Open library
The essential reading of generative AI and agents, open in a clean reading view, with Meow and Standard summaries when available. No account needed to read.
560 papers · 558 with a Meow summary · 333 posts
Every paper prepared on Reading + AI, newest first.
Yibo Wang, Lei Wang, Yue Deng et al.
Deep research systems are widely used for multi-step web research, analysis, and cross-source synthesis, yet their evaluation remains challenging. Existing benchmarks often require annotation-intensive task construction, rely on static evaluation dimensions, or fail to reliably verify facts when citations are missing.…
Yinger Zhang, Shutong Jiang, Renhao Li et al.
While agent evaluation has shifted toward long-horizon tasks, most benchmarks still emphasize local, step-level reasoning rather than the global constrained optimization (e.g., time and financial budgets) that demands genuine planning ability. Meanwhile, existing LLM planning benchmarks underrepresent the active infor…
Xiangyi Li, Yimin Liu, Wenbo Chen, Bingran You, Zonglin Di,
Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time. Despite rapid adoption, there is no standard way to measure whether they actually help. We present SkillsBench, a benchmark whose current inventory contains 87 tasks across 8 domains paired wi…
Alisia Lupidi, Bhavul Gauri, Thomas Simon Foster et al.
LLM agents hold significant promise for advancing scientific research. To accelerate this progress, we introduce AIRS-Bench (the AI Research Science Benchmark), a suite of 20 tasks sourced from state-of-the-art machine learning papers. These tasks span diverse domains, including language modeling, mathematics, bioinfo…
Ibragim Badertdinov, Maksim Nekrashevich, Anton Shevtsov et al.
Software engineering agents (SWE) are improving rapidly, with recent gains largely driven by reinforcement learning (RL). However, RL training is constrained by the scarcity of large-scale task collections with reproducible execution environments and reliable test suites. Although a growing number of benchmarks have e…
Multimodal Large Language Models (MLLMs) have advanced VQA and now support Vision-DeepResearch systems that use search engines for complex visual-textual fact-finding. However, evaluating these visual and textual search abilities is still difficult, and existing benchmarks have two major limitations. First, **existing…
Qiyuan Zhang, Junyi Zhou, Yufei Wang et al.
As Large Language Model (LLM) alignment evolves from simple completions to complex, highly sophisticated generation, Reward Models are increasingly shifting toward rubric-guided evaluation to mitigate surface-level biases. However, the community lacks a unified benchmark to assess this evaluation paradigm, as existing…
Shiva Krishna Reddy Malay, Shravan Nayak, Jishnu Nair et al.
Large language models are shifting from passive information providers to active agents intended for complex workflows. However, their deployment as reliable AI workers in enterprise is stalled by benchmarks that fail to capture the intricacies of professional environments, specifically, the need for long-horizon plann…
Zhuofeng Li Haoxiang Zhang Cong Wei Pan Lu Ping Nie, Yi Lu Yuyang Bai Shangbin Feng Hangxiao Zhu Ming Zhong Yuyu Zhang, Jianwen Xie Yejin Choi James Zou Jiawei Han Wenhu Chen Jimmy Lin et al.
Modern retrieval systems, whether lexical or semantic, expose a corpus through a fixed similarity interface that compresses access into a single top-$k$ retrieval step before reasoning. This abstraction is efficient, but for agentic search, it becomes a bottleneck: exact lexical constraints, sparse clue conjunctions,…
Video-MME Team
With the rapid advancement of video understanding, existing benchmarks are becoming increasingly saturated, exposing a critical discrepancy between inflated leaderboard scores and real-world model capabilities. To address this widening gap, we introduce **Video-MME-v2**, a comprehensive benchmark designed to rigorousl…
Yujian Liu Jiabao Ji Li An Tommi Jaakkola Yang Zhang Shiyu Chang
Agent skills, which are reusable, domain-specific knowledge artifacts, have become a popular mechanism for extending LLM-based agents, yet formally benchmarking skill usage performance remains scarce. Existing skill benchmarking efforts focus on overly idealized conditions, where LLMs are directly provided with hand-c…
Bowen Ye, Rang Li, Qibin Yang et al.
Large language models are increasingly deployed as autonomous agents for multi-step workflows in real-world software environments. However, existing agent benchmarks are limited by trajectory-opaque grading, underspecified safety and robustness evaluation, and narrow coverage of modalities and interaction paradigms. W…
Shuangrui Ding, Xuanlang Dai, Long Xing et al.
Large language and vision-language models increasingly power agents that act on a user’s behalf through command-line interface (CLI) harnesses. However, most agent benchmarks still rely on synthetic sandboxes, short-horizon tasks, mock-service APIs, and final-answer checks, leaving open whether agents can complete rea…
Yuxuan Zhang , Yubo Wang , Yipeng Zhu , Penghui Du , Junwen Miao , Xuan Lu ,
AI agents may be able to assist with emails and documents, but can they reliably complete everyday online workflows on real websites? Everyday online tasks offer a realistic yet unsolved testbed for evaluating the next generation of AI agents. To this end, we introduce ClawBench, an evaluation framework comprising 153…
Shaoqiu Zhang, Yuhang Wang, Jialiang Liang et al.
Repository-level coding benchmarks such as SWE-bench have driven a rapid surge in the capabilities of coding agents. Yet they usually treat coding tasks as a holistic, binary prediction problem (e.g., resolved or unresolved), neglecting fine-grained agent capabilities such as repository understanding, context retrieva…
Zongxia Li, Zhongzhi Li, Yucheng Shi et al.
AI agents have become increasingly capable of autonomously completing short, well-specified tasks. However, existing terminal benchmarks largely focus on relatively simple problems that finish within a few minutes and are typically evaluated only by their final outcome. This setup overlooks intermediate progress and p…
Grégoire Mialon, Clémentine Fourrier, Craig Swift et al.
We introduce GAIA, a benchmark for General AI Assistants that, if solved, would represent a milestone in AI research. GAIA proposes real-world questions that require a set of fundamental abilities such as reasoning, multi-modality handling, web browsing, and generally tool-use proficiency. GAIA questions are conceptua…
Wallyson Lemes de Oliveira, Mekhron Bobokhonov, Matteo Caorsi et al.
The Abstraction and Reasoning Corpus (ARC) is designed to assess generalization beyond pattern matching, requiring models to infer symbolic rules from very few examples. In this work, we present a transformer-based system that advances ARC performance by combining neural inference with structure-aware priors and onlin…
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional domains. We argue that this gap is largely an evaluation problem: widely used benchmarks lack sustained performance measurement on real and…
Mingchen Zhuge, Changsheng Zhao, Dylan R. Ashley et al.
Contemporary evaluation techniques are inadequate for agentic systems. These approaches either focus exclusively on final outcomes—ignoring the step-by-step nature of agentic systems, or require excessive manual labour. To address this, we introduce the **Agent-as-a-Judge** framework, wherein agentic systems are used…
Samuel Miserendino, Michele Wang, Tejal Patwardhan et al.
We introduce SWE-Lancer, a benchmark of over 1,400 freelance software engineering tasks from Upwork, valued at \$1 million USD total in real-world payouts. SWE-Lancer encompasses both independent engineering tasks — ranging from \$50 bug fixes to \$32,000 feature implementations — and managerial tasks, where models ch…
Mike A. Merrill, Alexander G. Shaw, Nicholas Carlini et al.
AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not sufficiently difficult to meaningfully measure frontier models. To this end, we present Terminal-Bench 2.0: a carefully curated hard benchm…
Naman Jain, King Han, Alex Gu et al.
Large Language Models (LLMs) applied to code-related applications have emerged as a prominent field, attracting significant interest from both academia and industry. However, as new and improved LLMs are developed, existing evaluation benchmarks (e.g., HumanEval, MBPP) are no longer sufficient for assessing their capa…
Hongliang He, Wenlin Yao, Kaixin Ma et al.
The rapid advancement of large language models (LLMs) has led to a new era marked by the development of autonomous applications in real-world scenarios, which drives innovation in creating advanced web agents. Existing web agents typically only handle one input modality and are evaluated only in simplified web simulat…
Mark Chen, Jerry Tworek, Heewoo Jun et al.
We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On [HumanEval](https://github.com/openai/human-eval), a new evaluation set we release to measure functional correctne…
Jacob Austin, Augustus Odena, Maxwell Nye et al.
This paper explores the limits of the current generation of large language models for program synthesis in general purpose programming languages. We evaluate a collection of such models (with between 244M and 137B parameters) on two new benchmarks, MBPP and MathQA-Python, in both the few-shot and fine-tuning regimes.…
Thomas Kwa, Ben West, Joel Becker et al.
Despite rapid progress on AI benchmarks, the real-world meaning of benchmark performance remains unclear. To quantify the capabilities of AI systems in terms of human capabilities, we propose a new metric: 50%-task-completion time horizon, the time humans typically take to complete tasks that AI models can complete wi…
Jason Wei Zhiqing Sun Spencer Papay
We present BrowseComp, a simple yet challenging benchmark for measuring the ability for agents to browse the web. BrowseComp comprises 1,266 questions that require persistently navigating the internet in search of hard-to-find, entangled information. Despite the difficulty of the questions, BrowseComp is simple and ea…
Chan Jun Shern, Neil Chowdhury, Oliver Jaffe et al.
We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering. To this end, we curate 75 ML engineering-related competitions from Kaggle, creating a diverse set of challenging tasks that test real-world ML engineering skills such as training models, preparing datasets, an…
Tianbao Xie Danyang Zhang Jixuan Chen Xiaochuan Li Siheng Zhao, Ruisheng Cao Toh Jing Hua Zhoujun Cheng Dongchan Shin Fangyu Lei Yitao Liu, Yiheng Xu Shuyan Zhou Silvio Savarese Caiming Xiong Victor Zhong Tao Yu
Autonomous agents that accomplish complex computer tasks with minimal human interventions have the potential to transform human-computer interaction, significantly enhancing accessibility and productivity. However, existing benchmarks either lack an interactive environment or are limited to environments specific to ce…
Shunyu Yao, Howard Chen, John Yang et al.
Existing benchmarks for grounding language in interactive environments either lack real-world linguistic elements, or prove difficult to scale up due to substantial human involvement in the collection of data or feedback signals. To bridge this gap, we develop WebShop – a simulated e-commerce website environment with…
Xiang Deng, Yu Gu, Boyuan Zheng et al.
We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for ge…
Shuyan Zhou, Frank F. Xu, Xuhui Zhou et al.
With advances in generative AI, there is now potential for autonomous agents to manage daily tasks via natural language commands. However, current agents are primarily created and tested in simplified synthetic environments, leading to a disconnect with real-world scenarios. In this paper, we build an environment for…
Victor Barres, Honghua Dong, Soham Ray et al.
Existing benchmarks for conversational AI agents simulate single-control environments, where only the AI agent can use tools to interact with the world, while the user remains a passive information provider. This differs from real-world scenarios like technical support, where users need to actively participate in modi…
Colin White, Samuel Dooley, Manley Roberts et al.
Test set contamination, wherein test data from a benchmark ends up in a newer model’s training set, is a well-documented obstacle for fair LLM evaluation and can quickly render benchmarks obsolete. To mitigate this, many recent benchmarks crowdsource new prompts and evaluations from human or LLM judges; however, these…
Shunyu Yao, Noah Shinn, Pedram Razavi et al.
Existing benchmarks do not test language agents on their interaction with human users or ability to follow domain-specific rules, both of which are vital for deploying them in real world applications. We propose $\tau$-bench, a benchmark emulating dynamic conversations between a user (simulated by language models) and…
Wei-Lin Chiang, Lianmin Zheng, Ying Sheng et al.
Large Language Models (LLMs) have unlocked new capabilities and applications; however, evaluating the alignment with human preferences still poses significant challenges. To address this issue, we introduce Chatbot Arena, an open platform for evaluating LLMs based on human preferences. Our methodology employs a pairwi…
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we…
Dan Hendrycks, Collin Burns, Saurav Kadavath et al.
Many intellectual endeavors require mathematical problem solving, but this skill remains beyond the capabilities of computers. To measure this ability in machine learning models, we introduce MATH, a new dataset of $12,\!500$ challenging competition mathematics problems. Each problem in MATH has a full step-by-step so…
Stephanie Lin, Jacob Hilton, Owain Evans
We propose a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. We crafted questions that some humans would answer falsely due to a false belief or misconception. To pe…