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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 ↗6039 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
KeraCare/glm-ocr-field-bbox-lora — model update ↗
# GLM-OCR Field + BBox Extraction (LoRA, full-scale) Fine-tuned from [`zai-org/GLM-OCR`](https://huggingface.co/zai-org/GLM-OCR) to jointly extract structured fields **and their bounding boxes** from prescription document images. Given a prescription photo, the model returns a single JSON object with every field below, each with its on-page location. This is a **LoRA adapter merged into the base weights** (rank 64, alpha 128, dropout 0.05, targeting the vision encoder attention, vision-language merger, and LM attention/MLP layers). A sibling full fine-tune of the same task is at [`KeraCare/glm
- GitHub · Repository created · First archived
Aimee51819/FileFlipper ↗
FileFlipper — Quick Edit for Mac & Windows. One-click Markdown: turn Word, PDF, PowerPoint and Excel into clean Markdown that AI reads with fewer tokens. Plus Shift-drag conversion in Finder and File Explorer: JPG, PNG, PDF, HEIC, MP4, crop, OCR. 100% on-device, free and open source. Mac 和 Windows 都能用，一键转 Markdown。
- arXiv · Published · First archived
Threat-Preserving Representation Sensitivity in Agent-Security Benchmarks ↗
Security benchmarks for LLM-based agents often report the attack success rate (ASR) as a measure of model robustness and use these scores to compare different models and defense mechanisms, assuming that they describe the security of the agent. In this paper, we explore whether it also influences the benchmark's measurement. To measure the effect of the benchmark representation, we introduce threat-preserving representation sensitivity (TPRS), which measures how much the ASR changes when we change the agent-visible representation while holding the underlying task, harmful action, security poli…
- Hacker News · Posted on Hacker News · First archived
OpenAI Deprecations ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
OpenAI's Astra model is shockingly good at robotics ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: GPT-6 Astra and Claude 5.5 Opus race to create the best StarCraft bot ↗
StarSkirmish: Hillclimb is a long horizon implementation of the StarCraft: Broodwar based LLM benchmark that I released last week. Each LLM writes a Protoss bot in C++ against BWAPI, played on OpenBW. They must climb five tiers of Protoss opponents, from scripted demo bots up to top rated human written bots like Stardust and PurpleWave. The StarSkirmish bench restricted LLMs to 1 hour of wall clock time for reasoning. With Hillclimb there is no f…
- Hacker News · Posted on Hacker News · First archived
Show HN: One line so LLM workers share a 429 wait and keep jobs an outage stops ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: Client rooms where calls and AI agents suggest edits, people approve ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: Taskrouter – One API Learning to Choose Models per AI Task ↗
Hacker News discussion.
- arXiv · Published · First archived
Reasoning Models Are Accurate but Unsound on Identification ↗
A reasoning model asked whether a causal effect is recoverable from observational data can fail in two ways: it refuses an identifiable query or answers a nonidentifiable one. The latter is more consequential, as no observational data can validate the claimed formula. Measuring this failure requires queries that are provably non-identifiable, which prior evaluations lack, and grading that accepts correct formulas in any equivalent form, which string matching cannot provide. We build CERTID, a formal identification pipeline that addresses both limitations. CERTID uses the sound and complete cau…
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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