arXiv AI
ASCEND: Personal AI Agents for Autonomous Scientific Computing Across HPC Clusters and GPU Workstations
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Sep 15
ECAS: An Edge-Controlled Agentic System for Validation-Gated Scientific Application Execution
arXiv:2609.14211v1 Announce Type: cross Abstract: Scientific applications increasingly rely on high-performance computing (HPC), yet translating a scientist's high-level goal into a correct target-sc...
arXiv AI
5d ago
Can AI Scientists Coordinate at Runtime?
The paper "Can AI Scientists Coordinate at Runtime?" introduces Runtime Agent Coordination (RAC), a system that dynamically selects agents from existing AI‑scientist hosts during execution, assigns scoped work contracts, and provides artifact‑grounded verification. An exploratory evaluation on ResearchClawBench across three hosts—Agent Laboratory, EvoScientist, and ARK—shows that runtime selection improves performance, while adding contracts and verification can reduce scores depending on the host. The study highlights the potential and limitations of runtime coordination under budget constraints.
arXiv AI
Sep 23
APEXA: Execution-Integrity Enforcement for Multi-Agent LLM Automation of Synchrotron Data Reduction
arXiv:2609.24165v1 Announce Type: new Abstract: Synchrotron data reduction, detector calibration followed by azimuthal integration of terabyte-scale diffraction series, is a multi-step, expert-bound...
arXiv AI
Sep 15
OpenAl4S: Code as Action, Science as Sessions
OpenAI4S is an open‑source scientific research agent that treats code as action and science as sessions, combining a persistent computing runtime with structured session management. It uses tool calls for orchestration, executes code cells in persistent Python and R kernels, and records an append‑only Action Ledger, per‑cell execution logs, versioned artifacts, environment snapshots, and workspace checkpoints to preserve provenance and enable session recovery, branching, and extension. Evaluated on 36 research scenarios—including retrosynthesis, molecular dynamics, and protein design—OpenAI4S achieved a higher overall score (7.83) than a general‑purpose coding harness, especially on long‑horizon, computation‑intensive workflows, though reproducibility remains an open challenge. whyItMatters":"The system demonstrates that persistent execution coupled with session‑level provenance can enhance the reliability of AI‑assisted scientific workflows, as evidenced by its superior performance across diverse research scenarios."
arXiv AI
Aug 13
Ready Cohorts: Bounding GPU Opportunity and Avoiding Host Round Trips in LLM-Agent Control
arXiv:2608. 12123v1 Announce Type: cross Abstract: LLM-agent services repeatedly execute small deterministic transitions between model and tool calls: route an outcome, update state, and emit the next effect.
arXiv AI
Aug 6
Architectural Implications of Agentic AI Workflows
arXiv:2608. 04458v1 Announce Type: new Abstract: Agentic AI is emerging in datacenters, but its architectural implications remain unexplored.