arxiv arXiv cs.CL · 8h ago

How Large Language Models Source Brand Reputation Across Languages and Markets

This study analyzes the citation sources used by large language models when answering questions about brands, focusing on the underlying web references rather than just the generated text. The researchers merged three Rankfor.AI datasets to examine 167,551 URL-grounded citations across 128 brands in 12 home markets and 13 languages. The analysis reveals that AI grounds brand answers overwhelmingly in third-party sources, with 85.7% of citations pointing to sites the brand does not own compared to only 14.3% for owned domains. The source base is highly concentrated and follows a Zipf law, where 80% of citations originate from approximately 18% of domains. Wikipedia emerges as the dominant reference site, being the most-cited domain in 11 of the 12 languages studied. The only exception is Lithuanian, where the business daily vz.lt slightly edges out Wikipedia with a 4.38% share. Additionally, the source mix shows market-specific variations, such as YouTube being the top cited domain for Polish national brands and HR portals supplying more citations than Polish Wikipedia.

arxiv arXiv cs.CL · 8h ago

ToolBench-X: Benchmarking Tool-Using Agents Under Unreliable Environments

The authors introduce ToolBench-X, a new benchmark designed to evaluate large language model agents under recoverable tool-environment unreliability. Unlike existing benchmarks that assume clean and stable environments, this framework injects five structured hazard types: Specification Drift, Invocation Error, Execution Failure, Output Drift, and Cross-source Conflict. The dataset contains executable multi-step tasks across diverse domains with deterministic tools and canonical final answers for automatic evaluation. Crucially, every injected instance remains solvable through valid recovery paths such as retrying, fallback, or verification. Experiments reveal a substantial reliability gap where agents performing well with reliable tools often fail under these hazards. Further analysis indicates that failures stem from limited hazard diagnosis and ineffective recovery rather than tool-use volume or inference budget. Targeted recovery hints successfully recover many failed tasks, whereas test-time scaling yields more limited gains. These findings suggest that evaluation must shift focus from function-call accuracy to task completion in unreliable environments.

media Hugging Face Forums · 11h ago

Community Inquiry on Model Benchmarking Methods

A user on the Hugging Face discussion forum posted a question seeking advice on how to benchmark machine learning models. The inquiry was initiated by an individual who is new to the field of fine-tuning and wishes to evaluate their models after completion. The post explicitly asks for established methods or strategies that the community uses for this purpose. It highlights a common need among practitioners to understand standard evaluation practices in model development. The discussion thread currently contains only one post from a single participant. No specific benchmarks, metrics, or technical solutions were provided within the visible content of the source.

arxiv arXiv cs.LG · 15h ago

Small Language Models Outperform Frontier LLMs in Relation Extraction

A fine-tuned 0.5B-parameter Qwen2.5 model achieves 0.83 micro-F1 in general-domain relation extraction, surpassing zero-shot GPT-5.4 and Claude Sonnet 4.6. On literary benchmarks, it reaches 0.92 on the Biographical dataset, outperforming GPT-5.4 and exceeding frontier models in accuracy, demonstrating that task-adapted small models can deliver high performance with minimal hardware and privacy overhead.

arxiv arXiv cs.AI · 16h ago

BabelJudge: Measuring LLM-as-a-Judge Reliability Across Languages and Agent Trajectories

BabelJudge introduces an open-source framework to measure four key bias modes in LLM judges across languages and agent trajectories. It reveals a significant reliability drop from Hindi to Swahili—0.714 to 0's 0.550—highlighting cross-lingual degradation invisible to raw accuracy. The framework enables bias-aware evaluation without human labels, using controlled perturbations to create known gold labels, and extends to agentic workflows with new metrics on tool accuracy and hallucination detection.

arxiv arXiv cs.AI · 16h ago

RoboMME-Interference: Benchmarking Robot Memory Under Interference

RoboMME-Interference introduces a cross-session benchmark to evaluate robot memory under interference. It adds unrelated sessions to prior demonstrations, revealing that perceptual memory variants degrade significantly as distractions increase, highlighting current systems' lack of robustness to interference and the need for long-context memory.

arxiv arXiv cs.AI · 16h ago

Flow Annealing Posterior Sampling for Function-Space Regression and Inverse Problems

FAPS is the first function-space posterior sampling framework that unifies stochastic-process regression and PDE inverse problems. It uses pretrained flow-matching priors and Langevin correction with low-rank covariance preconditioning to enable efficient, accurate posterior inference from sparse, noisy data with coherent uncertainty quantification.

media r/LocalLLaMA · 16h ago

Has anyone else found vLLM outputs worse than llama.cpp?

A user reports noticing less reliable outputs from vLLM compared to llama.cpp, including formatting errors, context forgetting, and lower code quality. They ask whether such differences stem from quantization, chat templates, parser issues, or configuration errors, and seek confirmation if others have observed similar quality discrepancies between inference backends.

arxiv arXiv cs.AI · 17h ago

SAFER: Reliable Test-Time Adaptation under Adversarial Streams

SAFER is a training-free framework that enhances robustness of test-time adaptation by using reliability-guided augmentation. It generates stochastic augmentations, pools predictions via correlation-weighted aggregation with outlier detection, and includes adaptive mixing to preserve clean performance under adversarial attacks. Evaluations on PACS, VLCS, and OfficeHome show improved resilience without sacrificing clean accuracy.

arxiv arXiv cs.AI · 17h ago

Reference-Free Assessment of Physical Consistency in Video Generation

A new method evaluates physical consistency in generated videos without requiring human voting or ground-truth references. It uses DROID-SLAM and SEA-RAFT to detect inconsistencies, improving task success rates by over 8% and enabling spatio-temporal localization of physical artifacts.

arxiv arXiv cs.AI · 17h ago

LLM-Assisted Label Cleaning in Chest CT Dataset

A large language model (LLM) assisted in identifying label-report discordance in the CT-RATE chest CT dataset. GPT-5.4 achieved 96.4% agreement with existing labels, with radiologist adjudication supporting LLM-derived labels in 74.2% of general and 91.9% of lymphadenopathy discordances. Multi-LLM majority-vote labels outperformed others in F1 score and kappa, and the cleaned dataset will be publicly released.

arxiv arXiv cs.AI · 17h ago

PlanBench-XL: Benchmark for Long-Horizon Tool-Use Planning

PlanBench-XL evaluates long-horizon planning in LLM agents across 1,665 tools through 327 retail tasks. It introduces a blocking mechanism to simulate real-world tool failures, revealing that agents like GPT-5.4 drop from 51.90% to 11.36% accuracy under severe disruptions, highlighting vulnerabilities in recovery and error handling.

arxiv arXiv cs.AI · 17h ago

Gold Points Sniper: Self-guided Visual Reasoning for Fine-grained Action Understanding

Gold Points Sniper (GPS) enables lightweight vision-language models to perform self-guided multimodal reasoning for fine-grained human action understanding. By integrating a Gold Points Extractor, Selective Socratic Questioner, and Semantic Entailment Evaluator, GPS achieves performance comparable to GPT-4o while maintaining superior factual accuracy on CAP benchmark-based instruction-tuning data.

arxiv arXiv cs.AI · 17h ago

Structural Codebase Index Improves Resolve Without Cost Penalty

A structural codebase index in coding agents enhances localization and resolve performance without increasing cost per cell. It outperforms agentic-grep baselines in both metrics and achieves lower cost per solved task, especially in workloads with multi-file changes.

lab Hugging Face Blog · 17h ago

Introducing the FFASR Leaderboard: Benchmarking ASR in the Real World

The FFASR Leaderboard was launched to evaluate speech recognition systems in real-world conditions. It provides a benchmark for assessing the performance of automatic speech recognition models across diverse environments and use cases.

arxiv arXiv cs.AI · 18h ago

MMGist: A Comprehensive Multimodal Benchmark for 2027

MMGist is a curated multimodal benchmark with 7,262 items, designed to address flaws in existing vision-language benchmarks. It reduces evaluation size by 69% and improves cross-model discrimination by 78%, while preserving model rankings with a Spearman correlation of 0.98. The benchmark highlights visual logic as a key weakness and emphasizes the importance of visual dependency, discriminative power, and reliability in evaluation.

arxiv arXiv cs.AI · 18h ago

Efficient Multimodal Models for Pulmonary Embolism Risk Assessment

A benchmark using efficient multimodal large language models evaluates PE diagnosis and risk prediction on the INSPECT dataset. Results show Gemma4 E4B and E2B outperform others when EHR data is available, with PE diagnosis achieving higher accuracy than prognostic tasks like readmission prediction.

arxiv arXiv cs.AI · 18h ago

A Differentiable Atari VCS for Explainable AI

A fully differentiable emulator of the Atari 2600 VCS is presented, reproducing all 64 ALE games with bit-for-bit accuracy in RAM and screen output. The system enables gradient-based explainable AI by providing a complex, fully known ground truth, with both Julia and JAX implementations validated against a reference emulator and capable of high-throughput differentiable rollouts on GPU.

arxiv arXiv cs.AI · 18h ago

Character Variety in LLM-Generated Stories

This study compares characters in LLM-generated and human-written stories using narratological dimensions. It finds that while LLMs produce characters with similar basic traits, they lack diversity in complex character features like stylization and wholeness. The research highlights key differences in character depth and variety between human and machine-generated narratives.

arxiv arXiv cs.AI · 19h ago

PRIME: Evaluating Prompt Resolution in Conflicting Instructions

PRIME introduces a framework to analyze how large language models handle conflicting instructions by generating calibrated conflicts in response length, format, and reasoning. The study finds that conflict type has a greater impact on model behavior than model size, revealing diverse failure modes across conflict categories. Results highlight the need for conflict awareness and suggest instruction following cannot be reliably assessed through isolated benchmarks alone.