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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 ↗5199 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.
- arXiv · Published · First archived
Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance ↗
Mobile Manipulation (MoMa) is a cornerstone of autonomous robotic systems, yet real-world fine-tuning remains a formidable challenge because of high-dimensional action spaces and intricate coupling between heterogeneous modules. In this paper, we propose HiRe-MoMa (Hierarchical Residual Mobile Manipulation), a systematic framework for stable and sample-efficient real-world fine-tuning of complex MoMa systems. At its core, HiRe-MoMa adopts a Hierarchical Residual Architecture that fine-tunes a frozen base policy. A Policy-Agnostic Residual learns additive corrections atop the base policy, while…
- arXiv · Published · First archived
Modeling and Understanding Neural Weights of Humanoid Policies with Diffusion ↗
Recent advances in robot learning have driven rapid progress in humanoid control, enabling increasingly agile behaviors in the real world. However, humanoid control networks are typically obtained through conventional training procedures, while comparatively little attention has been given to directly modeling the distribution of their learned weights and generating functional networks in weight space. In this work, we investigate whether high-performing humanoid control networks can be generatively modeled directly through their weights. We adopt a latent diffusion framework that couples a li…
- arXiv · Published · First archived
VITAL: From Visual Motion to Precise Action ↗
World-action models aim to turn visual anticipation into driving decisions, yet typically train action experts on trajectory representations defined independently of visual dynamics. This motivates deriving action targets directly from visual motion. We first examine a simpler setting in which trajectories are reconstructed from recorded future images. Even without the need for future prediction, reconstruction captures the dominant maneuver but leaves centimeter-scale errors. We therefore introduce VITAL (Vision-Induced Tokens for Action Learning), an action tokenizer that couples a visually…
- arXiv · Published · First archived
CommerceGen-Studio: An End-to-End Framework for Commercial-Grade Main-Image Generation ↗
We present CommerceGen-Studio, an end-to-end framework for generating commercial-grade main images for e-commerce Stock Keeping Units (SKUs). Our framework is organized along a three-stage pipeline of information extraction, semantic rewriting, and multi-condition generation: Prompt-Eval-Refine-Loop Agent (PERL-Agent) distills heterogeneous SKU metadata into concise selling-point text lists through an iterative closed loop of VLM and LLM roles scored by a reference-free rubric. Layout Thinker Optimization (LTO) aligns the rewriter with both caption and rendered-image quality via alternating do…
- Hugging Face · Model / dataset date · First archived
ahmadsy/tashkeel-nano-4p5m — model update ↗
# Tashkeel Nano 4.5M - Arabic Diacritization **4,488,160 parameters. DER (CE) 9.91 and WER (CE) 31.03 on the published **SadeedDiac-25** benchmark, scored with that benchmark's own evaluator - **ahead of every 7B-13B system on its leaderboard.** > **Superseded.** The smaller successor > [`ahmadsy/tashkeel-nano-1p1m`](https://huggingface.co/ahmadsy/tashkeel-nano-1p1m) > (1.08 M) scores **DER (CE) 3.78 / WER (CE) 12.73** on the same benchmark with > the same evaluator, at 4.14x fewer parameters. Prefer it. Every number in this card is either produced by a downloadable model or taken from a publi
- arXiv · Published · First archived
DynThinker: Unifying Static 3D and Dynamic 4D Spatial Reasoning ↗
Achieving general spatial intelligence requires a unified understanding of both static 3D and dynamic 4D. However, we find that existing models struggle to excel at both: 3D post-training can weaken dynamic understanding, while 4D-specialized models often lack strong static 3D reasoning. Our analysis reveals that this spatial--dynamic interference is highly non-uniform across parameter groups, with the balance between static and dynamic capabilities particularly sensitive to changes in late-layer MLPs. To address this interference, we propose DynThinker, a unified framework for jointly strengt…
- arXiv · Published · First archived
Co-Evolving Acting and Forecasting for Language Agents ↗
Training language agents is bottlenecked by the high cost of long-horizon rollouts, yet standard agentic reinforcement learning (RL) learns only from a terminal scalar reward, leaving the supervision in intermediate steps largely unused. Prior work exploits this supervision through value targets that estimate per-step success probabilities or observation targets that fit environment response. Both are hard to construct well: (1) value targets come from the same single scalar signal for each step in the trajectory, and (2) observation targets, such as web pages and terminal logs, are long and n…
- Hugging Face · Model / dataset date · First archived
ibm-granite/granitelib-guardian-r1.0 — model update ↗
# Granite Guardian Library The Guardian Library includes four families of LoRA adapters, each developed for a specific task related to safety, factuality, and policy compliance in LLM-based systems. We provide adapters for: * [ibm-granite/granite-4.0-micro](https://huggingface.co/ibm-granite/granite-4.0-micro) * [ibm-granite/granite-4.1-3b](https://huggingface.co/ibm-granite/granite-4.1-3b) * [ibm-granite/granite-4.1-8b](https://huggingface.co/ibm-granite/granite-4.1-8b) * [ibm-granite/granite-4.1-30b](https://huggingface.co/ibm-granite/granite-4.1-30b) We give a brief overview of the function
- Hugging Face · Model / dataset date · First archived
ibm-granite/granitelib-core-r1.0 — model update ↗
# Granite Core Library The Granite Core Library includes three families of LoRA adapters, each developed for a specific task that enhances the base model's capabilities for explainability, calibration, and constraint verification. We provide adapters for: * [ibm-granite/granite-4.0-micro](https://huggingface.co/ibm-granite/granite-4.0-micro) * [ibm-granite/granite-4.1-3b](https://huggingface.co/ibm-granite/granite-4.1-3b) * [ibm-granite/granite-4.1-8b](https://huggingface.co/ibm-granite/granite-4.1-8b) * [ibm-granite/granite-4.1-30b](https://huggingface.co/ibm-granite/granite-4.1-30b) We give
- arXiv · Published · First archived
CoWear: Towards Collaborative Wearable Inertial Localization with Multiple Smart Devices ↗
Consumer smart devices observe the same human motion from different body locations, yet existing inertial-localization benchmarks rarely provide synchronized heterogeneous wearables with device-specific motion ground truth. We present CoWear, a dataset and reference framework for collaborative inertial localization using a smartphone, smartwatch, and smart glasses. The dataset contains over 30 participants, over 300 walking sessions, and more than 30 hours and 100 km of aggregate device trajectories, with each device individually tracked by motion capture. To enable collaboration across distin…
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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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