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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 ↗5310 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.
- Hacker News · Posted on Hacker News · First archived
Scaledown.ai Launches Constrained Decoding Models ↗
Hacker News discussion.
- GitHub · Repository created · First archived
thegitai/thegitai-cli ↗
AI coding agent for your terminal: understands your repo, edits across files, runs and verifies changes with your approval. Source-visible CLI client.
- Hacker News · Posted on Hacker News · First archived
Linus Torvalds is using AI to develop Linux kernel code now ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
laion/Humaneness-Voice-Small-1.1-Alpha — model update ↗
# Humaneness Voice Small 1.1 Alpha Completed full fine-tune. Current download: step **6,109 / 6,109**. The verified complete weight file is selected by default.json, which updates automatically at training completion. Uploaded older snapshots remain individually labeled under checkpoints/. This is a full-parameter continuation, **not a LoRA**, and not a new pretraining from scratch. **[Public English listening demo](https://laion-humaneness-voice-1-1-alpha-listening.static.hf.space/?model=small)**: matched old/new checkpoints, seven-emotion and 40-emotion cases, natural/organic variants, casua
- arXiv · Published · First archived
ChunkAct: Action Chunking for Agentic Robotics ↗
Agentic robotics places a large language model (LLM) agent in the robot control loop, where it composes perception, motion, and vision-language-action (VLA) tools under the guidance of a memory and thereby generalizes to new tasks without retraining. This generality comes at a steep price in efficiency. Every primitive requires a round of LLM reasoning over the full interaction history, so the cost of an episode grows with the number of primitives. We find this reasoning largely redundant: once a sub-goal is chosen, its primitives follow a routine already prescribed by memory. We present Chunk…
- arXiv · Published · First archived
LoopWAM: Closed-Loop Generalist Policy Improvement from Deployment Rollouts ↗
Scaling expert demonstrations alone does not enable generalist robot policies to improve from deployment. Real-world rollouts contain successes, suboptimal behavior, and failures: imitating every recorded action can reinforce poor behavior, while filtering out failures discards evidence of physical interactions and their consequences. We introduce \textbf{LoopWAM}, a closed-loop world-action model that learns directly from this full quality spectrum, using rollouts from both itself and other deployed policies. Source-dependent noise sampling unifies behavior learning and action-conditioned wor…
- arXiv · Published · First archived
EgoExplorer: A Vision-Motion-Touch Observatory for Human Loco-Manipulation In Situ ↗
Learning humanoid loco-manipulation from human demonstrations rests on observing how locomotion, posture, and object interaction evolve together as tasks unfold. Scaling collection across diverse environments, in turn, favors lightweight, portable capture systems. Yet, existing datasets provide only partial views of this coordination. Thus, we introduce EgoExplorer, a 100-hour vision-motion-touch observatory of human loco-manipulation built with a wearable system. With an emphasis on professional activities, its continuous recordings align egocentric video with reconstructed whole-body motion,…
- arXiv · Published · First archived
Cepheid: Decoupling Scale from Structure for Cross-Family Motion Completion ↗
Sparse-to-dense motion completion recovers dense mesh motion from sparse vertex displacements. Residual priors trained on one geometry family frequently fail to beat parameter-free harmonic extension off-family. Moreover, cross-family training meets a scaling barrier: residual amplitude varies by more than an order of magnitude across geometry families and collapses as observations become denser. Since this scale depends on unknown target motion, it cannot be measured directly at test time. We decouple residual structure from physical scale by learning a dimensionless prior, estimating amplitu…
- arXiv · Published · First archived
Dog-Breed Text-to-image Flow Matching from Pretraining to Fine-Tuning on 4 Consumer GPUs ↗
We generally perceive that training text-to-image diffusion models requires heavy compute: it is true if we aim at a comprehensive frontier model. This understanding prevents people from training their own models when getting started in the field. We develop MiniDog, a framework that enables pretraining diffusion transformers in 55 minutes and fine-tuning in another 15 minutes on four 3090 GPUs. For data, we use 26k dog images from ImageNet and caption them for pretraining and use 2k images generated by FLUX.1-schnell for fine-tuning. We precompute the image and text features and cache them fo…
- arXiv · Published · First archived
What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study ↗
How should demonstration quality be used during vision-language-action (VLA) pretraining to improve performance after target-task fine-tuning? We compare weighting, filtering, and conditioning across reward definitions and label granularities using 5,000 naturally suboptimal human demonstrations and 24 target tasks. Within each VLA, we match demonstrations and training budgets. Additional comparisons examine pretraining data size and the roles of quality tags during fine-tuning and inference. Across three VLAs, we find that approach-aware episode conditioning improves mean success over plain b…
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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