{"total":12,"items":[{"citing_arxiv_id":"2605.18535","ref_index":63,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Beyond Scaling: Agents Are Heading to the Edge","primary_cat":"cs.LG","submitted_at":"2026-05-18T15:18:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Personal agents require edge deployment to preserve high-fidelity local context and zero-latency loops, as claimed through three structural shifts away from cloud-centric designs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17169","ref_index":70,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Responsible Agentic AI Requires Explicit Provenance","primary_cat":"cs.AI","submitted_at":"2026-05-16T21:56:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Explicit provenance across the full agentic AI lifecycle is the necessary condition for making responsibility computable and actionable.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.10325","ref_index":31,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Verifiable Process Rewards for Agentic Reasoning","primary_cat":"cs.AI","submitted_at":"2026-05-11T10:30:53+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"dense supervision is not automatically beneficial: weak verifiers can degrade both in-domain and OOD performance, so VPR additionally emphasizes the reliability and verifiability of the oracle. LLM Agents and Agentic Reinforcement Learning.LLMs are increasingly used as autonomous agents that interact with tools and environments over multiple turns [31, 27]. Despite rapid progress on multi-turn benchmarks, agentic RL has largely retained the outcome-only reward structure inherited from RLVR, leaving step-level supervision derived from the environment's structure comparatively under-explored. Inference-time methods such as ReAct [36], Reflexion [22], Tree of Thoughts [35], and LATS [39] enhance planning by reasoning, reflecting, or searching at decoding time, but do not"},{"citing_arxiv_id":"2605.04357","ref_index":47,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Coral: Cost-Efficient Multi-LLM Serving over Heterogeneous Cloud GPUs","primary_cat":"cs.DC","submitted_at":"2026-05-05T23:25:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Coral cuts multi-LLM serving costs by up to 2.79x and raises goodput by up to 2.39x on heterogeneous GPUs through adaptive joint optimization and a lossless two-stage decomposition that solves quickly.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.24351","ref_index":43,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Diffusion Templates: A Unified Plugin Framework for Controllable Diffusion","primary_cat":"cs.LG","submitted_at":"2026-04-27T11:44:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Diffusion Templates is a unified plugin framework that allows injecting various controllable capabilities into diffusion models through a standardized interface.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.23505","ref_index":31,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Uncertainty Propagation in LLM-Based Systems","primary_cat":"cs.SE","submitted_at":"2026-04-26T02:48:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"This paper introduces a systems-level conceptual framing and a three-level taxonomy (intra-model, system-level, socio-technical) for uncertainty propagation in compound LLM applications, along with engineering insights and open challenges.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.09378","ref_index":28,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"BadSkill: Backdoor Attacks on Agent Skills via Model-in-Skill Poisoning","primary_cat":"cs.CR","submitted_at":"2026-04-10T14:48:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"BadSkill poisons embedded models in agent skills to achieve up to 99.5% attack success rate on triggered tasks with only 3% poison rate while preserving normal behavior on non-trigger inputs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.02674","ref_index":67,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Do Agent Societies Develop Intellectual Elites? The Hidden Power Laws of Collective Cognition in LLM Multi-Agent Systems","primary_cat":"cs.MA","submitted_at":"2026-04-03T03:08:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"LLM agent societies develop power-law coordination cascades and intellectual elites through an integration bottleneck that grows with system size.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Evidence for a collective intelligence factor in the performance of human groups. science, 330(6004):686-688, 2010. [66] Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, et al. Autogen: Enabling next-gen llm applications via multi-agent conversations. InFirst conference on language modeling, 2024. [67] Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, et al. The rise and potential of large language model based agents: A survey.Science China Information Sciences, 68(2):121101, 2025. [68] Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models."},{"citing_arxiv_id":"2604.09574","ref_index":36,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Turing Test on Screen: A Benchmark for Mobile GUI Agent Humanization","primary_cat":"cs.AI","submitted_at":"2026-02-24T04:29:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"The work creates a new benchmark for humanizing GUI agent touch dynamics via a MinMax detector-agent model, a mobile touch dataset, and methods showing agents can match human behavior without losing task performance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.02334","ref_index":38,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Holos: A Web-Scale LLM-Based Multi-Agent System for the Agentic Web","primary_cat":"cs.AI","submitted_at":"2026-01-18T13:09:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Holos is a five-layer LLM-based multi-agent system architecture using the Nuwa engine for agent generation, a market-driven Orchestrator for coordination, and an endogenous value cycle for incentive-compatible persistence in the Agentic Web.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.07432","ref_index":17,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering","primary_cat":"cs.AI","submitted_at":"2025-10-08T18:31:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"TS-Agent is an agentic framework that uses LLMs only for evidence-based reasoning while delegating extraction to raw time series tools, matching or exceeding baselines on four benchmarks with largest gains on reasoning tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.06858","ref_index":4,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Disentangling Interaction and Bias Effects in Opinion Dynamics of Large Language Models","primary_cat":"physics.soc-ph","submitted_at":"2025-09-08T16:26:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A Bayesian framework disentangles topic, agreement, and anchoring biases from interaction effects in LLM multi-turn dialogues, revealing convergence to attractors that shift with fine-tuning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}