AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation
2026-07-09T08:52:33Z•2d2564b3e2418b9dd9257ac217ebc2bbc715a25ff2c72576abb72df65569fabd
LLM-augmented-ABMPOMDPSageMathadversarial-riskagentic-aibenchmarkingcode-agentscomputer-algebra-systemsdata-leakageefficiencyepidemic-simulationevaluationhardware-calibrationin-context-searchorchestrationprivacy-riskquantum-computingreflectionreproducibilitysampling-complexitysupply-chain-risktoken-economicstool-augmented-agentstrajectory-reviews
What happened
This ingest covers a set of 2026 arXiv submissions about agentic LLMs, evaluation benchmarks, and tool-augmented reasoning. Key papers: AgentLens — a production-style benchmark that evaluates full interaction trajectories (not just pass/fail) for code agents using formal checks plus LLM-written trajectory reviews; a theoretical sampling-complexity analysis of in‑context search and reflection-driven iterative reasoning; HALE — a hybrid LLM+agent-based modeling framework applied to COVID-19 simulation; QANTIS — a study treating a quantum processor as a calibrated belief-update service for POMDPs
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_ai
- Record identifier
- 2d2564b3e2418b9dd9257ac217ebc2bbc715a25ff2c72576abb72df65569fabd
- Enrichment time
- 2026-07-09T08:52:33Z
- AI-assisted enrichment
- Yes
This record may overlap with other records. Its enrichment can be incomplete or wrong, and machine assistance was used. Validate consequential decisions against the linked source and your own environment.