{"total":10,"items":[{"citing_arxiv_id":"2605.06992","ref_index":64,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Why Does Agentic Safety Fail to Generalize Across Tasks?","primary_cat":"cs.LG","submitted_at":"2026-05-07T22:16:03+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Agentic safety fails to generalize across tasks because the task-to-safe-controller mapping has a higher Lipschitz constant than the task-to-controller mapping alone, as proven in linear-quadratic control and demonstrated in quadcopter and LLM experiments.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"DJ Strouse, Steven Hansen, Angelos Filos, Ethan Brooks, et al. In-context reinforcement learning with algorithm distillation.arXiv preprint arXiv:2210.14215, 2022. [63] Ido Levy, Ben Wiesel, Sami Marreed, Alon Oved, Avi Yaeli, and Segev Shlomov. St-webagentbench: A benchmark for evaluating safety and trustworthiness in web agents.arXiv preprint arXiv:2410.06703, 2024. [64] Yuxi Li. Deep reinforcement learning: An overview.arXiv preprint arXiv:1701.07274, 2017. [65] Shiau Hong Lim, Huan Xu, and Shie Mannor. Reinforcement learning in robust markov decision processes.Advances in neural information processing systems, 26, 2013. 13 [66] Aixin Liu, Aoxue Mei, Bangcai Lin, Bing Xue, Bingxuan Wang, Bingzheng Xu, Bochao Wu, Bowei"},{"citing_arxiv_id":"2604.27354","ref_index":55,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"CoAX: Cognitive-Oriented Attribution eXplanation User Model of Human Understanding of AI Explanations","primary_cat":"cs.AI","submitted_at":"2026-04-30T03:12:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Cognitive models of user reasoning strategies with XAI methods on tabular data fit human forward-simulation decisions better than ML baselines and support hypothesis testing without new user studies.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.00861","ref_index":15,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs","primary_cat":"cs.CL","submitted_at":"2025-10-01T13:10:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"ERL trains LLMs to erase faulty reasoning steps and regenerate them in place, yielding gains of up to 8.48% EM on multi-hop QA benchmarks like HotpotQA.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2506.12382","ref_index":24,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Exploring the Secondary Risks of Large Language Models","primary_cat":"cs.LG","submitted_at":"2025-06-14T07:31:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces secondary risks as a new class of LLM failures from benign prompts, defines two primitives, proposes SecLens search framework, and releases SecRiskBench showing risks are widespread across 16 models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2412.12197","ref_index":17,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Anti-bullying Adaptive Cruise Control: A proactive right-of-way protection approach","primary_cat":"eess.SY","submitted_at":"2024-12-14T12:44:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"AACC combines online IOC for driving style identification with a Stackelberg game planner to proactively protect right-of-way against cut-ins, reporting up to 79.8% safety gains in simulation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2406.02430","ref_index":5,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Seed-TTS: A Family of High-Quality Versatile Speech Generation Models","primary_cat":"eess.AS","submitted_at":"2024-06-04T15:48:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Seed-TTS models produce speech matching human naturalness and speaker similarity, with added controllability via self-distillation and reinforcement learning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2405.14093","ref_index":299,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"A Survey on Vision-Language-Action Models for Embodied AI","primary_cat":"cs.RO","submitted_at":"2024-05-23T01:43:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"This is the first survey on vision-language-action models, providing a taxonomy across three lines, plus summaries of datasets, simulators, benchmarks, challenges, and future directions in embodied AI.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Xing, \"Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality,\" March 2023. [Online]. Available: https://lmsys.org/blog/2023-03-30-vicuna/ [297] Y . Li, \"Deep reinforcement learning: An overview,\"CoRR, vol. abs/1701.07274, 2017. [298] K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, \"A brief survey of deep reinforcement learning,\"CoRR, vol. abs/1708.05866, 2017. [299] W. Li, H. Luo, Z. Lin, C. Zhang, Z. Lu, and D. Ye, \"A survey on transformers in reinforcement learning,\"CoRR, vol. abs/2301.03044, 2023. [300] H. van Hasselt, A. Guez, and D. Silver, \"Deep reinforcement learning with double q-learning,\" inAAAI. AAAI Press, 2016, pp. 2094-2100. [301] M. Andrychowicz, D. Crow, A. Ray, J. Schneider, R. Fong, P. Welin-"},{"citing_arxiv_id":"2309.07864","ref_index":70,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Rise and Potential of Large Language Model Based Agents: A Survey","primary_cat":"cs.AI","submitted_at":"2023-09-14T17:12:03+00:00","verdict":"ACCEPT","verdict_confidence":"HIGH","novelty_score":4.0,"formal_verification":"none","one_line_summary":"The paper surveys the origins, frameworks, applications, and open challenges of AI agents built on large language models.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"optimization, exemplified by Q-learning [66] and SARSA [67]. With the rise of deep learning, the integration of deep neural networks and reinforcement learning, known as Deep Reinforcement Learning (DRL), has emerged [ 68; 69]. This allows agents to learn intricate policies from high- dimensional inputs, leading to numerous significant accomplishments like AlphaGo [70] and DQN [71]. The advantage of this approach lies in its capacity to enable agents to autonomously learn in unknown environments, without explicit human intervention. This allows for its wide application in an array of domains, from gaming to robot control and beyond. Nonetheless, reinforcement learning faces challenges including long training times, low sample efficiency, and stability concerns,"},{"citing_arxiv_id":"1907.11754","ref_index":35,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Deep Reinforcement Learning for Personalized Search Story Recommendation","primary_cat":"cs.LG","submitted_at":"2019-07-26T19:01:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"A deep RL architecture using imitation learning and reinforcement learning is proposed to model immediate and future values of search story recommendations in a Markov decision process framework.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1907.07273","ref_index":31,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"An Inductive Synthesis Framework for Verifiable Reinforcement Learning","primary_cat":"cs.LG","submitted_at":"2019-07-16T21:57:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"The paper introduces an inductive synthesis framework that generates verifiable deterministic program approximations of neural RL policies, preserving safety invariants via counterexample-guided search over state transition systems.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}