Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv:2603. 25157v3 Announce Type: replace-cross Abstract: Recent vision backbones, such as Transformer families and state-space models like Mamba, have achieved remarkable progress on image recognition.
arXiv:2606. 07666v1 Announce Type: cross Abstract: Noisy intermediate-scale quantum (NISQ) processors are entering an early fault-tolerance regime where full quantum error correction carries prohibitive resource costs, yet lightweight error detection can meaningfully improve algorithmic success rates.
arXiv:2606. 09433v1 Announce Type: new Abstract: Wastewater influenza surveillance can reveal community circulation before clinical reporting, but wastewater alone is not a fully identifiable proxy for human burden.
arXiv:2605. 03862v4 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards has become a common way to improve explicit reasoning in large language models, but final-answer correctness alone does not reveal whether the reasoning trace is faithful, reliable, or useful to the model that consumes it.
arXiv:2606. 09100v1 Announce Type: cross Abstract: Community detection is a fundamental problem in the analysis of complex networks.
arXiv:2606. 08327v1 Announce Type: cross Abstract: Standard transformers apply self-attention uniformly at every layer and token, regardless of whether the input requires dynamic cross-token interaction.
arXiv:2605. 23965v2 Announce Type: replace Abstract: Large Language Models (LLMs) achieve strong performance on logical reasoning benchmarks, yet their reliability remains uncertain.
arXiv:2606. 09169v1 Announce Type: new Abstract: In recent years, unified multimodal models (UMMs) have emerged to support both understanding and generation within a single framework.
arXiv:2606. 09064v1 Announce Type: cross Abstract: Recent advances in Video Large Language Models (Video-LLMs) have enabled performance on long-video understanding tasks.
arXiv:2606. 07645v1 Announce Type: cross Abstract: The scarcity of hard negative samples in current vision-language datasets significantly hinders fine-grained perception.
arXiv:2603. 29824v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning methods such as LoRA enable efficient adaptation of large pretrained models, but often lag behind full fine-tuning in both convergence speed and final performance.
arXiv:2606. 08493v1 Announce Type: cross Abstract: \textit{Tissue graph counterfactuals} ask how a cell's expression would change under altered spatial neighbor contexts.
arXiv:2606. 07808v1 Announce Type: new Abstract: Reasoning language models deployed in agentic workflows must follow an instruction hierarchy: when instructions from different sources conflict, the model should obey the highest-privilege applicable instruction.
arXiv:2606. 08018v1 Announce Type: new Abstract: Existing text-to-SQL benchmarks are largely centered on SQLite, making it difficult to evaluate whether models can generalize across heterogeneous SQL dialects.
arXiv:2503. 05169v2 Announce Type: replace Abstract: Applying machine learning to increasingly high-dimensional problems with sparse or biased training data increases the risk that a model is used on inputs outside its training domain.
arXiv:2604. 27810v2 Announce Type: replace Abstract: Computational molecular representations underpin virtual screening, property prediction, and materials discovery.
arXiv:2606. 07592v1 Announce Type: new Abstract: Offline reinforcement learning requires careful conservatism to mitigate distribution shift, yet most existing methods apply a fixed penalty uniformly across all states regardless of local data coverage.
arXiv:2606. 08735v1 Announce Type: new Abstract: Quality-diversity reinforcement learning (QD-RL) aims to construct policy repertoires that contain both high-performing and behaviorally diverse policies.
arXiv:2606. 09450v1 Announce Type: new Abstract: LLMs have recently achieved strong results on formal proving benchmarks.
arXiv:2606. 08376v1 Announce Type: cross Abstract: As artificial intelligence (AI) systems are increasingly deployed across socially consequential domains, reports of AI-related harms and failures have grown in frequency and diversity.