Paper Citation Record · LEDGER

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning

As of 21 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 4 inbound Pith citation observations for arXiv:2411.16313.

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

pith.paper-citation-record.v1
2411.16313 v3

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:19:07.096279Z

measured 69 of 69 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T01:02:23.260368Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T22:26:17.104855Z

Reference resolution

65 of 65 outbound references displayed

verified exact1
verified fuzzy35
unresolved29
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a75f2fbb-4022-4acf-bc5c-8a9fd914dd63 · outbound

This paper cites GPT-4 Technical Report.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning GPT-4 Technical Report

Reference 1

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Observation 86fa2b53-3f57-4ac3-afeb-5e04b7d0441c · outbound

This paper cites An optimistic perspective on offline reinforcement learning.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning An optimistic perspective on offline reinforcement learning

Reference 2

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Observation 97598037-281c-4290-9919-f936670b57c9 · outbound

This paper cites FinBERT: Financial Sentiment Analysis with Pre-trained Language Models.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning FinBERT: Financial Sentiment Analysis with Pre-trained Language Models

Reference 3

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Observation 41df5f01-d721-4b01-b4ca-8f8e5b2d2616 · outbound

This paper cites Layer Normalization.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Layer Normalization

Reference 4

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Observation 4eb9dfa8-d086-4187-9d36-73203706939f · outbound

This paper cites Guiding LLMs the right way: Fast, non-invasive constrained generation.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Guiding LLMs the right way: Fast, non-invasive constrained generation

Reference 5

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Observation 215705d2-9aa9-4d54-9fca-734b351a3080 · outbound

This paper cites End-to- end object detection with transformers.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning End-to- end object detection with transformers

Reference 6

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Observation 47aacfa4-ddef-4520-a89e-e28315ea6766 · outbound

This paper cites Unleashing the potential of prompt engineering for large language models.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Unleashing the potential of prompt engineering for large language models

Reference 7

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Observation 98537c47-4c16-4da9-bc1c-be7d79f0b687 · outbound

This paper cites Decision transformer: Reinforce- ment learning via sequence modeling.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Decision transformer: Reinforce- ment learning via sequence modeling

Reference 8

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Observation 49c265a4-1144-4d82-9d8a-f197e2e3b2d0 · outbound

This paper cites Swin2sr: Swinv2 transformer for compressed im- age super-resolution and restoration.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Swin2sr: Swinv2 transformer for compressed im- age super-resolution and restoration

Reference 9

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Observation 153feb5c-1716-4f38-8703-9b2f32a39ab8 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

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Observation 41cd6af5-93f5-4d76-b9b8-28eb3243f6c5 · outbound

This paper cites Instances as queries.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Instances as queries

Reference 11

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Observation df7c9e7f-93b1-42b8-ae12-c9ef31411703 · outbound

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Unresolved cited work

Reference 12

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Observation ef9c888e-765a-45c2-a6c4-a3bd27e867f9 · outbound

This paper cites AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn

Reference 13

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Observation 1c43b6ac-f963-4367-8261-9f4f9a9e88a6 · outbound

This paper cites Clova: A closed-loop visual assistant with tool usage and update.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Clova: A closed-loop visual assistant with tool usage and update

Reference 14

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Observation b218329b-c995-48a0-9334-85da9be4e789 · outbound

This paper cites Openagi: When llm meets domain experts.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Openagi: When llm meets domain experts

Reference 15

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Observation 8b359611-4963-421b-b509-47bf65baeffc · outbound

This paper cites ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

Reference 16

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Observation 1ffe51a2-259f-43ed-8e8f-a5be22a37e9f · outbound

This paper cites Visual program- ming: Compositional visual reasoning without training.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Visual program- ming: Compositional visual reasoning without training

Reference 17

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Observation f9b0f294-74bc-423d-ac8b-67f0efd65856 · outbound

This paper cites Toolkengpt: Augmenting frozen language models with mas- sive tools via tool embeddings.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Toolkengpt: Augmenting frozen language models with mas- sive tools via tool embeddings

Reference 18

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Observation 0b2ced1b-d699-4fcb-82ad-57b1d0a0b643 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning LoRA: Low-rank adaptation of large language models

Reference 19

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Observation cf0e8dc6-26e3-4d80-b818-e5bbfa22c849 · outbound

This paper cites Visual program distillation: Distilling tools and programmatic reasoning into vision-language models.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Visual program distillation: Distilling tools and programmatic reasoning into vision-language models

Reference 20

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Observation b41617bd-33e5-4a0b-ad4a-f81341ba7ac9 · outbound

This paper cites Can llms effectively leverage graph structural information through prompts, and why? Transactions on Machine Learn- ing Research, 2024.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Can llms effectively leverage graph structural information through prompts, and why? Transactions on Machine Learn- ing Research, 2024

Reference 21

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Observation 89569d4d-6e6e-4c3e-90c0-81c46cd390b2 · outbound

This paper cites Qwen2.5-Coder Technical Report.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Qwen2.5-Coder Technical Report

Reference 22

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Function as a service mar- ket size & share analysis - growth trends & fore- casts

Reference 23

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Observation 56557d38-beef-4ab2-ba2b-9af2d7aebf5f · outbound

This paper cites Hydra: A hyper agent for dynamic compositional visual reasoning.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Hydra: A hyper agent for dynamic compositional visual reasoning

Reference 24

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Observation 4cacd71c-b4a0-40e1-afb1-a05de51ffb5f · outbound

This paper cites Large language models are zero-shot reasoners.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Large language models are zero-shot reasoners

Reference 25

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Observation fae73cee-95cc-4e77-a28b-b3289143f339 · outbound

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Run code without thinking about servers

Reference 26

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Observation beb2312e-90a3-49d0-8ae4-ab1f8da8a5f9 · outbound

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 27

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Observation 709da84f-5a5c-43f4-8091-c9ab73659c1b · outbound

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Formal-LLM: Integrating Formal Language and Natural Language for Controllable LLM-based Agents

Reference 28

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This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in nat- ural language processing.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Pre-train, prompt, and predict: A systematic survey of prompting methods in nat- ural language processing

Reference 29

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Observation bf2fc6a3-4f20-43a2-9915-d27288acf7e7 · outbound

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents

Reference 30

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Language

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning ControlLLM: Augment Language Models with Tools by Searching on Graphs

Reference 32

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities

Reference 33

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 34

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Chameleon: Plug-and-play compositional reasoning with large language models

Reference 35

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Chatgpt, 2024

Reference 36

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Observation 8a97cb00-691c-4c1a-b182-8f9f2860b282 · outbound

This paper cites ART: Automatic multi-step reasoning and tool-use for large language models.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning ART: Automatic multi-step reasoning and tool-use for large language models

Reference 37

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Gonzalez

Reference 38

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Observation a768cc08-514b-41cc-8dda-4dff56efa989 · outbound

This paper cites Let Your Graph Do the Talking: Encoding Structured Data for LLMs.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Let Your Graph Do the Talking: Encoding Structured Data for LLMs

Reference 39

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Making language models better tool learners with execution feedback

Reference 40

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Observation 21083619-002d-47cb-a925-76c8a244c078 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 41

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Observation f94d3f62-5923-414a-a9aa-80cf4f2ec4ae · outbound

This paper cites A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 42

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Toolformer: Lan- guage models can teach themselves to use tools

Reference 43

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Observation 40bd2b82-0379-413e-98de-a17747125a02 · outbound

This paper cites Architectural implications of function-as-a-service computing.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Architectural implications of function-as-a-service computing

Reference 44

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Observation 89416237-1ce4-4b75-a7b4-f489bc3358e0 · outbound

This paper cites Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:07.923775Z

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Observation 7f4a8dc9-6794-45d8-a81b-113d55c31a5b · outbound

This paper cites TaskBench: Benchmarking Large Language Models for Task Automation.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning TaskBench: Benchmarking Large Language Models for Task Automation

Reference 46

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning StructuredRAG: JSON Response Formatting with Large Language Models

Reference 47

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Observation 8f8ce5e1-7166-42e5-88bc-ad1698f9064c · outbound

This paper cites Vipergpt: Visual inference via python execution for reasoning.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Vipergpt: Visual inference via python execution for reasoning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:07.903779Z

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Observation 3cf5c268-1259-4126-b38d-f66596ed87e4 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 49

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This paper cites GTA: A Benchmark for General Tool Agents.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning GTA: A Benchmark for General Tool Agents

Reference 50

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This paper cites Deep rein- forcement learning: A survey.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Deep rein- forcement learning: A survey

Reference 51

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Observation 6ed06bb2-f7b0-4843-aaad-8e37f3497684 · outbound

This paper cites Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models

Reference 52

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Observation c7ca23c1-d440-4345-a268-400a0603c885 · outbound

This paper cites Netllm: Adapt- ing large language models for networking.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Netllm: Adapt- ing large language models for networking

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:07.862413Z

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Observation 9e653124-4a89-49d0-b3e9-386188c32597 · outbound

This paper cites Mathchat: Converse to tackle challenging math problems with llm agents.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Mathchat: Converse to tackle challenging math problems with llm agents

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:07.842728Z

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Observation fb71f7ee-8faf-4cea-91e1-351413428093 · outbound

This paper cites Gpt4tools: Teaching large language model to use tools via self-instruction.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Gpt4tools: Teaching large language model to use tools via self-instruction

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:07.821697Z

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Observation 50a68692-0377-4f87-b4cf-50e9e41f1585 · outbound

This paper cites An empirical study of gpt-3 for few-shot knowledge-based vqa.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning An empirical study of gpt-3 for few-shot knowledge-based vqa

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:07.804801Z

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Observation f4f1384b-34dd-44c6-9e07-787e50a768bd · outbound

This paper cites MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T13:19:07.046694Z

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Observation 788f2b29-e920-43a2-8cd4-8bb041f4b6cb · outbound

This paper cites Open-domain Implicit Format Control for Large Language Model Generation.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Open-domain Implicit Format Control for Large Language Model Generation

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-12T13:19:07.194354Z

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Observation 4f3677c6-6d8c-4989-8443-714e89b13bb4 · outbound

This paper cites Restormer: Efficient transformer for high-resolution image restoration.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Restormer: Efficient transformer for high-resolution image restoration

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T13:19:07.058390Z

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Observation d1923b66-ec09-4391-8f83-bb120181291f · outbound

This paper cites Evaluating and improving tool-augmented computation-intensive math reasoning.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Evaluating and improving tool-augmented computation-intensive math reasoning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:07.774487Z

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Observation 4d3e0939-187c-4096-8a45-02d8e9e10049 · outbound

This paper cites TinyLlama: An Open-Source Small Language Model.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning TinyLlama: An Open-Source Small Language Model

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-12T13:19:07.071543Z

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Observation e589c378-0394-4665-a3e5-d425f6a90e2d · outbound

This paper cites Real-Time User-Guided Image Colorization with Learned Deep Priors.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Real-Time User-Guided Image Colorization with Learned Deep Priors

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T13:19:07.076786Z

Source-reported events for the cited work

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Observation ee8df903-bfd0-40cf-ab7b-7363a965b860 · outbound

This paper cites Wein- berger, and Yoav Artzi.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Wein- berger, and Yoav Artzi

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:07.755736Z

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Observation 2021cf95-f5c2-4d07-b307-0a5f7d27f1b8 · outbound

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning pay-as-you-go

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:07.739614Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c5b4b783-cbcf-4fe8-9f5b-32fad728123e · outbound

This paper cites This highlights CATP-LLM’s advan- tage in cost-aware tool planning to substantially reduce tool costs without sacrificing performance.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning This highlights CATP-LLM’s advan- tage in cost-aware tool planning to substantially reduce tool costs without sacrificing performance

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:07.720518Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 1973cc7f-9dc4-41c7-9e0c-34c8b713ecde · inbound

IoT-Brain: Grounding LLMs for Semantic-Spatial Sensor Scheduling cites this paper.

IoT-Brain: Grounding LLMs for Semantic-Spatial Sensor Scheduling CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning

Reference 76

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Observation c26cefaf-8deb-47f9-b0b4-449879e50d4d · inbound

ATLAS: Agentic Test-time Learning-to-Allocate Scaling cites this paper.

ATLAS: Agentic Test-time Learning-to-Allocate Scaling CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning

Reference 56

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Observation d7371775-7d00-4526-b3d1-a502e1bf6548 · inbound

Same Task, Different Work: Prompt-Induced Waste in Coding Agents cites this paper.

Same Task, Different Work: Prompt-Induced Waste in Coding Agents CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning

Reference 10

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Observation ebfeb76d-9c02-4aee-ba33-304cafd891c8 · inbound

Same Task, Different Work: Prompt-Induced Waste in Coding Agents cites this paper.

Same Task, Different Work: Prompt-Induced Waste in Coding Agents CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning

Reference 10

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