AI proxy to cap LLM API budgets ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: LLM verification pipeline against hallucinations ↗
Hacker News discussion.
- GitHub · Repository created · First archived
posstos/ai-relay-welfare ↗
AI 中转站福利导航：31 个中转站的邀请链接合集，含签到状态与实测备注
- arXiv · Published · First archived
QCATS: Query Context-Aware Transformer Slicing for Efficient Predictive Query Processing ↗
In-database predictive query processing increasingly applies Transformer-based models within relational pipelines. However, existing in-database inference typically exposes only tuple-level model inputs to the inference runtime, leaving relational predicates and metadata statistics invisible to neural execution planning. In this paper, we propose QCATS, a query context-aware transformer slicing framework that enables efficient sparse inference inside database systems. QCATS executes at query granularity: instead of routing individual tokens or tuples during inference, it uses query predicates…
- Hacker News · Posted on Hacker News · First archived
Show HN: LLM API prices with full price history and a cloud vs. local calculator ↗
Hacker News discussion.
- arXiv · Published · First archived
Fully Interpretable Minimal Transformers: From Geometry to Algorithm ↗
We present a framework for building and interpreting minimal transformer models. By constraining a transformer's embedding dimension and head size to 2, we enable full two-dimensional visualization of its internal representations. Embeddings, query/key/value transforms, attention outputs, residual streams, and decision boundaries can all be seen directly. Our central claim is that the learned geometry implies an algorithm; the arrangement of points and boundaries in R^2 can be read as a step-by-step procedure. We train a transformer on a simple task where it must produce the most recently obse…
- Hacker News · Posted on Hacker News · First archived
We tried to reproduce LLM judge position bias ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Streaming an LLM chat across a TypeScript/Python boundary: the details that bite ↗
Hacker News discussion.
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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
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These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.