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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 10 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 2026-08-10T06:31:04.303077+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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:16:44.685301Z digest=sha256:d6288008b1b6a5903f2d80fbe0e6aa9ca024747f56abb4c356f2c6972bfda34a

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T14:16:44.754187Z digest=sha256:b81831c0889f9c4ec6112ac7757d164b75bc7a0da58f7ba5dc296e36fc487be0

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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source=pdf_text observed=2026-08-07T14:16:44.824215Z digest=sha256:4959bae80cfd42c7c4a988f4e4833950614bb1967e32b7933b3a31da901691e1

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T14:16:44.909069Z digest=sha256:ab975cc101355660d93f22afbbad3cd8c2b68ed94e98d2f9401c2879dda02da2

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

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source=pdf_text observed=2026-08-07T14:16:45.026717Z digest=sha256:d6ca5be0c7aff4a330b80b7aed25a804e0f43298bc2c7df3ea9e32895e4f2ba0

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T14:16:45.172356Z digest=sha256:29c19c3b3b92fd60057b500bb57df6bbbec0f5a9cd35b156062d64e9af9a327b

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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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T14:16:45.310548Z digest=sha256:a56ba080d084aec3e69a404204687e1c91622c5551086db295e04c0b7127e611

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=pdf_text observed=2026-08-07T14:16:45.426202Z digest=sha256:a0b49c25e5d3133c42b81708bbaa463cdb36253eaf37557cadfc3f27765c1d5c

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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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T14:16:45.550536Z digest=sha256:aa535780e6b80db95c6a78b3e511c31b66466d1691d2e453549ff002825a8227

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T14:16:45.633428Z digest=sha256:8b730832f255081d1ca6a97873769a2876f66aae1c857cfed5fb498cc94d0e2a

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=pdf_text observed=2026-08-07T14:16:45.756234Z digest=sha256:0c8765f6bf3ffaaf8710db0daa63e3fef0b43b789d7351cadc9ce91f3c275779

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=pdf_text observed=2026-08-07T14:16:45.817948Z digest=sha256:81e920994fc30c93f6a87bf168eed483d426807585c8b0681bf3bdeadfb91a2f

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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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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T14:16:45.906545Z digest=sha256:20404f023d0d73399b188935aaa340cdab40b7f62405a226d47d4f09d19d5c4d

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=pdf_text observed=2026-08-07T14:16:46.018741Z digest=sha256:2dacdad50bee6a1c5219c4ec224767fc2fd5341645cf0878bc46fe16435712d0

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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source=pdf_text observed=2026-08-07T14:16:46.099993Z digest=sha256:d3e0c0ea60de709fdb73ff1fadba22f6465f1c449a9f2f7919353d73b7da304f

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=sha256:682636d28c6adeeafe287233a329d5db76114260a33cb23fafd9e627d21e1840

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T14:16:46.257128Z digest=sha256:9a200690411d633d9004f6c7e1998aab67705c389f3f88da265baa12052f0a11

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:e4dc87c1d39d12b8e5c79440b3a5a734a6afc7ff9e51bc0d635627e5adaa97a7

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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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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T14:16:46.386384Z digest=sha256:16657d0bd70f859c7dd978756929be5ebec373c27b56e093ba862a84de684a67

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=sha256:54461b1397557a974aed7aa16fa5b54801e83ed12b015966f9370f7d271bb597

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=sha256:870d5332b598efd8f63b289bbbb8f928c3075ecbd5f39377d475c4a370f591f9

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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source=pdf_text observed=2026-08-07T14:16:46.575546Z digest=sha256:65595583ec1ed8a29b84d3f0dfc26b91f62ef09b3c877e53c3e5dc02b97abb38

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=sha256:02aa902b28d471447692eaf85f850a0e1493dc4cce4b4eaed32ee1e45328c8fe

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=sha256:4800884c84aca2e17ffdfcbe0dae633a0d1c9c9682a7ccc73e55c9d4c29c3ed7

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T14:16:46.915619Z digest=sha256:fad0efcc579634f673ea2db194875ce0eeceab422b240cc8a47d7bfc940abc03

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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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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T14:16:47.026428Z digest=sha256:193bd1746b62869123138a59944c511b9465df52e262fad9af8d0422a4ad4a6e

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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source=pdf_text observed=2026-08-07T14:16:47.031138Z digest=sha256:dfe5e9dba068d410bcfc4762b21b21983d5484e863dfbd427b114345b66cb71d

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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source=pdf_text observed=2026-08-07T14:16:47.052422Z digest=sha256:eaba188d800e3f97c527128cf90e3e04c2a060f56542b7e20cd473a1008061af

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T14:16:47.190120Z digest=sha256:302579f08712239cde4c6b6023ba2d46e51f86402d788d719f8490b9f9fefec4

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=sha256:58fd901940ed7dea90429707f929bf6aa35deec6d5d2817e9da74eba78a6f143

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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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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T14:16:47.528330Z digest=sha256:ec134422a01bc8c2ec64abb11556fe102c6615be52985e75bb054c639c3a0e56

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=sha256:4aac5a4ee3a8ac342278de9d35f68a9164c216075e6b72b43cf7394a650b98b8

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=sha256:8e1a086018a98ecdaa2196265ba37d4a68afc28a4fe1edb28bce79d06785e70c

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:a4c15a4c3c641de0eeb152df76406521caf8869b12f818327956726388183df3

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.

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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=sha256:17cc7fb4d29932308a05ca21c1efde3f6765426c2087124b96be18575528e25e

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=sha256:000a475057dd1a97cb75269fc3be0967eb52c3f7a1db41a5b7e9e9a5a9311673