Binghamton University

Continuous-control reinforcement learning struggles with exploration and long-horizon credit assignment, and the usual fix, action noise or entropy regularization, explores without direction. GANGSTER uses a conditional generative adversarial network to propose candidate subgoals from the current state, scores them with structured losses for desirability, reachability and diversity, and feeds the chosen subgoal back only as intrinsic reward shaping, leaving the core TD3 and SAC update rules untouched. On MuJoCo's Ant-v5, GANGSTER with TD3 cuts time-to-solve by about 31% and raises final performance by 53% over baseline TD3; on HalfCheetah-v5 with SAC it raises final performance by 10% and lifts post-solution consistency above 99%.

Continuous-control reinforcement learning struggles with exploration and long-horizon credit assignment, and the usual fix, action noise or entropy regularization, explores without direction. GANGSTER uses a conditional generative adversarial network to propose candidate subgoals from the current state, scores them with structured losses for desirability, reachability and diversity, and feeds the chosen subgoal back only as intrinsic reward shaping, leaving the core TD3 and SAC update rules untouched. On MuJoCo's Ant-v5, GANGSTER with TD3 cuts time-to-solve by about 31% and raises final performance by 53% over baseline TD3; on HalfCheetah-v5 with SAC it raises final performance by 10% and lifts post-solution consistency above 99%.

Variational quantum classifiers usually buy accuracy with more entangling gates, yet CNOTs err and run far slower than single-qubit gates. OQMD folds the readout map into training, using trainable single-qubit rotations to choose the measurement basis without adding any CNOT. On a 30-point stratified Iris test split, the best 0-CNOT OQMD configuration reaches 83.33% against 56.67% for the best 18-CNOT control; at 18 CNOTs OQMD raises the peak from 56.67% to 66.67%, though the pooled mean gap is small.

Variational quantum classifiers usually buy accuracy with more entangling gates, yet CNOTs err and run far slower than single-qubit gates. OQMD folds the readout map into training, using trainable single-qubit rotations to choose the measurement basis without adding any CNOT. On a 30-point stratified Iris test split, the best 0-CNOT OQMD configuration reaches 83.33% against 56.67% for the best 18-CNOT control; at 18 CNOTs OQMD raises the peak from 56.67% to 66.67%, though the pooled mean gap is small.

The authors identify and study memory overreliance, an inference-time failure in which benign, correctly stored, and appropriately retrieved memory misleads reasoning when it only partially overlaps the current query, and propose MemTrim, a plug-and-play framework that decomposes memories into evidence units indexed in a trie at write time and, at read time, removes evidence repeated in or conflicting with the query, retains memory-only information, and suppresses a previous answer when its supporting evidence has changed, reducing memory-induced errors across benchmarks, models, and memory architectures while preserving the benefits of useful memory.

The authors identify and study memory overreliance, an inference-time failure in which benign, correctly stored, and appropriately retrieved memory misleads reasoning when it only partially overlaps the current query, and propose MemTrim, a plug-and-play framework that decomposes memories into evidence units indexed in a trie at write time and, at read time, removes evidence repeated in or conflicting with the query, retains memory-only information, and suppresses a previous answer when its supporting evidence has changed, reducing memory-induced errors across benchmarks, models, and memory architectures while preserving the benefits of useful memory.