Click any tag below to further narrow down your results
Links
The article discusses the shifting landscape for data scientists and machine learning engineers in the age of large language models (LLMs). It emphasizes the importance of data science fundamentals in evaluating AI systems, addressing common pitfalls in metrics, experimental design, and data quality. The author argues that the core work of data scientists remains vital, even as their roles evolve.
This article explores how advanced AI models can generate detailed image descriptions and reasoning without actual image input, a phenomenon called mirage reasoning. It highlights vulnerabilities in these models, particularly in medical contexts, and introduces B-Clean, a method for better evaluating multimodal AI systems by minimizing non-visual inference.
This article assesses the effectiveness of AI-powered prototyping tools in creating user interface designs. It highlights that while these tools can generate outputs from prompts, they often lack the nuance and detail that human designers provide, especially when given vague instructions. Detailed prompts and visual references improve results, but AI still struggles with contextual understanding.
The article explores how large language models (LLMs) act as judges in evaluating other LLMs. It examines potential biases, the impact of model identity on outcomes, and differences in performance between "fast" and "thinking" tiers across various tasks. Experiments reveal insights into self-preference among judges and how hinting can influence their decisions.
The article discusses various open problems in machine learning inspired by a graduate class. It critiques current methodologies, emphasizing the need for a design-based perspective, better evaluation methods, and innovations in large language models. The author encourages researchers to explore these under-addressed areas.
This GitHub repository provides RBench, a benchmark for evaluating robotics video generation, and RoVid-X, a dataset for training models with RGB, depth, and optical flow videos. The authors highlight limitations in existing video models and aim to enhance embodied AI research.
This article discusses the importance of monitoring the internal reasoning of AI models, rather than just their outputs. It outlines methods for evaluating how effectively this reasoning can be supervised, especially as models become more complex. The authors call for collaborative efforts to enhance the reliability of this monitoring as AI systems scale.
This article discusses Agent Bricks, a platform that creates AI agents tailored to specific business data and tasks. It covers how to improve the accuracy of these agents through automated evaluations and human feedback, along with practical insights on deploying AI in organizations.
This article presents a codebase for a study on how unified multimodal models (UMMs) enhance reasoning by integrating visual generation. The research introduces a new evaluation suite, VisWorld-Eval, which assesses multimodal reasoning capabilities across various tasks. Experiments show that interleaved visual-verbal reasoning outperforms purely verbal methods in specific contexts.
The author reviews ZeroBench and finds its visual reasoning tasks too simplistic, mainly involving basic counting of objects. They argue that improvements in evaluation scores do not equate to advancements in visual reasoning capabilities.
This article discusses the challenges of measuring advancements in robotics, emphasizing the limitations of offline datasets and simulations. It highlights the need for real-world evaluations and the emergence of platforms like RoboArena for testing robot policies in interactive environments.
This article introduces WebGym, an extensive open-source environment for training visual web agents using nearly 300,000 tasks from real websites. It details a reinforcement learning approach that improves agent performance, achieving a notable increase in success rates on unseen tasks compared to other models.
This article discusses the capabilities of AI models, particularly GPT-5, in advancing scientific research. It highlights the introduction of FrontierScience, a framework for assessing AI's scientific reasoning and its impact on research efficiency, while also addressing the limitations of traditional synthetic methods in chemistry.
AIRS-Bench evaluates the research capabilities of large language model agents across 20 tasks in machine learning. Each task includes a problem, dataset, metric, and state-of-the-art value, allowing for performance comparison among various agent configurations. The framework supports contributions from the AI research community for further development.
This article reviews the Claude Opus 4.6 system card, highlighting its new features like a 1M token context window and upgraded model capabilities. It raises concerns about the evaluation process, safety protocols, and the increasing reliance on self-assessment by the model itself.
SGI-Bench is a benchmark designed to assess AI systems' capabilities in scientific inquiry, covering stages like deliberation, conception, action, and perception. It includes over 1,000 expert-curated samples from 10 disciplines, focusing on tasks such as deep research, idea generation, and experimental reasoning.
This article introduces FinCDM, a framework for assessing financial large language models (LLMs) by evaluating their knowledge and skills rather than relying on a single score. It highlights the creation of a new dataset, CPA-KQA, based on CPA exam questions, which allows for a more nuanced analysis of LLM capabilities in financial contexts. The framework aims to uncover knowledge gaps and enhance model development for real-world applications.
Kaggle's Community Benchmarks allows users to create and share custom benchmarks for evaluating AI models. This initiative addresses the need for more flexible and transparent evaluations in the rapidly evolving AI landscape. Users can define tasks and group them into benchmarks for comprehensive model comparison.
The article critiques LMArena, an online leaderboard for AI models, arguing it prioritizes superficial metrics over accuracy. Users often vote based on presentation rather than correctness, leading to misleading rankings that harm the industry. It calls for a shift towards more rigorous evaluation methods.
This article presents a collection of skills focused on context engineering for AI agents. It covers the principles of managing context, designing memory systems, and optimizing agent operations. The skills are platform-agnostic and include practical examples for implementation.
Terminal-Bench 2.0 launches with a new testing framework, Harbor, aimed at improving the evaluation of AI agents in terminal-based tasks. The update includes 89 validated tasks and addresses previous inconsistencies, while Harbor supports scalable testing in cloud environments.
This article examines the safety features and evaluation integrity of Claude Opus 4.6, focusing on risks like sabotage and deception. It critiques the model's performance, particularly in comparison to its predecessor, Opus 4.5, while highlighting areas where it excels and where it struggles, especially in writing tasks. The author emphasizes the need for improved evaluation processes as the technology evolves.
This article discusses how fine-tuning open-source LLM judges using Direct Preference Optimization (DPO) can lead to performance that matches or exceeds GPT-5.2 in evaluating model outputs. The authors trained models like GPT-OSS 120B and Qwen 3 235B on human preference data, achieving better accuracy and efficiency at a lower cost.
Youtu-Agent is a modular framework for creating and evaluating autonomous agents. It allows developers to define agents, environments, and toolkits using a configuration system based on YAML files. The framework supports both single-agent and multi-agent paradigms, facilitating complex task execution.
This article discusses how AI has shifted the focus from production to evaluation in professional work. While AI can generate content quickly, true value now lies in the ability to judge and refine that output, making expertise more important than ever.
This article discusses the importance of thorough evaluation when deploying AI agents. It outlines how AI development differs from traditional software, identifies three essential evaluation components, and provides a practical five-step process for effective assessments.
This article details how Datadog's teams used LLM Observability to enhance their natural language query (NLQ) agent for analyzing cloud costs. It covers the creation of a ground truth dataset, the challenges of evaluating AI-generated queries, and the implementation of a structured debugging process to identify and address errors.
Open Deep Research is an open-source agent designed for deep research tasks, compatible with various model providers and search tools. It ranks high on the Deep Research Bench leaderboard and offers flexibility for customization through its API. The platform supports multiple LLMs and search APIs, making it versatile for different research needs.
LMArena, a startup that tracks AI model performance, recently raised $150 million, bringing its valuation to $1.7 billion. The platform, which began as a research project at UC Berkeley, allows users to evaluate and compare AI models through a public leaderboard. It has quickly become a key player in an industry needing independent assessments.
This guide explains how AI can streamline software operations in production environments. It covers decision-making frameworks for building or buying solutions, outlines an evaluation plan to assess value, and identifies key factors for enterprise readiness.
The article critiques the METR plot, which measures task completion times for AI models, highlighting its reliance on only 14 samples in the 1-4 hour range. The author argues that using such a limited dataset to draw conclusions about AI progress and safety timelines is misleading and calls for more robust metrics.
The article critiques the pass@k metric used to measure AI agents' success, arguing that it can create a misleadingly positive view of performance. It highlights that while pass@k may show high success rates through multiple attempts, real user experiences are often less forgiving. The author calls for more careful consideration and justification when using this metric in evaluating AI.
The article explores the limitations of current evaluation methods for AI models, particularly in assessing design capabilities and reducing the need for constant oversight. It highlights the advancements of Gemini 3 and Opus 4.5 in design and coding tasks, suggesting that existing benchmarks fail to capture these qualities. The author argues for a shift toward more qualitative assessments to better reflect the capabilities of LLMs.
This article details the implementation of Google's Nested Learning (HOPE) architecture, focusing on its mechanism-level components and testing procedures. It provides guidance on installation, usage, and evaluation, including various training configurations and memory management strategies for machine learning models.
This article discusses a framework for measuring how well different compression methods preserve context in AI agent sessions. It compares three approaches, finding that structured summarization from Factory maintains more critical information than methods from OpenAI and Anthropic. The evaluation highlights the importance of context retention for effective task completion in software development.
Bloom is an open source framework that automates the evaluation of AI model behaviors, allowing researchers to specify a desired behavior and generate relevant scenarios for assessment. The tool produces evaluations quickly and offers flexibility in measuring different behavioral traits, complementing existing tools like Petri.
This article outlines the LLM-as-judge evaluation method, which uses AI to assess the quality of AI outputs. It discusses its advantages, limitations, and offers best practices for effective implementation based on recent research and practical experiences.
The article discusses the shortcomings of achieving high accuracy in Text-to-SQL systems, emphasizing that 90% accuracy is insufficient for enterprise applications. It highlights the need for rigorous evaluation frameworks, like Spider 2.0, to ensure reliability and trust in AI-driven analytics.
The article discusses how the rise of AI tools, particularly LLMs, has affected software engineering and data work. While some engineers are concerned about the declining quality of code, data professionals find value in these tools for generating quick, low-maintenance solutions. It emphasizes the need for careful evaluation of the new data generated by these systems.
Andrei Kaparthy's insights on AI's role in work resonate with many, prompting a reflection on how to integrate these ideas into data engineering practices. The article emphasizes the importance of mastering fundamentals to effectively evaluate AI-generated work and encourages active participation in the evolving landscape of technology.
OpenAI reflects on the oversight of sycophantic behavior in its model updates, particularly with GPT-4o. The article outlines the evaluation process, identifies shortcomings in testing, and emphasizes the importance of integrating qualitative assessments and user feedback into future model deployments.
LLM-SRBench is a new benchmark aimed at enhancing scientific equation discovery using large language models, featuring comprehensive evaluation methods and open-source implementation. It includes a structured setup guide for running and contributing new search methods, as well as the necessary configurations for various datasets. The benchmark has been recognized for its significance, being selected for oral presentation at ICML 2025.
The article focuses on enhancing one's design taste by exploring various facets of design appreciation and evaluation. It encourages readers to critically analyze design elements in everyday life and develop a more discerning eye for aesthetics. The content emphasizes the importance of exposure to diverse design styles and the value of informed taste in both personal and professional contexts.
SpatialScore introduces a comprehensive benchmark for evaluating multimodal large language models (MLLMs) in spatial understanding, consisting of the VGBench dataset and an extensive collection of 28K samples. It features the SpatialAgent, a multi-agent system designed for enhanced spatial reasoning, and reveals persistent challenges and improvements in spatial tasks through quantitative and qualitative evaluations.
The article evaluates various language models (LLMs) to determine which one generates the most effective SQL queries. It compares the performance of these models based on their accuracy, efficiency, and ease of use in writing SQL code. The findings aim to guide users in selecting the best LLM for their SQL-related tasks.
The author evaluates various large language models (LLMs) for personal use, focusing on practical tasks related to programming and sysadmin queries. By using real prompts from their bash history, they assess models based on cost, speed, and quality of responses, revealing insights about the effectiveness of open versus closed models and the role of reasoning in generating answers.
A new benchmark for generative world models (WMs) is introduced, focusing on their effectiveness in closed-loop environments that reflect real agent-environment interactions. This research emphasizes task success over visual quality and reveals that controllability and effective post-training data scaling are crucial for improving embodied agents' performance. The study establishes a systematic evaluation framework for future research in generative world models.
The framework presented in the article aims to evaluate and address fears associated with mathematical concepts and their applications. It delves into the psychological barriers that hinder understanding and encourages a more approachable perspective on mathematics. By reframing these fears, the framework seeks to empower individuals in their mathematical journey.
GDPval is a new evaluation framework designed to measure AI model performance on economically valuable tasks across 44 occupations. By focusing on real-world applications, GDPval aims to provide insights into AI's potential impact on productivity and the job market, helping to ground discussions about future advancements in AI technology.
The article discusses the evolving landscape of AI infrastructures, emphasizing the importance of creating robust environments and evaluation systems for assessing AI performance. It highlights the need for improved user experience and interaction within these infrastructures to foster better AI development and applications.
MotifBench offers a comprehensive repository for motif-scaffolding methods, featuring 30 test cases with detailed evaluation instructions, performance tracking, and a call for community contributions. It provides necessary PDB files and scripts for generating scaffold structures, alongside guidance for benchmarking performance and submitting results. Feedback from users is encouraged to enhance the repository and its resources.
The article provides an overview of a codebase for training language and vision-language models using PyTorch, highlighting installation instructions, model inference, and training setup. It details the required dependencies, configuration paths, and methods for integrating new datasets and models, while also addressing the usage of various GPU resources for efficient training and evaluation.
To improve your strategy skills, focus on exploring various resources, such as public engineering blogs and private networks, while also forming learning communities. Evaluate the strategies you've collected using a structured rubric, and implement policies to practice and enhance your strategic abilities within your organization. Ultimately, developing personal accountability and ongoing learning will be key to mastering engineering strategy.
Arabic Leaderboards has launched a new platform to centralize evaluations of Arabic AI models, featuring updates to the AraGen benchmark and the introduction of the Arabic Instruction Following leaderboard. The AraGen-03-25 release includes expanded datasets and improvements in evaluation methodologies, emphasizing the need for accurate assessments in Arabic language tasks. Ongoing analysis of ranking consistency among models highlights the robust nature of the evaluation framework amidst dynamic updates.
The content of the article appears to be corrupted or unreadable, making it impossible to extract meaningful information or insights about the evaluation of GPT-5. Therefore, no summary can be provided based on the current text.
Evaluating large language model (LLM) systems is complex due to their probabilistic nature, necessitating specialized evaluation techniques called 'evals.' These evals are crucial for establishing performance standards, ensuring consistent outputs, providing insights for improvement, and enabling regression testing throughout the development lifecycle. Pre-deployment evaluations focus on benchmarking and preventing performance regressions, highlighting the importance of creating robust ground truth datasets and selecting appropriate evaluation metrics tailored to specific use cases.
The mostlyai-qa library provides tools for assessing the fidelity and novelty of synthetic samples compared to original datasets, allowing users to compute various accuracy and similarity metrics while generating easy-to-share HTML reports. With just a few lines of Python code, users can visualize statistics and perform detailed analyses on both single-table and sequential data. Installation is straightforward via pip, making it accessible for developers and researchers working with synthetic tabular data.
HELMET (How to Evaluate Long-Context Models Effectively and Thoroughly) is introduced as a comprehensive benchmark for evaluating long-context language models (LCLMs), addressing limitations in existing evaluation methods. The blog outlines HELMET's design, key findings from evaluations of 59 recent LCLMs, and offers a quickstart guide for practitioners to utilize HELMET in their research and applications.
The proposed scoring model for WCAG 3 aims to enhance accessibility evaluation by shifting focus from binary pass/fail metrics to a more nuanced scoring system. This change is intended to better reflect user experiences and the diverse needs of individuals with disabilities. The article discusses the implications of this shift and the potential benefits for web accessibility standards.
TextQuests introduces a benchmark to evaluate the performance of Large Language Models (LLMs) in classic text-based video games, focusing on their ability to engage in long-context reasoning and learning through exploration. The evaluation involves assessing agents' progress and ethical behavior across various interactive fiction games, revealing challenges such as hallucination and inefficiency in dynamic thinking. The aim is to help researchers better understand LLM capabilities in complex, exploratory environments.
Multiple loopholes have been discovered in SWE Bench Verified, allowing agents to access future repository states, including solutions and detailed approaches to problems. Examples include using commands that reveal future commits and fixes in various projects, necessitating measures to remove any artifacts that could leak this information. The team is assessing the broader impact of these findings on evaluations and trajectories for sources of leakage.
LLMs are being developed to generate CAD models for simple 3D mechanical parts, leveraging techniques like OpenSCAD for programmatic CAD design. Initial tests show promising results, with evaluations revealing that LLMs have recently improved their capabilities in generating accurate solid models and understanding mechanical design principles. A GitHub repository is available for further exploration of the evaluation processes and tasks involved.
OpenAI MRCR (Multi-round co-reference resolution) is a long context dataset designed to evaluate a language model's ability to identify multiple instances of similar requests embedded in a conversation. This dataset incorporates varying levels of complexity by including multiple identical asks within long, multi-turn dialogues, challenging the model to accurately differentiate and respond to specific instances. Implementation details and grading methods for assessing model performance are also provided.
Effective data quality evaluation is essential for making informed decisions and involves a six-step framework. By defining clear goals, ensuring appropriate data sources, identifying anomalies, and using data observability tools, individuals can enhance the trustworthiness of their data and avoid the pitfalls of poor data quality.
The article discusses various sources of truth and how they shape our understanding of reality. It explores the implications of different narratives and the importance of critically evaluating information in the digital age. The piece emphasizes the need for discernment in seeking reliable knowledge amidst misinformation.
AI is entering a new phase where the focus shifts from developing methods to defining and evaluating problems, marking a transition to the "second half" of AI. This change is driven by the success of reinforcement learning (RL) that now generalizes across various complex tasks, requiring a reassessment of how we approach AI training and evaluation. The article emphasizes the importance of language pre-training and reasoning in enhancing AI capabilities beyond traditional benchmarks.
The B.R.E.W. framework provides a structured approach for evaluating marketing ideas based on four key criteria: Business potential, Reach, Effort, and Who. This method helps teams prioritize initiatives by assessing their viability and resource allocation, ultimately leading to more strategic decision-making in marketing efforts.
The article discusses the complexities of measuring engineering productivity, highlighting the challenges in defining and quantifying productivity metrics. It emphasizes the importance of context and multiple factors that influence productivity beyond mere output metrics, advocating for a more nuanced approach to understanding and evaluating engineering work.
Language models often generate false information, known as hallucinations, due to training methods that reward guessing over acknowledging uncertainty. The article discusses how evaluation procedures can incentivize this behavior and suggests that improving scoring systems to penalize confident errors could help reduce hallucinations in AI systems.
The article discusses the importance of effective evaluation methods for quality assurance in various fields. It emphasizes the need for clear criteria and structured feedback to improve performance and outcomes. Additionally, it highlights the role of continuous learning in refining these evaluation processes.
The article provides a guide on how to build a cold email scorecard to evaluate the effectiveness of cold email outreach strategies. It outlines the key components to include in the scorecard, helping marketers and sales professionals assess their email campaigns more systematically for better results.
Humanity's Last Exam (HLE), an AI benchmark for evaluating PhD-level research, has been criticized for having a significant percentage of its biology and chemistry questions (29 ± 3.7%) contradicting peer-reviewed literature. An independent follow-up revealed 18% of a subset of questions were problematic, prompting the HLE team to initiate a rolling revision process to improve the evaluation. The review process's design may have led to confusing and incorrect questions that do not reflect true scientific knowledge.
JudgeLRM introduces a novel approach to using Large Language Models (LLMs) as evaluators, particularly in complex reasoning tasks. By employing reinforcement learning with judge-wise rewards, JudgeLRM models significantly outperform traditional Supervised Fine-Tuning methods and current leading models, demonstrating superior performance in tasks that require deep reasoning.
ScreenSuite is introduced as the most comprehensive evaluation suite for GUI agents, designed to benchmark vision language models (VLMs) across various capabilities such as perception, grounding, and multi-step actions. It provides a modular and vision-only framework for evaluating GUI agents in realistic scenarios, allowing for easier integration and reproducibility in AI research.
WavReward is a novel reward feedback model designed to evaluate spoken dialogue systems by assessing both their intelligence quotient (IQ) and emotional quotient (EQ) through audio language models. It introduces a specialized evaluator using multi-sample feedback and reinforcement learning, along with the ChatReward-30K dataset, significantly outperforming existing evaluation models in accuracy and subjective testing across various spoken dialogue scenarios.
ReVisiT is a decoding-time algorithm designed for language-vision models (LVLMs) that enhances visual grounding by utilizing internal vision tokens as references. It aligns text generation with visual semantics without altering the underlying model, requiring specific implementations for various Transformer versions. The repository offers setup instructions, evaluation scripts, and integration guidance for users looking to incorporate ReVisiT into their own environments.
LRAGE is an open-source toolkit designed for evaluating Large Language Models in a Retrieval-Augmented Generation context, specifically for legal applications. It integrates various tools and datasets to streamline the evaluation process, allowing researchers to effectively assess model performance with minimal engineering effort. Key features include a modular architecture for retrievers and rerankers, a user-friendly GUI, and support for LLM-as-a-Judge evaluations.
The article discusses the importance of conducting after-action reviews to evaluate the effectiveness of actions taken during various projects or events. It emphasizes the value of reflective practices in improving future performance and decision-making processes. Key components of a successful review include gathering diverse perspectives and openly discussing successes and failures.
AI Note Writers can propose notes on posts, with their effectiveness evaluated by human contributors. They must meet specific criteria in `test_mode` to earn the ability to write notes that are visible to other users. The process includes a review by an automated evaluator to ensure notes are helpful and non-abusive.
The article discusses essential strategies for effective planning for the year 2026, emphasizing the importance of setting clear objectives and evaluating past performances to enhance future outcomes. It provides insights into identifying both strengths and weaknesses to improve decision-making processes in personal and professional contexts.
TextRegion is a training-free framework that generates text-aligned region tokens using frozen image-text models and segmentation masks, achieving remarkable zero-shot performance in tasks like semantic segmentation and multi-object grounding. The framework allows for direct evaluation and inference on custom images, provided users follow the setup and dataset preparation guidelines. It builds on various existing models and is available for use and citation under the MIT License.
ZeroSumEval is a framework designed for evaluating large language models (LLMs) through competitive games, dynamically scaling in difficulty as models improve. It features multi-agent simulations with clear win conditions to assess various capabilities such as knowledge, reasoning, and planning, while enabling easy extension for new games and integration with optimization tools. The framework supports multiple games including chess, poker, and math quizzes, and provides comprehensive logging and analysis tools for performance evaluation.
Researchers at Ai2 propose a method for evaluating language models by measuring the signal-to-noise ratio (SNR) of benchmarks. They demonstrate that higher SNR in benchmarks leads to more reliable model evaluations and suggest interventions to enhance benchmark quality, ultimately improving decision-making in language model training and scaling predictions. A dataset of 900K evaluation results on 465 models is also released to support further research in evaluation methodologies.
The study evaluates the capabilities of autonomous web agents based on large language models, revealing a disparity between perceived and actual competencies due to flaws in current benchmarks. It introduces Online-Mind2Web, a new evaluation benchmark comprising 300 tasks across 136 websites, and presents a novel LLM-as-a-Judge method that aligns closely with human assessment. The findings highlight the strengths and limitations of existing web agents to guide future research directions.