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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 ↗5844 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
CedM/oc_mlops_projet_2 — dataset update ↗
# Dataset Card for : oc_mlops_projet_2 - "Prêt à dépenser" Vous trouverez ici, les logs de l'API et les données traitées par l'API (input/output - Pour analyse de dérive).\ Vous trouverez également des fichiers d'exemple à uploader depuis le frontend Streamlit (pour faire des prédicitions en lot).
- Hacker News · Posted on Hacker News · First archived
Biohub leads $1.8B initiative to build AI-ready biological datasets ↗
Hacker News discussion.
- arXiv · Published · First archived
Physics-Aligned Electronic Ground-State Learning Improves Generalization ↗
Machine-learned interatomic potentials (MLIPs) excel at in-distribution tasks, accelerating drug and material development, yet they struggle to generalize out-of-distribution. We propose to push the cost-accuracy Pareto frontier by designing observable-agnostic electronic ground-state descriptor models (GSMs) with computational costs situated between MLIPs and Kohn-Sham density functional theory (KS-DFT). We align the learning objectives and architectures of GSMs with the governing equations of KS-DFT by enforcing physical constraints and removing optimization pressure on unphysical or irrelev…
- Hacker News · Posted on Hacker News · First archived
LC-3 Assembly Tutor-AI tutor that explains what the machine is doing ↗
Hacker News discussion.
- arXiv · Published · First archived
PatchBench: Measuring Collateral Damage in Activation Patching ↗
An LLM safety patch can pass a benchmark while still being a poor repair. This risk is especially acute for jailbreak repairs, where the goal is to correct a specific unsafe behaviour without changing unrelated behaviours. A patch may block exact evaluation prompts yet fail on close harmful variants, or suppress harmful behaviour by over-refusing benign prompts that share its wording or structure. Existing protocols primarily test whether models can be broken, while aggregate metrics (attack success, refusal rates, global capability) cannot distinguish selective repairs from broader local supp…
- Hacker News · Posted on Hacker News · First archived
Show HN: The Last SaaS – An open source headless SaaS for agents and assistants ↗
This kind of started as a joke, and it still kind of is. But it's also kind of fun to think about what the minimum number of primitives would cover all possible typical SaaS applications. And then drive that from an agent/assistant. There is obvious stuff like CRUD, and then permissions, users, notifications etc. So basically I made a start of a "headless SaaS" that has no UI and is just a CLI/API/MCP that you can connect your Claude etc to and i…
- arXiv · Published · First archived
Using Small Language Models to Reverse-Engineer Machine Learning Pipelines Structures ↗
Context: Once defined a taxonomy of stages structuring Machine Learning (ML) pipelines (e.g. Data Preprocessing, Modeling...), extracting these stages from source code is key for better understanding ML practices. However, the diversity caused by the constant evolution of ML (e.g., algorithms, datasets) makes this task challenging. Existing approaches either rely on non-scalable manual labeling or on classifiers that do not properly support domain's diversity. These limitations call for more reliable solutions. Objective: We evaluate whether Small Language Models (SLMs) can leverage their code…
- arXiv · Published · First archived
PairAudit: Guiding Human Review with Graph Tokens under Distribution Shift ↗
Intrusion detectors can confidently misclassify attacks that were not seen during training. Human review can correct these errors, but only a limited number of cases can be checked. Uncertainty-based review may overlook confident errors, while anomaly scores alone do not show whether changing the review plan will correct more errors. We introduce PairAudit to find overlooked errors and improve review under a fixed budget. Its graph tokens capture prediction patterns across connected nodes. Rather than building another predictor through feature aggregation, PairAudit uses unusual relational pat…
- Hacker News · Posted on Hacker News · First archived
Friday – A voice-activated personal AI companion built with Flutter ↗
Hacker News discussion.
- arXiv · Published · First archived
QuSema: Detecting Silent Bugs in Quantum Libraries via Quantum-knowledge-enhanced Agents ↗
Quantum libraries are now critical infrastructure for quantum algorithm development, yet their correctness remains difficult to test. Existing testing techniques mainly rely on failure-based or comparison-based oracles, exposing bugs only when executions fail, violate runtime checks, or disagree with another implementation. Their applicability is limited when suitable execution-based oracles are unavailable, leaving some silent bugs undetected. Such missed bugs can produce incorrect results that propagate into experimental conclusions, simulation studies, and algorithmic designs. Here we prese…
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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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These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.