Benchmark Everything Everywhere All at Once
Shiyun Xiong, Dongming Wu, Peiwen Sun, Yuang Ai, Bokang Yang, Wencheng Han, Xiao-Hui Li, Xiangyu Yue
Abstract
Benchmarks are fundamental for evaluating and advancing LLMs and MLLMs by providing standardized and explicit measures of performance. However, their construction is labor-intensive and hard to reuse, raising concerns about sustainability and scalability. Moreover, existing benchmarks often quickly reach performance saturation after their release, resulting in insufficient discrimination among state-of-the-art models. To address these challenges, we introduce Benchmark Agent, a fully autonomous agentic system designed for benchmark building. Our framework orchestrates the complete benchmark construction pipeline, from user query analysis and subtask design to data annotation and quality control. To assess Benchmark Agent, we implement it to produce 15 representative benchmarks, spanning diverse evaluation scenarios, including text understanding, multimodal understanding, and domain-specific reasoning. Extensive experiments, including human evaluation, LLM-as-a-judge assessment, and consistency checks, demonstrate Benchmark Agent can generate high-quality benchmark samples with minimal human involvement. More importantly, through continual evaluation, we observe several insightful findings, including that current models struggle with certain domain-specific reasoning tasks. We believe that rapidly evolving benchmarks can contribute significantly to the research community. The preview and code will be publicly available at the demo page and code repository.
AI Impact Assessments
(1 models)Scientific Impact Assessment: "Benchmark Everything Everywhere All at Once"
1. Core Contribution
The paper introduces Benchmark Agent, a fully autonomous agentic framework that automates the entire benchmark construction pipeline—from interpreting user evaluation requirements, through subtask design and dataset grounding, to sample generation and quality control. The system uses a dual-component architecture (Benchmark Planner + Benchmark Executor) inspired by brain-cerebellum hierarchical organization. The Planner decomposes user queries into subtasks via Design, Grounding, and Allocation agents, while the Executor realizes samples through tool-based transformations with quality verification loops.
The core problem addressed is twofold: (1) the labor-intensive, non-reusable nature of manual benchmark construction, and (2) rapid performance saturation of existing benchmarks, which diminishes their discriminative utility. The proposed solution enables on-demand, customizable benchmark generation with minimal human involvement.
2. Methodological Rigor
The evaluation framework is reasonably comprehensive, employing three complementary assessment approaches: human evaluation (acceptance rates 96–98%), LLM-as-a-Judge scoring across six dimensions, and consistency checks showing expected scaling trends across model families (e.g., Qwen3.5 2B→27B). The paper constructs 15 benchmarks spanning text, audio, image, and omni-modal settings.
However, several methodological concerns exist:
3. Potential Impact
The paper addresses a genuine pain point in AI evaluation infrastructure. If the system works reliably, it could:
However, the impact is tempered by concerns about benchmark contamination and validity. LLM-generated benchmarks risk testing artifacts of LLM reasoning patterns rather than genuine capabilities. The paper does not sufficiently address whether these benchmarks can reveal failure modes that the generating LLM itself would not anticipate—a fundamental limitation for benchmarks meant to push the frontier.
4. Timeliness & Relevance
The paper is highly timely. Benchmark saturation is a well-documented problem in the field, with MMLU, GSM8K, and similar benchmarks becoming less discriminative as models improve. The explosion of new benchmarks (often manually constructed) creates sustainability concerns. The paper's framing directly addresses current community needs.
The agent-based approach also aligns with the broader trend of using LLMs for meta-scientific tasks. The 2026 publication date positions it well relative to the maturation of agentic LLM systems.
5. Strengths & Limitations
Strengths:
Limitations:
6. Additional Observations
The paper's title ("Benchmark Everything Everywhere All at Once") is catchy but somewhat overpromising—the system still requires a curated dataset pool and specific tool implementations. The "everything everywhere" framing masks genuine scope constraints. The reliance on frontier commercial models (GPT-5.1) as backbone also limits accessibility for the broader research community, though the ablation with open-source models partially addresses this.
The concept of autonomous benchmark construction is genuinely novel as a fully integrated system, though individual components (LLM-based data synthesis, agentic evaluation) have precedent. The paper's contribution is primarily in systems integration and pipeline design rather than fundamental algorithmic innovation.
Generated Jun 5, 2026
Comparison History (26)
Paper 2 addresses a widely recognized and pressing challenge in the LLM/MLLM community—benchmark saturation and the unsustainable cost of benchmark construction. By automating benchmark creation through an agentic system, it has broad applicability across NLP, multimodal AI, and domain-specific reasoning, potentially transforming how the field evaluates models. Paper 1, while methodologically rigorous and novel in combining OPE with strategic behavior and explainability, targets a narrower intersection of causal inference and algorithmic decision-making, limiting its breadth of impact. Paper 2's timeliness given the rapid LLM advancement cycle further amplifies its potential influence.
Paper 2 has higher likely scientific impact: it proposes a concrete, systems-level rethinking of KV cache management (head-aware decomposition + SegPagedAttention) that directly targets a major bottleneck in long-context LLM serving. The approach is timely for industry and research infrastructure, has clear real-world applicability (memory, throughput, concurrency, distributed serving) and can influence multiple subareas (serving systems, memory management, distributed systems, attention optimization) without retraining models. Paper 1 is useful but may face reproducibility/quality and benchmark-validity concerns and is less foundational than a broadly deployable infrastructure advance.
Paper 1 offers a foundational systems contribution by addressing a critical bottleneck in LLM scaling: efficient sparse attention for long-context generation. By providing a programmable framework that achieves up to 4.7x throughput improvements on cutting-edge hardware (B200 GPUs) and massive models (229B parameters), it delivers immediate, highly quantifiable real-world value. While Paper 2's automated benchmarking is useful, Paper 1's deep hardware/software co-design enables the broader AI community to actually deploy and scale frontier models, giving it a more profound and lasting scientific impact.
Paper 1 has higher potential impact due to its broader, timely contribution to LLM/MLLM evaluation: an autonomous system for creating and updating benchmarks addresses a major scalability bottleneck and could affect many subfields that rely on standardized evaluation. Its applications extend across NLP, multimodal learning, and domain reasoning, potentially reshaping how benchmarks are produced and maintained. Paper 2 is a solid, application-focused reframing for infrastructure inspection, but its scope is narrower (traffic signs/DT monitoring) and likely impacts a more limited set of domains.
Paper 1 addresses a fundamental bottleneck in AI: the rapid saturation and high cost of creating LLM benchmarks. An autonomous benchmark generation system offers massive scalability, high novelty, and broad applicability across all AI domains. Paper 2 presents a rigorous methodological improvement for model quantization, but its impact is more specialized to efficiency and deployment, whereas Paper 1 could fundamentally shift the overarching evaluation paradigm of the entire field.
Paper 1 addresses a fundamental infrastructure challenge for the entire LLM/MLLM research community by automating benchmark construction, which is broadly applicable across AI research. Its autonomous agentic framework for benchmark generation is novel, timely given rapid LLM advancement, and could impact how the field evaluates models at scale. Paper 2, while methodologically sound, addresses a narrower domain (solar irradiance forecasting) with incremental improvements combining existing techniques. Paper 1's breadth of impact across AI research fields and its timeliness give it significantly higher potential scientific impact.
Paper 1 targets a widely felt bottleneck in LLM/MLLM evaluation: benchmark creation cost and rapid saturation. An autonomous, end-to-end benchmark-building agent with demonstrated ability to generate many benchmarks could immediately affect how models are evaluated across NLP, multimodal, and domain reasoning, with broad downstream impact and strong timeliness. Paper 2 is novel and methodologically grounded, but its impact is likely narrower (constrained optimization + specific domains like OPF) and depends on adoption in specialized workflows. Overall breadth, relevance, and practical applicability favor Paper 1.
Paper 2 likely has higher scientific impact: it identifies a fundamental failure mode (information self-locking) in outcome-based RL for LLM agents, provides a coupled AS/BT theoretical framing, and proposes a broadly applicable mitigation (AREW) with large gains across diverse agentic tasks. This targets a timely bottleneck for interactive LLM agents and can influence RL, agent design, and evaluation practice. Paper 1 is useful infrastructure, but agentic benchmark generation may face adoption/validity concerns and its impact depends on sustained community uptake.
Paper 1 introduces a rigorous causal evaluation protocol addressing a fundamental question about LLM reasoning pipelines—whether intermediate structures actually causally mediate outputs. This has deep implications for interpretability, controllability, and trust in LLM systems. The finding that intermediate structures act as 'influential context rather than stable causal mediators' is a significant conceptual contribution that could reshape how the field designs and relies on chain-of-thought and schema-guided approaches. Paper 2, while practically useful, addresses benchmark automation—a more incremental engineering contribution with less conceptual depth and narrower theoretical impact.
Paper 1 addresses a fundamental infrastructure problem in AI evaluation—automated benchmark construction—which has broad impact across the entire ML community. The ability to rapidly generate high-quality, non-saturated benchmarks addresses a critical bottleneck as models rapidly improve. Its breadth (text, multimodal, domain-specific) and potential to become standard infrastructure give it wider impact. Paper 2, while innovative in distilling transferable skills from trajectories, addresses a narrower problem in LLM agent optimization. Both are methodologically sound, but Paper 1's utility spans more research areas.
Paper 2 addresses a fundamental and broadly relevant challenge in AI research—benchmark construction and sustainability—with a fully autonomous system applicable across the entire LLM/MLLM community. Its potential to accelerate evaluation methodology, combat benchmark saturation, and enable continual assessment gives it wider scientific impact. Paper 1, while practically valuable, is more narrowly focused on enterprise knowledge management with a single deployment study. Paper 2's open-source tools, broader applicability across research fields, and methodological contribution to evaluation science give it greater potential for widespread adoption and citation.
Benchmark Agent addresses a fundamental infrastructure challenge in AI research—the scalability and sustainability of benchmark creation—with broad applicability across the entire field. Its fully autonomous pipeline for generating benchmarks across diverse domains (text, multimodal, domain-specific reasoning) could accelerate evaluation methodology community-wide. While TokenMizer solves a practical but relatively narrow engineering problem (LLM context management), Benchmark Agent's potential to transform how the community creates and maintains benchmarks gives it broader impact, greater novelty, and wider cross-field relevance.
Paper 1 tackles a profound, forward-looking theoretical challenge—evaluating AI models that surpass human comprehension—by proposing a novel adversarial evaluation framework. This addresses fundamental questions in AI alignment and future progress measurement. While Paper 2 offers a highly practical engineering solution for automating benchmark creation, Paper 1 introduces a methodological paradigm shift that will become increasingly critical and impactful as frontier models continue to scale beyond human evaluation capabilities.
Paper 1 introduces Benchmark Agent, an autonomous system that addresses the critical and timely challenge of benchmark saturation and scalability for LLMs/MLLMs. It demonstrates concrete results across 15 benchmarks with rigorous validation (human evaluation, LLM-as-judge, consistency checks). Its potential to continuously generate evolving benchmarks addresses a fundamental bottleneck in AI research. Paper 2 proposes entropy-based metrics for agent evaluation—a useful but more incremental contribution that serves as a complementary diagnostic tool without the same breadth of impact or demonstrated empirical validation at scale.
Paper 1 has higher potential impact due to strong novelty and broad applicability: an autonomous, end-to-end benchmark-construction agent addresses a major scalability bottleneck in LLM/MLLM evaluation and could be adopted across many domains, influencing how models are measured and improved. It is timely given rapid benchmark saturation and continual model releases, and its outputs (multiple benchmarks + tooling) can propagate widely. Paper 2 is valuable and application-driven, but its contribution is narrower (epidemiological simulation with LLM decisions) and more sensitive to methodological concerns about LLM validity/calibration for real behavior.
Paper 2 addresses a fundamental gap in AI agent evaluation by proposing a rigorous reliability framework grounded in safety-critical engineering principles. Its 12 concrete metrics across four dimensions (consistency, robustness, predictability, safety) provide a reusable conceptual contribution with broad applicability beyond any single benchmark. The finding that capability gains haven't translated to reliability improvements is a timely and important insight as agents are increasingly deployed. Paper 1, while useful, automates benchmark construction—a more incremental contribution that addresses scalability but not the deeper evaluation paradigm shift that Paper 2 proposes.
Paper 1 addresses a critical bottleneck in the rapidly advancing field of AI: benchmark saturation and creation latency. By introducing an empirically tested, fully autonomous system for benchmark generation, it provides an immediate, highly scalable tool for researchers, ensuring broad and rapid citation. Paper 2 is a perspective paper offering a valuable overview of hybrid modeling in neurology, but lacks the immediate empirical breakthrough and broad cross-domain utility of the concrete tool presented in Paper 1.
Paper 2 addresses a critical bottleneck in AI research—rapid benchmark saturation and the labor-intensive nature of evaluating LLMs. An autonomous benchmark-generation agent has broad, immediate utility across the entire AI community. Paper 1 offers strong advancements in neuroAI and brain-computer interfaces, but its impact is relatively confined to the specialized field of fMRI decoding. Therefore, Paper 2 possesses greater breadth of impact and timeliness.
Paper 2 likely has higher impact: it proposes an autonomous, reusable benchmark-construction system for LLM/MLLM evaluation, a timely bottleneck affecting much of current AI research. If robust, it can quickly influence community practices across NLP, multimodal learning, and domain reasoning via new benchmarks, continual evaluation, and released code. Paper 1 is a strong theoretical advance (strongly-polynomial policy iteration for L∞ robust MDPs) resolving an open question, but its immediate audience is narrower and applications more specialized than broad benchmarking infrastructure for frontier model development.
Paper 2 addresses a critical and highly timely bottleneck in AI research: the rapid saturation and labor-intensive nature of LLM benchmarks. By automating benchmark creation, it offers immense scalability and relevance across the entire LLM and MLLM ecosystem. While Paper 1 provides a solid methodological contribution to imbalanced learning, Paper 2's potential to continuously generate high-quality evaluations gives it a much broader and more immediate impact on accelerating general AI progress.