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Paper Citation Record · LEDGER

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning

As of 19 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2505.18831.

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

pith.paper-citation-record.v1
2505.18831 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:27:50.052728Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:20:04.591495Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T05:20:04.993495Z

Reference resolution

39 of 39 outbound references displayed

  • verified exact3
  • verified fuzzy0
  • unresolved34
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 275b4e78-d049-43e7-9e7d-499a0839ab6b · outbound

This paper cites an unresolved cited work.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Unresolved cited work

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:27:49.741454Z digest=sha256:5ee2853d579db8b0ffa2d887aaaffe6930aeb22f32a2a0a8e11072a8f7aaf340

Observation 031634fd-eb23-4a55-9843-b3d924c0a3cd · outbound

This paper cites Adapting Knowledge Prompt Tuning for Enhanced Automated Program Repair.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Adapting Knowledge Prompt Tuning for Enhanced Automated Program Repair

Reference 2

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local_arxiv, observed 2026-08-07T14:27:51.097726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:27:49.757251Z digest=sha256:7980cdf78faaaf99cfad1f345815ded59b58edb53197b31b5d0e06b7ed6f740e

Observation 64778890-b6a6-4654-a09a-bf2c576f6b6b · outbound

This paper cites MARS: a Multimodal Alignment and Ranking System for Few-Shot Segmentation.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning MARS: a Multimodal Alignment and Ranking System for Few-Shot Segmentation

Reference 3

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local_arxiv, observed 2026-08-07T14:27:51.072304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:27:49.765545Z digest=sha256:97734b9d0034b1da77268c94e180cf18a53c62bf94b426b7b0df0ab2e8c6ff2b

Observation 26421d0a-b553-4bbe-8545-ea9fa2490670 · outbound

This paper cites an unresolved cited work.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Unresolved cited work

Reference 4

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:27:49.775596Z digest=sha256:cb59eb94320ed1bc4ae53dd87b1617c676c544b7c0d9b0b5e0523df6e06a2ec6

Observation 8b14969c-8e5d-4b38-a3a8-68e785e093ae · outbound

This paper cites Deep reinforcement learning from human preferences.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Deep reinforcement learning from human preferences

Reference 5

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source=pdf_text observed=2026-08-07T14:27:49.783064Z digest=sha256:dcf423664996b986ead9256ee0f272c4ee37b48185c5dae6b4bb1132bb6e1727

Observation a1385966-e092-4749-9504-d1e5f63e5406 · outbound

This paper cites Neural Spacetimes for DAG Representation Learning.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Neural Spacetimes for DAG Representation Learning

Reference 6

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local_arxiv, observed 2026-08-07T14:27:50.935300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:27:49.790379Z digest=sha256:0f8519870aae4351b5e61685e51eca5f93dea48a57dc9b1c147eacfdc4b695fe

Observation e9ccada1-68ee-43c4-ba63-fc0410daa3ce · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 7

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source=pdf_text observed=2026-08-07T14:27:49.799189Z digest=sha256:d3604a10aef0377298a8d918a764ac7139be08f88a648414f714e4e12c6b661f

Observation e589d879-6482-434a-914b-44d8abc6423e · outbound

This paper cites MM-IFEngine: Towards Multimodal Instruction Following.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning MM-IFEngine: Towards Multimodal Instruction Following

Reference 8

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source=pdf_text observed=2026-08-07T14:27:49.807752Z digest=sha256:e426a4eb7fc6650de51c72885f2cbf0416cc8628f750388b566f70dc79a18d96

Observation d80d2367-28ca-4496-964d-aa9e971b0b74 · outbound

This paper cites Specializing Smaller Language Models towards Multi-Step Reasoning.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Specializing Smaller Language Models towards Multi-Step Reasoning

Reference 9

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source=pdf_text observed=2026-08-07T14:27:49.818904Z digest=sha256:5341311280fc6b7b429d8ff45dc120d7f612ca6da4649d97d49522098f13af68

Observation cdaeaf76-f5ee-4ca1-9485-b665ec98a63e · outbound

This paper cites Benchmarking Multimodal CoT Reward Model Stepwise by Visual Program.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Benchmarking Multimodal CoT Reward Model Stepwise by Visual Program

Reference 10

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source=pdf_text observed=2026-08-07T14:27:49.827346Z digest=sha256:87c63b60556cbbddc162f8e724e3ef7c793ef1b7f181d8285b8ec62181bc1708

Observation 392c4abf-df88-4a78-b058-ab2240a985fb · outbound

This paper cites an unresolved cited work.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Unresolved cited work

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:27:49.834225Z digest=sha256:ebb2c6ff21339fb9b87695dab343616b786eee6c3b5277428ec920d4753eff0e

Observation 91f6d3af-5d5b-4245-abe8-cda51feb0c9d · outbound

This paper cites Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning

Reference 12

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source=pdf_text observed=2026-08-07T14:27:49.847670Z digest=sha256:0b5bc7c03d916aea951273b06904b9b34e23d3d9b2eac7b4ccce5b8d2d3e79bf

Observation ea22e792-a2e9-4167-834f-3ea8266a9e64 · outbound

This paper cites LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search

Reference 13

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local_arxiv, observed 2026-08-07T14:27:50.825725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:27:49.840646Z digest=sha256:7fa052b86353845abdd27909ec2f7f1f26292e0d9625ebd971269923f5150897

Observation de8244f9-100b-431b-b4c8-1a2cba1335e1 · outbound

This paper cites MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines

Reference 14

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source=pdf_text observed=2026-08-07T14:27:49.863423Z digest=sha256:1c5e8c2df79b282ee49091309a2263d0b5ff451821c88bb4298cf9988976663d

Observation 1b27107d-9ac8-4be0-88f6-40b1ce23ec4e · outbound

This paper cites FinSphere, a Real-Time Stock Analysis Agent Powered by Instruction-Tuned LLMs and Domain Tools.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning FinSphere, a Real-Time Stock Analysis Agent Powered by Instruction-Tuned LLMs and Domain Tools

Reference 15

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source=pdf_text observed=2026-08-07T14:27:49.856183Z digest=sha256:5d858fa80cf77778cb656f0a393477b57b877f28b2248a645f87a6761009081c

Observation ae46c352-c78f-49c3-8cff-7b10dd8c232d · outbound

This paper cites Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 16

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source=pdf_text observed=2026-08-07T14:27:49.881434Z digest=sha256:0cddcae245d8cdcb56107e48518168db61d759331d9709b9e5b4cb6b22d585d2

Observation fa6067af-3fdc-4d38-b2e0-94fb346c2aa7 · outbound

This paper cites an unresolved cited work.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Unresolved cited work

Reference 17

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source=pdf_text observed=2026-08-07T14:27:49.873650Z digest=sha256:cbefb1187f7dcdfb7ff54173bceffd1aeca66bb2fa729858dd42e07e33ca3a7f

Observation 22e71aaf-666b-4a64-87f1-da82be82236f · outbound

This paper cites RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Reference 18

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source=pdf_text observed=2026-08-07T14:27:49.901444Z digest=sha256:d9c7e8d8ae96f0e134482398161e9865b1b07d95b7ce502fa7cf3e8fc3becfb1

Observation 9b5d2bbe-8e6a-4cff-85f2-8db749303448 · outbound

This paper cites The Impact of Reasoning Step Length on Large Language Models.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning The Impact of Reasoning Step Length on Large Language Models

Reference 19

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source=pdf_text observed=2026-08-07T14:27:49.887553Z digest=sha256:f051f3fe05fc1eb02136a79687ff6dda6471e261144a0a1fc0d44bac79388cb7

Observation 750792ca-cc5a-478a-8f98-0e2f4d552475 · outbound

This paper cites an unresolved cited work.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Unresolved cited work

Reference 20

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source=pdf_text observed=2026-08-07T14:27:49.918527Z digest=sha256:d660f8c157145e0eab3a2a3212b194908dfa05bd1660e9d0ea99e8ca1b5d5125

Observation 0d0d412e-0076-4204-94fe-b559e03ca013 · outbound

This paper cites CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model

Reference 21

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source=pdf_text observed=2026-08-07T14:27:49.909403Z digest=sha256:1f0b1cd60921404fa22e5e44726d2dc6fd569b87c5e18c7023259e15523e6f2d

Observation 36cf3bfc-5601-4f4d-bc80-a95622c814d3 · outbound

This paper cites An Agent Framework for Real-Time Financial Information Searching with Large Language Models.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning An Agent Framework for Real-Time Financial Information Searching with Large Language Models

Reference 22

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source=pdf_text observed=2026-08-07T14:27:49.935581Z digest=sha256:208c8d6a9a0f718363982b533e878edfb402520fca1297e3ecb0e4b5cbaebee1

Observation d0bd0eeb-95f8-4056-8104-7f10d90f1f7a · outbound

This paper cites BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Reference 23

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source=pdf_text observed=2026-08-07T14:27:49.926983Z digest=sha256:bac93748246919f3a3f53a889cbee554f94058d876a560308e4b3a5befc9e448

Observation f08b0495-f438-4f42-bebb-920bf9c62329 · outbound

This paper cites Guerreiro, Ricardo Rei, and André F.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Guerreiro, Ricardo Rei, and André F

Reference 24

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source=pdf_text observed=2026-08-07T14:27:49.950393Z digest=sha256:b160fbf4f2a1deb1ed0a3e416a2a20ffc2d1e8ea98c33a5efa6dd327c28b05de

Observation 73b135fb-f751-41b0-9faa-c6c38078de3a · outbound

This paper cites Scalable Diffusion Models with Transformers.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Scalable Diffusion Models with Transformers

Reference 25

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source=pdf_text observed=2026-08-07T14:27:49.942606Z digest=sha256:7c383643278cd7e35a23c722817bc8157a4af9bac6287ece18a3572d2f860484

Observation c09c6818-50c4-4fb2-984c-f7c2a686d488 · outbound

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

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Comparing Traditional and LLM-based Search for Consumer Choice: A Randomized Experiment

Reference 26

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source=pdf_text observed=2026-08-07T14:27:49.963186Z digest=sha256:8598bb2369bc9c2e07b435713f1dc77efbcccce5049f89fd530404b2640d345f

Observation 7cea74e3-a911-4837-9650-50ffeb60436d · outbound

This paper cites ChatDev: Communicative Agents for Software Development.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning ChatDev: Communicative Agents for Software Development

Reference 27

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source=pdf_text observed=2026-08-07T14:27:49.956005Z digest=sha256:6e6745898186a805c60f55c13a6e2262d8da4a2df75f6e8d74fe8e816bb89837

Observation edd306ae-02fe-48cf-aa94-51706cb421df · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Gemini: A Family of Highly Capable Multimodal Models

Reference 28

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source=pdf_text observed=2026-08-07T14:27:49.977250Z digest=sha256:b61bcc54f6fb3b4d7dc37d10f5d3a4d408a98026239bebdd9e3d7e95c839ed45

Observation b6349f8a-556b-4ac1-9675-7700c1b0c9c4 · outbound

This paper cites Qwen2.5-32B: Leveraging Self-Consistent Tool-Integrated Reasoning for Bengali Mathematical Olympiad Problem Solving.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Qwen2.5-32B: Leveraging Self-Consistent Tool-Integrated Reasoning for Bengali Mathematical Olympiad Problem Solving

Reference 29

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source=pdf_text observed=2026-08-07T14:27:49.970944Z digest=sha256:102ee823a58cde09783291e012e9464b1383143668bd66ef57c2171b464b175a

Observation 75960073-c17a-4b22-a1eb-b9bd92624d67 · outbound

This paper cites AgentRM: Enhancing Agent Generalization with Reward Modeling.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning AgentRM: Enhancing Agent Generalization with Reward Modeling

Reference 30

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source=pdf_text observed=2026-08-07T14:27:49.996079Z digest=sha256:6334717b76f93440b5c8aabeaffd701037f659711a5c9c27848f0fb4aba40a20

Observation f6c886b5-483f-4cf4-8968-c21974c3a241 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning LLaMA: Open and Efficient Foundation Language Models

Reference 31

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source=pdf_text observed=2026-08-07T14:27:49.987872Z digest=sha256:afa9957279b355a0fddbe532e95f8d7eae5bfb3e123c35f0d6e06ebe821dc276

Observation 3425af78-980c-4011-bb1d-316548ee1f9e · outbound

This paper cites Qwen2 Technical Report.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Qwen2 Technical Report

Reference 32

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source=pdf_text observed=2026-08-07T14:27:50.016325Z digest=sha256:4d1b3f438c7e25209d6483e56ec0dbb61fb227087076f9b32949b17cc7870f63

Observation e473578f-6892-4132-860e-fda9dfc47eca · outbound

This paper cites When Search Engine Services meet Large Language Models: Visions and Challenges.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning When Search Engine Services meet Large Language Models: Visions and Challenges

Reference 33

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source=pdf_text observed=2026-08-07T14:27:50.005082Z digest=sha256:72fc5c5af1e93f6ba00ce13b70500b790109bc9ad816ed07651785342938477e

Observation 3a082e5b-f3c2-4673-82a7-e5dce44208d8 · outbound

This paper cites KoLA: Carefully Benchmarking World Knowledge of Large Language Models.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning KoLA: Carefully Benchmarking World Knowledge of Large Language Models

Reference 34

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source=pdf_text observed=2026-08-07T14:27:50.030313Z digest=sha256:71bfba69616bbc11c3cbdd8e5bd8c8940998b6c465c4ebcaa39df583ab237887

Observation 96fa39a9-a874-41d9-b13b-7de76a7f2b20 · outbound

This paper cites Rethinking Prompt-based Debiasing in Large Language Models.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Rethinking Prompt-based Debiasing in Large Language Models

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:27:50.023055Z digest=sha256:fa55989f8fc60cc64939aafc880ac2326b37cead65db33d155eccf440cfacf03

Observation 295697fa-3ccd-42ca-879d-7ef5f9d16560 · outbound

This paper cites Supervised Fine-Tuning Achieve Rapid Task Adaption Via Alternating Attention Head Activation Patterns.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Supervised Fine-Tuning Achieve Rapid Task Adaption Via Alternating Attention Head Activation Patterns

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T14:27:50.045237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:27:50.045237Z digest=sha256:9c1ba9e0aac51b3dc060aecd2d1195dc06c0ef31ca215ab12e0813d5b3679490

Observation cc5701c9-e022-4f44-91c4-62ebc5f5da33 · outbound

This paper cites FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Models.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Models

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T14:27:50.037756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:27:50.037756Z digest=sha256:d6b50d4ad74971b718b4eee821f750edf9101fc4404264b5c1a1878dfb3a1db5

Observation 4a305ed3-4c3d-454d-af58-5eb73bb3d940 · outbound

This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T14:27:50.052728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:27:50.052728Z digest=sha256:11b3c2d455fa276384a48ecdfbd6722ab1a47240b7725ee3d75e277896f7c858

Observation a46bd610-488d-4298-9323-952222564da3 · outbound

This paper cites Recurrence-Enhanced Vision-and-Language Transformers for Robust Multimodal Document Retrieval.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning Recurrence-Enhanced Vision-and-Language Transformers for Robust Multimodal Document Retrieval

Reference 2025

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:27:51.120580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:27:49.750523Z digest=sha256:f7d1e90223fb9ff25f47ca33cb1cd8feb443594106192eb87cfd543438ffe42e

Pith citing papers

Observation bf6c5fa1-85b2-49ab-a248-e5411c7e1338 · inbound

Reinforcement Fine-Tuning for Reasoning towards Multi-Step Multi-Source Search in Large Language Models cites this paper.

Reinforcement Fine-Tuning for Reasoning towards Multi-Step Multi-Source Search in Large Language Models Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning

Reference 16

Resolution
verified exact
local_arxiv, observed 2026-08-07T05:20:04.997818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:20:04.591495Z digest=sha256:d97e9cf68119651f55fc3aeee1e10be4ae5c921a53402bd4dd11cf4fdb8aca8f