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Paper Citation Record · LEDGER

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs

As of 8 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2505.19481.

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

pith.paper-citation-record.v1
2505.19481 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:16:47.986431Z

measured 37 of 37 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 2026-08-02T08:31:01.469310Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

  • verified exact2
  • verified fuzzy9
  • unresolved25
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 84d2d838-09e4-437a-bb71-a379e9e77c1c · outbound

This paper cites Phi-4 Technical Report.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs Phi-4 Technical Report

Reference 1

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no resolver link, observed 2026-08-07T14:16:44.685301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:16:44.685301Z digest=sha256:fbb290a2bc66f633cf7921d3086943d49f7f65a60a179dcbbf565f9ceccc3a9c

Observation 3ddd0015-a0aa-45d3-930b-5045c122ada8 · outbound

This paper cites Risk and return in high- frequency trading.Journal of Financial and Quantitative Analysis,54993–1024.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs Risk and return in high- frequency trading.Journal of Financial and Quantitative Analysis,54993–1024

Reference 2

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raw_fallback, observed 2026-08-07T14:16:50.282112Z

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=2026-08-07T14:16:44.754187Z digest=sha256:78f7a0e1c6453a939ce12f83f556891854498ce5cdfe64774894b252105ce5ea

Observation 2c98747c-c0a5-448b-aa32-ccfaadd69797 · outbound

This paper cites Improving Factuality and Reasoning in Language Models through Multiagent Debate.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs Improving Factuality and Reasoning in Language Models through Multiagent Debate

Reference 3

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no resolver link, observed 2026-08-07T14:16:44.824215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:16:44.824215Z digest=sha256:12f4b57384fec4eb5efdfa073fe20716d415437a793c7ab8ee9c4f719efb4c60

Observation a51b0919-7104-4e7b-a15e-6b35930f6708 · outbound

This paper cites E.(1967).

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs E.(1967)

Reference 4

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raw_fallback, observed 2026-08-07T14:16:50.123459Z

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=2026-08-07T14:16:44.909069Z digest=sha256:ebba414b5c5bdb0ad80852410fa4fde39020170fbf3d254a290e8ff95dbbf68e

Observation 2109fd91-028e-47fc-acf6-72e90429ab73 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:16:45.026717Z digest=sha256:a00aa931a12528d812535c058f63f963871c35b7197f93766f279f98b1076495

Observation 0a04d155-2ff9-4621-967e-c0eac87d9acf · outbound

This paper cites Reinforcement Learning Equilibrium in Limit Order Markets.Journal of Economic Dynamics and Control,144.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs Reinforcement Learning Equilibrium in Limit Order Markets.Journal of Economic Dynamics and Control,144

Reference 6

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verified fuzzy
raw_fallback, observed 2026-08-07T14:16:49.925155Z

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=2026-08-07T14:16:45.172356Z digest=sha256:c5a2eae361db3e84a0aecbe6dcfcf56a1639a20b0c72dd83db459ee13c265a68

Observation 061c7b6a-8acf-416f-8cd8-aeb6b27e50cf · outbound

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

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

Reference 7

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no resolver link, observed 2026-08-07T14:16:45.310548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:16:45.310548Z digest=sha256:e36c004c195abee267ce4069b49f66b1e56d35c749d7bce8f9653551d04d5200

Observation 5ef6b394-a704-4f99-9dea-3ab8253335be · outbound

This paper cites TurboAttention: Efficient Attention Approximation For High Throughputs LLMs.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs TurboAttention: Efficient Attention Approximation For High Throughputs LLMs

Reference 8

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T14:16:45.426202Z digest=sha256:8cead5d2fa640b50b41c1bb49743c4c02d302a8deb62b7aa8894bca8a93fcefb

Observation 704c6af7-e828-4af3-a14c-e0b581fc656f · outbound

This paper cites GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM

Reference 9

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no resolver link, observed 2026-08-07T14:16:45.550536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:16:45.550536Z digest=sha256:6e6da242e8ea5eed8e3dcaa86ae1f301551ea0e5ce78da87d71c37fd5e8807a6

Observation 7784396c-b9d5-4298-b30f-27e412081587 · outbound

This paper cites M.,Uszkoreit, J.,Le, Q.andPetrov, S.(2019).

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs M.,Uszkoreit, J.,Le, Q.andPetrov, S.(2019)

Reference 10

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raw_fallback, observed 2026-08-07T14:16:49.780697Z

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=2026-08-07T14:16:45.633428Z digest=sha256:4db43f8c509e18a2257bb595b062416c02ea0a4d9bf7c9b08c884219d16e1296

Observation db4c5c80-bcd4-4458-8166-3f2475342433 · outbound

This paper cites Efficient Memory Management for Large Language Model Serving with PagedAttention.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs Efficient Memory Management for Large Language Model Serving with PagedAttention

Reference 11

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T14:16:45.756234Z digest=sha256:8666a5a0bab1aad9df5b6b5fb0fb0844f7db4e238082f29fd77ce4b9333a795b

Observation 8fb75c14-1379-48bc-bb2b-e1624725b7a9 · outbound

This paper cites OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:16:45.817948Z digest=sha256:ded11bc0c3083dec2baf3e420370b15ec8d87fd7feebc7952e2faaf13fc591cb

Observation bf0183ab-eb04-4312-9e9d-257fab5dba5a · outbound

This paper cites Camel: Communicative agents for" mind" exploration of large language model society.Advances in Neural Information Processing Systems,3651991–52008.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs Camel: Communicative agents for" mind" exploration of large language model society.Advances in Neural Information Processing Systems,3651991–52008

Reference 13

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verified fuzzy
raw_fallback, observed 2026-08-07T14:16:49.582197Z

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=2026-08-07T14:16:45.906545Z digest=sha256:753a8b5bd621c4a11f04d2266f0b5ae9bbb9bf501dab68dca2127591d71cfc9a

Observation cdabc844-48c4-4402-99c3-b3fd05469cb3 · outbound

This paper cites Svdquant: Absorbing outliers by low-rank components for 4-bit diffusion models.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs Svdquant: Absorbing outliers by low-rank components for 4-bit diffusion models

Reference 14

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T14:16:46.018741Z digest=sha256:684319316bf0258f11ea6bf9c63fd968135bb3c916acc5f0ae8d0e61da132566

Observation 770390de-bbde-42c8-987f-66949810bf77 · outbound

This paper cites QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving

Reference 15

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no resolver link, observed 2026-08-07T14:16:46.099993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:16:46.099993Z digest=sha256:a0ea18e44d5b8590aa8199cb551a7e99a7a2af37607141697331f5d8b5176011

Observation dba7ae56-c724-417d-b318-3223159b3d4a · outbound

This paper cites Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach

Reference 16

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source=pdf_text observed=2026-08-07T14:16:46.176908Z digest=sha256:4a47306677113eda14d66c0936ea18de8c7edf5eb2678573c960949b4180cde9

Observation 91f7cd50-d972-48c5-a496-4a51452519dc · outbound

This paper cites Pointer sentinel mixture models.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs Pointer sentinel mixture models

Reference 17

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raw_fallback, observed 2026-08-07T14:16:49.407735Z

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=2026-08-07T14:16:46.257128Z digest=sha256:86506dbb70cd69009019a173f28219e00c1f0c610a4594745210f470bd393bc2

Observation b98d44a7-93d2-4a11-9e1c-b97f5cae9958 · outbound

This paper cites FP8 Formats for Deep Learning.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs FP8 Formats for Deep Learning

Reference 18

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source=pdf_text observed=2026-08-07T14:16:46.324314Z digest=sha256:2852cfe002490f9a36042046d15d169ddcef9d92ad81426ea8a59f0960ad33b1

Observation 8c873734-c063-4d01-b4e4-58e9d511d51c · outbound

This paper cites DIAMBRA Arena: a New Reinforcement Learning Platform for Research and Experimentation.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs DIAMBRA Arena: a New Reinforcement Learning Platform for Research and Experimentation

Reference 19

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verified exact
local_arxiv, observed 2026-08-07T14:16:48.580691Z

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=2026-08-07T14:16:46.386384Z digest=sha256:e72956047959dce9cdad9ce23dfa515d61c39be21a311da0899ccafc83a69e83

Observation c3820474-25b1-4a27-a874-f80bbc3cef51 · outbound

This paper cites A Practical Mixed Precision Algorithm for Post-Training Quantization.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs A Practical Mixed Precision Algorithm for Post-Training Quantization

Reference 20

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source=pdf_text observed=2026-08-07T14:16:46.449162Z digest=sha256:188e4dbcdfe2e02258fcf7f788d6beb955904601aa1f4b90888c4154cfd5807d

Observation d09da9e9-10f6-40ae-9728-7f9ce9385803 · outbound

This paper cites Qwen2.5 Technical Report.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs Qwen2.5 Technical Report

Reference 21

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source=pdf_text observed=2026-08-07T14:16:46.527711Z digest=sha256:cbd4721bb63ca5ebd1f15cfa0db06ed87c241e3f416ae1574937eb3b6d685b35

Observation 69770212-85d2-4c43-9cde-adfd7935c680 · outbound

This paper cites The StarCraft Multi-Agent Challenge.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs The StarCraft Multi-Agent Challenge

Reference 22

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no resolver link, observed 2026-08-07T14:16:46.575546Z

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source=pdf_text observed=2026-08-07T14:16:46.575546Z digest=sha256:5631f92c8bf46f835bbd66913760e8e54ed9af90f5b54f0510f30d12138c912d

Observation eb7d2383-827a-42cf-b567-47861ccc6daf · outbound

This paper cites SCROLLS: Standardized CompaRison Over Long Language Sequences.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs SCROLLS: Standardized CompaRison Over Long Language Sequences

Reference 23

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no resolver link, observed 2026-08-07T14:16:46.664378Z

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source=pdf_text observed=2026-08-07T14:16:46.664378Z digest=sha256:9adf86b38854297683b34f2a02e3b092ee6f1a2327c388f41af911ffa9dfc304

Observation 21bb93ef-e647-4911-8c7d-282f4508b882 · outbound

This paper cites Reflexion: Language Agents with Verbal Reinforcement Learning.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs Reflexion: Language Agents with Verbal Reinforcement Learning

Reference 24

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source=pdf_text observed=2026-08-07T14:16:46.781573Z digest=sha256:4d558c93ac7864fff8fe248dd4f112ac8fc17b9b4829719022ef704e7dfacdb4

Observation 1bbdd1aa-68d7-42be-bd80-cb373336e275 · outbound

This paper cites M.(2010).

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs M.(2010)

Reference 25

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raw_fallback, observed 2026-08-07T14:16:49.255214Z

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=2026-08-07T14:16:46.915619Z digest=sha256:5cfdd83f7abf256d5a53f97e56d7f9254889e1122859bf95d3ee0878ae42a3a0

Observation e0c4bd7e-11da-4a31-8a9d-3046f10ecb7f · outbound

This paper cites Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance

Reference 26

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verified exact
local_arxiv, observed 2026-08-07T14:16:48.351117Z

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=2026-08-07T14:16:47.026428Z digest=sha256:880babbad6d1ec0aeba1bce7448903f888d72f5d118199c6a455f46bb6ac17b6

Observation 0f9e06fa-926e-432a-b86a-4d2bedd47ee3 · outbound

This paper cites Gemma 3 Technical Report.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs Gemma 3 Technical Report

Reference 27

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no resolver link, observed 2026-08-07T14:16:47.031138Z

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source=pdf_text observed=2026-08-07T14:16:47.031138Z digest=sha256:d073691cca1003ed5fcc2549d2303dc20147dab6f756e43e769d0f820f67243b

Observation 36b0b688-e51f-4466-842c-d5b6b85c391b · outbound

This paper cites SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 28

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no resolver link, observed 2026-08-07T14:16:47.052422Z

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source=pdf_text observed=2026-08-07T14:16:47.052422Z digest=sha256:71a3436a1b1bf25967a927ef4bed28319f1196dbffc92fffc6cc644452801fa4

Observation 3c57875b-06e5-4221-8c4f-c999c258cb9a · outbound

This paper cites J.,Han, X.,Fu, X.,Zhong, T .,Zeng, J.,Song, M.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs J.,Han, X.,Fu, X.,Zhong, T .,Zeng, J.,Song, M

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-07T14:16:49.086346Z

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=2026-08-07T14:16:47.190120Z digest=sha256:98aebacf365c9731728498b57649349903dd89c7771e229f18bb4a25adef60fe

Observation f25ee928-77ca-4228-97cf-f5bf27d343fc · outbound

This paper cites AI Metropolis: Scaling Large Language Model-based Multi-Agent Simulation with Out-of-order Execution.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs AI Metropolis: Scaling Large Language Model-based Multi-Agent Simulation with Out-of-order Execution

Reference 30

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source=pdf_text observed=2026-08-07T14:16:47.386502Z digest=sha256:dfdaf403b0bde6019f0dcee0fd230296c95dc32721809e16c4c45bcc4bb5c270

Observation fa336cfe-16eb-4ce2-a618-5c0dbaac1711 · outbound

This paper cites R.andCao, Y .(2023).

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs R.andCao, Y .(2023)

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-07T14:16:48.928927Z

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=2026-08-07T14:16:47.528330Z digest=sha256:eb9329c777df5e19100af113e2f4a184435a3b461c2636b8609a6ef528f80396

Observation 2d2632fa-ff0d-4efb-8bf8-dc3a53a713a8 · outbound

This paper cites FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design

Reference 32

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no resolver link, observed 2026-08-07T14:16:47.619235Z

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source=pdf_text observed=2026-08-07T14:16:47.619235Z digest=sha256:466b6de18c29ceabfef94ca67610c500d59a249c510835afa2936fb43eb908c0

Observation 5e2ce555-b285-4428-a4e5-b1e7c38b7e90 · outbound

This paper cites A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist

Reference 33

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source=pdf_text observed=2026-08-07T14:16:47.697800Z digest=sha256:b7b3a34535df2d48652e599d4f458721e41ac147010180b4a98904c78e316117

Observation da10163d-d38c-4102-ba5e-e01fcc73c35f · outbound

This paper cites Atom: Low-bit Quantization for Efficient and Accurate LLM Serving.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs Atom: Low-bit Quantization for Efficient and Accurate LLM Serving

Reference 34

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:16:47.771772Z digest=sha256:11bb0d7b70d30d360775ca2599dc518d7d3d1b57727aef07bf1cae8bfbb13386

Observation 5d7a3c2d-ba7d-49ac-908b-6ffb0a82c59f · outbound

This paper cites SGLang: Efficient Execution of Structured Language Model Programs.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs SGLang: Efficient Execution of Structured Language Model Programs

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-07T14:16:47.871852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:16:47.871852Z digest=sha256:590afd9938711208d82e3043574ed321f1506c6f90eed4817d18bad06a9531e5

Observation 5e5b4846-3827-4d00-9ba3-99b763f58db1 · outbound

This paper cites BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T14:16:47.986431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:16:47.986431Z digest=sha256:a6558c4a05706a92dd66ce3a868ffba35b5f34ea6e5ee9d82d20fae5be841c88

Pith citing papers

Observation 5fd66efb-c8a1-42e6-9e11-6413dbdd1f35 · inbound

Memory in the Loop: In-Process Retrieval as Extended Working Memory for Language Agents cites this paper.

Memory in the Loop: In-Process Retrieval as Extended Working Memory for Language Agents Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-02T08:31:01.469310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T08:31:01.469310Z digest=sha256:43ef385007bfca196aca43f8e46bce9a2c9e29ed36bf824a13121a5df7e07ad0