arXiv subjects

Zhaoming Yin

Publications and source records attributed to Zhaoming Yin.

5 recordsLinked to original sources

arXiv2026

Everything Is a VisionBlock: Conversational Authoring over Git-Versioned Content for Spatial Computing

Spatial applications compile their content into shipped binaries, so every change costs a build-and-redeploy cycle. We present the VisionBlock system, which splits an application into an engine -- a generic binary with a fixed set of capabilities (render panels, volumes, and immersive scenes; fetch data; run gestures) -- and themes: complete applications expressed as trees of VisionBlocks, units of declarative content the engine renders. Themes are data: creating, changing, or publishing one never touches the binary. Authoring is a chat -- each turn produces a VisionBlock's next version -- and versioning is plain git. The model is five-dimensional: dimensions 1-3 are space (panel, volume, room); dimension 4 is time (git history -- revert to roll back, branch to try variants); dimension 5 is the principal (the per-user domain: the same path resolves differently per person). The engine renders one point, (x, y, z, version, principal). One consequence follows per non-spatial axis: iteration collapses to chat turns and reverts; ownership and permission are properties of content; and together they make applications items -- grantable, forkable, sellable subtrees, an economy of apps inside one binary. A blockchain explorer, a document reader, an immersive showroom all run on the same engine; none requires a deploy to change. This paper presents the design; a production implementation is underway, and a subsequent version will report implementation and evaluation.

cs.HC↗

arXiv2026

Safety Invariants for Agents Orchestrating Irreversible State Transitions: A Four-Dimensional Formalism Evaluated on Public Ledgers

Autonomous agents are increasingly asked to produce irreversible effects on external systems - transferring funds, writing to durable storage, actuating hardware. Existing agent frameworks (ReAct, Reflexion, MCP) optimize task success on benchmarks and give little attention to the safety of irreversible side-effects. We formalize one such setting, movement of value across public ledgers, as state transitions in a four-dimensional space indexed by (wallet, chain, address, protocol), and use that formalism to state and prove a guarantee we call execution fidelity: under a fault model admitting planner mis-mapping, ambiguous outcomes, retries, at-least-once delivery, and delegated non-human callers, a session's realized effect on the ledger is either nothing at all or exactly the transition that was rendered to the user, exactly once. The theorem deliberately does not claim that the rendered transition matches the user's intent - no runtime layer can decide that - but it confines that unbounded question to a single predicate over a finite object, which is what makes a preview a sufficient control rather than a formality. Seven safety invariants, derived from the fidelity condition rather than enumerated from experience, discharge the guarantee. Empirically, on a controlled N=60 adversarial suite the stack lifts pass rate by ~74 percentage points over a naive-ReAct baseline on two write-aggressive backing models, but by only ~3 points on a write-cautious one - evidence that single-model evaluations of agent safety stacks are close to unfalsifiable. The system is deployed; 108 production write operations across 8 chains back the failure taxonomy. Although the evaluation setting is public ledgers, the formalism and invariants apply to any probabilistic agent acting on irreversible external state.

cs.CR↗

arXiv2020

StreamNet: A DAG System with Streaming Graph Computing

To achieve high throughput in the POW based blockchain systems, researchers proposed a series of methods, and DAG is one of the most active and promising fields. We designed and implemented the StreamNet, aiming to engineer a scalable and endurable DAG system. When attaching a new block in the DAG, only two tips are selected. One is the parent tip whose definition is the same as in Conflux[1]; another is using Markov Chain Monte Carlo (MCMC) technique by which the definition is the same as IOTA [2]. We infer a pivotal chain along the path of each epoch in the graph, and a total order of the graph could be calculated without a centralized authority. To scale up, we leveraged the graph streaming property; high transaction validation speed will be achieved even if the DAG is growing. To scale out, we designed the direct signal gossip protocol to help disseminate block updates in the network, such that messages can be passed in the network more efficiently. We implemented our system based on IOTA's reference code (IRI) and ran comprehensive experiments over the different sizes of clusters of multiple network topologies.

cs.DC↗

arXiv2020

Taming Near Repeat Calculation for Crime Analysis via Cohesive Subgraph Computing

Near repeat (NR) is a well known phenomenon in crime analysis assuming that crime events exhibit correlations within a given time and space frame. Traditional NR calculation generates 2 event pairs if 2 events happened within a given space and time limit. When the number of events is large, however, NR calculation is time consuming and how these pairs are organized are not yet explored. In this paper, we designed a new approach to calculate clusters of NR events efficiently. To begin with, R-tree is utilized to index crime events, a single event is represented by a vertex whereas edges are constructed by range querying the vertex in R-tree, and a graph is formed. Cohesive subgraph approaches are applied to identify the event chains. k-clique, k-truss, k-core plus DBSCAN algorithms are implemented in sequence with respect to their varied range of ability to find cohesive subgraphs. Real world crime data in Chicago, New York and Washington DC are utilized to conduct experiments. The experiment confirmed that near repeat is a solid effect in real big crime data by conducting Mapreduce empowered knox tests. The performance of 4 different algorithms are validated, while the quality of the algorithms are gauged by the distribution of number of cohesive subgraphs and their clustering coefficients. The proposed framework is the first to process the real crime data of million record scale, and is the first to detect NR events with size of more than 2.

cs.DS↗

arXiv2017

Exemplar or Matching: Modeling DCJ Problems with Unequal Content Genome Data

The edit distance under the DCJ model can be computed in linear time for genomes with equal content or with Indels. But it becomes NP-Hard in the presence of duplications, a problem largely unsolved especially when Indels are considered. In this paper, we compare two mainstream methods to deal with duplications and associate them with Indels: one by deletion, namely DCJ-Indel-Exemplar distance; versus the other by gene matching, namely DCJ-Indel-Matching distance. We design branch-and-bound algorithms with set of optimization methods to compute exact distances for both. Furthermore, median problems are discussed in alignment with both of these distance methods, which are to find a median genome that minimizes distances between itself and three given genomes. Lin-Kernighan (LK) heuristic is leveraged and powered up by sub-graph decomposition and search space reduction technologies to handle median computation. A wide range of experiments are conducted on synthetic data sets and real data sets to show pros and cons of these two distance metrics per se, as well as putting them in the median computation scenario.

cs.DS↗