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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 ↗5217 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
AvinashRicky/avigpt-250m-instruct — model update ↗
# 👑 AviGPT-250M-Instruct: Semi-Parametric Edge Intelligence ### Semi-Parametric 250M Small Language Model with a Native NVMe Hardware Memory Bus > **AviGPT is a proud member of the NVIDIA Inception Program** **Architect, System Designer & Sole Creator:** Yadlapalli Avinash Ricky (India 🇮🇳) **Model Parameters:** 250,269,696 (~250M) **Resident VRAM Footprint:** **488 MB** (FP16 Edge Mode) **Checkpointed Weights:** 477.5 MB **Status:** SFT 2.0 Production Release --- [](#) [. It holds ONLY the ` _ .extra.json` files, under the **same relative path** as in the main repo: ``` / / / _ .extra.json ``` For an alignment array at `a/arb/arb_vdv/GEN_1a2b3.json` in the main repo, this layer's file is `a/arb/arb_vdv/GEN_1a2b3.extra.json` here. The ` ` is the main file's content hash, so the two always belong together. Not every book has this layer; a missing file is a 404, not an error. Content: the same com
- arXiv · Published · First archived
LIBERO-PeRM: Benchmarking Personalized Robotic Manipulation ↗
Personalization is essential for general-purpose robots to transition into household environments and achieve true practicality. While general robotic manipulation has been extensively studied, personalized robotic manipulation (PeRM) lacks a controlled benchmark for separating task completion from preference satisfaction. We introduce LIBERO-PeRM, a benchmark for preference learning in embodied agents. LIBERO-PeRM studies five questions: 1) how policies follow preferences in zero-shot settings; 2) how they learn single preferences from demonstrations; 3) how efficiently they acquire preferenc…
- arXiv · Published · First archived
Gen4P: Video Generators are 4D Perceivers ↗
We introduce Gen4P, a unified framework for recovering 4D geometry and motion from monocular video. We show that a pretrained video generator can be repurposed into a feed-forward 4D perceiver without iterative denoising. Building on on-demand querying, Gen4P processes clean video latents in a single encoder pass and predicts the requested 4D attributes through a lightweight cross-attention decoder. The on-demand query formulation enables flexible spatial sampling, and our proposed continuous-time queries further provide temporal flexibility. To strengthen geometric knowledge, we propose a str…
- arXiv · Published · First archived
Which Tokens to Merge? Diffusion Dynamics for Efficient Image Generation ↗
Token merging has emerged as an effective training-free strategy for accelerating diffusion models by reducing redundant token computation while minimizing generation degradation. A key challenge is determining which tokens can be safely merged and which should be preserved for subsequent computation. Existing method addresses this by preserving prompt-aligned foreground regions, thereby protecting semantically relevant structures. However, this text-relevance criterion overlooks background objects, local details that are not explicitly mentioned in the prompt, leading to degraded structural c…
- arXiv · Published · First archived
SoftCache: Adaptive Step Size Caching for Diffusion Transformers ↗
Diffusion and flow models generate a sample over iterative steps, each a full pass through a large network, and that pass dominates generation cost. Training-free caching accelerates them by reusing activations from recent evaluations instead of recomputing them. Dynamic caching methods decide when to refresh by accumulating a cheap probe of the network input into a staleness signal and spending a fresh evaluation once it crosses a threshold. They perform hard caching: the signal is a binary switch between refresh and reuse, and every step, fresh or cached, advances by the prescribed step size…
- arXiv · Published · First archived
Value as Memory: Online Value-Guided Post-training for General Robot Policies ↗
Real-world post-training of generalist robot policies faces two challenges: partial observability makes action selection ambiguous, while offline post-training methods delay feedback from deployment experience. We present Value as Memory (VaM), a framework that addresses these challenges through value caching and online real-world reinforcement learning (RL). Value caching conditions actions on recent value predictions as task-progress memory, while online updates enable timely policy improvement by combining offline demonstrations, off-policy interventions, and on-policy rollouts for sample-e…
- arXiv · Published · First archived
Discovering state-of-the-art quantum circuits with large language models ↗
Significant efforts are currently being directed toward the implementation of large-scale quantum information processors, but realizing useful quantum advantage also requires the development of efficient quantum algorithms. While for many practical problems the dominant resource costs are associated with a small set of computational primitives, optimizing them in the quantum setting presents unique challenges, including a reversibility constraint absent from ordinary classical computation. Here we introduce an evolutionary framework in which large language models (LLMs) propose circuit designs…
- arXiv · Published · First archived
T2-z: Test-Time Optimization of Environment Latents for Robotic World Models ↗
Predicting how the world responds to an action requires more than what can be seen: robots must account for environmental factors that are neither directly observable nor explicitly specified, from physical properties such as friction and mass to human-defined environmental rules. We introduce T2-Z for test-time optimization of environment latents, which augments a robotic world model with a learnable environment latent to condition world predictions. This compact latent captures underlying environmental variation without supervision of what environmental factors exist or what values they take…
- arXiv · Published · First archived
Perceive, Predict, and Plan: a Unified World Action Model for Egocentric Dexterous Manipulation ↗
We introduce P$^{3}$-WAM, a unified world action model for egocentric dexterous manipulation that learns 3D perception, action-conditioned prediction, and goal-directed planning through three complementary modes: Perceive, which reconstructs articulated hand motion and camera trajectories from video; Predict, which generates visual futures under prescribed actions; and Plan, which jointly generates action trajectories and their anticipated visual consequences conditioned on an initial image, a language instruction, and an optional visual goal. Our model integrates a compact, structured 3D acti…
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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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