A new dataset and multi-agent framework for legal consultation question answering that outperforms standard LLMs.
arXiv preprint arXiv:2602.14038 , year=
6 Pith papers cite this work. Polarity classification is still indexing.
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Introduces Active Task Driving Memory (ATMem) and STR-GRPO to move GUI agents from passive record storage to actively maintained task states, tested on a new mobile benchmark with progress and scope-aware metrics.
A lightweight supervised router using frozen-LLM embeddings for memory admission decisions outperforms LLM-based memory managers in both F1 score and latency on the LoCoMo benchmark.
SkillPCF is a closed-loop agent framework with a physics-guided memory skill bank, reinforcement-learned skill selection, and simulator-grounded evolution that improves design quality and efficiency for photonic crystal fiber inverse design under limited simulation budgets.
CPO++ adapts reinforcement fine-tuning of MLLMs to endogenous multi-modal concept drift through counterfactual reasoning and preference optimization, yielding better coherence and cross-domain robustness in safety-critical settings.
MemForest reformulates agent memory as a temporal data management problem using a hierarchical index (MemTree) for parallel construction and localized updates, reporting 79.8% accuracy and 6x throughput on LongMemEval-S and LoCoMo benchmarks.
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MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing
MemForest reformulates agent memory as a temporal data management problem using a hierarchical index (MemTree) for parallel construction and localized updates, reporting 79.8% accuracy and 6x throughput on LongMemEval-S and LoCoMo benchmarks.