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Paper Citation Record · LEDGER

Lossless Token Sequence Compression via Meta-Tokens

As of 8 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2506.00307.

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

pith.paper-citation-record.v1
2506.00307 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:14:30.282576Z

measured 40 of 40 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T06:44:04.493625Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T06:47:27.317442Z

Reference resolution

39 of 39 outbound references displayed

  • verified exact0
  • verified fuzzy4
  • unresolved34
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e9221553-ea76-47ae-84c0-374a7b835547 · outbound

This paper cites Adapting language models to compress contexts.

Lossless Token Sequence Compression via Meta-Tokens Adapting language models to compress contexts

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:25.522663Z digest=sha256:6dc2dfbf20af47e061de864809842f1e98d8292158847563c38b93b25a39bce6

Observation 8bd80a06-de56-401b-a359-e5c6e36af44e · outbound

This paper cites Learning to compress prompt in natural language formats.

Lossless Token Sequence Compression via Meta-Tokens Learning to compress prompt in natural language formats

Reference 2

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:25.667638Z digest=sha256:aba6f02273377b7b1a85ab4fc6c697b635a9ae5890b778a41e0ffa65ef1ea83a

Observation 7d41701c-fe70-4ef4-8a44-a30a99dc1cf3 · outbound

This paper cites UniICL: An Efficient Unified Framework Unifying Compression, Selection, and Generation.

Lossless Token Sequence Compression via Meta-Tokens UniICL: An Efficient Unified Framework Unifying Compression, Selection, and Generation

Reference 3

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metadata mismatch
local_arxiv, observed 2026-08-07T12:14:31.069309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T12:14:25.847485Z digest=sha256:d99a01ba00900334a6a36c49acf86e25cdd9b4bf1b26e37caa0248322e2aa77e

Observation 0eda27cb-3467-45c1-b0c5-3b684314a4a1 · outbound

This paper cites In-context Autoencoder for Context Compression in a Large Language Model.

Lossless Token Sequence Compression via Meta-Tokens In-context Autoencoder for Context Compression in a Large Language Model

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:26.038257Z digest=sha256:5d881dc584768fdf73a49d437fc640d1c9ed10054d5491df6143d85b26c318d8

Observation f1d54dbe-9cea-4123-a62e-f44cb3307b52 · outbound

This paper cites CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution.

Lossless Token Sequence Compression via Meta-Tokens CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:26.122187Z digest=sha256:7b85ecd1b8e530cf5b7a0051889514a43129e33803cce282d614bdb19689fe99

Observation 9d95336f-2acc-4672-83ab-f461671f1728 · outbound

This paper cites Deliberative Alignment: Reasoning Enables Safer Language Models.

Lossless Token Sequence Compression via Meta-Tokens Deliberative Alignment: Reasoning Enables Safer Language Models

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:26.266557Z digest=sha256:090c87b0a2e747ed8e8e063412aaf70878086968d7f6e90c950ce2ae7653245e

Observation 27065203-7717-4a98-9a2c-3d1abdc51a74 · outbound

This paper cites Long short-term memory.

Lossless Token Sequence Compression via Meta-Tokens Long short-term memory

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-07T12:14:32.045494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T12:14:26.393403Z digest=sha256:51d141c3702e004f7ad5925c88370156561a8d2f7ea5ec159975315fb7a9e2f2

Observation 46a2604c-b7c8-4a68-98c4-c86139737605 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Lossless Token Sequence Compression via Meta-Tokens LoRA: Low-Rank Adaptation of Large Language Models

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:26.494764Z digest=sha256:1ed4b1c5096fbd82fd087839c4322d203ba720bb9ded8e25d647bbac92dc0836

Observation 0427e401-f35a-4281-b7c8-c7fb90d3b9e6 · outbound

This paper cites LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models.

Lossless Token Sequence Compression via Meta-Tokens LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models

Reference 9

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source=arxiv_source observed=2026-08-07T12:14:26.584176Z digest=sha256:2a5013f16694c0d00b084fa93137f647ef173400eb84048074cb56a398548b7a

Observation e04842d1-f9f4-42cc-a4fc-9731001c7f75 · outbound

This paper cites LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression.

Lossless Token Sequence Compression via Meta-Tokens LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:26.649414Z digest=sha256:810dfbfe2a48af7fa37e02ec58ddc2aea761721b0901d10e9c14dcd16e42de98

Observation 4fcd7c68-7826-47ea-a0a4-89a456b443ee · outbound

This paper cites Discrete prompt compression with reinforcement learning.

Lossless Token Sequence Compression via Meta-Tokens Discrete prompt compression with reinforcement learning

Reference 11

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source=arxiv_source observed=2026-08-07T12:14:26.747807Z digest=sha256:361c7d0490d92e7a37e4a8b4a1217654c7988c0dd9d9e70b94f9e7262227ad5b

Observation ca719f35-be5f-4958-865f-d3220a6b7ee5 · outbound

This paper cites Scaling Laws for Neural Language Models.

Lossless Token Sequence Compression via Meta-Tokens Scaling Laws for Neural Language Models

Reference 12

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T12:14:26.868395Z digest=sha256:c8fca99644a9a1236ed03c030d4bd419b7756167879a819e8193c4df338183d4

Observation cbddda90-5c48-4764-9ee9-9bbb0d7ebe00 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

Lossless Token Sequence Compression via Meta-Tokens The power of scale for parameter-efficient prompt tuning

Reference 13

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source=arxiv_source observed=2026-08-07T12:14:27.009456Z digest=sha256:875f2c4bb1005b0d7ffed090e37a8f15dfbfaa86fb6007aecc6673ec7f20aeae

Observation e9abc0f7-4e3a-46d9-bfc6-eae1b3346e33 · outbound

This paper cites How do students use chatgpt as a writing support? Journal of Adolescent & Adult Literacy, 2024.

Lossless Token Sequence Compression via Meta-Tokens How do students use chatgpt as a writing support? Journal of Adolescent & Adult Literacy, 2024

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-07T12:14:31.815120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T12:14:27.138015Z digest=sha256:cee9434b26f9a91024218214fcfc713e3a95ddb27a366ccdbaecf1413c3f9035

Observation 1aacc649-e483-421b-8b98-6d4f55fb8b33 · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation.

Lossless Token Sequence Compression via Meta-Tokens Prefix-tuning: Optimizing continuous prompts for generation

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:27.260705Z digest=sha256:d517a68c55f8eb3f3f375aacc1a2db8f0d91c819a332e72e55b2d3161228f53d

Observation 5e3deea6-3218-4d7f-a50d-0ef26660a5bc · outbound

This paper cites Compressing context to enhance inference efficiency of large language models.

Lossless Token Sequence Compression via Meta-Tokens Compressing context to enhance inference efficiency of large language models

Reference 16

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source=arxiv_source observed=2026-08-07T12:14:27.378173Z digest=sha256:1f7fee8ff5eb7dda68de3ea48a6954bef2a96cec5eed415a562daea739923361

Observation ed4f9f04-76a1-4e96-8940-f9de2ac3c9cb · outbound

This paper cites Prompt Compression for Large Language Models: A Survey.

Lossless Token Sequence Compression via Meta-Tokens Prompt Compression for Large Language Models: A Survey

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:27.469882Z digest=sha256:d750de254fac08043afa40bcf3ac7b753856d0cbdf4328cc385aad22930c4eac

Observation 08a17c38-6d27-44a4-8d2e-13424f9efd05 · outbound

This paper cites 500xCompressor: Generalized Prompt Compression for Large Language Models.

Lossless Token Sequence Compression via Meta-Tokens 500xCompressor: Generalized Prompt Compression for Large Language Models

Reference 18

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source=arxiv_source observed=2026-08-07T12:14:27.618067Z digest=sha256:5d129b511063fc793520b3270e2c50608c0679767ca55804cbe8a87494b9f7ef

Observation 753afd03-84e0-4329-840b-2669a6ce501f · outbound

This paper cites Prompt Compression with Context-Aware Sentence Encoding for Fast and Improved LLM Inference.

Lossless Token Sequence Compression via Meta-Tokens Prompt Compression with Context-Aware Sentence Encoding for Fast and Improved LLM Inference

Reference 19

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:27.735279Z digest=sha256:b7af0b8c4b3069b333327a8f34321d3da2e8eb762cabbae78324d210aee2d75d

Observation 3b229f09-5265-49f3-8d39-652069a6c340 · outbound

This paper cites TCRA - LLM : Token compression retrieval augmented large language model for inference cost reduction.

Lossless Token Sequence Compression via Meta-Tokens TCRA - LLM : Token compression retrieval augmented large language model for inference cost reduction

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:27.817728Z digest=sha256:b01558379d5f778e5f7e184447dbe77a4960006b2ffdcf92aa86c3e09d7bd207

Observation 4548edf7-0830-400b-99fb-d2ac8ce6b213 · outbound

This paper cites RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems.

Lossless Token Sequence Compression via Meta-Tokens RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:27.901241Z digest=sha256:d31e8ee62dd688e6f9366b2a7041805fbd327a5ef83f29e0c6586d7a9b1b0696

Observation a78ab5a1-1be5-48c9-b87d-8bae6b9d3c0e · outbound

This paper cites Learning to compress prompts with gist tokens.

Lossless Token Sequence Compression via Meta-Tokens Learning to compress prompts with gist tokens

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:28.008324Z digest=sha256:cb20877a985aae07bdbe6d33627f859e4259e578d2aff5429d6712c40df57447

Observation a449005e-46f0-4086-a812-121c7abd423e · outbound

This paper cites OctoPack: Instruction Tuning Code Large Language Models.

Lossless Token Sequence Compression via Meta-Tokens OctoPack: Instruction Tuning Code Large Language Models

Reference 23

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:28.092042Z digest=sha256:4ba7360a1e205f11c1c90dc2f4265dfc78f7a7de80033c32a2863728e7f580eb

Observation ad3b4654-ff9b-4a18-a4dd-08d836e0bf38 · outbound

This paper cites Training Software Engineering Agents and Verifiers with SWE-Gym.

Lossless Token Sequence Compression via Meta-Tokens Training Software Engineering Agents and Verifiers with SWE-Gym

Reference 24

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source=arxiv_source observed=2026-08-07T12:14:28.193970Z digest=sha256:175960fdb5522db526c14f01d2cc8bd94850b058c9b0d72cc9bba3d6d720f3ae

Observation 7834589b-7ae5-42c2-9a10-b13825ec2e6b · outbound

This paper cites LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression.

Lossless Token Sequence Compression via Meta-Tokens LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression

Reference 25

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source=arxiv_source observed=2026-08-07T12:14:28.276503Z digest=sha256:95725dfaebdeafcff3201884dbaa0ceff62766c7aed5fea17be6fded43639db2

Observation 1dc2aa01-0efd-4286-984f-54937473e45e · outbound

This paper cites Using the output embedding to improve language models.

Lossless Token Sequence Compression via Meta-Tokens Using the output embedding to improve language models

Reference 26

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verified fuzzy
raw_fallback, observed 2026-08-07T12:14:31.579673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T12:14:28.454678Z digest=sha256:013a0e2eb8de14aefa77be196c1f77149d4049a8a8b0973a5c353933b6500c69

Observation db0c97ab-c68d-4187-896c-51724358ff2d · outbound

This paper cites Bidirectional recurrent neural networks.

Lossless Token Sequence Compression via Meta-Tokens Bidirectional recurrent neural networks

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-07T12:14:31.333868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T12:14:28.601133Z digest=sha256:f51666fd0f5c4127dc83f77cb841a323924f483f150dfce559e7df790035d24e

Observation 2554f203-01e5-4850-be7c-3a50c4507750 · outbound

This paper cites TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning.

Lossless Token Sequence Compression via Meta-Tokens TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning

Reference 28

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source=arxiv_source observed=2026-08-07T12:14:28.757576Z digest=sha256:045eaaf2be50685b273ed8cdf956a8a3265eed6e6bcd8b7178ee566be31d6c47

Observation 2f7a11e8-fa37-429b-8916-9641decd5af2 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Lossless Token Sequence Compression via Meta-Tokens DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 29

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:28.867666Z digest=sha256:0f1eea6eecc2b0e8344b3660baf88e1471b7a3aac9575c695712e03845f8a832

Observation 5e3db8e9-d316-44cd-852d-d0241e984291 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Lossless Token Sequence Compression via Meta-Tokens Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 30

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source=arxiv_source observed=2026-08-07T12:14:29.027570Z digest=sha256:c529953c1c0e4a3ac636662b8fd049a202bdaabdfe521a23462cb6f12da677fd

Observation 513505d5-7868-4ab6-89f9-8e3a7959e2bc · outbound

This paper cites Comparing Traditional and LLM-based Search for Consumer Choice: A Randomized Experiment.

Lossless Token Sequence Compression via Meta-Tokens Comparing Traditional and LLM-based Search for Consumer Choice: A Randomized Experiment

Reference 31

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T12:14:29.141052Z digest=sha256:bf9121d3aa907e785aec2508b561d480b5c15c2ca20f56ec3979b4b412eb5072

Observation b62951e7-98e5-42b9-b8e2-262261f52fbe · outbound

This paper cites Is ChatGPT the Ultimate Programming Assistant -- How far is it?.

Lossless Token Sequence Compression via Meta-Tokens Is ChatGPT the Ultimate Programming Assistant -- How far is it?

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:29.237283Z digest=sha256:16dfd38d2a8e467b4afa9f5e09afbb579b5148a80d81fd5a4c1899e09d855692

Observation f41a06a5-7621-4374-bbb3-96ef156476a1 · outbound

This paper cites Visualizing data using t-sne.

Lossless Token Sequence Compression via Meta-Tokens Visualizing data using t-sne

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:29.347201Z digest=sha256:a4b724ca5698c07af937cb90f21c790b9ab08a37378530084a3892411a53c8bf

Observation f14546f5-dd64-4a54-96de-cf65cb57e2fa · outbound

This paper cites Attention is all you need.

Lossless Token Sequence Compression via Meta-Tokens Attention is all you need

Reference 34

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source=arxiv_source observed=2026-08-07T12:14:29.504040Z digest=sha256:14cdfd24adb986f4c054c38e1a0e13bd4f26b42843cb88a0a31f25823c87aa8b

Observation 6d923931-460a-42b1-9d62-32265b3754ff · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Lossless Token Sequence Compression via Meta-Tokens Chain-of-thought prompting elicits reasoning in large language models

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:29.678041Z digest=sha256:f804a9b276963df8ac1347b7ea5945a63d4dfa457bd9e78c180473490e4e0602

Observation fea7c8d2-72b5-4957-a520-fad00302d1b7 · outbound

This paper cites Scaling embedding layers in language models.

Lossless Token Sequence Compression via Meta-Tokens Scaling embedding layers in language models

Reference 36

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:29.853601Z digest=sha256:e1ea6038934329ea57296925cae81fb1ae5f26212e28381eef6c1a0f656ab8c7

Observation 5ad16a02-f095-4694-b772-8c7b0a6f3dde · outbound

This paper cites AdaComp: Extractive Context Compression with Adaptive Predictor for Retrieval-Augmented Large Language Models.

Lossless Token Sequence Compression via Meta-Tokens AdaComp: Extractive Context Compression with Adaptive Predictor for Retrieval-Augmented Large Language Models

Reference 37

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:29.976307Z digest=sha256:daa537b9054241d71685c8ec12ba1985cd8e56b1523a5a4d33aaa6d3552df34e

Observation 293d88d4-6bc0-41e5-93fd-81208bc22271 · outbound

This paper cites LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models.

Lossless Token Sequence Compression via Meta-Tokens LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models

Reference 38

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no resolver link, observed 2026-08-07T12:14:30.164602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:30.164602Z digest=sha256:53b4e5442b189e5e61bcd35230c80fbb127e4854ee47192ac0c6d45f8e518974

Observation 853e204c-d461-41f6-8903-bd3e65ed74ee · outbound

This paper cites Ziv and A.

Lossless Token Sequence Compression via Meta-Tokens Ziv and A

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T12:14:30.282576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:30.282576Z digest=sha256:cb0daf56f73fdeca6b0d97183b098f1b48d852f1b6aa06627cc7758de4459cf1

Pith citing papers

Observation 8416e4d6-413f-4073-a2d2-65699fc57ff2 · inbound

From Token to Token Pair: Efficient Prompt Compression for Large Language Models in Clinical Prediction cites this paper.

From Token to Token Pair: Efficient Prompt Compression for Large Language Models in Clinical Prediction Lossless Token Sequence Compression via Meta-Tokens

Reference 9

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verified exact
arxiv_id, observed 2026-05-13T06:47:27.320539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-13T06:44:04.493625Z digest=sha256:42837f8962fba838afbb4695d50357030b8af8fae73bbd182e93b2256baca0f5