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Paper Citation Record · LEDGER

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs

As of 14 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2506.06401.

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

pith.paper-citation-record.v1
2506.06401 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:19:36.207209Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

38 of 38 outbound references displayed

  • verified exact0
  • verified fuzzy8
  • unresolved28
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 84c211cc-f138-49d4-b275-f7286cd7d03e · outbound

This paper cites StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving

Reference 3

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Observation 478b8067-e0d1-4b46-84e7-f5feee773f5a · outbound

This paper cites Decomposed Prompting: A Modular Approach for Solving Complex Tasks.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Decomposed Prompting: A Modular Approach for Solving Complex Tasks

Reference 6

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source=pdf_text observed=2026-08-07T10:19:33.585059Z digest=sha256:7a1933b0df957006ac437027afe20234015150d051fb4704a298246ba66eeaca

Observation 3c8a36d9-357a-45b8-bae0-74e0b38c34b1 · outbound

This paper cites Less is More: Summary of Long Instructions is Better for Program Synthesis.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Less is More: Summary of Long Instructions is Better for Program Synthesis

Reference 7

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local_arxiv, observed 2026-08-07T10:19:36.532192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 141d7c1f-ec64-41d8-b636-43957099702b · outbound

This paper cites Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models

Reference 8

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source=pdf_text observed=2026-08-07T10:19:33.726727Z digest=sha256:c3989f91b24786ea67c361dc867e5a407fd889bdc5d0fdff62980f7b4fd368b7

Observation d0814cf7-7aaf-448f-8f77-61840729be00 · outbound

This paper cites Faithful Chain-of-Thought Reasoning.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Faithful Chain-of-Thought Reasoning

Reference 10

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source=pdf_text observed=2026-08-07T10:19:33.925730Z digest=sha256:e4d801a3fff94519701be5e969af54d0e750086d022b74e098ac58210f21afcd

Observation b656e7f2-59e4-4b75-816f-06d10f376c0a · outbound

This paper cites Coherence boosting: When your pretrained language model is not paying enough attention.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Coherence boosting: When your pretrained language model is not paying enough attention

Reference 11

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source=pdf_text observed=2026-08-07T10:19:34.045731Z digest=sha256:f7a0df8c5b70c4f4258b8fb09bd0e944a4ab137c515db174bcb4687024309257

Observation a72b3b7a-e075-41a1-a8f9-6ab10daa98dd · outbound

This paper cites Hugging Face.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Hugging Face

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation d46cbc78-a18d-4a5e-8f48-114d9fbd4dd6 · outbound

This paper cites SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning

Reference 14

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source=pdf_text observed=2026-08-07T10:19:34.293483Z digest=sha256:47bd227b3a8874de7e6468480495b2233c809f7745e993f7868198319e902d24

Observation 2391460a-aa37-4a85-8b16-94b5fc9b06b0 · outbound

This paper cites Lila: A Unified Benchmark for Mathematical Reasoning.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Lila: A Unified Benchmark for Mathematical Reasoning

Reference 15

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source=pdf_text observed=2026-08-07T10:19:34.414500Z digest=sha256:ab37105a93b07561351a03039343d218a1b97a6d6b6b7dcaf277c5841b813003

Observation 335962a1-218e-42d1-8b1f-0498faa6135b · outbound

This paper cites an unresolved cited work.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Unresolved cited work

Reference 16

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 5dd6f083-aaf7-4fdd-b60c-20ccd126c663 · outbound

This paper cites Accessed: 2025- 02-14.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Accessed: 2025- 02-14

Reference 17

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T10:19:34.461311Z digest=sha256:bda5fbe96cfdaa7a5b4d114cb5efd6d0f6e236aaeb155e13674767c6d24371f0

Observation d8794b37-2891-4372-9247-1828c320de3f · outbound

This paper cites Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies

Reference 18

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source=pdf_text observed=2026-08-07T10:19:34.523636Z digest=sha256:92ed3a776194ff9c93b2e806b7ffda2e33429123f41a7a51cbc0095ca1459bc5

Observation f24c5fe0-5490-45fb-b1ff-36682edfd381 · outbound

This paper cites Measuring and Narrowing the Compositionality Gap in Language Models.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Measuring and Narrowing the Compositionality Gap in Language Models

Reference 19

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source=pdf_text observed=2026-08-07T10:19:34.611121Z digest=sha256:0bee45969cc2ac700ded7b8cb7b5361ec8a119da0d5ec9e67cb7cf9eae8cd0c8

Observation c1c5b9b8-fa9c-4d5b-8bfe-23dae174d260 · outbound

This paper cites Automatic Prompt Optimization with "Gradient Descent" and Beam Search.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 20

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source=pdf_text observed=2026-08-07T10:19:34.725290Z digest=sha256:3f5fd57cadbe9771c03e58ec2dd2c197de6212b85cb48f485ae75866ec02a887

Observation 0a37a750-c4fe-4062-b1d0-b2aeb49622d9 · outbound

This paper cites https://www.reuters.com/technology/chatgpt- fever-spreads-us-workplace-sounding-alarm-some- 2023-08-11.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs https://www.reuters.com/technology/chatgpt- fever-spreads-us-workplace-sounding-alarm-some- 2023-08-11

Reference 21

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation df38ad69-e782-4b7e-9d30-5a9c74ea715b · outbound

This paper cites Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models

Reference 22

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Observation e0cc99d7-c345-4fed-b8b9-667a29d40b7e · outbound

This paper cites Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 23

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source=pdf_text observed=2026-08-07T10:19:34.916672Z digest=sha256:7a92fa322bdb23bf606e602b4b6a88cd4621abbeee3613ede2476e42f3887653

Observation e7116de8-2f33-4d51-8430-0cfbd7a757eb · outbound

This paper cites AutoHint: Automatic Prompt Optimization with Hint Generation.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs AutoHint: Automatic Prompt Optimization with Hint Generation

Reference 24

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source=pdf_text observed=2026-08-07T10:19:34.977087Z digest=sha256:7be267ed86193a1431b7ac5560963af1b27dac5df93905a0ea75dfeb0d30c892

Observation c037001f-5feb-4870-bffa-896416e120b3 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 25

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source=pdf_text observed=2026-08-07T10:19:35.030366Z digest=sha256:608c3855c2d0cd7edd10f101ce89c9dec80e549908666474ad7c9b1f790c370c

Observation 0e60339d-5a4a-41cb-b92a-c1acc6c5b01e · outbound

This paper cites Efficient Large Language Models: A Survey.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Efficient Large Language Models: A Survey

Reference 26

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source=pdf_text observed=2026-08-07T10:19:35.095186Z digest=sha256:ab039bc1814d9c18ddb349b37dac22ae9b118bb98aaa7f17f916f3a1c4516def

Observation 680eaec7-e165-4dc5-aaab-9bc77f0b5f07 · outbound

This paper cites PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization

Reference 27

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source=pdf_text observed=2026-08-07T10:19:35.176132Z digest=sha256:fdac7e88961f9f0dbd90898acac2375209503367452da6888bbc9df925f7eaec

Observation 8688fd6e-c713-4618-8bef-ff6bcb32303a · outbound

This paper cites A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems

Reference 28

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source=pdf_text observed=2026-08-07T10:19:35.285497Z digest=sha256:5e9db0c3c976c416e95be16e10e01f748e97dddba98eb47d1448701657a7c3fd

Observation a42c6d9c-072e-4a7e-9288-e4fba7b6bcd0 · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 29

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source=pdf_text observed=2026-08-07T10:19:35.384672Z digest=sha256:4a1b27aa5b79892d2b8c8266ae8bb13cd13400f2f9e58405f1a6769172dab491

Observation e67e8a53-bd98-4acd-991f-e98fa4efd42a · outbound

This paper cites Self-Discover: Large Language Models Self-Compose Reasoning Structures.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Self-Discover: Large Language Models Self-Compose Reasoning Structures

Reference 30

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source=pdf_text observed=2026-08-07T10:19:35.465699Z digest=sha256:af4f616d944788f015c368fa91da29a1b11f2b43f554483dc74154e372d246a6

Observation 8e9aa06f-3744-4b70-b859-eea49344e367 · outbound

This paper cites Today is 3/5, and it is Jane’s second time in the year 1973 to see a me- teor shower. What is the date 24 hours later in MM/DD/YYYY?.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Today is 3/5, and it is Jane’s second time in the year 1973 to see a me- teor shower. What is the date 24 hours later in MM/DD/YYYY?

Reference 31

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 55bcc3b6-e191-4906-a515-786d6e96d56e · outbound

This paper cites Therefore, we can get the general and instructive solution:.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Therefore, we can get the general and instructive solution:

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T10:19:35.632877Z digest=sha256:b08ee97c5f3e79b0cfd30daa4911e99b4064cc71f363f8d188a0de8af46a185c

Observation d787bcc5-ee38-46a0-9136-3f75b6c3e5b4 · outbound

This paper cites So the answer is (D).

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs So the answer is (D)

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T10:19:35.824123Z digest=sha256:3bb7d25243a3787505a70bc3bb27cc378dd126215300bcbd8747a44c6f33a8d8

Observation 2c4e3490-0212-48de-a4f3-5a2931cdf04b · outbound

This paper cites an unresolved cited work.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Unresolved cited work

Reference 1937

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 121ae80f-ce96-420b-9ccb-30f655b76f3b · outbound

This paper cites an unresolved cited work.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Unresolved cited work

Reference 1958

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source=pdf_text observed=2026-08-07T10:19:35.928053Z digest=sha256:d05a168729c025e1b55d90aedfa18dc7e85cd733a7c0da85be3cba3667049828

Observation 509487d3-cdbc-480f-af2f-cfd66753086b · outbound

This paper cites they" might be referring to. There are two possible referents for.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs they" might be referring to. There are two possible referents for

Reference 1963

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verified fuzzy
raw_fallback, observed 2026-08-07T10:19:37.053128Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T10:19:35.994927Z digest=sha256:05891ca2b433e1f80d54a8803e4bed0f113a97e5a40bcbb417df85755b2ea6d4

Observation fba009c1-c80b-4882-8269-07421df1aae6 · outbound

This paper cites an unresolved cited work.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Unresolved cited work

Reference 1994

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T10:19:36.207209Z digest=sha256:8b5b61a0f7168a9ba509271a85b19f4dc79ec00de1c83a33447bbd268a502b16

Observation 07f959b0-6a52-41db-856c-a0e9ac1c73a0 · outbound

This paper cites Amongst all the options, the only movie similar to these ones seems to be Forrest Gump (comedy, drama, romance; 1994).

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Amongst all the options, the only movie similar to these ones seems to be Forrest Gump (comedy, drama, romance; 1994)

Reference 1995

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verified fuzzy
raw_fallback, observed 2026-08-07T10:19:36.791037Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T10:19:36.132943Z digest=sha256:52d6ab01b10130a37202566a9daecfd918e10e80da6c279adf829b0a00f48395

Observation 2d5a219a-2126-4569-acc2-e368acba5b2d · outbound

This paper cites So the answer is (C).

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs So the answer is (C)

Reference 2000

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verified fuzzy
raw_fallback, observed 2026-08-07T10:19:36.907655Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T10:19:36.067775Z digest=sha256:2b38ee7971a230ff9b18c01c698ae742e65e15651f8b662848a0c26e9a16e356

Observation d1c38d42-6efb-4843-8285-af388f1a5f6e · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs On the Opportunities and Risks of Foundation Models

Reference 2021

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:33.234769Z digest=sha256:162940f4cd5cfd1709705dde589a9269b2a7b9f628fa68d78c973a6dc6722e2f

Observation 8b87cf41-03fa-48db-8086-101b0e1995aa · outbound

This paper cites Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks

Reference 2022

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source=pdf_text observed=2026-08-07T10:19:33.303554Z digest=sha256:ef5e48eda8972358ce9c6185e186ebffec394c87159b7e132f72156aa20a4e32

Observation ea991fb9-6da8-45a8-9831-72fc6250e744 · outbound

This paper cites MathPrompter: Mathematical Reasoning using Large Language Models.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs MathPrompter: Mathematical Reasoning using Large Language Models

Reference 2023

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unresolved
no resolver link, observed 2026-08-07T10:19:33.505707Z

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source=pdf_text observed=2026-08-07T10:19:33.505707Z digest=sha256:0a1b08663ef67cd5438eefb93ac7f98319c61444ccbffac8c5fbd7431a710be9

Observation 765b85e9-5ef7-497a-a058-7063fb065305 · outbound

This paper cites Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts

Reference 2024

Resolution
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no resolver link, observed 2026-08-07T10:19:33.848237Z

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source=pdf_text observed=2026-08-07T10:19:33.848237Z digest=sha256:84cd6dbf5c8699aeaf3ee9746c49b519278f1b278415c8b35a556b4f70ac5c07

Observation 9010ed4a-642e-4d79-94d5-c55e80172dd0 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2025

Resolution
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no resolver link, observed 2026-08-07T10:19:33.433284Z

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source=pdf_text observed=2026-08-07T10:19:33.433284Z digest=sha256:5ad4175f9051e99038815b794d08b64c8013e2e4a1524eb6555799bdcce50842

Pith citing papers

No inbound Pith citation observations are available.