Author name cluster

Arvind Narayanan

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8 papers

2 author rows

TMLR Journal 2025 Journal Article

AI Agents That Matter

Sayash Kapoor
Benedikt Stroebl
Zachary S Siegel
Nitya Nadgir
Arvind Narayanan

AI agents are an exciting new research direction, and agent development is driven by benchmarks. Our analysis of current agent benchmarks and evaluation practices reveals several shortcomings that hinder their usefulness in real-world applications. First, there is a narrow focus on accuracy without attention to other metrics. As a result, SOTA agents are needlessly complex and costly, and the community has reached mistaken conclusions about the sources of accuracy gains. Our focus on cost in addition to accuracy motivates the new goal of jointly optimizing the two metrics. We design and implement one such optimization, showing its potential to greatly reduce cost while maintaining accuracy. Second, the benchmarking needs of model and downstream developers have been conflated, making it hard to identify which agent would be best suited for a particular application. Third, many agent benchmarks have inadequate holdout sets, and sometimes none at all. This has led to agents that are fragile because they take shortcuts and overfit to the benchmark in various ways. We prescribe a principled framework for avoiding overfitting. Finally, there is a lack of standardization in evaluation practices, leading to a pervasive lack of reproducibility. We hope that the steps we introduce for addressing these shortcomings will spur the development of agents that are useful in the real world and not just accurate on benchmarks.

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NeurIPS Conference 2025 Conference Paper

Establishing Best Practices in Building Rigorous Agentic Benchmarks

Yuxuan Zhu
Tengjun Jin
Yada Pruksachatkun
Andy Zhang
Shu Liu
Sasha Cui
Sayash Kapoor
Shayne Longpre

Benchmarks are essential for quantitatively tracking progress in AI. As AI agents become increasingly capable, researchers and practitioners have introduced agentic benchmarks to evaluate agents on complex, real-world tasks. These benchmarks typically measure agent capabilities by evaluating task outcomes via specific reward designs. However, we show that many agentic benchmarks have issues in task setup or reward design. For example, SWE-bench-Verified uses insufficient test cases, while $\tau$-bench counts empty responses as successes. Such issues can lead to under- or overestimation of agents’ performance by up to 100% in relative terms. To make agentic evaluation rigorous, we introduce the Agentic Benchmark Checklist (ABC), a set of guidelines that we synthesized from our benchmark-building experience, a survey of best practices, and previously reported issues. When applied to CVE-Bench, a benchmark with a particularly complex evaluation design, ABC reduces performance overestimation by 33%.

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UAI Conference 2025 Conference Paper

Hindsight Merging: Diverse Data Generation with Language Models

Veniamin Veselovsky
Benedikt Stroebl
Gianluca M. Bencomo
Dilip Arumugam
Lisa Schut
Arvind Narayanan
Thomas L. Griffiths 0001

Pre-training a language model equips it with a broad understanding of the world, while fine- tuning refines it into a helpful assistant. However, fine-tuning does not exclusively enhance task- specific behaviors but also suppresses some of the beneficial variability from pre-training. This reduction in diversity is partly due to the optimization process, which theoretically decreases model entropy in exchange for task performance. To counteract this, we introduce hindsight merging, a technique that combines a fine-tuned model with a previous training checkpoint using linear interpolation to restore entropy and improve performance. Hindsight-merged models retain strong instruction-following capabilities and alignment while displaying increased diversity present in the base model. Additionally, this results in improved inference scaling, achieving a consistent 20-50% increase in pass@10 relative to the instruction tuned model across a coding benchmark and series of models. Our findings suggest that hindsight merging is an effective strategy for generating diverse generations that follow instructions.

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TMLR Journal 2024 Journal Article

CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark

Zachary S Siegel
Sayash Kapoor
Nitya Nadgir
Benedikt Stroebl
Arvind Narayanan

AI agents have the potential to aid users on a variety of consequential tasks, including conducting scientific research. To spur the development of useful agents, we need benchmarks that are challenging, but more crucially, directly correspond to real-world tasks of interest. This paper introduces such a benchmark, designed to measure the accuracy of AI agents in tackling a crucial yet surprisingly challenging aspect of scientific research: computational reproducibility. This task, fundamental to the scientific process, involves reproducing the results of a study using the provided code and data. We introduce CORE-Bench (Computational Reproducibility Agent Benchmark), a benchmark consisting of 270 tasks based on 90 scientific papers across three disciplines (computer science, social science, and medicine). Tasks in CORE-Bench consist of three difficulty levels and include both language-only and vision-language tasks. We provide an evaluation system to measure the accuracy of agents in a fast and parallelizable way, saving days of evaluation time for each run compared to a sequential implementation. We evaluated two baseline agents: the general-purpose AutoGPT and a task-specific agent called CORE-Agent. We tested both variants using two underlying language models: GPT-4o and GPT-4o-mini. The best agent achieved an accuracy of 19% on the hardest level of tasks, showing the vast scope for improvement in automating routine scientific tasks. Having agents that can reproduce existing work is a necessary step toward building agents that can conduct novel research and could verify and improve the performance of other research agents. We hope that CORE-Bench can improve the state of reproducibility and spur the development of future research agents.

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ICML Conference 2024 Conference Paper

Position: A Safe Harbor for AI Evaluation and Red Teaming

Shayne Longpre
Sayash Kapoor
Kevin Klyman
Ashwin Ramaswami
Rishi Bommasani
Borhane Blili-Hamelin
Yangsibo Huang
Aviya Skowron

Independent evaluation and red teaming are critical for identifying the risks posed by generative AI systems. However, the terms of service and enforcement strategies used by prominent AI companies to deter model misuse have disincentives on good faith safety evaluations. This causes some researchers to fear that conducting such research or releasing their findings will result in account suspensions or legal reprisal. Although some companies offer researcher access programs, they are an inadequate substitute for independent research access, as they have limited community representation, receive inadequate funding, and lack independence from corporate incentives. We propose that major generative AI developers commit to providing a legal and technical safe harbor, protecting public interest safety research and removing the threat of account suspensions or legal reprisal. These proposals emerged from our collective experience conducting safety, privacy, and trustworthiness research on generative AI systems, where norms and incentives could be better aligned with public interests, without exacerbating model misuse. We believe these commitments are a necessary step towards more inclusive and unimpeded community efforts to tackle the risks of generative AI.

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ICML Conference 2024 Conference Paper

Position: On the Societal Impact of Open Foundation Models

Sayash Kapoor
Rishi Bommasani
Kevin Klyman
Shayne Longpre
Ashwin Ramaswami
Peter Cihon
Aspen K. Hopkins
Kevin Bankston

Foundation models are powerful technologies: how they are released publicly directly shapes their societal impact. In this position paper, we focus on open foundation models, defined here as those with broadly available model weights (e. g. , Llama 3, Stable Diffusion XL). We identify five distinctive properties (e. g. , greater customizability, poor monitoring) that mediate their benefits and risks. Open foundation models present significant benefits, with some caveats, that span innovation, competition, the distribution of decision-making power, and transparency. To understand their risks of misuse, we design a risk assessment framework for analyzing their marginal risk. Across several misuse vectors (e. g. , cyberattacks, bioweapons), we find that current research is insufficient to effectively characterize the marginal risk of open foundation models relative to pre-existing technologies. The framework helps explain why the marginal risk is low in some cases, clarifies disagreements about misuse risks by revealing that past work has focused on different subsets of the framework with different assumptions, and articulates a way forward for more constructive debate. Overall, our work helps support a more grounded assessment of the societal impact of open foundation models by outlining what research is needed to empirically validate their theoretical benefits and risks.

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TMLR Journal 2024 Journal Article

The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources

Shayne Longpre
Stella Biderman
Alon Albalak
Hailey Schoelkopf
Daniel McDuff
Sayash Kapoor
Kevin Klyman
Kyle Lo

Foundation model development attracts a rapidly expanding body of contributors, scientists, and applications. To help shape responsible development practices, we introduce the Foundation Model Development Cheatsheet: a growing collection of 250+ tools and resources spanning text, vision, and speech modalities. We draw on a large body of prior work to survey resources (e.g. software, documentation, frameworks, guides, and practical tools) that support informed data selection, processing, and understanding, precise and limitation-aware artifact documentation, efficient model training, advance awareness of the environmental impact from training, careful model evaluation of capabilities, risks, and claims, as well as responsible model release, licensing and deployment practices. We hope this curated collection of resources helps guide more responsible development. The process of curating this list, enabled us to review the AI development ecosystem, revealing what tools are critically missing, misused, or over-used in existing practices. We find that (i) tools for data sourcing, model evaluation, and monitoring are critically under-serving ethical and real-world needs, (ii) evaluations for model safety, capabilities, and environmental impact all lack reproducibility and transparency, (iii) text and particularly English-centric analyses continue to dominate over multilingual and multi-modal analyses, and (iv) evaluation of systems, rather than just models, is needed so that capabilities and impact are assessed in context.

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NeurIPS Conference 2021 Conference Paper

Mitigating dataset harms requires stewardship: Lessons from 1000 papers

Kenneth Peng
Arunesh Mathur
Arvind Narayanan

Machine learning datasets have elicited concerns about privacy, bias, and unethical applications, leading to the retraction of prominent datasets such as DukeMTMC, MS-Celeb-1M, and Tiny Images. In response, the machine learning community has called for higher ethical standards in dataset creation. To help inform these efforts, we studied three influential but ethically problematic face and person recognition datasets---Labeled Faces in the Wild (LFW), MS-Celeb-1M, and DukeMTMC---by analyzing nearly 1000 papers that cite them. We found that the creation of derivative datasets and models, broader technological and social change, the lack of clarity of licenses, anddataset management practices can introduce a wide range of ethical concerns. We conclude by suggesting a distributed approach to harm mitigation that considers the entire life cycle of a dataset.

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