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Paper Citation Record · LEDGER

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training

As of 22 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2603.06642.

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

pith.paper-citation-record.v1
2603.06642 v2

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T20:43:37.635374Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

21 of 21 outbound references displayed

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External citation measurements

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Outbound references

Observation 75694d6f-4625-4823-8e68-928d4a3e55d6 · outbound

This paper cites (2017).Attention Is All You Need.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training (2017).Attention Is All You Need

Reference 1

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source=pdf_text observed=2026-08-02T20:43:35.881099Z digest=sha256:4350c1acab75834ba634e828e68bb148b9595d4f29fcce8e06f6b445b50d1964

Observation 3422a72c-4495-4ace-94be-257c14891e61 · outbound

This paper cites (2020).Test-Time Training with Self-Supervision for Generalization under Distribution Shifts.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training (2020).Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

Reference 2

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source=pdf_text observed=2026-08-02T20:43:35.950613Z digest=sha256:b992116da71d810e529e380a6bcb38cfd0218134033155548bd7ca559da5f67f

Observation d1bbf254-a46d-4612-8e47-76ae9f9f50aa · outbound

This paper cites (2024).Learning to (Learn at Test Time): RNNs with Expressive Hidden States.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training (2024).Learning to (Learn at Test Time): RNNs with Expressive Hidden States

Reference 3

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source=pdf_text observed=2026-08-02T20:43:36.021379Z digest=sha256:04392b6a5335c817e28bbb4202ddfff8fcfd772aafe7b05aeb486de946079b32

Observation 6dec8a8c-1dde-48dc-8d60-410414e3e872 · outbound

This paper cites Test-Time Training Done Right.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training Test-Time Training Done Right

Reference 4

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source=pdf_text observed=2026-08-02T20:43:36.082526Z digest=sha256:3bcd9b7f2865541ee9a515402b6f6caf9a0d27332b58d39eba5f595245a38a6f

Observation 3deafdcc-16e1-403b-83bb-0c50dcd08f2f · outbound

This paper cites (2026).Gated Differentiable Working Memory (GDWM) for Long-Context Language Modeling.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training (2026).Gated Differentiable Working Memory (GDWM) for Long-Context Language Modeling

Reference 5

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source=pdf_text observed=2026-08-02T20:43:36.191586Z digest=sha256:180704bafd7cf6eaa45e8feea19b95f58cfd615701d58bbdd829c33d3ac28519

Observation fbd984b9-f0c5-4bd9-8884-6e27320563b0 · outbound

This paper cites F., et al.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training F., et al

Reference 6

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source=pdf_text observed=2026-08-02T20:43:36.359871Z digest=sha256:0c6d6bf91d17ff0adafaf318ae93a36a458575bb26e9fe4eca2e5e3e9e11923d

Observation 8036a418-5918-4475-b936-76afb824ee1a · outbound

This paper cites (2023).Needle In A Haystack – Pressure Testing LLMs.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training (2023).Needle In A Haystack – Pressure Testing LLMs

Reference 7

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source=pdf_text observed=2026-08-02T20:43:36.426811Z digest=sha256:f6cf862a164055560ac7c4a7396063ee8d7223281f69957f1552628fbaf4ec73

Observation cb23690b-88a9-4288-a9e0-c74fb80aa210 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 8

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source=pdf_text observed=2026-08-02T20:43:36.501176Z digest=sha256:7ad0de8cad739483812c1fa69460cb3f93aca632f197026d893b0f57d82b626c

Observation c9b257b6-f772-4e0b-acf5-27871be06db9 · outbound

This paper cites (2023).RWKV: Reinventing RNNs for the Transformer Era.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training (2023).RWKV: Reinventing RNNs for the Transformer Era

Reference 9

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source=pdf_text observed=2026-08-02T20:43:36.612867Z digest=sha256:028d3b7194534e8b289aabc1f075e73e56a3d6bede99a71ea2f5f3829cf74f2b

Observation 0989ade6-e56f-49bd-a9cd-9f117b0b9eff · outbound

This paper cites Titans: Learning to Memorize at Test Time.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training Titans: Learning to Memorize at Test Time

Reference 10

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source=pdf_text observed=2026-08-02T20:43:36.742463Z digest=sha256:48b6a9ea2bfd20a77a962d46f54c40bbcb9b9e901be79fc581d3f22ef3c3856b

Observation f3edf507-b039-4c99-a4c5-eab6352b839c · outbound

This paper cites (2024).Simple Linear Attention Language Models Balance the Recall-Throughput Tradeoff.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training (2024).Simple Linear Attention Language Models Balance the Recall-Throughput Tradeoff

Reference 11

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source=pdf_text observed=2026-08-02T20:43:36.817413Z digest=sha256:f85d1b5290401ce744f6662d6f41b42d073b1395c55ce77a02899c1d3e517292

Observation 9e0559d0-6000-4a9c-ad3e-677e2188302a · outbound

This paper cites (2024).Zoology: Measuring and Improving Recall in Efficient Language Models.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training (2024).Zoology: Measuring and Improving Recall in Efficient Language Models

Reference 12

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source=pdf_text observed=2026-08-02T20:43:36.868262Z digest=sha256:a4e2ab37906eed2743f9babe6406ae9515d0630819d29fd6ac1a8123a65a115d

Observation 8dcd8aa9-e883-4b6c-adb1-558b0d38a0c6 · outbound

This paper cites (2024).Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training (2024).Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference

Reference 13

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source=pdf_text observed=2026-08-02T20:43:36.942675Z digest=sha256:0642be12d122cfd52bb8cbab580609b196e0ffc0368950f0f095a6d317a0d57e

Observation 0186dea8-15c6-4ffc-aefd-e06921973236 · outbound

This paper cites (2025).Cache What Lasts: Token Retention for Memory-Bounded KV Cache in LLMs.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training (2025).Cache What Lasts: Token Retention for Memory-Bounded KV Cache in LLMs

Reference 14

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source=pdf_text observed=2026-08-02T20:43:37.044877Z digest=sha256:8d7a9c79dad0b58c3d754e25f3742cb5a9ec590a014cacb83e07224102302eaa

Observation c6d67a6f-192d-4ea5-b37f-4d977bb567e5 · outbound

This paper cites (2024).MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training (2024).MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention

Reference 15

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source=pdf_text observed=2026-08-02T20:43:37.166781Z digest=sha256:3fbbd003e87f46cd2e774715da1c6d549aada544c63b3cb2e5246e0253b46577

Observation d8e2c0b5-458f-4baf-b766-d3d418fc5841 · outbound

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

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

Reference 16

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source=pdf_text observed=2026-08-02T20:43:37.256381Z digest=sha256:eb2522a817bff2774e383e2bc9ec0dacad1f2181f1e9ef0c1238adb82b62221b

Observation 27751f3e-0844-40e9-8b11-95a7306c453b · outbound

This paper cites (2024).H 2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training (2024).H 2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models

Reference 17

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source=pdf_text observed=2026-08-02T20:43:37.378095Z digest=sha256:313faa307a9bbe1b1fbe3940f99c151b2fea23038b21c331cd6e66a32bad2cb9

Observation fb62226e-15c4-4ea8-8d94-79f5bc76a46e · outbound

This paper cites (2024).Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training (2024).Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference

Reference 18

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source=pdf_text observed=2026-08-02T20:43:37.467237Z digest=sha256:9f7e573b8ebe796e1aa33f402148eb4afa8a544af51918ea4982a5649eef7509

Observation bb4bc3da-3819-4872-af09-d018dbcf2a08 · outbound

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

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training TinyStories: How Small Can Language Models Be and Still Speak Coherent English?

Reference 19

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source=pdf_text observed=2026-08-02T20:43:37.517425Z digest=sha256:e012db1d533d7a66472a397ab0780644ec4e4af8274e6e1d33da8325a37ea835

Observation 57b77d9f-222a-421a-8a8a-f7d04c599a57 · outbound

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

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 20

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source=pdf_text observed=2026-08-02T20:43:37.565942Z digest=sha256:9372b166ba90d5c35f6b88cd87f92d3ba9b36b1f08fcacb5a70edec834f8cbf1

Observation ee5d69d5-004b-496d-9762-d59351477fe4 · outbound

This paper cites YaRN: Efficient Context Window Extension of Large Language Models.

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training YaRN: Efficient Context Window Extension of Large Language Models

Reference 21

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source=pdf_text observed=2026-08-02T20:43:37.635374Z digest=sha256:550d043d2c0580fbd4a8c1bbec361d82be9f5fcdb3e658eaac5a75290fd56824

Pith citing papers

No inbound Pith citation observations are available.