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At a glance: five different ways to study agents
| Paper | What it investigates | How the evidence is framed | Key limitation |
|---|---|---|---|
| Voyager: An Open-Ended Embodied Agent with Large Language Models | Exploring Minecraft and building reusable skills | Comparisons in the authors’ Minecraft experiments | Results concern that setup, not general agent learning rates |
| Language Agents Achieve Superhuman Synthesis of Scientific Knowledge | Searching and synthesizing scientific literature | Human-AI comparison on three literature-research tasks | The comparison does not establish superiority across all research work |
| AI Agents That Matter | How to evaluate agents responsibly | Analysis of evaluation practices and their pitfalls | It is a critique and framework, not a single agent leaderboard |
| AgentBench: Evaluating LLMs as Agents | Agent performance across eight environments | Multi-environment evaluation | Scores depend on the included environments and tasks |
| Sleeper Agents: Training Deceptive LLMs That Persist Through Safety Training | Constructed backdoors and safety training | Controlled experiments with deliberately created behaviors | Does not show deployed systems are secretly deceptive |
1. Voyager makes agent learning concrete in Minecraft
Voyager is a vivid starting point because its agent has a world to explore, feedback to react to, and a way to retain useful actions. Wang and coauthors describe an open-ended Minecraft agent built around three ideas: an automatic curriculum that proposes new goals, an executable skill library that stores successful code, and iterative prompting that uses environmental feedback and errors to improve attempts. The paper says Voyager interacts with GPT-4 through black-box queries; it is not a newly trained foundation model.
In their Minecraft experiments, the authors report that Voyager collected 3.3 times more unique items, traveled 2.3 times farther, and unlocked key technology milestones up to 15.3 times faster than the prior state of the art they compared against. Those figures describe comparisons in the paper’s particular experimental setup. They are not a promise about how quickly agents learn in other games or real-world environments.
Read Voyager on arXiv. You do not need to own or play Minecraft to read the paper.
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2. PaperQA2 tests whether an agent can synthesize research
PaperQA2 focuses on a task that is easy to recognize and hard to do well: finding relevant scientific papers, extracting evidence, summarizing it, and noticing contradictions. The authors’ abstract says the system “matches or exceeds subject matter expert performance on three realistic literature research tasks” in their human-AI comparison.
The number matters: this is a result on three evaluated tasks, not evidence that AI generally out-researches experts or replaces scientific judgment. The work also introduces LitQA2, a benchmark intended to assess scientific literature research. It is an interesting paper for readers who want to see what “research agent” means when the work involves locating and reconciling sources rather than simply generating a plausible answer.
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Read Language Agents Achieve Superhuman Synthesis of Scientific Knowledge on arXiv.
3. AI Agents That Matter asks what a good evaluation should measure
Agent demos often emphasize whether a system completed a task. Kapoor, Stroebl, Siegel, Nadgir, and Narayanan argue that accuracy alone can hide important weaknesses. Their paper calls for evaluations that account for cost, distinguish model-level performance from what an application actually needs, and address benchmark overfitting and reproducibility.
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The authors warn: “This has led to agents that are fragile because they take shortcuts and overfit to the benchmark in various ways.” A high benchmark score is evidence about performance under a defined test; it does not automatically show that an agent will be dependable in a different workflow, or that the cost of using it makes sense.
Read AI Agents That Matter on arXiv.
4. AgentBench shows why there is no single agent score
AgentBench evaluates large language model agents across eight environments, framing agent capability around reasoning and decision-making. The paper is useful alongside AI Agents That Matter because it makes the evaluation problem concrete: a system is acting within environments with different tasks, constraints, and opportunities to fail.
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Its latest cited revision is dated October 4, 2025; the paper was first submitted August 7, 2023. Read the version and evaluation scope carefully when interpreting results. Performance across eight selected environments is more informative than one isolated task, but it still is not a universal measure of what an agent can do everywhere.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Sleeper Agents examines a controlled safety concern
Hubinger and coauthors constructed proof-of-concept backdoor behaviors to study whether they persisted through safety training. One setup changed code-generation behavior depending on a stated year. The paper examines supervised fine-tuning, reinforcement learning, and adversarial training as ways of trying to remove the behaviors.
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This is a cautionary experimental result, not proof that deployed AI systems are secretly deceptive. Its value is that it tests a specific safety concern under controlled conditions and gives researchers a way to reason about how constructed backdoors respond to training interventions.
How to choose what to read next
- Want an intuitive agent example? Start with Voyager: the Minecraft setting makes goals, feedback, and reusable skills easy to picture.
- Interested in AI-assisted scholarship? Read PaperQA2, keeping its three-task expert comparison in view.
- Want to judge agent claims more critically? Pair AI Agents That Matter with AgentBench to consider evaluation quality and environment coverage.
- Focused on safety? Read Sleeper Agents as a controlled study of backdoor persistence, not as evidence about the hidden intentions of deployed products.
These papers are not contestants in one shared ranking. They study different capabilities and questions, so their evidence is best read in context rather than collapsed into a single score.
Other benchmarks worth knowing about
Two additional resources illustrate how much evaluation focus can vary. AstaBench, from the Allen Institute for AI, is a benchmark suite for scientific tasks, including finding papers and creating literature-review tables. It is an evaluation resource, not an additional paper in this five-paper list.
AgentIF is a NeurIPS 2025 benchmark paper focused on instruction following in agentic scenarios. Its authors report 707 human-annotated instructions across 50 tasks. That focus differs from AstaBench’s scientific research tasks and from AgentBench’s eight environments; the results answer different questions and should not be treated as interchangeable.
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