Paper Citation Record · LEDGER

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers

As of 8 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2607.14427.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.14427 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T02:11:16.513360Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

23 of 23 outbound references displayed

verified exact0
verified fuzzy0
unresolved22
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bffdc4e3-6a1c-4939-9d5b-6a0cdf1f7e03 · outbound

This paper cites GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:13.508966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d1e1e34-15cd-4601-8118-0259002d4f67 · outbound

This paper cites SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:13.651051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15a154a3-b546-4fe2-b9f7-e10eb352530b · outbound

This paper cites Mixture-of-recursions: Learning dynamic recursive depths for adaptive token-level computation.arXiv preprint arXiv:2507.10524, 2025.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers Mixture-of-recursions: Learning dynamic recursive depths for adaptive token-level computation.arXiv preprint arXiv:2507.10524, 2025

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:13.810740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4575402-d871-496d-a3e7-56479f56c5e5 · outbound

This paper cites PonderNet: Learning to Ponder.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers PonderNet: Learning to Ponder

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:13.947284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 928e9500-c1ab-4903-abd3-4c5d5dc35b1b · outbound

This paper cites Universal Transformers Need Memory: Depth-State Trade-offs in Adaptive Recursive Reasoning.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers Universal Transformers Need Memory: Depth-State Trade-offs in Adaptive Recursive Reasoning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:14.129619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e5f5782-94e1-46eb-83a1-db0765b00862 · outbound

This paper cites Universal Transformers.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers Universal Transformers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:14.285419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf4ba506-0967-4a2d-ac28-8682fd074c09 · outbound

This paper cites SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:14.454774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e54e4c6a-e925-4a8e-8f66-61838f3d0209 · outbound

This paper cites TinyStories: How Small Can Language Models Be and Still Speak Coherent English?.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers TinyStories: How Small Can Language Models Be and Still Speak Coherent English?

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:14.582249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d93ce17-e426-441a-9ad9-6306c37e10a7 · outbound

This paper cites LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:14.732055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58ad91f2-0cb3-4941-b63f-47ceb72dffcc · outbound

This paper cites Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:14.888008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c007706-e6ab-490e-b29b-6e88e6bf5510 · outbound

This paper cites Adaptive Computation Time for Recurrent Neural Networks.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers Adaptive Computation Time for Recurrent Neural Networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:15.102556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13bef610-a4b3-48c8-baba-075bb40cfc3f · outbound

This paper cites LoopFormer: Elastic-depth looped transformers with shortcut consistency.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers LoopFormer: Elastic-depth looped transformers with shortcut consistency

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:15.291743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 637c62c6-6d6e-4921-a3d3-ef2872959bec · outbound

This paper cites Decoupled Weight Decay Regularization.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers Decoupled Weight Decay Regularization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:15.412312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f2c8873-1fa9-4001-8f17-ef43c54d9999 · outbound

This paper cites The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:15.588268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6bc1fa6-fa78-4563-a566-ccd52bbfd934 · outbound

This paper cites Rao et al.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers Rao et al

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:15.705842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48c0a03a-7d61-4feb-834d-905662959a56 · outbound

This paper cites Mixture-of-Depths: Dynamically allocating compute in transformer-based language models.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:15.817770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18876a6a-65bf-4505-bc80-fe044b0dc32f · outbound

This paper cites Confident Adaptive Language Modeling.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers Confident Adaptive Language Modeling

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:15.941481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f1548c6-79f8-4b6d-a462-085bd2862b19 · outbound

This paper cites GLU Variants Improve Transformer.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers GLU Variants Improve Transformer

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:16.062703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e03f9504-f999-46c5-b18b-1b566548980a · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:16.178719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33cc853a-d9e6-47dd-8e6c-fd41aa0c53cf · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers LLaMA: Open and Efficient Foundation Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:16.285397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aed49fa9-93f4-46df-805f-d5e66e9fb49f · outbound

This paper cites Whitfield et al.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers Whitfield et al

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:16.345368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea20cac2-1617-4a50-9906-1bf661cb5fe6 · outbound

This paper cites Root Mean Square Layer Normalization.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers Root Mean Square Layer Normalization

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T02:11:16.427367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec5d48a4-d7f2-4618-b9ce-e8426e8dc95f · outbound

This paper cites Scaling Latent Reasoning via Looped Language Models.

Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers Scaling Latent Reasoning via Looped Language Models

Reference 23

Resolution: malformed identifier; no resolver link, observed 2026-08-02T02:11:16.513360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.