THE INTERNET IS THINKING
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 ↗5531 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.
- GitHub · Repository created · First archived
kauakdev/kauak ↗
A Sims-style isometric office that shows what your Herdr coding agents are doing, live
- Hugging Face · Model / dataset date · First archived
fxtdstudios/RUDRA — model update ↗
# RUDRA **Radiometric Dynamic-Range Conditioning for HDR-Aware Diffusion Models** [FXTD Studios](https://fxtdstudios.com) · [github.com/fxtdstudios/RUDRA](https://github.com/fxtdstudios/RUDRA) Two independent families live in this repository. | family | what it does | files | |---|---|---| | **`sdr2hdr/`** | takes an ordinary 8-bit frame and reconstructs scene-linear HDR | 7 | | **decoders (root)** | decode diffusion latents straight to scene-linear HDR, skipping the tone-mapped VAE | 14 | They share a name and a radiometric convention and nothing else. Pick the one that matches your problem.
- arXiv · Published · First archived
Can Language Models Play the Wikipedia Game? ↗
Language models increasingly support or automate information retrieval from document collections too large to index. This task is difficult when it requires specialist linguistic or scientific knowledge. We used the Wikipedia Game to test language models on an analogous information retrieval task. In the game, players must reach a target article using only article links. We compared sentence embedding models and LLMs across five Wikipedia languages and five academic-literature datasets. We construct very large local graphs of Wikipedia pages and of paper citations using OpenAlex and provide co…
- arXiv · Published · First archived
Towards Physically Grounded Articulated 3D Assets: A Multi-Agent Pipeline and Benchmark ↗
In this paper, we study physically grounded articulated 3D asset generation---moving the goal from reproducing how objects look to capturing how they move and function. Inspired by the literature on procedural and agentic generation, we present Artisan, a system that extends the latest method Articraft by introducing a multi-agent pipeline with physical simulation feedback, enabling comprehensive attributes beyond geometry, appearance, and physically plausible articulation, such as collision-free motion, stability under gravity, and accurate mass and material properties. Furthermore, we constr…
- Hugging Face · Model / dataset date · First archived
francesca9805/swe-latn-10mb-after-ppt-Dp-100mb-packed-bfdiso-ckpt500_seed455 — model update ↗
# Model Card for swe-latn-10mb-after-ppt-Dp-100mb-packed-bfdiso-ckpt500_seed455 This model is a fine-tuned version of [francesca9805/swe-latn-10mb-ppt-Dp-100mb-packed-bfdiso_seed455](https://huggingface.co/francesca9805/swe-latn-10mb-ppt-Dp-100mb-packed-bfdiso_seed455). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="francesca9805/s
- Hugging Face · Model / dataset date · First archived
3hs4n/Ternary-Bonsai-2-27B-Uncensored-Heretic-v2-mlx-2bit-compact — model release ↗
# Ternary Bonsai 2 27B Uncensored Heretic v2: Compact MLX 2-bit A smaller native MLX variant of [the original Heretic v2 MLX pack](https://huggingface.co/3hs4n/Ternary-Bonsai-2-27B-Uncensored-Heretic-v2-mlx-2bit). **All 2,057 language tensors are copied byte for byte.** The size reduction comes from quantizing the 27 vision MLP expansion layers (`linear_fc1`) from FP16 to native MLX affine 2-bit/group-128 storage. Vision attention, MLP contraction layers, the merger, positional embeddings and all other vision tensors remain unchanged. The compact weights occupy **8,365,392,904 bytes**, approxi
- Hacker News · Posted on Hacker News · First archived
Agent-config&Claude Code mods ↗
Hacker News discussion.
- arXiv · Published · First archived
Splat2Act: From Captured Scenes to Personalized VLA Manipulation ↗
A user who brings a generalist manipulation policy into their home or office wants it to pick up their objects from their shelves, counters, and tables. Yet even a strong generalist like $π_{0.5}$ achieves 0% pickup success in our captured deployment scenes without adaptation, and collecting real demonstrations in every user's environment is prohibitively expensive. We present Splat2Act, an automated real-to-sim-to-real pipeline that personalizes a pretrained vision-language-action (VLA) policy to a specific scene and object set using only a handheld phone scan and images of the user's objects…
- arXiv · Published · First archived
InstantFusion: A Shared Latent Interface for Heterogeneous Image Generators ↗
Diffusion/flow models have emerged as the de facto paradigms for image generation, giving rise to a diverse family of modern generators with increasingly heterogeneous architectures and representations. Despite their remarkable generation capabilities, the cross-model alignment of latent representations and the consistency of their underlying denoising dynamics remain underexplored. In this work, we uncover an intriguing cross-model compatibility: independently trained generators exhibit similar latent spatial organization, while directly handing off intermediate states between unaligned model…
- arXiv · Published · First archived
Noise Space Learning for VLA Adaptation ↗
Pretrained vision-language-action (VLA) models encode broad manipulation priors, yet adapting them to specific tasks requires balancing limited robot interaction, computational resources, and safety constraints. Noise space learning (NSL) adapts flow-matching-based VLAs by learning a context-conditioned distribution over noise while keeping the pretrained VLA fixed. Building on prior noise-steering methods, we formulate NSL as an interface for VLA adaptation that accommodates different learning algorithms and instantiate it with PPO, SAC, and AWR. Theoretically, we provide an information-theor…
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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
Follow a thread
Explore related regions
These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.