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9 storiesInnovation
TOPAS proposes workflow-aware scheduling to shorten multi-agent LLM jobs
A new arXiv paper introduces TOPAS, a scheduler that jointly manages cached LLM prefixes and request execution in multi-stage agent workflows. The authors report lower job-completion times than their tested baselines on synthetic workflows and two MetaGPT software-development workloads.arxiv.orgInnovation
EmoVec steers language models toward controllable emotional responses without retraining
A new arXiv paper describes EmoVec, an inference-time method that injects emotion-specific vectors into language models to adjust emotional intensity while preserving the response’s meaning, fluency and coherence, according to experiments reported by the authors.arxiv.orgInnovation
Researchers target attention heads linked to object hallucinations in LLaVA
A preprint reports that targeting 32 attention heads in LLaVA-1.5-7B reduced hallucinated objects in captions on 400 held-out COCO images.arxiv.orgInnovation
JIT-Agent proposes on-the-fly harness generation for AI agents
A new arXiv preprint presents JIT-Agent, a model designed to generate, repair and improve the software harnesses that guide AI agents. The authors report sizable benchmark gains across several language-model families, but the work remains an unreviewed preprint without independent validation in the source.arxiv.orgInnovation
$R^3$ trains robots to use natural-language reasoning during manipulation
An arXiv preprint introduces $R^3$, a post-training method that uses free-form language reasoning to guide robotic manipulation policies. The authors report gains on two controlled benchmarks, while leaving real-world performance and the size of those gains unspecified.arxiv.orgInnovation
Audit Finds Physical AI Benchmarks Share Redundant Information
A new arXiv preprint reports that several physical-AI benchmarks measure overlapping information, potentially changing how researchers rank models and choose evaluation suites.arxiv.orgInnovation
Radiology Vision-Language Models Show Hidden Failures Under Data Shifts, Preprint Finds
A new preprint reports that medical vision-language models can appear reliable on familiar data while failing cross-dataset transfer, multimodal alignment and shortcut tests.arxiv.orgInnovation
ProViP uses head-aware pruning to cut visual tokens in vision-language models
A new arXiv preprint proposes ProViP, a training-free method that progressively removes redundant visual tokens and focuses pruning on the attention heads most useful for selecting critical visual information. In one reported LLaVA-1.5-7B experiment, it retained 95.9% of the original performance while delivering a…arxiv.orgInnovation
Frozen Hematology AI Models Lose Accuracy and Calibration Under Acquisition Shift, Study Finds
An audit of 15 frozen hematology, pathology and general-vision foundation models reports steep drops in cross-dataset accuracy and confidence calibration when white-blood-cell images come from different acquisition conditions.arxiv.org
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