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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 ↗460 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.
- GitHub · Repository created · First archived
Alpha-Park/genpark-multi-head-scaled-dot-product-attention-skill ↗
Transformer multi-head self-attention engine with causal triangular masking and softmax normalization
- GitHub · Repository created · First archived
alphaparkinc/genpark-multi-head-scaled-dot-product-attention-skill ↗
Transformer multi-head self-attention engine with causal triangular masking and softmax normalization
- Hacker News · Posted on Hacker News · First archived
Show HN: Redthread – autonomous LLM pentesting with proof-of-concept exploits ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
LLM capabilities can transfer through unrelated text ↗
Hacker News discussion.
- GitHub · Repository created · First archived
pnnnhan99/awesome-free-ai-apis ↗
A curated collection of free & free-tier AI APIs for developers, vibe coding, AI apps, and modern software development.
- Hacker News · Posted on Hacker News · First archived
Foundations of Large Language Models ↗
Hacker News discussion.
- arXiv · Published · First archived
Steering Language Model Goals with Value Transplant ↗
Reasoning models often act as if they pursue goals, but their efforts are not always directed toward what users intend, sometimes leading them to pursue unintended outcomes. Previous work has examined how models may internally track their progress toward their goals through a "value axis." We study whether changing such a signal can retarget the model's search toward a different goal. We test value transplant: at each token, we shift the host model's activation along a candidate value axis by the donor-host difference in value coordinates (multiplied by a large scalar), aiming to redirect the…
- arXiv · Published · First archived
Kafila: Serving Large Language Models on a Trusted Set of Heterogeneous Commodity Machines ↗
Between them, the members of a research group or a circle of friends own several consumer computers, none large enough to run a capable large language model. Existing systems pool such capacity across open swarms anyone may join, which a group admitting only trusted machines cannot use. Bounding membership removes what they depend on: a swarm holds each part of the model on several peers and routes around a slow one. A bounded session must use every device it admits. Its pipeline advances at the pace of whichever device received a share it cannot serve quickly, so the division has to be right…
- arXiv · Published · First archived
SCOPD: Sparse-Context On-Policy Self-Distillation for Efficient Vision-Language Models ↗
Reasoning vision-language models (VLMs) process images and videos as long sequences of visual tokens, making inference expensive. Training-free token pruning reduces this cost, but aggressive compression can sharply degrade performance, often attributed to irreversible loss of task-relevant visual information. We show that this explanation is incomplete. In a fixed-context Pass@K analysis, repeated sampling from the same pruned visual representation recovers many examples missed by greedy decoding, indicating that useful visual evidence can remain accessible but be used unreliably. We call thi…
- arXiv · Published · First archived
Large Language Models for Structured Clinical Data Analysis: Dual-Agent Grounding and Validation ↗
Objective: To develop and characterize CLEAR-Med, a dual-agent framework for natural-language analysis of structured clinical data that separates SQL-based invocation from independent validation. Methods: CLEAR-Med uses one agent to translate a question into executable Structured Query Language (SQL), retain the executed query and database result, and produce a draft. Deterministic checks and a separately invoked cross-provider Validation Agent then accept the draft, request one bounded repair, or abstain. We formalized the system as a bounded selective pipeline and evaluated CLEAR-Med's confi…
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.