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9 storiesInnovation
Thinkingbox benchmark finds AI agents struggle to reliably complete stateful business workflows
A new arXiv paper introduces Thinkingbox, a sandbox and benchmark for testing AI agents on multi-step business tasks. The strongest tested model achieved a 65.36% pass@1 score but only a 25.25% pass^20 score, highlighting the gap between occasional success and dependable execution.arxiv.orgInnovation
Preprint reports asymmetric risks that LLM compression metrics can miss
An arXiv preprint evaluating 3 LLMs across 11 compression methods reports that aggregate accuracy and perplexity can conceal uneven knowledge loss, overconfidence on newly lost information, and offsetting shifts in subgroup bias.arxiv.orgInnovation
Preprint finds irrelevant text can shift multimodal model judgments predictably
An arXiv preprint reports that task-irrelevant text consistently biases multimodal language models in visual judgments, and describes the effect as a measurable affine shift in decision margins.arxiv.orgInnovation
Preprint proposes SingularClip to preserve learning plasticity in changing tasks
An arXiv preprint identifies growing imbalance among neural-network weight singular values as a possible cause of lost adaptability, then proposes periodically clipping those values in continual and reinforcement learning.arxiv.orgInnovation
Preprint proposes a probe-tested gate for dynamic ensembles under distribution shift
A new arXiv preprint introduces a diagnostic for deciding when region-specific combinations of regression models can outperform a fixed blend. In tests described by the authors, a small labeled target-domain probe predicted gains and helped reject one deployment that produced more than 30 times the static loss.arxiv.orgInnovation
Preprint traces how Llama 3.1 8B models numerical sequence structure
An arXiv preprint reports evidence that Llama 3.1 8B internally tracks first differences in specially designed numerical sequences. The study offers a proposed mechanism for this behavior, but its scope and robustness remain unclear from the supplied abstract.arxiv.orgInnovation
Preprint proposes adaptive feature selection for personalized fall prevention
A preprint accepted at MLHC 2026 describes PAFIR, a reinforcement-learning framework that selects changing, person-specific fall-risk signals from repeated multimodal health measurements. The paper reports better pattern capture than baselines, but the supplied record gives no effect sizes or evidence of reduced falls.arxiv.orgInnovation
Preprint proposes ECG model for atrial-fibrillation detection across lead configurations
An arXiv preprint describes DCGCNet, a model that combines ECG reconstruction and atrial-fibrillation classification, reporting AUC above 0.98 across seven cross-dataset settings and resilience to several noise types.arxiv.orgInnovation
Preprint proposes physics-guided neural operator for wireless radio maps
A new arXiv preprint introduces PU-HNO, a three-stage model for predicting detailed indoor radio maps from lower-fidelity simulations and scene information. The authors report improvements over several baseline approaches, but the supplied record does not provide numerical results or evidence of real-world deployment.arxiv.org
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