Multi-Layer AI Malware Detector
SecureScan uses logistic regression, heuristics, and VirusTotal for URL/file/binary triage. Achieves 93.1% accuracy with balanced precision/recall. Employs gray-zone logic to cut false positives.
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Tag: #research297 results
MoE for Drift-Aware Malicious Traffic Detection
MalMoE detects encrypted malicious traffic using graph-based Mixture-of-Experts to handle graph drift. It selects optimal 1-hop-GNN experts via a redesigned gate model. Trained with two-stage strategy and augmentation for real-time precision.
MLLMs Survey on Chart Fusion
Survey organizes MLLM evolution for chart understanding via multimodal fusion. Introduces taxonomy of tasks and datasets. Highlights limitations in perception and reasoning, suggesting alignment and RL enhancements.
MIPLIB-NL for Industrial Optimization Benchmarks
MIPLIB-NL creates natural-language optimization benchmarks from real MIPLIB 2017 instances via structure-aware reverse engineering. Includes 223 validated reconstructions tying NL specs to solver code. Reveals LLM failures on large-scale problems.
MetaphorStar Masters Image Metaphor Reasoning
MetaphorStar uses end-to-end visual RL for image metaphor understanding, featuring TFQ-Data dataset, TFQ-GRPO method, and TFQ-Bench. MetaphorStar-32B sets SOTA on implication benchmarks, outperforming 20+ MLLMs including Gemini-3.0-pro. Improves general visual reasoning via scaling analyses.
MERIT Boosts LLM Negotiation Skills
AgoraBench tests LLMs in nine bargaining scenarios like deception; utility metrics measure human alignment. MERIT feedback via prompting/finetuning elicits deeper strategy and opponent awareness. Outperforms baselines in negotiation power and acquisition.
MEL Boosts LLM Reasoning via Meta-Experience
Meta-Experience Learning (MEL) enhances RLVR by internalizing error-derived meta-experience into LLM memory. Uses self-verification for contrastive analysis of trajectories. Achieves 3.92%-4.73% Pass@1 gains across model sizes.
MeCSAFNet Boosts Multispectral Segmentation
MeCSAFNet uses dual ConvNeXt encoders for visible and non-visible channels in multispectral land cover segmentation. It employs smooth attentional feature fusion with CBAM and ASAU activation. Outperforms baselines like U-Net and SegFormer by up to 19% mIoU on FBP and Potsdam datasets.
LRMs Fail to Transfer Reasoning to ToM
Study compares reasoning vs non-reasoning LLMs on ToM benchmarks, finding no consistent gains and sometimes worse performance. Insights reveal slow thinking collapse, need for adaptive reasoning, and option-matching shortcuts. Interventions like S2F and T2M mitigate issues.
LOREN: Low-Rank Adaptation for Neural Receivers
LOREN introduces low-rank adapters to enable code-rate adaptation in neural receivers without storing separate weights. It freezes a shared base network and trains lightweight adapters per code rate. Achieves comparable performance with major hardware savings.