Paper Citation Record · LEDGER

Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers

As of 16 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2607.15456.

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

pith.paper-citation-record.v1
2607.15456 v2

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T23:22:32.056496Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

13 of 13 outbound references displayed

verified exact0
verified fuzzy0
unresolved12
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f209d013-055a-42de-910e-a9ccc06c6dd2 · outbound

This paper cites GQA: Training generalized multi-query transformer models from multi-head checkpoints.

Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers GQA: Training generalized multi-query transformer models from multi-head checkpoints

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T23:22:30.397930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation deea2628-b632-4963-9166-cd3aaaa8d1b1 · outbound

This paper cites The precision baseline cannot reach the largest ratios without collapse.

Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers The precision baseline cannot reach the largest ratios without collapse

Reference 6

Resolution: malformed identifier; no resolver link, observed 2026-08-01T23:22:32.056496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8d1992b-01f9-420e-859b-a421862a3b67 · outbound

This paper cites Fast Transformer Decoding: One Write-Head is All You Need.

Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers Fast Transformer Decoding: One Write-Head is All You Need

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T23:22:31.282517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a5bd9ba-0748-4db5-ad3d-6190de13475b · outbound

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

Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T23:22:31.448925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e3029da-0566-4fe0-9ac5-5a4c8edb74b9 · outbound

This paper cites Memory-Efficient Looped Transformer: Decoupling Compute from Memory in Looped Language Models.

Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers Memory-Efficient Looped Transformer: Decoupling Compute from Memory in Looped Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T23:22:31.783125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39ec6a33-488c-40fa-8478-a23de9a4ac15 · outbound

This paper cites Scaling Latent Reasoning via Looped Language Models.

Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers Scaling Latent Reasoning via Looped Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T23:22:31.822685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5514fa0e-c77e-4487-bf68-30c432402a2f · outbound

This paper cites an unresolved cited work.

Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T23:22:31.901786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44f54169-daf6-428e-94a0-ecc10c537f48 · outbound

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

Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-01T23:22:30.789143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2348b8b4-20be-4ce9-ac7f-1ae1c7839e96 · outbound

This paper cites You Only Cache Once: Decoder-Decoder Architectures for Language Models.

Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers You Only Cache Once: Decoder-Decoder Architectures for Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-01T23:22:31.614891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f43cf07-6576-47ee-b798-22ec9e9db6d5 · outbound

This paper cites MiniCache: KV Cache Compression in Depth Dimension for Large Language Models.

Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers MiniCache: KV Cache Compression in Depth Dimension for Large Language Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-01T23:22:31.121825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56c12b71-a98f-4f61-b13e-c553f0514438 · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-01T23:22:30.635042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c399909b-00ee-449b-aa0e-cdb766908015 · outbound

This paper cites KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization.

Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-01T23:22:30.957456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a53af8d-45c1-48bc-aad2-09f2e4f742d3 · outbound

This paper cites A Mechanistic Analysis of Looped Reasoning Language Models.

Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers A Mechanistic Analysis of Looped Reasoning Language Models

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-01T23:22:30.480923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.