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MC-CPO: Mastery-Conditioned Constrained Policy Optimization for Pedagogically Safe Intelligent Tutoring Systems
arXiv:2604.04251v2 Announce Type: replace Abstract: Intelligent tutoring systems increasingly rely on reinforcement learning to personalise instruction, yet optimising for observable engagement signals can systematically decouple learner activity from genuine knowledge acquisition. Analysing over 21 million student interactions across two deployed platforms, we find engagement events without corresponding mastery gains occur in 26.5% of interactions on Junyi Academy (72,758 students) and...
Synthetic Contrastive Reasoning for Multi-Table Q&A
Announce Type: new Abstract: Multi-table question answering requires models to retrieve relevant evidence, link schemas, and perform compositional reasoning across relational tables. Existing multi-table Q&A resources typically provide questions and final answers but lack reasoning supervision that explains how answers are derived. To address this gap, we construct a synthetic contrastive reasoning-trace dataset for MMQA by generating validated positive traces and plausible negative...
Marvell enters the AI network fray with 102.4 Tbps switch silicon
Marvell enjoyed a fillip from Nvidia chief Jensen Huang at Computex, who praised the firm as it unveiled the latest 102.4 Tbps switch silicon it has purpose-built for AI infrastructure. The fabless semiconductor biz announced upcoming availability of its Teralynx T100 chip to coincide with the Taiwanese trade show, claiming that it needs 25 percent lower power than competitive solutions with lower latency for AI training and inference workloads. But the firm is late to this party, as other...