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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 ↗5985 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
CACFG: Curvature-Aware Classifier-Free Guidance and Optimal Control ↗
Diffusion models generate samples by learning to reverse a fixed corruption process, and classifier-free guidance (CFG) is the standard mechanism for conditioning this process on a desired class or prompt. CFG can be applied at varying guidance strengths, and while higher strengths improve image quality and conditional alignment, too high a guidance strength can degrade image quality and diversity. Furthermore, CFG violates principled diffusion sampling dynamics, and existing explanations for why it works despite the violation disagree on the underlying theory or do not extend to deterministic…
- Hugging Face · Model / dataset date · First archived
mulligan/sim-square-broad-c03-mulligan-policy-rollouts — dataset update ↗
# sim-square-broad-c03-mulligan-policy-rollouts Part of [Mulligan](https://mulligan.page). Browse the release at [Policy Arena](https://arena.mulligan.page). | Property | Value | |---|---| | Task | sim-square-broad | | Role | policy-rollouts | | Release grouping | supporting | | Variant | mulligan | | Model rounds | See collection provenance | | Episodes | 100 | | Frames | 22176 | | Recording FPS | 20 | | Cameras | State-only; no camera recordings | | Release tag | `release-1` | | Parent | Original recording or policy-rollout source; see provenance below. | ## Provenance Trajectory parquet, ep
- Hugging Face · Model / dataset date · First archived
mulligan/real-square-d2-r05-mulligan-dp — model update ↗
# real-square-d2-r05-mulligan-dp Part of [Mulligan](https://mulligan.page). Browse evaluations at [Policy Arena](https://arena.mulligan.page); the datasets are in the [mulligan](https://huggingface.co/mulligan) organization. Diffusion-policy actor in LeRobot's checkpoint layout (`config.json`, `model.safetensors`, pre/post-processor normalizers). The config also carries Mulligan fields (camera crop boxes, action target and frame) that stock LeRobot `DiffusionConfig` rejects; loading needs the Mulligan training code. | Property | Value | |---|---| | Task | real-square-d2 | | Model round | R5 |
- arXiv · Published · First archived
PhaseMatcher: Autoregressive Phase-Set Identification with Spectral Decomposition ↗
Recovering complete phase sets from powder X-ray diffraction (PXRD) is challenging when weak-phase peaks overlap stronger signals. A natural strategy is to identify phases iteratively, removing the contribution of each identified phase from the observed pattern before predicting the next. However, even after a phase is correctly identified, misestimating its contribution can distort the residual and cause subsequent errors. We introduce PhaseMatcher, an autoregressive framework for complete phase-set identification with physics-guided spectral decomposition. After each phase prediction, PhaseM…
- arXiv · Published · First archived
AnchorPose for Geometry-Aware MOF Assembly through Meso-Grained Pose Generation ↗
Predicting metal-organic framework (MOF) structures from given building blocks requires recovering their positions and orientations in a periodic crystal. The spatial effects of rotation errors are geometry-dependent and anisotropic. The same angular error can produce different atomic displacements depending on block size, shape, and rotation axis. Angular error alone, without reference to the specific block geometry, therefore cannot fully describe the spatial consequences of a pose error. We introduce AnchorPose, a meso-grained pose generation framework that incorporates this geometric depen…
- arXiv · Published · First archived
HLA: Expressive Hybrid Linear Attention via Chunk-Wise Dynamic Mixing ↗
Linear attention enables efficient long-context autoregressive decoding by compressing history into recurrent states, but this compression can make selective access to sparse and distant information difficult. Existing chunk-based extensions increase memory capacity, yet learned chunk-mixing coefficients may remain fixed with respect to input content and therefore cannot adapt historical access to each query. We introduce \emph{Hybrid Linear Attention} (HLA), a query-dependent chunk-level attention mechanism for Gated DeltaNet (GDN). HLA represents each completed chunk as an exact affine state…
- arXiv · Published · First archived
Dual-Rate Force-Image Control with Model-Based Orientation Limits for Robotic Ultrasound ↗
Robotic ultrasound couples a high-rate contact-force loop with slower, delayed image feedback, so image-guided ultrasound probe rotation can perturb contact force before the resulting image response is observed. We derive a closed-form orientation-rate limit that bounds the modeled rotation-induced estimated-force excursion over a finite horizon while accounting for disturbance rejection by the fast force loop. The limit depends on local contact stiffness, force-loop gains, a conservative rotation-to-force gain bound, the excursion budget, and the prediction horizon. We implement this model in…
- Hacker News · Posted on Hacker News · First archived
The Hottest Debate in Tech: Ads in AI ↗
Hacker News discussion.
- arXiv · Published · First archived
HiER-BLS: A Hierarchy-Guided and Error-Correcting Robust Incremental Broad Learning System ↗
Broad Learning System (BLS) supports analytical training and incremental expansion, but its growth needs guidance on which inputs new blocks should learn from. Weight errors pose a further challenge by displacing learned outputs across class boundaries. We propose HiER-BLS to couple hierarchy-guided representation growth with error-correcting learning. Successive blocks focus on inputs selected by feature importance and correlation while preserving earlier representations. The evolving branch guides encoded learners through subspace size and sample confidence, so its learning experience inform…
- arXiv · Published · First archived
Request Order Matters: Cache-History Sensitivity in Selective KV-Cache Reuse for Rolling Agents ↗
Long-running agents repeatedly call an LLM while retaining most of their document window, evicting old documents, and appending new ones. These rolling updates break exact prefix caching and motivate non-prefix KV-cache reuse with selective recomputation. We show that persistent KV-cache reuse with selective recomputation can be history-dependent: in our rolling-agent workload, an unchanged prompt can produce different answers depending on the requests processed before it. At a matched 5% recomputation budget, document-aligned recomputation reduces answer variation across request orders from 6…
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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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