Search: “evaluation”

Figure 1: CUDA-to-MLX optimization translation map. CUDA optimization knowledge can be translated into architecture-native MLX strategies rather than copied instruction-for-instruction. We face a new

arXiv:2607.23524v1 Announce Type: new Abstract: Deep search is becoming a core capability of modern agent systems, yet it is typically evaluated solely based on end-to-end answer accuracy. This couple

arXiv:2607.24210v1 Announce Type: new Abstract: In clinical oncology studies, metastatic cancer is commonly evaluated using 'Response Evaluation Criteria in Solid Tumors' (RECIST), in which the diamet

arXiv:2607.24177v1 Announce Type: cross Abstract: Due to the lack of systematic evaluations, we are not yet able to determine which AI-based Windows malware detector to deploy in production, since exi

arXiv:2607.23696v1 Announce Type: new Abstract: Ad creative optimization is increasingly constrained by evaluation rather than generation. Generative models can produce many plausible creatives, but r

arXiv:2607.22045v1 Announce Type: new Abstract: Counterfactual explanations are a prominent approach in explainable artificial intelligence (xAI), providing actionable guidance on what input changes w

arXiv:2607.21998v1 Announce Type: new Abstract: This paper introduces a new benchmark test, Medical-Checklist, for assessing medical multimodal models. The recent advancements in multimodal models hav

arXiv:2607.21065v1 Announce Type: new Abstract: Pathology vision-language models (VLMs) have recently progressed rapidly and are commonly evaluated by answer accuracy on pathology VQA benchmarks. Howe

arXiv:2607.20739v1 Announce Type: cross Abstract: In large-scale machine learning, distributed training commonly involves multiple workers evaluating the gradients of the model on different dataset pa

arXiv:2607.20520v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated on mathematical problem solving, yet prior work often treats representationally equivalent formu

Arize and Fireworks benchmarked 10 AI models across 2,400 agent runs. Learn why cost per successful task beats token price for model evaluation and routing. The post Cost per successful task: Benchmar

arXiv:2607.19772v1 Announce Type: new Abstract: Dynamic RGBT (DRGBT) tracking aims to continuously localize a target when the available sensing modalities and observation platforms vary over time. Com

arXiv:2607.20219v1 Announce Type: new Abstract: Large language models (LLMs) can generate fluent Arabic answers, yet factual errors remain difficult to detect, localize, explain, and verify. Existing

arXiv:2607.18448v1 Announce Type: cross Abstract: Pathology report generation from whole-slide images (WSIs) is a rapidly growing multimodal learning problem, yet progress is difficult to measure beca

arXiv:2607.19701v1 Announce Type: new Abstract: VLMs are increasingly deployed in AD systems, creating an urgent need for rigorous safety evaluation under rare yet safety-critical scenarios. Among the

This story is wild. The short version: OpenAI were running a cybersecurity test against an unreleased model, with the model's guardrail features turned off. Rather than solve the test, the model broke

arXiv:2607.11891v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) in specialized domains like medical diagnostics and financial advisory necessitates evaluating capabili

arXiv:2607.12338v1 Announce Type: new Abstract: Agent benchmarks often compare two agents after all tasks have run, but costly evaluations make partial runs tempting. A task fraction alone does not sh

arXiv:2607.12523v1 Announce Type: cross Abstract: Reliable reinforcement learning (RL) agents must maintain operational integrity amidst sensor malfunctions, dynamic disturbances, and slow environment

arXiv:2509.26076v2 Announce Type: replace Abstract: As the mathematical capabilities of large language models (LLMs) improve, it becomes increasingly important to evaluate their performance on researc

arXiv:2607.08768v1 Announce Type: new Abstract: The rapid development of large language models and multimodal large language models has accelerated the emergence of proactive agents capable of operati

arXiv:2607.08535v1 Announce Type: cross Abstract: An LLM-as-judge score can move even when the candidate responses stay fixed, simply because the evaluator has changed. We treat this evaluator-replace

arXiv:2607.04008v1 Announce Type: new Abstract: We present a candidate-constrained retrieval-augmented generation system for LongEval-RAG, where each query is associated with an organizer-provided can

arXiv:2607.03862v1 Announce Type: new Abstract: AI-generated images have proliferated rapidly, motivating extensive research. Most existing AI-generated image detectors are developed and evaluated und

arXiv:2607.02131v1 Announce Type: cross Abstract: Restoring archival film remains a fundamentally challenging problem due to the absence of paired training data and the lack of standardized evaluation

arXiv:2603.17212v2 Announce Type: replace-cross Abstract: When organizations delegate text generation tasks to AI providers via pay-for-performance contracts, expected payments rise when evaluation is

arXiv:2607.01830v1 Announce Type: new Abstract: Reliable reward and preference signals are critical for evaluating and optimizing large language models on open-ended tasks. Rubric-based judges offer a

arXiv:2607.01751v1 Announce Type: cross Abstract: Existing medical video benchmarks primarily evaluate whether a model produces the correct answer, but rarely assess whether it answers at the right ti

arXiv:2607.00948v1 Announce Type: new Abstract: The visual quality of AI-generated videos has improved drastically in recent years, making it increasingly difficult for humans to distinguish between r

arXiv:2607.00159v1 Announce Type: new Abstract: Knowledge-Based Visual Question Answering (KB-VQA) aims to evaluate whether Visual Language Models (VLMs) can retrieve, ground, and reason over external

arXiv:2607.01086v1 Announce Type: cross Abstract: The evaluation of long-term video quality understanding remains an open challenge for large vision-language models (LVLMs). Existing video quality ben

arXiv:2606.30931v1 Announce Type: new Abstract: The LLM Jury, a Panel of LLM Evaluators (PoLL) reporting consensus scores, has become a practical alternative to single-judge LLM evaluation, yet its st

arXiv:2606.28578v1 Announce Type: cross Abstract: Closed-loop materials discovery iterates between proposing candidate structures and evaluating their properties, and property evaluation dominates the

arXiv:2606.31672v1 Announce Type: cross Abstract: Despite rapid progress in interactive world models (IWMs), existing benchmarks evaluate action following only at trajectory level and ignore memory an

arXiv:2606.28863v1 Announce Type: cross Abstract: AI systems increasingly exhibit behavior that differs systematically between evaluation and deployment contexts. Alignment faking, sandbagging, benchm

arXiv:2604.05318v2 Announce Type: replace Abstract: Harmful content detectors, particularly disinformation classifiers, are predominantly developed and evaluated on Standard American English (SAE), le

arXiv:2606.29088v1 Announce Type: cross Abstract: There are various benchmarks to evaluate bugfixing capabilities of Large Language Models. However, most widespread benchmarks do not fully reflect rea

arXiv:2606.29887v1 Announce Type: new Abstract: In real-world applications, guardrails are often expected to identify unsafe user-model interactions according to application-specific safety policies,

arXiv:2507.07445v3 Announce Type: replace Abstract: Autonomous agents navigating human society must master both production activities and social interactions, yet existing benchmarks rarely evaluate t

arXiv:2606.28235v1 Announce Type: cross Abstract: Autonomous coding agents now open and merge pull requests in shared repositories at scale, and the field evaluates them the way it has always evaluate

arXiv:2606.27537v1 Announce Type: new Abstract: Video generation models aspire to simulate dynamic environments, and several benchmarks now evaluate memory consistency across frames. However, most ass

arXiv:2606.28061v1 Announce Type: cross Abstract: Large language models (LLMs) have increasingly moved from standalone text generation systems to agents that invoke external tools, access environments

Fascinating paper on self-improving agents. (bookmark it) If you are working on agentic loops, you will quickly realize that they are only as good as the effectiveness of the evaluator. Self-improveme

arXiv:2606.26216v1 Announce Type: cross Abstract: We present CyberChainBench, a benchmark for evaluating LLM-based agents on smart contract security across three complementary tasks: vulnerability det

arXiv:2606.26657v1 Announce Type: new Abstract: Identifying high-utility candidates from massive discrete spaces under expensive evaluations is a recurring challenge across the sciences, with structur

arXiv:2606.26984v1 Announce Type: new Abstract: Unified multimodal models capable of both understanding and generation have achieved remarkable strides. However, despite their unified designs, existin