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arxiv arXiv cs.LG · 17h ago

VRA-FedSGD: Variance-Reduced Federated Learning for Heavy-Tailed Noise

The authors propose VRA-FedSGD, a variance-reduction based algorithm designed for federated learning in environments with heavy-tailed gradient and communication noise. This approach addresses challenges prevalent in large-scale machine learning over wireless networks and Internet of Things deployments. The method employs momentum variance reduction combined with nonlinear mapping to mitigate heavy-tailed gradient noise. It also utilizes a variance-reduced aggregation mechanism to suppress heavy-tailed communication noise. For nonconvex objective functions, VRA-FedSGD achieves a mean convergence rate of O(K^(-(p-1)/(2p-1))), where p is the tail index. In the almost sure sense, it reaches a rate of Õ(K^(-(1-1/(p-ε))) for strongly convex objectives, with ε being an arbitrarily small constant. Simulated experiments on logistic regression with real-world data verify the algorithm's effectiveness.
media Hugging Face Forums · 19h 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 · 21h ago

AdaR: Adaptive Recurrent Message Passing for Graph Test-Time Computing

AdaR enables flexible test-time computing on graphs without parameter changes by using adaptive recurrence. It derives step dependence as a necessary and sufficient condition for convergence and incorporates normalized step information and representation-target relations into recurrent updates, guided by gradient-based supervision signals. Empirical results show AdaR outperforms strong baselines in both inductive and transductive graph learning settings.
arxiv arXiv cs.LG · 21h ago

LLM-Integrated App Bug Seams Reveal Testing Gaps

A rental-search assistant with LLM features and multi-market support faced persistent user defects despite 1,553 passing automated tests. Analysis of 252 bug-fix commits showed 44% of fixes occurred at four unseen seams: browser runtime, non-default market, end-to-end flows, and whole-system level. A fix without a seam guard caused a defect to ship twice, highlighting the need for targeted testing at these boundaries.
arxiv arXiv cs.LG · 22h ago

The Scissors Effect: Resize Diversity Hurts Robust Surrogate Transfer

Input diversity, a common practice in transfer attacks, improves success on standard surrogates but reduces it on robust ones. This regime-dependent effect, called the Scissors Effect, is driven by gradient geometry, with resize operations degrading alignment in robust models. A training-free rule (CG-DI) adjusts diversity based on local gradient consistency to preserve attack success across surrogate types.
arxiv arXiv cs.LG · 22h ago

HERTA: Automated Testing for FHE Framework Vulnerabilities

HERTA is the first automated testing tool designed for fully homomorphic encryption frameworks. It uses metamorphic testing with novel relations derived from FHE semantics to detect deep-seated logic bugs that can silently corrupt encrypted computations. Evaluation on three industry frameworks revealed 21 previously unknown bugs, several of which have been confirmed and fixed by developers, with significant implications for security and service integrity.
arxiv arXiv cs.LG · 23h 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 · 23h 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 · 1d 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 · 1d 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.