{"total":27,"items":[{"citing_arxiv_id":"2607.05377","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Cortex: A Bidirectionally Aligned Embodied Agent Framework for Long-horizon Manipulation","primary_cat":"cs.RO","submitted_at":"2026-07-06T17:55:05+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A dual-system framework with a structured subtask interface, event-balanced training, and inference harness enables VLM-guided long-horizon robotic manipulation, achieving 95.5% on LIBERO-Long and 65% on real-world chemistry tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.29774","ref_index":34,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Analytic Concept-Centric Memory for Agentic Embodied Manipulation","primary_cat":"cs.RO","submitted_at":"2026-06-29T04:33:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Proposes a structured concept-centric memory system for embodied agents that connects object, scene, transition, and skill memories to support coarse-to-fine retrieval and improve task performance over baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.25136","ref_index":40,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Memory Retrieval in Visuomotor Policies for Long-Horizon Robot Control","primary_cat":"cs.RO","submitted_at":"2026-06-23T20:07:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"HALO distills VLM priors via question-answering objectives and applies sparse attention to enable reliable memory retrieval from up to eight minutes of history in imitation-learned visuomotor policies.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.23589","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"KEMO: Event-Driven Keyframe Memory for Long-Horizon Robot Manipulation with VLA Policies","primary_cat":"cs.RO","submitted_at":"2026-06-22T16:57:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"KEMO is an event-driven keyframe memory system that improves VLA policy success rates by 23.6% on real dual-arm tasks by selectively preserving task-relevant history via kinematics-visual event detection and gated fusion.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.22338","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Benchmarking Robot Memory Under Interference","primary_cat":"cs.RO","submitted_at":"2026-06-21T05:06:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Introduces RoboMME-Interference benchmark showing memory-augmented VLAs improve without distractors but decay steadily as unrelated sessions accumulate in history.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.21188","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Remember what you did?: Learning Behavioral Memories for Partially Observable Object Manipulation","primary_cat":"cs.RO","submitted_at":"2026-06-19T07:56:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CAMP learns a compressed behavioral memory from action history to enable success in long-horizon partially observable object manipulation without extra supervision, showing gains over baselines in real-robot and simulation tests.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20562","ref_index":58,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MemoryWAM: Efficient World Action Modeling with Persistent Memory","primary_cat":"cs.RO","submitted_at":"2026-06-18T17:59:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"MemoryWAM is a world action model with a hybrid memory design using recent frames, anchor frames, and gist tokens for efficient long-horizon robotic manipulation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20092","ref_index":7,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies","primary_cat":"cs.CV","submitted_at":"2026-06-18T11:11:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"EventVLA introduces foundational visual anchors and a Keyframe Evidence Memory module that predicts future keyframe probabilities from VLA embeddings to improve long-horizon task success by an average of 40% on 17 simulation and 4 real-world tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.18144","ref_index":55,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Memory as a Wasting Asset: Pricing Flash Endurance for Embodied Agents, and the Limits of Doing So","primary_cat":"cs.AI","submitted_at":"2026-06-16T16:43:19+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Flash endurance is priced via shadow price η making placement cost-optimal for any sign of value-write correlation χ, with χ positive only in recurrent long-horizon manipulation and the budget binding only on low-endurance commodity hardware.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.17463","ref_index":32,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"WeaveLA: Event Driven Cross-Subtask Latent Memory Weaving for Repetitive Robot Manipulation","primary_cat":"cs.CV","submitted_at":"2026-06-16T03:25:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"WeaveLA improves VLA policies for repetitive robot manipulation by event-triggered cross-subtask latent memory weaving, raising success on the hardest repetition tasks from 0% to 47.8% while leaving single-execution performance unchanged.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20679","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MemoryVAM: Integrating Memory into Video Action Model for Robot Manipulation","primary_cat":"cs.RO","submitted_at":"2026-06-13T08:54:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"MemoryVAM integrates a Perceiver-based Recap Compressor and Cue Gate into video action models, raising success rates on long-horizon manipulation from 5% to 42.5% on LIBERO-Mem and 75-80% on real-robot counting, spatial recall, and tracking tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.12105","ref_index":22,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model","primary_cat":"cs.RO","submitted_at":"2026-06-10T13:59:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DAM-VLA decouples per-modality temporal processing in vision-language-action models via latent buffers refreshed at sensor rates, achieving 95.2% average success versus 40.95% for synchronous baselines on seven real-world manipulation tasks while enabling 100 Hz control.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.12497","ref_index":57,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"$\\mu$VLA: On Recurrent Memory for Partially Observable Manipulation in VLA Models","primary_cat":"cs.LG","submitted_at":"2026-06-10T13:26:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Adding recurrent memory tokens to VLA models raises success rates on partially observable manipulation tasks from 0.42 to 0.84 on training and 0.07 to 0.23 on held-out tasks while preserving performance under full observability.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10363","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"HiMem-WAM: Hierarchical Memory-Gated World Action Models for Robotic Manipulation","primary_cat":"cs.RO","submitted_at":"2026-06-09T03:22:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"HiMem-WAM integrates hierarchical latent actions and boundary-aware memory gates into world action models to enhance robustness and performance on memory-dependent long-horizon robotic tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09827","ref_index":59,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MemoryVLA++: Temporal Modeling via Memory and Imagination in Vision-Language-Action Models","primary_cat":"cs.RO","submitted_at":"2026-06-08T17:59:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"MemoryVLA++ integrates a perceptual-cognitive memory bank and denoising world model into VLA models to enable temporal reasoning, yielding performance gains on manipulation benchmarks and real-robot tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.02775","ref_index":50,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"AURA: Action-Gated Memory for Robot Policies at Constant VRAM","primary_cat":"cs.AI","submitted_at":"2026-06-01T18:38:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"AURA-Mem uses an action-gated recurrent memory trained on closed-loop action error to deliver constant 4,224-byte state and 5-9x fewer writes than baselines while matching base policy success on LIBERO-Long.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.30877","ref_index":95,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Wall-OSS-0.5 Technical Report","primary_cat":"cs.RO","submitted_at":"2026-05-29T06:04:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Wall-OSS-0.5 is a 4B VLA model pretrained across many embodiments that achieves zero-shot real-robot performance on a 17-task suite and outperforms π_0.5 after fine-tuning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.20894","ref_index":33,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Mobile UMI: Cross-View Diffusion Policy with Decoupled Kinematics for Mobile Manipulation","primary_cat":"cs.RO","submitted_at":"2026-05-20T08:33:53+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A hardware-free dual-camera capture framework with ChArUco spatial unification and receding-horizon state alignment enables decoupled SE(3) manipulation and SE(2) base trajectories for diffusion policies, yielding 83.8% average success on four long-horizon household tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.03269","ref_index":95,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"RLDX-1 Technical Report","primary_cat":"cs.RO","submitted_at":"2026-05-05T01:40:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"RLDX-1 outperforms frontier VLAs such as π0.5 and GR00T N1.6 on dexterous manipulation benchmarks, reaching 86.8% success on ALLEX humanoid tasks versus around 40% for the baselines.","context_count":1,"top_context_role":"background","top_context_polarity":"unclear","context_text":"In International Conference on Learning Representations, 2026. [94] Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg. Progprompt: Generating situated robot task plans using large language models. In IEEE International Conference on Robotics and Automation, 2023. [95] Ajay Sridhar, Jennifer Pan, Satvik Sharma, and Chelsea Finn. MemER: Scaling up memory for robot control via experience retrieval.arXiv preprint arXiv:2510.20328, 2025. [96] Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 2024. [97] Priya Sundaresan, Quan Vuong, Jiayuan Gu, Peng Xu, Ted Xiao, Sean Kirmani, Tianhe Yu, Michael Stark, Ajinkya"},{"citing_arxiv_id":"2604.18933","ref_index":46,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Gated Memory Policy","primary_cat":"cs.RO","submitted_at":"2026-04-21T00:14:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"GMP selectively activates and represents memory via a gate and lightweight cross-attention, yielding 30.1% higher success on non-Markovian robotic tasks while staying competitive on Markovian ones.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Memer: Scaling up memory for robot control via experience retrieval, 2025. URL https://arxiv.org/abs/ 2510.20328. [45] Yuval Tassa, Tom Erez, and Emanuel Todorov. Synthesis and stabilization of complex behaviors through online trajectory optimization. In2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 4906-4913, 2012. doi: 10.1109/IROS.2012.6386025. [46] Emanuel Todorov, Tom Erez, and Yuval Tassa. Mu- joco: A physics engine for model-based control. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 5026-5033. IEEE, 2012. doi: 10.1109/IROS.2012.6386109. [47] Marcel Torne, Andy Tang, Yuejiang Liu, and Chelsea Finn. Learning long-context diffusion policies via past- token prediction."},{"citing_arxiv_id":"2604.15483","ref_index":30,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"${\\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities","primary_cat":"cs.LG","submitted_at":"2026-04-16T19:18:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"π₀.₇ is a steerable generalist robotic model that uses rich multimodal prompts including language, subgoal images, and performance metadata to achieve out-of-the-box generalization across tasks and robot bodies.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"scale knowledge for robot manipulation.arXiv preprint arXiv:2410.06158, 2024. 3 [29] Ruijie Zheng, Yongyuan Liang, Shuaiyi Huang, Jianfeng Gao, Hal Daum 'e III, Andrey Kolobov, Furong Huang, and Jianwei Yang. Tracevla: Vi- sual trace prompting enhances spatial-temporal aware- ness for generalist robotic policies.arXiv preprint arXiv:2412.10345, 2024. 3 [30] Ajay Sridhar, Jennifer Pan, Satvik Sharma, and Chelsea Finn. Memer: Scaling up memory for robot control via experience retrieval.arXiv preprint arXiv:2510.20328, 2025. [31] Hao Shi, Bin Xie, Yingfei Liu, Lin Sun, Fengrong Liu, Tiancai Wang, Erjin Zhou, Haoqiang Fan, Xi- angyu Zhang, and Gao Huang. Memoryvla: Perceptual- cognitive memory in vision-language-action models for"},{"citing_arxiv_id":"2604.14125","ref_index":34,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System","primary_cat":"cs.CV","submitted_at":"2026-04-15T17:50:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"HiVLA decouples VLM-based semantic planning with visual grounding from a cascaded cross-attention DiT action expert, outperforming end-to-end VLAs on long-horizon and fine-grained manipulation.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"To circumvent this limitation, hierarchical models explicitly decouple high- level task planning from low-level policy execution via interpretable interme- diate representations. This modularity retains the VLM's zero-shot reasoning power while allowing the action expert to specialize in precise motor control. These intermediate bridges take various forms, including textual subtasks in Hi- Robot [32]and MemER [34] or spatial keypoints in HAMSTER [22]. By isolating cognitive processes from high-frequency control, hierarchical systems provide a robust and scalable foundation for advancing embodied intelligence. 2.2 Visual-Grounded-Centric VLA A critical challenge in manipulation is precise visual grounding, which accurately maps high-level instructions to specific spatial regions within the visual input."},{"citing_arxiv_id":"2604.13942","ref_index":14,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection","primary_cat":"cs.RO","submitted_at":"2026-04-15T14:53:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A dual VLM-VLA framework for long-horizon robot manipulation achieves 32.4% success on RMBench tasks versus 9.8% for the strongest baseline via structured memory and closed-loop adaptive replanning.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Formally, giventhefailurecontext Fk = (otend k , τk, ck,W tend k ), the reflection step is defined as: (dk, ρ k) = Φreflect(Fk,E t),(13) where dk represents a natural-language diagnosis and ρk ∈ {retry,adjust-param,replan} denotes the rec- ommended recovery action. The error register is subse- quently updated as follows: Et+1 =E t ∪ {(τk, d k, ρ k)}.(14) The recommendationρk dictates the recovery strategy in the decision logic. For instance, ifρk = adjust-param, the high-level planner modifies specific sub-task param- eters, such as the approach direction, grasp-pose hint, or distractor constraintsBk, before re-issuing the same sub-task. This mechanism facilitates targeted local cor- rections while maintaining the validity of the existing"},{"citing_arxiv_id":"2603.04639","ref_index":36,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies","primary_cat":"cs.RO","submitted_at":"2026-03-04T21:59:32+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":"2603.03243","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations","primary_cat":"cs.RO","submitted_at":"2026-03-03T18:36:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"HoMMI learns whole-body mobile manipulation policies from robot-free human demonstrations by augmenting UMI with egocentric sensing and bridging the embodiment gap through an agnostic visual representation, relaxed head actions, and a whole-body controller.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.20323","ref_index":55,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"PhysMem: Scaling Test-Time Memory for Embodied Physical Reasoning","primary_cat":"cs.RO","submitted_at":"2026-02-23T20:18:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"PhysMem enables VLM-based robot planners to learn and verify physical properties through test-time interaction and hypothesis testing, raising success on a brick insertion task from 23% to 76%.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"How should a deployed VLM/VLA planner improve as it interacts with the world? The literature offers four broad answers, summarized in Table I. The most ambitious option updates the model itself, through online reinforcement learn- ing [41, 62, 52, 47], meta-learning [22], test-time training [57], or imitation finetuning of a memory-augmented backbone, as in MemER [55], MemoryVLA [51, 35], MEM [61], and SAM2Act [18]. A second family keeps the base frozen and feeds it context through retrieval [34, 24, 36] or natural- language reflection [53, 42]. The piece they leave out is a check on whether a remembered experience still applies in the current scene; our experiments make this concrete: selective retrieval matches the no-memory baseline (53% on medium),"},{"citing_arxiv_id":"2601.21998","ref_index":68,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Causal World Modeling for Robot Control","primary_cat":"cs.CV","submitted_at":"2026-01-29T17:07:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LingBot-VA combines video world modeling with policy learning via Mixture-of-Transformers, closed-loop rollouts, and asynchronous inference to improve robot manipulation in simulation and real settings.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[67] Mustafa Shukor, Dana Aubakirova, Francesco Capuano, Pepijn Kooijmans, Steven Palma, Adil Zouitine, Michel Aractingi, Caroline Pascal, Martino Russi, Andres Marafioti, Simon Alibert, Matthieu Cord, Thomas Wolf, and Remi Cadene. Smolvla: A vision-language-action model for affordable and efficient robotics.arXiv preprint arXiv:2506.01844, 2025. [68] Ajay Sridhar, Jennifer Pan, Satvik Sharma, and Chelsea Finn. Memer: Scaling up memory for robot control via experience retrieval.arXiv preprint arXiv:2510.20328, 2025. [69] Deborah Sulsky, Shi-Jian Zhou, and Howard L Schreyer. Application of a particle-in-cell method to solid mechanics.Computer physics communications, 87(1-2):236-252, 1995. [70] Jiaming Tang, Yufei Sun, Yilong Zhao, Shang Yang, Yujun Lin, Zhuoyang Zhang, James Hou, Yao Lu, Zhijian Liu, and Song"}],"limit":50,"offset":0}