{"total":11,"items":[{"citing_arxiv_id":"2606.05946","ref_index":53,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Short paper: Models in the dark -- Rectification and erasure under GDPR in ML supply chains","primary_cat":"cs.LG","submitted_at":"2026-06-04T09:46:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Survey identifying technical and supply-chain barriers to GDPR data subject rights in ML, with new framing of 'models in the dark' for downstream opacity.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.02293","ref_index":62,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"AI as a Tool for Simulation-Based Experiments in Literary Studies","primary_cat":"cs.CL","submitted_at":"2026-06-01T14:16:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Proposes AI-driven simulations for literary-historical experiments and reports preliminary text-generation results claiming the first limited in-distribution outputs matching human novels.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16776","ref_index":111,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning","primary_cat":"cs.LG","submitted_at":"2026-05-16T03:15:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Distinguishable Deletion unifies knowledge erasure and refusal for LLM unlearning via an energy index that enforces boundaries during training and enables refusal at inference.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.15425","ref_index":52,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Runtime-Structured Task Decomposition for Agentic Coding Systems","primary_cat":"cs.SE","submitted_at":"2026-05-14T21:16:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Runtime-structured task decomposition reduces retry costs in agentic coding systems by up to 51.7% versus monolithic prompts by rerunning only failed subtasks on two software engineering workloads.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.11685","ref_index":13,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Robust LLM Unlearning Against Relearning Attacks: The Minor Components in Representations Matter","primary_cat":"cs.CL","submitted_at":"2026-05-12T07:43:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Targeting minor components in LLM representations during unlearning yields substantially better resistance to relearning attacks than prior methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.21251","ref_index":85,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"CAP: Controllable Alignment Prompting for Unlearning in LLMs","primary_cat":"cs.LG","submitted_at":"2026-04-23T03:42:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CAP is a reinforcement-learning-driven prompt optimization framework that suppresses target knowledge in LLMs while preserving general capabilities, enabling reversible unlearning without any parameter updates.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.17396","ref_index":153,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Representation-Guided Parameter-Efficient LLM Unlearning","primary_cat":"cs.CL","submitted_at":"2026-04-19T11:59:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"REGLU guides LoRA-based unlearning via representation subspaces and orthogonal regularization to outperform prior methods on forget-retain trade-off in LLM benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.14644","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"CURaTE: Continual Unlearning in Real Time with Ensured Preservation of LLM Knowledge","primary_cat":"cs.CL","submitted_at":"2026-04-16T05:49:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CURaTE performs continual unlearning in LLMs in real time by using sentence embeddings to detect and refuse forget requests without changing model parameters, achieving effective forgetting and perfect knowledge preservation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.03114","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Can VLMs Truly Forget? Benchmarking Training-Free Visual Concept Unlearning","primary_cat":"cs.CV","submitted_at":"2026-04-03T15:36:00+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"VLM-UnBench demonstrates that prompt-based training-free unlearning in VLMs leaves forget accuracy near the no-instruction baseline except under oracle conditions that reveal the target concept.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.00761","ref_index":27,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Downgrade to Upgrade: Optimizer Simplification Enhances Robustness in LLM Unlearning","primary_cat":"cs.LG","submitted_at":"2025-10-01T10:50:14+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Downgrading optimizers to lower-information variants during LLM unlearning yields more robust forgetting on MUSE and WMDP benchmarks by converging to harder-to-perturb loss basins.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2501.19202","ref_index":32,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Improving LLM Unlearning Robustness via Random Perturbations","primary_cat":"cs.CL","submitted_at":"2025-01-31T15:12:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"LLM unlearning is reframed as inadvertently installing backdoor triggers on forget-tokens; Random Noise Augmentation is introduced as a defense that improves robustness with theoretical guarantees.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}