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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 ↗5552 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
Pete Hegseth's Military of the Future: Defined by AI and Utter Lack of Oversight ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
yuzhewu207/BOSSV2V — dataset update ↗
# BOSSV2V：3D BOSS 战斗与脚本条件视频配对数据 ## 补充批次：古树庭院 / 红甲 BOSS（局部恢复） [目录和编码说明](batches/20260925_forest_redarmor_recovered/README.md)：**25段独立连续片段，共17分09秒、30,870对帧、50个720p/30fps视频**。从原60分钟录制中恢复局部有效区间，保留同一源帧、相机、动作和朝向。旧整段排除不表示41分钟全坏；本批是其中连续不少于30秒的补充区间，不是新录制或连续17分钟镜头。含RGB、粉色17关节条件、数值状态、逐帧朝向及重建脚本；技术校验、图像抽查和远端逐文件校验通过。 RGB明确标记为2026-09-25资产版本重渲染，不承诺与历史有损视频逐像素相同；原有动画、粗场景和VFX表示限制见批次说明。原19分钟森林及独立4分钟环绕保留，追加后森林相关已发布时长为40分09秒。 ## 新增批次：2026-09-16 赤甲 BOSS / 古树庭院（慢速360度环绕） [本批目录与说明](batches/20260916_forest_redarmor_orbit360/README.md)：**4段×60秒，共4分钟，8个720p/30fps视频，7,200对帧；两段战斗环绕、两段定格环绕**。包含逐帧玩家、BOSS和相机朝向、完整60Hz原始状态及条件重建
- arXiv · Published · First archived
Dex-One2Many: Learning Dexterous Manipulation from a Single Human Demonstration ↗
While learning dexterous manipulation from a single human video offers a promising alternative to costly robot demonstrations, many recent methods predominantly imitate demonstrated motions. Such strict motion matching often limits generalization to initial object poses, goal poses, and grasps not shown in the video. Alternatively, discovering a policy via reinforcement learning (RL) allows for broad generalization, but without prior guidance, it struggles with high-dimensional exploration in complex, multi-stage tasks. To address these coupled generalization and exploration challenges, we pre…
- arXiv · Published · First archived
Rubric-CEPR: Self-Evolving Image Editing via Reward-Verified Self-Distillation ↗
Instruction-guided image editors have become highly capable, yet improving them further still depends on human-edited training pairs or external reward models. Such supervision is costly to obtain and can reward plausible failures: a realistic output may leave the requested change undone or alter content that should be preserved. In this work, we strive to improve a pretrained image editor using only its own generations, without human-edited targets or an external training-time reward model. To this end, we propose a self-evolving framework, named Rubric-CEPR, that verifies the editor's own sa…
- arXiv · Published · First archived
DreamTrue: Action-Faithful Robot World Model with Counterfactual Post-Training ↗
We present DreamTrue, a multi-view, cross-embodiment robot world model for action-faithful and physically plausible video prediction. Training such a model on existing robot datasets faces two obstacles: imprecise calibration can impair action following, while limited coverage of unsuccessful interactions can bias predictions toward successful outcomes. To improve action following across embodiments, we render action trajectories into image-space conditions and introduce offline geometric calibration to align these conditions with the target videos. To broaden interaction coverage, we introduc…
- Hugging Face · Model / dataset date · First archived
ducanhdinh/Qwen1.5-MoE-A2.7B-Finetuning — model update ↗
# Qwen1.5-MoE-A2.7B-Finetuning LoRA adapter finetune tu [`Qwen/Qwen1.5-MoE-A2.7B`](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) (Mixture-of-Experts) tren **mix 3 task**: SNLI (NLI), SQuAD (extractive QA), MMLU (trac nghiem), train chung trong 1 lan (cac tap duoc tron lai, shuffle). Task: `snli, squad, mmlu` — so sample sau khi loc: snli: 549367, squad: 87599, mmlu: 87691. ## Prompt format (giu nguyen nhu khi train de eval zero-shot) SNLI: ``` Premise: Hypothesis: Question: What is the relationship between the premise and the hypothesis? Choose one: entailment, neutral, or contradiction. Answ
- arXiv · Published · First archived
CSF: Contextual Safety Filtering for Motion Generators ↗
Text-conditioned motion generators produce trackable whole-body motion, but they have no notion of scene-dependent safety: the same action may target an object or a person. Existing safeguards either inspect the prompt, require labeled motion data, or enforce geometric constraints; therefore, they do not directly account for how scene context changes a motion's meaning. We introduce contextual safety filtering (CSF), a training-free filter that grounds natural-language safety rules in safe and unsafe reference trajectories produced by the generator. For each active rule, safe and unsafe refere…
- arXiv · Published · First archived
On the estimation and validity of AI time horizons---a statistical look at the METR plot ↗
METR's 50\% time horizon measures the human completion time of software tasks that an AI solves with 50\% probability, allowing AI capabilities to be expressed in interpretable units. On 228 tasks and 26 AIs, we recompute the time horizons using splines and item-response theory to relax the assumption that the AI difficulty of a task depends linearly on the log of human time. Our fitted spline can be interpreted as a function that \emph{converts} human time to AI difficulty; it is nearly flat in a region from 2--30 min but close to linear elsewhere. Hence, a time-horizon jump from 3 min to 30…
- arXiv · Published · First archived
A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Control ↗
General-purpose robots must perform a wide range of tasks from agile locomotion to dexterous manipulation. While sim-to-real reinforcement learning (RL) has proven to be a useful tool for this goal, current RL pipelines depend on engineering-heavy, per-task structural priors such as shaped rewards and demonstrations. Recent work has shown that diverse simulator resets, combined with massively parallel simulation, can alleviate much of this engineering burden on several manipulation problems. However, we find that naively scaling this paradigm to more precise or dynamic problems remains non-tri…
- arXiv · Published · First archived
What 30,000 Hours of Ego-centric Video Does Not Teach ↗
World models offer a promising alternative to physics-based simulators, yet remain far from practical deployment. We ask how far scaling ego-centric human video takes them, using a dataset of 30,000 hours spanning over 1,000 scene types and 14,000 contributors. Rather than relying on opaque downstream metrics, we directly evaluate agent and object-interaction fidelity on a challenging out-of-distribution benchmark. Increasing training data by 100x improves both, but unevenly: the agent is modeled well, while object fidelity remains far lower and improves slowly. We show that the agent gains ne…
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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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