MCP-Atlas is a new benchmark with 1000 tasks on production MCP servers that uses claim-level scoring to evaluate LLM agents on realistic multi-step tool-use competency.
(arXiv:2403.07714)
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CRAB-Bench and RUSE create a new evaluation framework for LLM agents on constraint-graph tasks with realistic human-like user behaviors, reporting 61% pass@1 for the best model and up to 57% further drops under RUSE.
TRON cuts tokens up to 27% with accuracy within 14pp of JSON on agentic benchmarks while TOON reaches 18% savings but triggers multi-turn parsing failures and parallel-call collapse on most models.
CTM-AI combines a formal consciousness model with foundation models to report state-of-the-art results on sarcasm detection, humor, and agentic tool-use benchmarks.
ToolOmni combines supervised fine-tuning on a cold-start multi-turn dataset with Decoupled Multi-Objective GRPO to enable proactive retrieval and grounded execution, yielding +10.8% higher end-to-end tool-use success and better generalization to unseen tools.
Kimi K2 is a 1-trillion-parameter MoE model that leads open-source non-thinking models on agentic benchmarks including 65.8 on SWE-Bench Verified and 66.1 on Tau2-Bench.
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Notation Matters: A Benchmark Study of Token-Optimized Formats in Agentic AI Systems
TRON cuts tokens up to 27% with accuracy within 14pp of JSON on agentic benchmarks while TOON reaches 18% savings but triggers multi-turn parsing failures and parallel-call collapse on most models.