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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 ↗6050 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.
- Hugging Face · Model / dataset date · First archived
TigreGotico/synthetic-wakeword-wake_up — dataset update ↗
# synthetic-wakeword-wake_up Synthetic wake-word audio for training and benchmarking OVOS wake-word plugins, covering the phrase "wake up". Every clip is machine-generated: text-to-speech synthesis followed by voice conversion to simulate multiple speakers. No human recording is included, and no natural voice is reproduced. Machine-generated audio carries no copyright of its own, so this dataset is published CC-BY-4.0 and is free to use, redistribute and build on, including for model training. Generated with the scripts in [TigreGotico/synthetic_dataset_generator](https://github.com/TigreGotic
- Hugging Face · Model / dataset date · First archived
Dewprisim/Prisim_Workflows — model update ↗
Workflows that I use. The newer model ones are updated as I tweak things Tested on RTX 3060 6GB VRAM /w 16GB RAM
- arXiv · Published · First archived
Gains and Collapse in On-Policy Distillation:A Reinforcement Learning Perspective ↗
On-policy distillation (OPD) has become an important approach to language model post-training. However, despite its performance gains, OPD can also collapse into excessively long and repetitive generation, and the mechanism underlying these divergent outcomes remains poorly understood. We explain these outcomes through a reinforcement learning perspective: the teacher implicitly rewards student behaviors, even those it rarely exhibits itself. From this perspective, our experiments show that OPD improves performance without expanding the student's capabilities. When the implicit reward model is…
- arXiv · Published · First archived
Hindsight-Guided Rationale Distillation for Rare Disease Diagnosis ↗
We study hindsight-guided distillation for rare disease diagnosis on ZebraMap: a 1.5B student is fine-tuned on chain-of-thought traces from a 8B teacher that observes the ground-truth diagnosis during generation. Absolute accuracy remains low for all models - the task is hard at this scale - but within this ceiling a filtered variant (StudentF) achieves a small, statistically significant accuracy advantage over the teacher (p < 0.001), concentrated in better-represented diseases. The unfiltered student does not significantly outperform the teacher (p = 0.129), establishing that contamination f…
- Hacker News · Posted on Hacker News · First archived
Sozly AI ↗
Hacker News discussion.
- GitHub · Repository created · First archived
FrederikRogalski/tinycoral ↗
tinygrad on Google's Coral Edge TPU: reverse-engineered ISA, our own code generator and USB driver. A whole transformer runs on the stick. Written by AI agents.
- arXiv · Published · First archived
LiBRA: Detection-Aware Image Watermark Removal via Bidirectional Latent Optimization ↗
Digital watermarking supports source attribution for AI-generated images, but its reliability depends on resistance to removal attacks. Some attacks attempt to remove watermarks by forcing the decoded watermark to differ from the original. However, this can produce an inverted watermark that remains detectable, causing removal to fail, while further attempts to alter the watermark may unnecessarily degrade image quality. To address these limitations, we present LiBRA (Latent In-band Bidirectional Removal Attack), which aims to make watermarks undetectable while preserving image quality. Instea…
- Hacker News · Posted on Hacker News · First archived
An AI-assembled crew exfiltrating ERP data from Spanish SMBs ↗
Hacker News discussion.
- arXiv · Published · First archived
EvoRiskBench: An Evolving Benchmark for Runtime Security Risks in Workspace Agents ↗
Workspace agents combine large language models with execution harnesses to perform stateful, multi-step tasks that access or modify external resources. Existing benchmarks leave gaps in executable coverage of their runtime security risks, while evolving model capabilities, harnesses, tools, and threats motivate benchmark evolution. We introduce EvoRiskBench, an evolving benchmark organized around the EP-Path-EF framework, which links an initial risk entry point to a one-hop technical effect through an agent-mediated risk path. The framework defines nine entry-point categories and five effect c…
- GitHub · Repository created · First archived
pooops15/muse-bridge ↗
Turn Muse (AI agent) into an OpenAI-compatible model (ms/muse) inside self-hosted 9Router for Hermes — file-queue bridge with native tool calls, unlimited batching & image understanding. Beginner tutorials in English & Indonesian
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
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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