Indian Institute of Technology Guwahati

Judgment prediction has mostly reasoned over case facts, yet real proceedings are shaped by exchanges among judge, petitioner and respondent. ArguGenCourt assigns three independent GPT-4o agents these roles and lets them converse in rounds, seeding prompts with courtroom utterances retrieved from the factually closest precedent document. On criminal petitioner arguments, local precedent retrieval in the multi-agent setting reaches a Legal-sim of 0.8921 against 0.8288 for fact-based conversation under the same retrieval; feeding the generated arguments into judgment prediction gives 0.55 accuracy versus 0.48 for zero-shot.

Judgment prediction has mostly reasoned over case facts, yet real proceedings are shaped by exchanges among judge, petitioner and respondent. ArguGenCourt assigns three independent GPT-4o agents these roles and lets them converse in rounds, seeding prompts with courtroom utterances retrieved from the factually closest precedent document. On criminal petitioner arguments, local precedent retrieval in the multi-agent setting reaches a Legal-sim of 0.8921 against 0.8288 for fact-based conversation under the same retrieval; feeding the generated arguments into judgment prediction gives 0.55 accuracy versus 0.48 for zero-shot.

Training data for wearable fall detection is compressed into a very small class-balanced synthetic set. Earlier pipelines either used hand-crafted features with shallow classifiers or trained deep networks on the full imbalanced corpus with re-sampling, at high data and compute cost. FallNest aligns synthetic and real samples in both time and frequency domains through gradient direction and embedding distribution, initialized by class-balanced K-Means and augmented with multi-view Fourier views. On SisFall, KFall and MobiFall under subject-wise splits with naturally imbalanced test sets, a 102K-parameter TinyCNN trained on only 32 to 146 synthetic windows reaches 93.5 to 95.8 percent mean accuracy and 0.89 to 0.98 PR-AUC over five seeds.

Training data for wearable fall detection is compressed into a very small class-balanced synthetic set. Earlier pipelines either used hand-crafted features with shallow classifiers or trained deep networks on the full imbalanced corpus with re-sampling, at high data and compute cost. FallNest aligns synthetic and real samples in both time and frequency domains through gradient direction and embedding distribution, initialized by class-balanced K-Means and augmented with multi-view Fourier views. On SisFall, KFall and MobiFall under subject-wise splits with naturally imbalanced test sets, a 102K-parameter TinyCNN trained on only 32 to 146 synthetic windows reaches 93.5 to 95.8 percent mean accuracy and 0.89 to 0.98 PR-AUC over five seeds.