QUIET: Multi-Blank Cascaded Story Cloze Benchmark for LLM Creative Generation
QUIET (Quality Understanding via Interlocked Evaluation Testing) is a new benchmark designed to evaluate LLM creative generation capability rather than discriminative recognition, addressing limitations of benchmarks like Story Cloze Test and HellaSwag. The benchmark places 10-20 blanks with explicit content constraints and cascade dependencies into complete stories, requiring open-ended generation rather than multiple-choice selection. Scoring uses an information-theoretic automated protocol operationalizing a 'calibrated surprise' framework: score = satisfy * (1 + lambda * surprise), combining constraint satisfaction with a surprise measure, enabling objective automated evaluation without human graders or LLM-as-Judge subjectivity.
Related guides (2)
Related events (8)
DEFINED: Data-efficient framework for fine-grained creativity assessment in debate using LLMs
DEFINED is a computational framework for automated creativity assessment in debate scenarios, operationalizing creativity through an eight-dimensional hierarchical metric system implemented via a pretrained autoregressive language model with a hierarchical scoring head. The system addresses data scarcity through constrained data augmentation and mixed-granularity training from limited expert-annotated data. It outperforms prompt-based LLM evaluators and existing debate scoring methods on authentic competition data. The work is relevant to AI evaluation methodology and the broader question of whether LLMs can reliably assess complex human cognitive outputs.
TextQuests: How Good are LLMs at Text-Based Video Games?
A Hugging Face blog post introduces TextQuests, an evaluation framework that tests LLMs on text-based video games as a proxy for interactive reasoning, planning, and language understanding. The benchmark assesses how well models can navigate, solve puzzles, and maintain state across multi-turn interactions in classic interactive fiction environments. This type of evaluation targets agentic capabilities including long-horizon planning and grounded language understanding.
SkillGenBench: Benchmarking Skill Generation Pipelines for LLM Agents
SkillGenBench is a new benchmark designed to evaluate the ability of LLM agents to generate correct, reusable, and executable skills from raw repositories and documents, rather than merely using pre-provided skills. It covers two generation regimes (task-conditioned and task-agnostic) and two procedural sources (repository-grounded and document-grounded), with standardized execution-based evaluation protocols. Experiments across multiple skill-generation methods reveal substantial performance variation and distinct failure modes depending on source type. The benchmark aims to establish skill generation as an independent research problem within agent systems.
Rethinking LLM Evaluation with 3C3H: AraGen Benchmark and Leaderboard
Hugging Face introduces AraGen, a new Arabic-language LLM benchmark and leaderboard built around the 3C3H evaluation framework (Correctness, Completeness, Conciseness, Helpfulness, Harmlessness, Honesty). The benchmark targets a gap in non-English LLM evaluation, specifically for Arabic, using a structured multi-criteria rubric rather than simple accuracy metrics. The leaderboard is hosted on Hugging Face and aims to provide a more holistic assessment of Arabic generative capabilities across frontier and open-weight models.
Creative Quality Alignment: Expert Tacit Knowledge Transfer via Chain-of-Thought Fine-Tuning
This paper empirically validates a creative quality metric from a companion work (Calibrated Surprise, Zou & Xu 2026a) under strict low-resource conditions: ~100 expert chain-of-thought annotations and a small base model. The authors introduce Creative Quality Alignment (CQA) as a class of engineering methods and identify a systematic bias in public alignment datasets toward craft knowledge, with weak coverage of audience modeling and reality-logic. A theoretical argument based on 'architectural duality' in single conditional distribution LLMs is offered to explain why so few examples suffice, distinguishing the result from purely empirical findings like LIMA.
NCRE-based benchmark reveals frontier LLMs top out at 68.8% on professional Office automation tasks
Researchers introduce an evaluation suite derived from China's National Computer Rank Examination (NCRE), comprising 200 practical tasks across Word, Excel, and PowerPoint scored via 7,118 machine-gradable criteria. Seven frontier LLMs are benchmarked: single-turn models peak at 36.6% Score Rate, while a full agentic system with execution feedback and iterative repair reaches 68.8%, still well below the 95.5% community-reference score. The results demonstrate that fine-grained, long-horizon Office document automation remains a significant unsolved challenge for current LLM and agent systems despite strong code-generation capabilities.
Clustered Self-Assessment: LLM uncertainty quantification via semantic clustering and multiple-choice self-evaluation
A new arXiv preprint proposes Clustered Self-Assessment, a method for uncertainty quantification in LLMs that groups sampled generations into semantically distinct clusters, reformats them as multiple-choice options, and uses the model's own probability assignments as confidence estimates. The approach outperforms entropy-based baselines across multiple models and datasets, achieving competitive performance with as few as two additional samples. The method is notable for directly leveraging the model's self-assessment capability rather than relying on indirect distributional signals.
AgentCL: A Rigorous Evaluation Framework for Continual Learning in Language Agents
AgentCL is a new benchmark and evaluation framework designed to rigorously assess continual learning in language agents, addressing gaps in existing benchmarks that focus on retrieval over long-context documents or use naive task streams with limited cross-task analysis. The framework constructs compositional task streams where earlier sub-solutions, evidence, or workflows are intentionally reusable in later tasks, contrasting them with naive streams to measure transfer gains. The authors also introduce MemProbe, a probing method that stores interactions, insights, and skills while filtering unreliable experiences during consolidation. Empirical results across coding, deep research, and language understanding tasks show that controlled streams better distinguish memory design quality, and that naive streams can mask memory-induced degradation.