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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 ↗458 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.
- Europe PMC · Published · First archived
Integrating Multimodal MRI Habitat and Transformer-Based Pathomics to Predict High-Risk Molecular Subtypes and Explore Biological Mechanisms in Adult Diffuse Gliomas. ↗
Background This study aims to achieve accurate prediction of high-risk molecular subtypes of gliomas through a cross-scale Combined model, matching the model's classification metrics with patient risk stratification and exploring the underlying biological mechanisms. Methods This study retrospectively collected preoperative MRI, postoperative whole-slide pathological images, molecular markers, and clinical data from 456 adult diffuse glioma patients. We separately constructed an MRI habitat prediction model, a WSI Transformer-based deep learning pathomics (PDL) model, and a Combined model. A d
- Hacker News · Posted on Hacker News · First archived
Context Language Models (CLMs) ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: FGRF v3.0 – Scale-dependent structural probing of LLM weight topologies ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Coop: A small language model pretrained by volunteers ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
I made Jev to talk to us like an LLM (almost) ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: SideTap – an LLM drives a real iPhone from Windows, no Mac or jailbreak ↗
Hacker News discussion.
- arXiv · Published · First archived
Is Weight Tying Still Beneficial for Decoder-Only LLMs in Private Settings Under DP-SGD? ↗
Differentially Private Stochastic Gradient Descent (DP-SGD) is a leading approach for privacy-preserving fine-tuning of large language models (LLMs). Many decoder-only LLMs employ weight tying between input and output embeddings, a design choice originally introduced for parameter efficiency and improved language modeling performance in the non-private setting. However, the impact of weight tying under differentially private training remains largely unexplored. In this work, we investigate the role of weight tying in the DP setting using GPT2 and DistilGPT2 as representative decoder-only archi…
- arXiv · Published · First archived
Looped Diffusion Transformer ↗
Improving text-to-image models has traditionally relied on increasing model size or the number of denoising steps. In this work, we explore an alternative way to scale computation by repeatedly running shared Transformer blocks within each denoising step, effectively increasing computational depth while keeping the parameter count fixed. This looped computation enables iterative refinement of internal representations without explicit reasoning tokens. However, naive looping fails to consistently improve image quality. We trace this problem to weak supervision across intermediate loops and unre…
- arXiv · Published · First archived
Linguistic Loopholes in LLM Unlearning: From a 174-Language Benchmark to Coverage-Aware Unlearning ↗
Unlearning a fact in one language does not guarantee its removal in others as changing the query or even the requested answer language can reopen seemingly forgotten knowledge -- a cross-lingual loophole. The most straightforward solution to this challenge -- unlearning in all languages -- is neither scalable nor desirable as it amplifies damage to unrelated model capabilities. We introduce the task of language budgeted multilingual unlearning where the goal is to select a subset of languages that maximizes cross-lingual erasure. To study this task we introduce the Cross-Lingual Unlearning Ten…
- arXiv · Published · First archived
OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoning ↗
Real-world time-series applications increasingly require models that can handle time series forecasting, context-conditioned prediction, and language-based temporal reasoning. Yet current time-series foundation models remain fragmented across these capabilities: numerical specialists often provide the strongest forecasts, while language-based models offer broader contextual understanding and analysis. A central challenge is to unify these heterogeneous capabilities without reducing their individual performance. We introduce OpenTSLM TeeMoE, a generalist time-series language model that can fore…
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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