{"total":11,"items":[{"citing_arxiv_id":"2605.28773","ref_index":40,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Rethinking Memory as Continuously Evolving Connectivity","primary_cat":"cs.CL","submitted_at":"2026-05-27T17:35:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"FluxMem evolves memory as a heterogeneous graph via three refinement stages and reports consistent state-of-the-art results on LoCoMo, Mind2Web, and GAIA benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07358","ref_index":66,"ref_count":4,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications","primary_cat":"cs.IR","submitted_at":"2026-05-08T07:10:26+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":2,"top_context_role":"background","top_context_polarity":"background","context_text":"Code-Backed V oyager [12], SkillCraft [44], PolySkill [45], ASI [46], CUA-Skill [47], MetaGPT [6], Eureka [48], DS-Agent [49], LDB [50], CodeAct [51], SWE-agent [52], ToolCoder [53], PSN [54] Hybrid-BasedJARVIS-1 [55], Synapse [56], SkillWeaver [57], AgentSkillOS [58], TPTU [59], talker-reasoner [60], DAMCS [61], GraphSkill [62], Alita [63] Skill Acquisition (§IV) Human-DerivedSkillNet [64], AgentSkillOS [58], Agentic Skills [65], SkillOS [66], Agent Hospital [67] Experience-Derived V oyager [12], SkillCraft [44], Reflexion [19], ExpeL [23], BoT [24], Trace2Skill [27], EverMemOS [68], HyperMem [69], AWM [26], Synapse [56], PolySkill [45], GITM [31], Retroformer [33], MemGPT [34], Eureka [48], TiM [35], M+ [39], Learned Memory Bank [40], G-Memory [70], Nemori [41], AgentEvolver [71], STULIFE [72], AutoRefine [73],"},{"citing_arxiv_id":"2605.07180","ref_index":25,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Learning Agent Routing From Early Experience","primary_cat":"cs.CL","submitted_at":"2026-05-08T03:18:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"BoundaryRouter routes queries to LLM or agent using early experience memory from a seed set, cutting inference time 60.6% versus always using agents and raising performance 28.6% versus always using direct LLM inference.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.15034","ref_index":11,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Autogenesis: A Self-Evolving Agent Protocol","primary_cat":"cs.AI","submitted_at":"2026-04-16T14:04:06+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2601.02553","ref_index":2,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SimpleMem: Efficient Lifelong Memory for LLM Agents","primary_cat":"cs.AI","submitted_at":"2026-01-05T21:02:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SimpleMem proposes semantic structured compression, online synthesis, and intent-aware retrieval to create efficient lifelong memory for LLM agents, reporting 26.4% F1 gains and up to 30x lower token use on LoCoMo benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.18746","ref_index":15,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MemEvolve: Meta-Evolution of Agent Memory Systems","primary_cat":"cs.CL","submitted_at":"2025-12-21T14:26:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"MemEvolve jointly evolves agent experiential knowledge and memory architectures via a modular codebase, delivering up to 17% gains on agent benchmarks with cross-task and cross-model generalization.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.24168","ref_index":23,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MGA: Memory-Driven GUI Agent for Observation-Centric Interaction","primary_cat":"cs.AI","submitted_at":"2025-10-28T08:19:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MGA is a memory-driven GUI agent that uses an observer for bias-free screen reading and structured memory for compact state transitions to enable efficient long-horizon automation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.02544","ref_index":51,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning","primary_cat":"cs.AI","submitted_at":"2025-09-02T17:44:45+00:00","verdict":"CONDITIONAL","verdict_confidence":"UNKNOWN","novelty_score":5.0,"formal_verification":"none","one_line_summary":"UI-TARS-2 reaches 88.2 on Online-Mind2Web, 47.5 on OSWorld, 50.6 on WindowsAgentArena, and 73.3 on AndroidWorld while attaining 59.8 mean normalized score on a 15-game suite through multi-turn RL and scalable data generation.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[50] Jiahao Qiu, Xuan Qi, Tongcheng Zhang, Xinzhe Juan, Jiacheng Guo, Yifu Lu, Yimin Wang, Zixin Yao, Qihan Ren, Xun Jiang, Xing Zhou, Dongrui Liu, Ling Yang, Yue Wu, Kaixuan Huang, Shilong Liu, Hongru Wang, and Mengdi Wang. Alita: Generalist agent enabling scalable agentic reasoning with minimal predefinition and maximal self-evolution, 2025. URLhttps://arxiv.org/abs/2505.20286. [51] Maria Abi Raad, Arun Ahuja, Catarina Barros, Frederic Besse, Andrew Bolt, Adrian Bolton, Bethanie Brownfield, Gavin Buttimore, Max Cant, Sarah Chakera, et al. Scaling instructable agents across many simulated worlds. arXiv preprint arXiv:2404.10179, 2024. [52] Christopher Rawles, Sarah Clinckemaillie, Yifan Chang, Jonathan Waltz, Gabrielle Lau, Marybeth Fair, Alice Li,"},{"citing_arxiv_id":"2508.07407","ref_index":75,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems","primary_cat":"cs.AI","submitted_at":"2025-08-10T16:07:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A comprehensive review of self-evolving AI agents that improve themselves over time, organized via a framework of inputs, agent system, environment, and optimizers, with domain-specific and safety discussions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2508.04149","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Difficulty-Based Preference Data Selection by DPO Implicit Reward Gap","primary_cat":"cs.CL","submitted_at":"2025-08-06T07:24:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Selecting preference pairs whose DPO implicit reward gap is small yields better LLM alignment than random or baseline selection while using only 10% of the data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2504.01990","ref_index":172,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems","primary_cat":"cs.AI","submitted_at":"2025-03-31T18:00:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"This survey frames foundation agents using brain-inspired modular architectures and reviews challenges in evolution, collaboration, and safety.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"agents can accumulate basic environmental knowledge through continuous interaction, while Learn-by- Interact [127] shows that meaningful understanding can emerge from direct environmental engagement without explicit reward mechanisms. These foundational approaches establish the groundwork for more sophisticatedlearningparadigms,withrecentworkonTest-TimeInteraction[ 172]demonstratingthatscaling interaction horizon-rather than just reasoning depth-enables agents to dynamically balance exploration and exploitation through extended environmental engagement. Building upon basic interaction capabilities, agents require systematic mechanisms toprocess and organize their accumulated experiences. More sophisticated approaches are exemplified by DESP [93] and Voyager [74]"}],"limit":50,"offset":0}