arXiv AI
Delegating Earth science model calibration to knowledge-guided LLM agents
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arXiv AI
Jun 12
TerraBench: Can Agents Reason Over Heterogeneous Earth-System Data?
arXiv:2606. 13148v1 Announce Type: new Abstract: Climate and environmental decision-making increasingly requires reasoning across heterogeneous inputs, including gridded physical data, satellite imagery, geospatial context, and simulator outputs.
arXiv AI
Sep 30
Reconstructing Implicit Scientific Knowledge: Evaluating LLM Agents through End-to-End Reproduction of Astronomy
The paper introduces a framework for evaluating large language model agents by attempting to end‑to‑end reproduce published astronomy studies, separating execution from verification and distinguishing computational failures from methodological ambiguities. Applying this to fourteen papers—one from The Astrophysical Journal and thirteen from Nature—revealed that eleven contained ambiguities that prevented a uniquely specified reproduction path. In a controlled case study, twelve different analysis paths produced distance estimates ranging from 2.16 to 3.53 kpc, with only one matching the published value of ~2.70 kpc, demonstrating that matching outcomes does not guarantee that the agent has reconstructed the underlying reasoning. whyItMatters":"The study shows that end‑to‑end reproduction can expose gaps in implicit scientific knowledge within AI systems, highlighting the need for better integration of causal relevance in LLM agents."
arXiv AI
Aug 25
EarthVerse: Benchmarking Scientific Agents Across Dynamic Earth Systems and Natural Hazards
arXiv:2608.23525v1 Announce Type: new Abstract: Earth-system analysis reconstructs changing physical processes from observations that differ in source, scale, timing, and modality. Natural hazards ma...
arXiv Computation and Language
Sep 14
Earth-Agent-Pro: Towards Real-World Full-Chain Earth Observation with Agents
arXiv:2609.12533v1 Announce Type: cross Abstract: Real-world Earth observation (EO) agents must translate high-level scientific questions into executable workflows to acquire observations, prepare da...
arXiv AI
Oct 2
VeriHarness: Scaling Agentic Verification for Long-Horizon Tasks
VeriHarness is a method that enhances verification for large language model agents tackling long‑horizon tasks without needing reference answers at test time. It transforms the base LLM into an agentic verifier by providing a workspace, evidence tools, and reusable verification skills, using disagreement resolution and consensus challenge to evaluate competing claims. Across five benchmarks and two frontier models, VeriHarness outperforms baselines, achieving significant performance gains and demonstrating self‑improvement of verification skills from failure feedback.
arXiv AI
Oct 5
From Migration to Calibration: Preserving Agent Capabilities across Models, Jurisdictions, and Scale
The paper proposes a framework for agent calibration that ensures consistent capabilities when agents are deployed, migrated, or scaled across different models, harnesses, infrastructures, and user contexts. It defines standards for basic capabilities, technical environments, and user contexts, diagnoses gaps, applies revisions, and rechecks within fixed budgets, using diagnostic source behavior and mandatory tests to qualify agents. The framework includes semantic checkpoints, repair localization, and reusable configuration artifacts, and outlines an automatic calibration tool that relies on limited authorized user trajectories and tests, though empirical validation remains pending.