Researchers adapt a mixture-of-experts diffusion language model, DiffusionGemma-26B, for medical visual question answering, benchmarking it against its autoregressive sibling. The diffusion model matches or exceeds AR performance, decodes 3.5-4.4x faster, and offers any-order infill for drafting radiology reports, a capability inherently absent in autoregressive models.

CreativityNeuro is a data-free method that enhances divergent thinking in LLMs via contrastive weight steering, achieving up to 14 human percentile points improvement on creativity tests and reducing mode collapse.

This paper proposes a difficulty-routed service-control architecture for autonomous customer-service agents performing operational tasks like refunds and cancellations. A lightweight router keeps routine sessions on a low-cost baseline path and routes operationally coupled sessions to an escalated workflow that uses conflict-aware communication and write-triggered reconsideration to focus safeguards before consequential backend writes. Evaluated on retail and airline tasks, the method improves reliability consistently on conflicting requests, and improvements are not due to indiscriminate interaction expansion.

Understanding large, complex codebases remains challenging. Agent4cs is a multi-agent framework that summarizes codebases bottom-up using three agents: summarization, keyword extraction, and quality assurance. Evaluated on 7 models, it improves semantic consistency by 8% and keyword coverage by up to 38%.

Wiola is a fully original small language model architecture built from first principles, unrelated to existing families like GPT, LLaMA, Mistral, or Falcon. It introduces five novel components: Spiral Rotary Positional Encoding (SRPE), Gated Cross-Layer Attention (GCLA), Adaptive Token Merging (ATM), Dual Stream Feed-Forward (DSFF), and WiolaRMSNorm. Released in four sizes (120M to 1.5B parameters), it is fully compatible with HuggingFace Transformers.

Auto-FL-Research (AFR) is a constrained coding-agent workflow for federated learning algorithmic recipe search. It is evaluated on five healthcare FLamby tasks and six LEAF profiles, showing gains on multiple tasks but also revealing seed-sensitive and search-selected failures.

PACE is a modular neuro-symbolic framework that generates feasibility-aware counterfactual explanations by combining neural predictive models with symbolic reasoning. It enforces domain-specific constraints to produce realistic and actionable recommendations, as demonstrated on the Adult Income dataset.