As enterprise agent tool catalogs scale from 10 to 110 agents, routing accuracy drops 16--23 percentage points on under-specified requests. An oracle analysis identifies retrieval and confusion gaps, with embedding-based shortlisting recovering +10--11pp F1. A human-annotated study of 1,435 utterances confirms real-world recovery of +10--17pp despite lower absolute performance.
Routing Accuracy Degradation and Recovery in Enterprise Agent Systems
GPT-5 outperforms humans in inducing belief states via planning
A new study evaluates Large Language Models' ability to induce specific belief states in other agents through actions rather than conversation, a capability termed Non-Conversational Planning ToM (NCP-ToM). Using the NCP-ExploreToM framework, researchers tested six frontier models and human participants on 600 task instances where agents had to move objects or direct characters to achieve belief goals.
Automated grading of Linux/bash examinations using large language models
This study evaluates whether four frontier Large Language Models (GPT, Claude Opus, Gemini, and GLM) can approximate expert judgment when grading short Linux/bash command responses. The research demonstrates that structured prompts significantly improve agreement with human graders, establishing a framework for AI-assisted assessment in computing education.
OpenBioRQ: Benchmark for Agentic Biomedical Research Faithfulness
OpenBioRQ introduces a benchmark of 12,553 unsolved biomedical research questions across 12 domains, designed to test agentic models' faithfulness and abstention. It evaluates models in a tool-using setting without answer keys, using real follow-up evidence rather than parametric knowledge, and reveals significant agentic collapse on the hardest questions where tools are no longer used despite being critical.
Large Language Models Fail to Translate Fongbe Accurately
Evaluations show Fongbe translations achieve poor quality (1.0-2.2/5) compared to Hausa's acceptable scores (4.0-4.5/5), with a consistent 3x BLEU gap. Automatic metrics like BERTScore show embedding collapse and weak human correlation, especially for Hausa, while Gemini outperforms others for Fongbe and GPT-4o for Hausa in human judgments. Minimum sample sizes of 2,500 sentences are needed for stable model rankings.
MacAgentBench Launches macOS AI Agent Benchmark
MacAgentBench introduces a comprehensive benchmark with 676 tasks across 25 applications, 60% of which involve both GUI and CLI interactions. It uses deterministic rule-based evaluation and fine-grained multi-checkpoint scoring, revealing that Claude Opus 4.6 on OpenClaw achieves 73.7% Pass@1, primarily due to its skill library rather than framework design.