Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-13T06:26:23.775879Z
Paper Citation Record · LEDGER
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning
As of 31 July 2026, this Paper Citation Record lists 45 of 45 outbound references and 2 inbound Pith citation observations for arXiv:2605.12206.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-07-31T06:34:12.847434+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-30T22:21:16.608148Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-01T14:05:46.368588Z
Reference resolution
45 of 45 outbound references displayed
- verified exact10
- verified fuzzy33
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation f53a3d65-4832-4511-9958-3b83c9a93de6 · outbound
This paper cites A Survey Analyzing Generalization in Deep Reinforcement Learning.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning A Survey Analyzing Generalization in Deep Reinforcement Learning
Reference 1
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T06:27:24.339868Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation fe1b4cfc-7baa-4e10-94d1-83b810aa959f · outbound
This paper cites Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability.Advances in neural information processing systems, 34:25502–25515.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability.Advances in neural information processing systems, 34:25502–25515
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.030130Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 79f4fa72-a798-47ae-a79b-f6f1c2a7524f · outbound
This paper cites Horizon generalization in reinforcement learning.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Horizon generalization in reinforcement learning
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.024286Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 996766d5-f2d8-4c3c-95e4-0e3a66b01871 · outbound
This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Combining recurrent, convolutional, and continuous-time models with linear state space layers
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.047409Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation d6b50539-914b-4f13-9ee9-f9f49ee2b0e5 · outbound
This paper cites Efficiently modeling long sequences with structured state spaces.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Efficiently modeling long sequences with structured state spaces
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.040639Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation d75161ce-276d-4845-aac4-27d6c6cead1b · outbound
This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.026105Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 1ccfb9b3-16e4-4fce-b57c-89acfd9c6425 · outbound
This paper cites Parallelizing linear recurrent neural nets over sequence length.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Parallelizing linear recurrent neural nets over sequence length
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.012630Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation c2c92c02-25c3-438a-a7c1-b3723eee467b · outbound
This paper cites Hierarchically Gated Recurrent Neural Network for Sequence Modeling.Advances in Neural Information Processing Systems, 36:33202–33221, December 2023.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Hierarchically Gated Recurrent Neural Network for Sequence Modeling.Advances in Neural Information Processing Systems, 36:33202–33221, December 2023
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.036296Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 3b97049c-3e30-497f-a661-3036b3989910 · outbound
This paper cites xLSTM: Extended Long Short-Term Memory.Advances in Neural Information Processing Systems, 37: 107547–107603, December 2024.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning xLSTM: Extended Long Short-Term Memory.Advances in Neural Information Processing Systems, 37: 107547–107603, December 2024
Reference 9
- Resolution
- verified exact
- doi, observed 2026-05-13T06:27:23.902867Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation d59f8b96-057d-4802-bc9c-bb0c98f0c249 · outbound
This paper cites Were RNNs All We Needed?.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Were RNNs All We Needed?
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T06:27:24.345615Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 51fa9aec-2a64-41f1-867b-9e639205bc70 · outbound
This paper cites Scalable MatMul-free Language Modeling.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Scalable MatMul-free Language Modeling
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T06:27:24.360868Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 00ab07a7-e090-42ff-bf20-bf31f29873bf · outbound
This paper cites Parallelizable memory recurrent units.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Parallelizable memory recurrent units
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T00:05:40.495941Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 3c8081b7-9a7f-404e-882f-d7f339cdf903 · outbound
This paper cites Long short-term memory.Neural Computation, 9 (8):1735–1780.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Long short-term memory.Neural Computation, 9 (8):1735–1780
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.004891Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation d1e055ac-ddc5-43ce-913d-562f37d27055 · outbound
This paper cites Learning phrase representations using RNN encoder- decoder for statistical machine translation.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Learning phrase representations using RNN encoder- decoder for statistical machine translation
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.006751Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 14c9468a-81b5-4926-9212-a7a83d624b71 · outbound
This paper cites The unreasonable effectiveness of the forget gate.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning The unreasonable effectiveness of the forget gate
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T06:27:24.363857Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 3904592b-17ce-4f6d-b90c-70731cdb5b49 · outbound
This paper cites A bio-inspired bistable recurrent cell allows for long-lasting memory.PLoS ONE, 16(6):e0252676.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning A bio-inspired bistable recurrent cell allows for long-lasting memory.PLoS ONE, 16(6):e0252676
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.042593Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 9d40c520-aad4-403e-a224-e95295de6084 · outbound
This paper cites Minimal gated unit for recurrent neural networks.International Journal of Automation and Computing, 13(3):226–234.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Minimal gated unit for recurrent neural networks.International Journal of Automation and Computing, 13(3):226–234
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.010715Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 22abc59d-c91a-4bdb-aae4-ecfaec1208af · outbound
This paper cites Warming up recurrent neural networks to maximise reachable multistability greatly improves learning.Neural Networks, 166:645–669.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Warming up recurrent neural networks to maximise reachable multistability greatly improves learning.Neural Networks, 166:645–669
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.056389Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 65335429-b5d2-459b-a3cd-fb8f8e7326b1 · outbound
This paper cites Blelloch.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Blelloch
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.003025Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation bc08914f-2fdd-4c5e-ade7-0df9e4e1d0a8 · outbound
This paper cites Reinforcement learning with long short-term memory.Advances in neural information processing systems, 14.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Reinforcement learning with long short-term memory.Advances in neural information processing systems, 14
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.064045Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 69360c71-7fc5-4250-a8b0-4b4f7425ec9e · outbound
This paper cites A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T06:27:24.357587Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 65974669-fdaf-4951-a779-ed669ba918e8 · outbound
This paper cites Quantifying generalization in reinforcement learning.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Quantifying generalization in reinforcement learning
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:24.996709Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 7faf40e9-a157-4dca-a707-6671f729214d · outbound
This paper cites Leveraging procedural generation to benchmark reinforcement learning.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Leveraging procedural generation to benchmark reinforcement learning
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.001109Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 3400a388-31b8-4914-b43b-1321d94d6eb0 · outbound
This paper cites A survey of zero-shot generalisation in deep reinforcement learning.Journal of Artificial Intelligence Research, 76: 201–264.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning A survey of zero-shot generalisation in deep reinforcement learning.Journal of Artificial Intelligence Research, 76: 201–264
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.028039Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation aa290809-5619-4bc6-a053-9c63d0e7dfd7 · outbound
This paper cites Curriculum learning.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Curriculum learning
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.022551Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 77c0575a-e5a3-4da6-8c95-98c3cafeead3 · outbound
This paper cites Curriculum learning for reinforcement learning domains: A framework and survey.Journal of Machine Learning Research, 21(181):1–50.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Curriculum learning for reinforcement learning domains: A framework and survey.Journal of Machine Learning Research, 21(181):1–50
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.020683Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 72eccd51-a25f-4623-933a-0331574b034c · outbound
This paper cites Domain randomization for transferring deep neural networks from simulation to the real world.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Domain randomization for transferring deep neural networks from simulation to the real world
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.016699Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 9189b4fc-4128-4b1c-88f7-56990946a1a5 · outbound
This paper cites Planning and acting in partially observable stochastic domains.Artificial intelligence, 101(1-2):99–134.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Planning and acting in partially observable stochastic domains.Artificial intelligence, 101(1-2):99–134
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.058375Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation d90777aa-f3aa-4e15-bb69-38a7a97fd585 · outbound
This paper cites Deep recurrent q-learning for partially observable mdps.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Deep recurrent q-learning for partially observable mdps
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.053289Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation ec1490bb-73c0-4413-afe7-2a6ef4b90b7a · outbound
This paper cites Human-level control through deep reinforcement learning.nature, 518(7540):529–533.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Human-level control through deep reinforcement learning.nature, 518(7540):529–533
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.049271Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 00903e15-941b-4939-9eac-f6f4a596b93a · outbound
This paper cites Proximal Policy Optimization Algorithms.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Proximal Policy Optimization Algorithms
Reference 32
- Resolution
- verified exact
- local_arxiv, observed 2026-05-13T06:27:24.354461Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 276573ae-2930-49f3-ba94-754a8a4b3b98 · outbound
This paper cites Neural Turing Machines.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Neural Turing Machines
Reference 33
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T07:34:44.411886Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 16889386-4f01-4b6c-a695-9dfce77c304a · outbound
This paper cites Hybrid computing using a neural network with dynamic external memory.Nature, 538 (7626):471–476.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Hybrid computing using a neural network with dynamic external memory.Nature, 538 (7626):471–476
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.008806Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation a52d0092-2cc5-420e-912f-1d6a459d9e12 · outbound
This paper cites Stabilizing transformers for reinforcement learning.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Stabilizing transformers for reinforcement learning
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.051088Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation f7a61c60-8dd6-45d2-9a3e-87bee941f347 · outbound
This paper cites Dream to Control: Learning Behaviors by Latent Imagination.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Dream to Control: Learning Behaviors by Latent Imagination
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.014521Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation aa719012-f94c-490d-b482-21326ee174bb · outbound
This paper cites Mastering atari with discrete world models.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Mastering atari with discrete world models
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.045502Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation af7175cb-89b9-4b98-a284-0f8ef44270bc · outbound
This paper cites Mastering diverse control tasks through world models.Nature, 640(8059):647–653.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Mastering diverse control tasks through world models.Nature, 640(8059):647–653
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.038491Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation df3b5acb-e0ac-43b0-82ac-69e53fd3d3d8 · outbound
This paper cites Varibad: Variational bayes-adaptive deep rl via meta-learning.Journal of Machine Learning Research, 22(289):1–39.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Varibad: Variational bayes-adaptive deep rl via meta-learning.Journal of Machine Learning Research, 22(289):1–39
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.034359Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 83359859-bddb-4af3-88e3-6db40538ddc6 · outbound
This paper cites an unresolved cited work.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Unresolved cited work
Reference 40
- Resolution
- unresolved
- raw_fallback, observed 2026-05-13T06:27:25.032153Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 75bd0970-6d24-44ba-86d7-186a9892b521 · outbound
This paper cites On the difficulty of training recurrent neural networks.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning On the difficulty of training recurrent neural networks
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.018537Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation c08c151a-7592-4d07-ae50-3f9b71d44ac9 · outbound
This paper cites State-space fading memory.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning State-space fading memory
Reference 42
- Resolution
- verified exact
- local_arxiv, observed 2026-05-13T06:27:24.342655Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation de6ce2f5-830a-4d1e-8bd8-5b2a722b8e17 · outbound
This paper cites An overview of the stability analysis of recurrent neural networks with multiple equilibria.IEEE Transactions on Neural Networks and Learning Systems, 34(3):1098–1111.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning An overview of the stability analysis of recurrent neural networks with multiple equilibria.IEEE Transactions on Neural Networks and Learning Systems, 34(3):1098–1111
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T06:27:23.899846Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 0f12b3c5-cdc8-4ae3-8a06-1966f886d81e · outbound
This paper cites Asynchronous methods for deep reinforce- ment learning.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Asynchronous methods for deep reinforce- ment learning
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.062189Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation d2a68500-f561-4cc1-b656-0ca101c04152 · outbound
This paper cites Cleanrl: High-quality single-file implementations of deep reinforcement learning algorithms.Journal of Machine Learning Research, 23(274):1–18.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Cleanrl: High-quality single-file implementations of deep reinforcement learning algorithms.Journal of Machine Learning Research, 23(274):1–18
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:25.060231Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation b78a76e3-d258-4ade-bc84-eb5a853c3301 · outbound
This paper cites Beyond standard RNNs, various approaches have been proposed to enhance the memory capacity accessible to the agent.
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Beyond standard RNNs, various approaches have been proposed to enhance the memory capacity accessible to the agent
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T06:27:24.999158Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Pith citing papers
Observation dba68bda-5e87-4077-9a08-9c9cf00c0f37 · inbound
Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations cites this paper.
Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations On the Importance of Multistability for Horizon Generalization in Reinforcement Learning
Reference 65
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T22:09:07.294855Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 0a36af8b-7006-41a8-88b2-65759948ef48 · inbound
Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations cites this paper.
Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations On the Importance of Multistability for Horizon Generalization in Reinforcement Learning
Reference 65
- Resolution
- verified exact
- local_arxiv, observed 2026-07-01T14:05:46.370900Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.