{"total":29,"items":[{"citing_arxiv_id":"2605.13370","ref_index":6,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Phasor Memory Networks: Stable Backpropagation Through Time for Scalable Explicit Memory","primary_cat":"cs.LG","submitted_at":"2026-05-13T11:28:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PMNet uses unitary phasor dynamics and hierarchical anchors to make explicit memory stable for long sequences, matching a 3x larger Mamba model on long-context robustness with a 119M parameter network.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13179","ref_index":5,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Does Engram Do Memory Retrieval in Autoregressive Image 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at.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2104.13478","ref_index":34,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","primary_cat":"cs.LG","submitted_at":"2021-04-27T21:09:51+00:00","verdict":"ACCEPT","verdict_confidence":"HIGH","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Geometric deep learning provides a unified mathematical framework based on grids, groups, graphs, geodesics, and gauges to explain and extend neural network architectures by incorporating physical regularities.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1807.03819","ref_index":13,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Universal 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For instance, generalized expectation criteria [94, 45] ask the user to provide population-level statistics (e.g. telling the system that on average each sentence contains at least one noun); the DeepDive sys- tem [139] asks users to supply rules that each generate many weak labels; and [65] extrapolates more general patterns from an initial set of low-recall labeling rules. This general approach is often referred to as distant supervision, and has also received recent attention in the natural language processing community (see e.g. [60, 99] as well as several of the references above). Expanding these lines of work and finding a way to apply them to the case of agents, where"},{"citing_arxiv_id":"1603.08983","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Adaptive Computation Time for Recurrent Neural Networks","primary_cat":"cs.NE","submitted_at":"2016-03-29T22:09:00+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":8.0,"formal_verification":"none","one_line_summary":"ACT lets RNNs dynamically adapt computation depth per input via a differentiable halting unit, yielding large gains on synthetic tasks and structural insights on language data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}