3arXiv cs.CL (Computation and Language)·
VEHBench: Stage-local diagnostic benchmark for LLM-assisted vibration energy harvester design
Researchers introduce VEHBench, a 763-task engineering benchmark for evaluating LLMs across distinct stages of vibration energy harvester (VEH) design, scored by an analytical physical oracle. The benchmark covers four design roles: specification triage, verifier-guided search, corrupted-state recovery, and policy-conditioned selection. Key finding is that LLM capability is strongly stage-dependent — no single model dominates the full workflow — suggesting that model routing and specialization matter for engineering applications.
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PhysTool-Bench reveals severe gaps in MLLM physical tool use and embodied planning
Researchers introduce PhysTool-Bench, the first benchmark evaluating multimodal LLMs on physical tool use across 2,510 queries and 2,678 real-world tools spanning manufacturing, electrical work, agriculture, and healthcare. Evaluation of 13 leading MLLMs shows even the best model (Gemini-3.1-Pro) identifies only 58.7% of tools in a scene and completes just 21.0% of queries end-to-end. The results expose a two-level deficit: poor tool perception in realistic scenes and a much larger drop at the planning stage, indicating a lack of functional commonsense for mapping tools to task semantics. This pinpoints a critical bottleneck for embodied AI development.
LLM-as-a-Verifier: Training-free verification framework scales along granularity, repetition, and criteria decomposition
Researchers introduce LLM-as-a-Verifier, a general-purpose verification framework that treats verification as a new scaling axis for LLMs, computing continuous scores from token logit distributions rather than discrete judge outputs. The framework scales along three dimensions—score granularity, repeated evaluation, and criteria decomposition—and achieves state-of-the-art results on Terminal-Bench V2 (86.5%), SWE-Bench Verified (78.2%), RoboRewardBench (87.4%), and MedAgentBench (73.3%) without requiring additional training. The authors also demonstrate that the framework's fine-grained signals can serve as dense RL feedback, improving sample efficiency for SAC and GRPO on robotics and math benchmarks, and build a Claude Code extension for monitoring agentic systems.
PowerCodeBench: Knowledge Boundary Probing and Intervention for LLM-Based Power System Code Generation
This paper introduces PowerCodeBench, an execution-validated benchmark for evaluating LLMs on power-system simulation code generation using the pandapower library. The authors identify that failures are dominated by API-knowledge boundary errors (hallucinated function names, misused parameters) rather than reasoning failures, and propose a boundary-aware intervention combining API demand estimation with targeted documentation injection. Evaluated across ten open-weight models (1.5B–480B) and four commercial APIs on 2,000 tasks, the intervention yields 32–56 accuracy point improvements while using only 41% of baseline prompt-token cost. Open-weight models in the 70B–120B range match commercial mid-tier accuracy, with Llama-3.1-405B and Qwen3-Coder-480B leading.
PlanBench-XL: Benchmark for LLM Agent Planning in Large-Scale Tool Ecosystems
Researchers introduce PlanBench-XL, an interactive benchmark of 327 retail tasks spanning 1,665 tools designed to evaluate LLM agents on long-horizon planning under retrieval-limited tool visibility. The benchmark includes a blocking mechanism simulating real-world disruptions such as missing or failing tools, forcing agents to detect and recover from broken execution paths. Experiments on ten leading LLMs reveal severe performance degradation: GPT-5.4 drops from 51.90% accuracy in unblocked settings to 11.36% under the most severe blocking condition, highlighting fragility in adaptive planning for large, imperfect tool environments.
HypoArena benchmark evaluates LLMs on open-ended hypothesis discovery before conclusions are known
Researchers introduce Prospective Hypothesis Discovery (PHD), a new capability framing that tests whether LLMs can autonomously generate grounded, testable hypotheses from inconclusive evidence rather than answering pre-specified questions. The benchmark HypoArena includes 988 cases across six scientific and analytical domains, constructed via a novel Retrospective Context Regression pipeline that strips conclusions from expert documents. Evaluation of 15 frontier LLMs using a Bradley-Terry-Davidson arena framework reveals clear capability stratification and model-dependent effects of structured analytical prompting, with some models regressing under structured guidance. The work positions hypothesis formulation as a distinct, underexplored evaluation target for LLMs.
RRBench: evaluating open-weight LLMs on longitudinal data preparation tasks in governance-restricted settings
Researchers from UCL introduce RRBench, an open-source benchmark and evaluation framework for assessing open-weight LLMs on data preparation tasks in longitudinal population studies, where cloud-based models are prohibited by data governance rules. The benchmark uses ground-truth cleaning scripts from six sweeps of a British cohort study, covering tasks like category harmonization and multi-wave merging, with automated evaluation of LLM-generated R code. Current 31–35B parameter open-weight models achieve up to 87.9% average task completion on consumer-grade hardware, suggesting a viable path for AI-assisted data preparation in privacy-sensitive research contexts.
Judge Arena: Benchmarking LLMs as Evaluators
Hugging Face and Atla have launched Judge Arena, a platform for benchmarking large language models in their role as automated evaluators. The initiative uses an Elo-based ranking system to compare how well different LLMs judge the quality of model outputs, addressing the growing reliance on LLM-as-judge paradigms in evaluation pipelines. This fills a meta-evaluation gap: as LLM judges become standard practice, understanding their relative reliability and biases becomes critical infrastructure for the field.
TriggerBench: A benchmark for evaluating prospective memory in LLMs
Researchers introduce TriggerBench, a benchmark evaluating prospective memory (PM) in LLMs — the ability to spontaneously recall and act on latent constraints without explicit prompting. The benchmark spans five dimensions across daily assistant and professional workflow scenarios, and reveals that PM is substantially harder than retrospective memory, decaying sharply with context length while retrospective memory near-saturates at 100K tokens. Key findings include a precision-recall trade-off in PM, attentional fragility under concurrent requests, and a novel result that PM accuracy correlates with spare reasoning capacity as measured against AIME-2025 math performance.