While model and agent evaluation are inextricably linked, their technical benchmarks and metrics for success are fundamentally different.
Mastering Agentic Techniques: AI Agent Evaluation | NVIDIA Technical Blog…May 11 Submitted by Keming Wu on May 12 UniX Lab Authors: , , , , , , Zuhao Yang , , , , , Ping Nie , , Abstract WorldReasonBench and WorldRewardBench are introduced as benchmarks for evaluating video generation models' ability to reason…
…The following papers were recommended by the Semantic Scholar API Omni-WorldBench: Towards a Comprehensive Interaction-Centric Evaluation for World Models (2026) WorldMark: A Unified Benchmark Suite for Interactive Video World Models…
…The following papers were recommended by the Semantic Scholar API Systematic Capability Benchmarking of Frontier Large Language Models for Offensive Cyber Tasks (2026) CyberCertBench: Evaluating LLMs in Cybersecurity Certification Knowledge (2026) Evaluating…
…Evaluating Adaptive Planning in Large Language Model Agents under World and User Constraints Published on Jun 4 Submitted by Jeff on Jun 5 University of Illinois at Urbana-Champaign Authors: , , , , , , , , , , Abstract AdaPlanBench…
Two years ago we released VQAScore: ask a VLM "does this image show {prompt}?" and use P(Yes) as the score. It became a go-to evaluation metric and reward model for image generation, replacing CLIPScore across the field …
Current LLM benchmarks are broken. We think long horizon "world" building could be an interesting additional way to evaluate LLMs, since it combines many aspects such as need for advanced reasoning, tool calling, working…
I built AptSelect to stop writing throwaway scripts every time I needed to test how different LLMs handle specific instructions and prompt edge cases.What it does:Parallel Execution: Send a single prompt to OpenAI, Anthr…
I'm excited to announce Elmo, an MIT-licensed, open source AEO/GEO tool.We help you scrape ChatGPT/Google AI Mode/etc using web scrapers like BrightData/Olostep/etc, evaluate prompts against the OpenAI/Anthropic/Mistral …
TLDR: Forked pytorch and triton internals . Changed attention so its linear first layer , middle quadratic layer, last linear layer Inference got much faster with a low perplexity hit in tests .Full attention O(n²): 17.9…
…Benchmarking Agentic Procedural 3D Modeling Via Code Published on May 31 Submitted by taesiri on Jun 2 Deepmind Authors: Yipeng Gao , , , , , , , Abstract Vision-language models are evaluated for procedural 3D modeling tasks…
…where a model repurposes available objects by reasoning about their affordances and attributes rather than relying on canonical usage. As a first step, we introduce CreativityBench , a benchmark for evaluating affordance-based…
…Generated by Qwen/Qwen2.5-Coder-32B-Instruct Measuring structured object understanding in vision foundation models remains challenging due to inconsistent evaluation protocols and limited part-level supervision . Semantic correspondence (SC) evaluates…
…Procedural generation with configurable complexity enables an effectively infinite, contamination-resistant evaluation suite, and deterministic pixel-level evaluation eliminates reliance on bias-prone judge models. Across 11 image editing models, we find…
…Generated by Qwen/Qwen2.5-Coder-32B-Instruct Charts are a primary medium for conveying quantitative and relational information, yet systematically evaluating chart parsing models remains difficult. Existing benchmarks focus on narrow…
…The following papers were recommended by the Semantic Scholar API BenHalluEval: A Multi-Task Hallucination Evaluation Framework for Large Language Models on Bengali (2026) Cultural Benchmarking of LLMs in Standard and Dialectal…