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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
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 ↗6055 classified signals in the fourteen-day window. Recent momentum: steady. 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.
- OpenAlex · Published · First archived
Plug-and-Play Dramaturge: A Divide-and-Conquer Approach for Iterative Narrative Script Refinement via Collaborative LLM Agents ↗
Although LLMs have been widely adopted for creative content generation, a single-pass process often struggles to produce high-quality long narratives. How to effectively revise and improve long narrative scripts like scriptwriters remains a significant challenge, as it demands a comprehensive understanding of the entire context to identify global structural issues and local detailed flaws, as well as coordinating revisions at multiple granularities and locations. Direct modifications by LLMs typically introduce inconsistencies between local edits and the overall narrative requirements. To addr…
- OpenAlex · Published · First archived
MCGMark: An Encodable and Robust Online Watermark for Tracing LLM-Generated Malicious Code ↗
With the advent of large language models (LLMs), numerous software service providers are developing LLMs tailored for code generation, such as CodeLlama. However, these models can be exploited by malicious developers to generate malicious code, posing severe threats to the software ecosystem. To address this issue, we first conducted an empirical study and built MCGTest , a dataset of \(406\) prompts designed to elicit malicious code from LLMs. Leveraging this dataset, we propose MCGMark , a watermarking method to trace and attribute LLM-generated malicious code. MCGMark subtly embeds user-spe…
- OpenAlex · Published · First archived
Fair distributed machine learning with imbalanced data as a Stackelberg evolutionary game ↗
Abstract Decentralised machine learning systems trained on heterogeneous and imbalanced data face a fundamental tension between predictive performance and equitable contribution across participating nodes. Here, we introduce two contribution-weighting mechanisms inspired by the principles of Stackelberg evolutionary games: the Deterministic Stackelberg Weighting Model (DSWM) and the Adaptive Stackelberg Weighting Model (ASWM), which dynamically regulate each node’s influence on the global model during training. We evaluate both methods in Single-Leader–Multiple-Follower (SLMF) and Multi-Leader…
- Europe PMC · Published · First archived
EdiProPred: Prediction of Edible Plant Tissue-Associated Proteins Using Large Language Models ↗
Ensuring the safety and suitability of proteins considered for food and biotechnology applications is an important challenge. Computational approaches can facilitate the prioritisation of candidate proteins for further investigation, particularly given the rapid growth of genomic and metagenomic sequence datasets. In this study, we developed computational models to distinguish edible plant tissue-associated (EPTA) proteins from non-EPTA proteins. The positive class comprised 939 proteins with experimentally validated evidence of expression in plant tissues commonly consumed by humans, hereafte
- Hacker News · Posted on Hacker News · First archived
Ask HN: Are there AI models for generating sounds based on a text and reference? ↗
I've been having a hard time finding a solution. Is there really no commercialized model that I can feed in a reference sound and text instruction and get another sound out? Right now having a multimodal inputs to image or text output is a commodotized, solved problem. IE you can put a prompt for some image and use a reference image to guide the model on what you want. After all, a picture is worth a thousand words right? Ive also used a text and…
- Hacker News · Posted on Hacker News · First archived
Nova Sprint. Coordinate AI sprints across multiple models and harnesses ↗
Hacker News discussion.
- arXiv · Published · First archived
The interface of data assimilation and machine learning ↗
Data assimilation (DA) is the process of combining forecasts from a model with observations in order to optimally estimate the state of a system. This is critical for chaotic systems, such as the atmosphere, since if observations are not continually assimilated the model will quickly lose skill. DA is routinely performed (usually every 6 hours) at operational forecasting centres around the world. In this article we discuss the interface of machine learning (ML) and DA. This is still an emerging and quickly developing field, and this article tries to give an overview of some of the main topics…
- GitHub · Repository created · First archived
carverauto/agentboard ↗
Self-organized multi-agent orchestration framework with shared context, messaging, job board, and deep CI/git integration
- GitHub · Repository created · First archived
madcodeorg/nota ↗
A local-first workspace for documents, whiteboards, meetings, and on-device AI.
- GitHub · Repository created · First archived
igrbible/Ruach_Studio ↗
A full music studio and WebUI for YuE2. Score first (ABC/MIDI), LoRA training, stems, remaster, upscale, lyrics check, DAW export. Songs from words on your own GPU. yue2.cpp · 7 languages · OSEM: Open Source, Engaged & Musical
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How to assess evidence about Artificial Intelligence
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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