NeuroRisk is a physics-informed deep unrolled optimizer for risk-aware traffic engineering that achieves small optimality gaps and 100-100000x speedup over solvers while outperforming neural baselines on throughput.
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T3R applies multiple Rotograd matrices and a rotation technique to create surrogate gradients, enabling deeper test-time adaptation in GNNs and yielding 0.172 MAE reduction plus 9.37% relative gains on OGB benchmarks.
TravelEval is a new benchmark with a six-dimensional evaluation framework, realistic data sandbox, and simulation-based global assessment for LLM-powered travel planning agents.
Hash-based key-dependent filtering of covert carriers improves detection resistance in network storage and timing covert channels with low processing overhead.
citing papers explorer
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NeuroRisk: Physics-Informed Neural Optimization for Risk-Aware Traffic Engineering
NeuroRisk is a physics-informed deep unrolled optimizer for risk-aware traffic engineering that achieves small optimality gaps and 100-100000x speedup over solvers while outperforming neural baselines on throughput.
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T3R: Deeper Test-Time Adaptation for Graph Neural Networks via Gradient Rotation
T3R applies multiple Rotograd matrices and a rotation technique to create surrogate gradients, enabling deeper test-time adaptation in GNNs and yielding 0.172 MAE reduction plus 9.37% relative gains on OGB benchmarks.
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TravelEval: A Comprehensive Benchmarking Framework for Evaluating LLM-Powered Travel Planning Agents
TravelEval is a new benchmark with a six-dimensional evaluation framework, realistic data sandbox, and simulation-based global assessment for LLM-powered travel planning agents.
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Hiding the Trees in the Forest: Building Network Covert Channels with Hash-Based Covert Carrier Filtering
Hash-based key-dependent filtering of covert carriers improves detection resistance in network storage and timing covert channels with low processing overhead.