Deterministic Engineering
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Not Every Byte Gets a Vote
Not Every Byte Gets a Vote In a deterministic game engine, replay starts simple: record inputs, run the same ticks again, and compare the result. When I started wiring replay for the sim, my first instinct was simple: Easy. For the first few fields, that feels right.
3DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Code
arXiv:2606.01057v1 Announce Type: new Abstract: Procedural 3D modeling through code is emerging as a versatile paradigm, offering deterministic, engine-ready, and precisely editable assets that neural 3D generators inherently lack. Authoring such procedural content, however, demands deep expertise in 3D software APIs, parametric design, and code-level geometric reasoning. In this paper, we propose 3DCodeBench, a systematic benchmark for evaluating vision-language model (VLM) agents for...
VeRO: A Harness for Agents to Optimize Agents
arXiv:2602.22480v4 Announce Type: replace Abstract: An important emerging application of coding agents is agent harness optimization: the iterative improvement of a target agent by editing and evaluating its code. Despite its relevance, the community lacks a systematic understanding of coding agent performance on this task. Harness optimization differs from conventional software engineering: agent harnesses interleave deterministic code with stochastic LLM completions, requiring structured...
Alibaba/Open-Code-Review
The open source AI code review agent. English | 简体中文 Open Code Review is an AI-powered code review CLI tool. It originated as Alibaba Group's internal official AI code review assistant — over the past two years, it has served tens of thousands of developers and identified millions of code defects.
Post-Deterministic Distributed Systems: A New Foundation for Trustworthy Autonomous Infrastructure
Announce Type: new Abstract: For decades, distributed systems have typically assumed that correct participants execute protocol-specified behavior with stable, externally defined, and deterministic semantics. Classical theory has extensively parameterized network timing, communication topologies, and failure domains, but this participant model has remained comparatively fixed. The integration of autonomous reasoning engines, stochastic model-driven agents, and policy-driven actors into cloud...
Autonomous heterogeneous catalyst discovery with a self-evolving multi-agent digital twin
arXiv:2606.05050v1 Announce Type: cross Abstract: Theoretical heterogeneous catalysis promises rapid catalyst discovery, yet computational and machine-learning predictions often deviate from experiment and stay confined to narrow material families, for want of a faithful, condition-aware catalytic simulator. We present CatDT (Catalysis Digital Twin), a self-evolving multi-agent system that builds an autonomous digital twin of a working catalyst, unifying gas-solid and liquid-solid modeling....
Autonomous heterogeneous catalyst discovery with a self-evolving multi-agent digital twin
arXiv:2606.05050v1 Announce Type: cross Abstract: Theoretical heterogeneous catalysis promises rapid catalyst discovery, yet computational and machine-learning predictions often deviate from experiment and stay confined to narrow material families, for want of a faithful, condition-aware catalytic simulator. We present CatDT (Catalysis Digital Twin), a self-evolving multi-agent system that builds an autonomous digital twin of a working catalyst, unifying gas-solid and liquid-solid modeling....
Marvell enters the AI network fray with 102.4 Tbps switch silicon
Marvell enjoyed a fillip from Nvidia chief Jensen Huang at Computex, who praised the firm as it unveiled the latest 102.4 Tbps switch silicon it has purpose-built for AI infrastructure. The fabless semiconductor biz announced upcoming availability of its Teralynx T100 chip to coincide with the Taiwanese trade show, claiming that it needs 25 percent lower power than competitive solutions with lower latency for AI training and inference workloads. But the firm is late to this party, as other...
eMoT: evolving Memory-of-Thought via Symbolic Anchoring and Memory Corrosion
Announce Type: new Abstract: While Large Language Models (LLMs) achieve impressive performance on multi-step reasoning tasks, their reliability is persistently hindered by critical limitations such as unconstrained hallucinations and poor numerical computation. Fundamentally, these issues arise because standard models treat reasoning as a transient, one-off generation process rather than retaining and refining successful procedural logic. To address these challenges, we propose eMoT...