Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
As of 28 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2507.22448.
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 2026-07-27T06:30:09.085275+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-11T10:45:46.618668Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T01:46:26.624174Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 2704d2cb-c3e6-4f6a-be39-29bc7fb70629 · inbound
SpikingBrain: Spiking Brain-inspired Large Models cites this paper.
SpikingBrain: Spiking Brain-inspired Large Models Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Reference 45
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T18:51:45.834693Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation aef477d0-1c57-42a5-a152-02b5b177e2b9 · inbound
RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QA cites this paper.
RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QA Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T04:55:54.139038Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation d605d63f-c9ad-4dfc-ba12-a6fefa308afa · inbound
Kimi Linear: An Expressive, Efficient Attention Architecture cites this paper.
Kimi Linear: An Expressive, Efficient Attention Architecture Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Reference 129
- Resolution
- malformed identifier
- arxiv_id, observed 2026-05-13T23:49:10.984633Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 17a632e8-d8fe-45da-aa80-d13aa97c9ee9 · inbound
S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models cites this paper.
S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T22:23:21.391466Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 4dcf20f3-d19c-4764-ad29-598907d510cb · inbound
Super Apriel: One Checkpoint, Many Speeds cites this paper.
Super Apriel: One Checkpoint, Many Speeds Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Reference 72
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:01:24.853106Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation dbf90b1d-db8e-40bc-ad1d-7030b0754a37 · inbound
SpikingBrain2.0: Brain-Inspired Foundation Models for Efficient Long-Context and Cross-Platform Inference cites this paper.
SpikingBrain2.0: Brain-Inspired Foundation Models for Efficient Long-Context and Cross-Platform Inference Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Reference 41
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T19:21:06.834096Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 509c05d9-b8bf-4656-803c-e219c63b6005 · inbound
Component-Aware Self-Speculative Decoding in Hybrid Language Models cites this paper.
Component-Aware Self-Speculative Decoding in Hybrid Language Models Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T16:06:40.648369Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 1f426a32-4a49-41d5-af73-67d496314406 · inbound
XL-SafetyBench: A Country-Grounded Cross-Cultural Benchmark for LLM Safety and Cultural Sensitivity cites this paper.
XL-SafetyBench: A Country-Grounded Cross-Cultural Benchmark for LLM Safety and Cultural Sensitivity Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Reference 60
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T19:41:11.065650Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 22d69211-ca82-44fe-a18e-d700c1bd060d · inbound
LLMForge: Multi-Backend Hardware-Aware Neural Architecture Search with Infinite-Head Attention for Edge Language Models cites this paper.
LLMForge: Multi-Backend Hardware-Aware Neural Architecture Search with Infinite-Head Attention for Edge Language Models Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T14:03:20.524249Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 7c32fa78-ac57-4f48-8389-41d785d5e3a1 · inbound
Forget Attention: Importance-Aware Attention Is All You Need cites this paper.
Forget Attention: Importance-Aware Attention Is All You Need Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T23:06:21.045608Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 1008ae7b-3e77-4e5b-9f84-1fcfff013c6e · inbound
MOSAIC: Efficient Mixture-of-Agent Scheduling via Adaptive Aggregation and Inference Concurrency cites this paper.
MOSAIC: Efficient Mixture-of-Agent Scheduling via Adaptive Aggregation and Inference Concurrency Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Reference 8
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T01:46:26.630023Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 4e091ae8-936e-4da1-9189-ce5823b5a42a · inbound
Morphing into Hybrid Attention Models cites this paper.
Morphing into Hybrid Attention Models Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Reference 72
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T08:44:28.064214Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation dff1143d-d849-420a-9631-c9316323ac43 · inbound
Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training cites this paper.
Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T10:45:46.618668Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.