Claims Browser

GRAIN demonstrates superior structural generalization, halving the out-of-distribution gap of SFT models from 15.77% to 7.80%

"it demonstrates superior structural generalization, halving the out-of-distribution (OOD) gap of SFT models (from 15.77\% to 7.80\%)"

A Structure Invariance Reward forces LLMs to learn robust text-to-structure mappings rather than memorizing linguistic artifacts

"By validating extracted intermediate graphs against ground-truth topologies, this reward forces the LLM to learn robust text-to-structure mappings rather than memorizing linguistic artifacts."

Graph anomaly detection methods based on frequency-domain filtering have achieved promising results in recent years

"In recent years, graph anomaly detection (GAD) based on frequency-domain filtering have achieved promising results."

Existing graph anomaly detection approaches use static basic functions for graph filters which cannot effectively adapt to frequency-domain distribution of graph data

"they use static basic function to constructed graph filter which cannot effectively adapt to the frequency-domain distribution of graph data"

Current graph anomaly detection methods fail to adequately consider the importance of each attribute in node feature vectors, leading to loss of fine-grained information

"they fail to adequately consider the importance information of each attribute in the node feature vector, leading to the loss of fine-grained information"

Existing graph anomaly detection approaches insufficiently utilize node labels

"they insufficiently utilize node labels for GAD"

The proposed JPGFN method significantly outperforms mainstream graph anomaly detection approaches on multiple real-world datasets

"Experimental results on multiple real-world datasets demonstrate that the proposed method significantly outperforms mainstream approaches."

Tool-augmented LLM agents face a security risk when tool outputs specify concrete actions that can drive real-world side effects beyond user intent

"when tool outputs no longer merely provide data but begin to specify concrete actions, they effectively become ``commands'' that can drive real-world side effects beyond user intent"

The security risk in tool-augmented LLM agents arises from conflating action induction with execution authorization

"this risk arises from conflating action induction with execution authorization"

SARA limits attack success rate to no more than 0.63% across four primary evaluation settings while maintaining competitive task utility

"SARA limits ASR to no more than \(0.63\%\) across four primary evaluation settings while maintaining competitive task utility"

SARA consistently reduces attack success rate across additional agent backbones beyond the primary evaluation

"consistently reduces ASR across additional Agent backbones"

Separating action induction from execution authorization through distinct runtime roles is an effective approach to securing tool-augmented LLM agents

"we propose SARA, which treats action induction and execution authorization as distinct runtime roles and separates action provenance from execution authority"

Korean eojeols (morphologically complex spacing units) create a representational challenge for constituency parsing because terminal units of phrase-structure trees do not straightforwardly correspond to simple surface words

"Korean constituency parsing raises a representational challenge because the terminal units of a phrase-structure tree do not straightforwardly correspond to simple surface words. Korean eojeols are morphologically complex spacing units"

Morpheme+XPOS representation provides stronger parsing results than Eojeol-based representations, even when predictions are projected to the eojeol terminal domain

"Morpheme+XPOS gives the strongest results even after its predictions are projected to the eojeol terminal domain"

Fine-grained morphological and XPOS representations provide valuable evidence for constituency parsers under gold-annotation conditions

"Under these gold-annotation conditions, the results show that fine-grained morphological and XPOS representations provide valuable evidence for the evaluated parsers"

Explorable image-to-scene generation is essential for applications in gaming, robotics, and virtual reality

"Explorable image-to-scene generation is essential for applications in gaming, robotics, and virtual reality."

Eojeol+UPOS parsing substantially underperforms morphologically richer conditions despite eojeol terminals yielding shorter transition sequences

"Eojeol terminals yield shorter transition sequences, but Eojeol+UPOS parsing substantially underperforms the morphologically richer conditions"

Eojeol is a stable and interpretable surface domain for phrase-structure annotation when morpheme-level and XPOS information is retained as aligned morphosyntactic evidence

"linguistic and resource-design considerations motivate eojeol as a stable and interpretable surface domain for phrase-structure annotation, with morpheme-level and XPOS information retained as aligned morphosyntactic evidence"