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Paper Citation Record · LEDGER

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning

As of 22 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 5 inbound Pith citation observations for arXiv:2512.13278.

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

pith.paper-citation-record.v1
2512.13278 v2

Coverage vector

measured 79 of 79 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T16:30:43.415659Z

measured 84 of 84 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T06:36:48.004746Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T10:58:14.489388Z

Reference resolution

79 of 79 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved78
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7802d973-3736-450e-87ad-c03ac29802b9 · outbound

This paper cites write newline.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning write newline

Reference 1

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:35.726120Z digest=sha256:e457777db1c9a011988d681618a4ca671a115c13712ed948a5fa6b88605baaae

Observation e8afbcd0-c981-41ce-b0a2-994104e260a2 · outbound

This paper cites Vqa: Visual question answering.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Vqa: Visual question answering

Reference 2

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source=arxiv_source observed=2026-08-03T16:30:35.860242Z digest=sha256:8d9fb138633346fd5f112b013731f3bfec6a3efe7f3915e675cd91a890efbe8c

Observation a7498ee3-3b0b-40b6-b5e3-6e75b3830887 · outbound

This paper cites Program Synthesis with Large Language Models.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Program Synthesis with Large Language Models

Reference 3

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source=arxiv_source observed=2026-08-03T16:30:36.000310Z digest=sha256:64653368ee6cdbf9bc52987d6f2de72cd9e81a965d5da2b4116c0221a1f5e6c9

Observation bc05cd5a-71a1-44bb-8a37-8dbd3878832c · outbound

This paper cites Qwen2.5-VL Technical Report.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Qwen2.5-VL Technical Report

Reference 4

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source=arxiv_source observed=2026-08-03T16:30:36.085077Z digest=sha256:2919d25ab67302970c462b823f09892ee36c71cf9dab0bb66d774d9235e33f7e

Observation 4b4f81a2-e654-4e2a-a53e-6123c2600f30 · outbound

This paper cites Large Language Models as Tool Makers.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Large Language Models as Tool Makers

Reference 5

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source=arxiv_source observed=2026-08-03T16:30:36.292953Z digest=sha256:43d8b37fe41d855562b6464abce5f2e31e53bae2131da53e8214c6a0e96feb1f

Observation dbc9488b-12a3-409f-b1c2-0a5d5b5879ee · outbound

This paper cites Evaluating Large Language Models Trained on Code.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Evaluating Large Language Models Trained on Code

Reference 6

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source=arxiv_source observed=2026-08-03T16:30:36.393857Z digest=sha256:eab8b8524d601b0cad11f9bd4a6d1ab132b43ed0259881010432cbc1beefe4da

Observation 6b0c5751-23e3-45b6-88c9-91290596d5c9 · outbound

This paper cites ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning

Reference 7

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source=arxiv_source observed=2026-08-03T16:30:36.533019Z digest=sha256:1dfca2a4ed9316591808e89710d0f614d965e7f8a328979ab56c7cd802ed71c6

Observation a0eabbb1-0637-4248-94b0-21408e1e7196 · outbound

This paper cites Label ranking methods based on the plackett-luce model.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Label ranking methods based on the plackett-luce model

Reference 8

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source=arxiv_source observed=2026-08-03T16:30:36.601866Z digest=sha256:7548ac841a9a54dedb2bcd6c906f95257982319c49b82eaaea475f6d45c8e376

Observation 09d07d95-40c3-47bb-8ffb-44799d80d21f · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Training Verifiers to Solve Math Word Problems

Reference 9

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source=arxiv_source observed=2026-08-03T16:30:36.699110Z digest=sha256:11055be74a099b76bba5de43dc74187409715316f584b0cb4eddea518850279d

Observation 807d581e-51a8-4dbc-9b7d-635d2706fb9e · outbound

This paper cites ToolRegistry: A Protocol-Agnostic Tool Management Library for Function-Calling LLMs.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning ToolRegistry: A Protocol-Agnostic Tool Management Library for Function-Calling LLMs

Reference 10

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source=arxiv_source observed=2026-08-03T16:30:36.820854Z digest=sha256:b5dd725f883ee616cdfb18e061c4e61d5fb644097b7c8c88be290c2598a2a68e

Observation 55fdfde1-aba8-4bb5-954e-8078e2f33f11 · outbound

This paper cites Image super-resolution using deep convolutional networks.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Image super-resolution using deep convolutional networks

Reference 11

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source=arxiv_source observed=2026-08-03T16:30:36.925982Z digest=sha256:b0193541a2436e260863f2209c1a56cf09ffea2fe85f2f08fd6fc0d0546ede8a

Observation 47e76a85-9398-46d5-a90b-0face57c7871 · outbound

This paper cites Agentic Reinforced Policy Optimization.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Agentic Reinforced Policy Optimization

Reference 12

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source=arxiv_source observed=2026-08-03T16:30:37.032798Z digest=sha256:8ba7b452ec110dbdd6af06fe5b4c8b09ee83212b5c668d64ee9fb74686ac0500

Observation 36cde8fd-ba7a-47c2-bf78-05620f93caef · outbound

This paper cites AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls

Reference 13

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source=arxiv_source observed=2026-08-03T16:30:37.182971Z digest=sha256:9b8664a705452bfa64175609154bf1ad62971434f05c01d2fb7c8a657e226e32

Observation 786f8328-7614-43e1-ba0d-0f872bca4cf8 · outbound

This paper cites A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems

Reference 14

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source=arxiv_source observed=2026-08-03T16:30:37.273216Z digest=sha256:c48bc1749ac36fd5c082650053d7f4c0e098de88812c04b3f0fd0ba9160965e1

Observation 0e1f51ac-bb8d-49e5-8120-8e77ac11502e · outbound

This paper cites ReTool: Reinforcement Learning for Strategic Tool Use in LLMs.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning ReTool: Reinforcement Learning for Strategic Tool Use in LLMs

Reference 15

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source=arxiv_source observed=2026-08-03T16:30:37.378465Z digest=sha256:ce2ee753a90674adffb3beb331e62f375745770556e098ac436c7b23684b3b71

Observation 71c7ce0b-b6f5-46dd-8289-82734685cd1a · outbound

This paper cites Group-in-Group Policy Optimization for LLM Agent Training.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Group-in-Group Policy Optimization for LLM Agent Training

Reference 16

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source=arxiv_source observed=2026-08-03T16:30:37.558400Z digest=sha256:9710fc1280f5f8c5a60a66d3c2ee45216d48a48ea6e1ebfb207c83dec5267526

Observation 92e4ba94-006b-485f-94ad-70fde13b62f7 · outbound

This paper cites Word embedding based generalized language model for information retrieval.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Word embedding based generalized language model for information retrieval

Reference 17

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source=arxiv_source observed=2026-08-03T16:30:37.635610Z digest=sha256:f4924c88efb5f3a0f812e145505103fe7ceae0f2358d8d027e52435c42c9f823

Observation 98a23b74-9607-4ba0-a537-01b9fe0eed3d · outbound

This paper cites A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence

Reference 18

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source=arxiv_source observed=2026-08-03T16:30:37.724497Z digest=sha256:6e4fa91ba7e8237f782fb9affd55e52b613cd9ddd7ffa7dd74b171aff92cfab2

Observation c2fc79bd-453b-489a-82ef-a5d41c38d5a9 · outbound

This paper cites Multi-modal agent tuning: Building a vlm-driven agent for efficient tool usage.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Multi-modal agent tuning: Building a vlm-driven agent for efficient tool usage

Reference 19

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source=arxiv_source observed=2026-08-03T16:30:37.850809Z digest=sha256:78fa8a5c7303c9a30b723235b333b468e75553d06c1b676ef834d6b69519a22c

Observation 218bbb1e-07e9-45e8-af6b-414e19c0be8d · outbound

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

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 20

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source=arxiv_source observed=2026-08-03T16:30:37.996559Z digest=sha256:d12d86365bc3dbd422a27049bffe0e3b8f9dbd599ee8ba3e1d886b44bcfa7f4e

Observation c98c48de-7d5b-41cb-a0df-86cef427f83a · outbound

This paper cites Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

Reference 21

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source=arxiv_source observed=2026-08-03T16:30:38.105728Z digest=sha256:5d8dc6701879ac83aded23352e0c48be8c301066ae455f6c6bf2d77e8920e2f8

Observation aaf1a65c-70c8-4941-8e0e-6822d9edd0b6 · outbound

This paper cites Visual sketchpad: Sketching as a visual chain of thought for multimodal language models.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Visual sketchpad: Sketching as a visual chain of thought for multimodal language models

Reference 22

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source=arxiv_source observed=2026-08-03T16:30:38.221520Z digest=sha256:5e5ce2d5ee0ade704a207683c286283a05bdcb8ef217d053aba435ceaeeba2ad

Observation 2a4e0c9b-d55f-4610-87d8-01dba886eb55 · outbound

This paper cites GPT-4o System Card.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning GPT-4o System Card

Reference 23

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source=arxiv_source observed=2026-08-03T16:30:38.374646Z digest=sha256:6222ed3f4fffd8748e045aa2284a9a69369887632c10b1b6c72a374a6585d986

Observation 910f97c4-68e0-47d1-b251-89d120dc6e50 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 24

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source=arxiv_source observed=2026-08-03T16:30:38.549816Z digest=sha256:33347884ced05f5dd54aeaed4ab77e22696c8c8a8ddfe657d6e9a66c9a7d4900

Observation 84160818-54ca-46b1-83d7-0aefc975da2c · outbound

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

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines

Reference 25

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source=arxiv_source observed=2026-08-03T16:30:38.621012Z digest=sha256:9d2dd7ad69d84dfa5c22c2374b7222fa97ab8e2c8371a41702584070e816972a

Observation cc39ebd3-b2d4-4689-8c04-0d5b5fa67350 · outbound

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

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 26

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source=arxiv_source observed=2026-08-03T16:30:38.779404Z digest=sha256:e587d771d90cf6f5a0cd79d636824c564d74b2be2aa21ccd664f8c6254ff1d90

Observation 7c19b975-2f23-481b-b647-baed2b00b1d1 · outbound

This paper cites TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems

Reference 27

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source=arxiv_source observed=2026-08-03T16:30:38.891928Z digest=sha256:a78c1706029c372b07f4b1d8dedcfb82e6854a66b86a16d18ddac6488e0e1182

Observation 6e8ce0c6-01c1-4173-a998-023706ab5700 · outbound

This paper cites RePO: Replay-Enhanced Policy Optimization.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning RePO: Replay-Enhanced Policy Optimization

Reference 28

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source=arxiv_source observed=2026-08-03T16:30:39.076602Z digest=sha256:544ada043d3b2a3d6dd4264da1e863202cbdeb8eca6224ad79003ce2cabb0146

Observation 05ca345c-a7b1-49c2-ad60-c18244b49695 · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Grounding dino: Marrying dino with grounded pre-training for open-set object detection

Reference 29

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source=arxiv_source observed=2026-08-03T16:30:39.172353Z digest=sha256:39f9bab49c83643debbdf1ec37e419c248ac37644b194871cc56b25a7c636a0f

Observation 42402e87-b025-450b-a377-100de4be1e48 · outbound

This paper cites Understanding R1-Zero-Like Training: A Critical Perspective.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Understanding R1-Zero-Like Training: A Critical Perspective

Reference 30

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source=arxiv_source observed=2026-08-03T16:30:39.262531Z digest=sha256:6d00e00dacef54efab68f9b576afd3e5c97c12791e57b3363b8b7dcee57d364c

Observation 61c88db6-3fd5-4294-852b-ee3cc6395ca1 · outbound

This paper cites ARPO:End-to-End Policy Optimization for GUI Agents with Experience Replay.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning ARPO:End-to-End Policy Optimization for GUI Agents with Experience Replay

Reference 31

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source=arxiv_source observed=2026-08-03T16:30:39.322149Z digest=sha256:be9b9ff84e33526730afade59e80c797223e03a1e7a0682ba6166bd3fe4db5f1

Observation d73a13c9-2588-484a-9a52-8efaebc0fba7 · outbound

This paper cites Individual choice behavior, volume 4.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Individual choice behavior, volume 4

Reference 32

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source=arxiv_source observed=2026-08-03T16:30:39.444683Z digest=sha256:c9034c9d099f107ffe55ca59e26f978e5fe17790f4cc6dc96fb831810ce823f1

Observation 19d6a823-d3f4-43b7-b473-f5f0fbd076c8 · outbound

This paper cites Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem Solving.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem Solving

Reference 33

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source=arxiv_source observed=2026-08-03T16:30:39.500962Z digest=sha256:b9cf8eac00cee5e22f9ae6bd174ff5b5cc35cb11faf65cbe42b135483d9c0664

Observation 10c3b398-fcee-43b8-b618-abfdc0f132ca · outbound

This paper cites ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild

Reference 34

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source=arxiv_source observed=2026-08-03T16:30:39.610066Z digest=sha256:2de1f2d032cfae48d786ef3b534ecc005e4cac91b4b63bafa77682d09448db5a

Observation 09da1f62-8102-48d3-a580-6066ff3d0b19 · outbound

This paper cites From REST to MCP: An Empirical Study of API Wrapping and Automated Server Generation for LLM Agents.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning From REST to MCP: An Empirical Study of API Wrapping and Automated Server Generation for LLM Agents

Reference 35

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source=arxiv_source observed=2026-08-03T16:30:39.690713Z digest=sha256:a12b3893203d680fa0011980561a3d5bd152d0f29e59b8544213212e87d353ba

Observation 10d9d692-f031-470b-806d-c8ee1f30bb0b · outbound

This paper cites AIME 2025 dataset.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning AIME 2025 dataset

Reference 36

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source=arxiv_source observed=2026-08-03T16:30:39.754411Z digest=sha256:71500116d145f004335e18328ca5104cd406b527828f627989c8bb97598174ed

Observation 4c93fb0f-afa6-4dbc-b2d0-45d2213eaf93 · outbound

This paper cites AIME 2024 dataset.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning AIME 2024 dataset

Reference 37

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source=arxiv_source observed=2026-08-03T16:30:39.827154Z digest=sha256:0d3c574d29ba92942a6ec323667c7f9da9fc7a3c5644dbddf34404b37a223562

Observation abbbab6c-92ae-4e16-9407-3567680fa6b9 · outbound

This paper cites Distributed representations of words and phrases and their compositionality.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Distributed representations of words and phrases and their compositionality

Reference 38

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:39.883412Z digest=sha256:36e464bbb34dfeaeab236bd9ac3c3d64351f696052e0ee7dac3f44cb130b647b

Observation b9500de1-7fc7-4109-bccc-8cfa9a59983a · outbound

This paper cites WebGPT: Browser-assisted question-answering with human feedback.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning WebGPT: Browser-assisted question-answering with human feedback

Reference 39

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source=arxiv_source observed=2026-08-03T16:30:39.994599Z digest=sha256:cbb317da2c2e91b78e4ecba8234578656d60037f9563e4edf37387432db2c3b6

Observation 3a79f34c-73bb-4317-a148-95833cb3c12d · outbound

This paper cites Gorilla: Large language model connected with massive apis.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Gorilla: Large language model connected with massive apis

Reference 40

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source=arxiv_source observed=2026-08-03T16:30:40.064748Z digest=sha256:f38a52b81e54c5e0fc826b70b45c5f0eb099926d956bac31191dd710608766c7

Observation 4d441b42-10db-4dd6-84e5-404224d3e987 · outbound

This paper cites LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL

Reference 41

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source=arxiv_source observed=2026-08-03T16:30:40.120866Z digest=sha256:2dbd96e0dcecb5efd4453c8cd712ff1635a059907004818e0d8c80523f9312bc

Observation 653f238e-876b-4446-96af-a6ef1aadbf7d · outbound

This paper cites SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers

Reference 42

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no resolver link, observed 2026-08-03T16:30:40.193101Z

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source=arxiv_source observed=2026-08-03T16:30:40.193101Z digest=sha256:abd1c37d727100253013d04e3db61455b2cde81aac2097543041ad0fb485bf99

Observation 604adde6-5414-4f08-bfb5-8d3236232c3c · outbound

This paper cites Measuring and narrowing the compositionality gap in language models.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Measuring and narrowing the compositionality gap in language models

Reference 43

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source=arxiv_source observed=2026-08-03T16:30:40.316632Z digest=sha256:7a27e9bcc19572c37e676068e40852e894b93884704094352be8739d8e88a674

Observation 334ee213-0e5c-4e29-8f56-e2a7fc593c14 · outbound

This paper cites Fung, Yujia Qin, Zhiyuan Liu, and Heng Ji.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Fung, Yujia Qin, Zhiyuan Liu, and Heng Ji

Reference 44

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source=arxiv_source observed=2026-08-03T16:30:40.446119Z digest=sha256:3fd519f7960dab8d06f85688f958d1adbb3e3761b93ff41fdb8e0baf45b4b88c

Observation 2d4e3a45-f79e-45ed-833f-f409a3ba3f96 · outbound

This paper cites ToolRL: Reward is All Tool Learning Needs.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning ToolRL: Reward is All Tool Learning Needs

Reference 45

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no resolver link, observed 2026-08-03T16:30:40.569618Z

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source=arxiv_source observed=2026-08-03T16:30:40.569618Z digest=sha256:7664c65e38cf43cb70ecb4b801f49fb4b8d622c493e4cb3fa91ba5fc90606d25

Observation 637293b8-7030-4554-a112-9500a0b6eb0e · outbound

This paper cites Tool LLM : Facilitating large language models to master 16000+ real-world API s.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Tool LLM : Facilitating large language models to master 16000+ real-world API s

Reference 46

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source=arxiv_source observed=2026-08-03T16:30:40.731915Z digest=sha256:5fdefe5c630481140ae8687020370a52b47d3f17200dbaab6a75281fd495cab4

Observation 0e70231f-b571-454a-8e19-a5fa283745a6 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Direct preference optimization: Your language model is secretly a reward model

Reference 47

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no resolver link, observed 2026-08-03T16:30:40.888390Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:40.888390Z digest=sha256:3fad31c6165304a590f77c9e0134d46185c1e7ecdba04a61f1f49aa86a6a36c6

Observation d4e18bd9-660d-4d01-bc93-cacf73869345 · outbound

This paper cites Gpqa: A graduate-level google-proof q&a benchmark.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Gpqa: A graduate-level google-proof q&a benchmark

Reference 48

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no resolver link, observed 2026-08-03T16:30:41.030616Z

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source=arxiv_source observed=2026-08-03T16:30:41.030616Z digest=sha256:b3ac2b40ff30a60955c23ebc07c0ecfe42eb04fe1101d44a4fe16148a1aa9f21

Observation 1916f24a-caa9-46eb-8ad6-6f26a1f1cf6f · outbound

This paper cites Toolformer: Language models can teach themselves to use tools.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Toolformer: Language models can teach themselves to use tools

Reference 49

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source=arxiv_source observed=2026-08-03T16:30:41.134476Z digest=sha256:7637aaa4a13a59e9bf80a2e0ebb8b49d32bd23a05681fab1892297f0d1fc5d15

Observation f340a7c4-f091-4672-a84c-911b5e96cf17 · outbound

This paper cites Proximal Policy Optimization Algorithms.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Proximal Policy Optimization Algorithms

Reference 50

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source=arxiv_source observed=2026-08-03T16:30:41.228038Z digest=sha256:1ca7f894e9323d9da8f2e492f0a79251edb64811ed5aeaa360b499767fdfbabd

Observation 6ba424a7-071a-451d-853e-4bd2233f05a6 · outbound

This paper cites rStar2-Agent: Agentic Reasoning Technical Report.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning rStar2-Agent: Agentic Reasoning Technical Report

Reference 51

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source=arxiv_source observed=2026-08-03T16:30:41.340195Z digest=sha256:a38c5f18f7116fd7ff2591f2b25f10c2ba79a266915cea2cc05e11cb22f3012c

Observation 154dc57d-b5eb-4c0d-a355-a638e8accac1 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 52

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source=arxiv_source observed=2026-08-03T16:30:41.462065Z digest=sha256:750a27a9b07492cb71381646c3ac982a6f184cb70b2a026edd784e617e78695f

Observation 7f74ff72-6eb1-4282-adc2-25b8ca65366f · outbound

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

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face

Reference 53

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no resolver link, observed 2026-08-03T16:30:41.583725Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:41.583725Z digest=sha256:bed08de1a6cd62679ed22f50928958fbc466b10a074ed6f949df37fa0d37dbf6

Observation e36af9b7-1f4b-4857-aeb6-6cb27689f1cc · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning HybridFlow: A Flexible and Efficient RLHF Framework

Reference 54

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no resolver link, observed 2026-08-03T16:30:41.643374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:41.643374Z digest=sha256:c0ef5384f1ce8ef4af5abef6b699b50b98e1149aae835e5d051097fff0d6f819

Observation 72e0c638-e0c5-4633-a02d-3bd412ed7cbd · outbound

This paper cites Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning

Reference 55

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:41.727966Z digest=sha256:d0171ba6ca6279c606dd194f82731a8f4cceb9e8f10a7108d838f4d041c12e53

Observation a2c1d218-c55f-43ec-9d6a-05c445687d40 · outbound

This paper cites RestGPT: Connecting Large Language Models with Real-World RESTful APIs.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning RestGPT: Connecting Large Language Models with Real-World RESTful APIs

Reference 56

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unresolved
no resolver link, observed 2026-08-03T16:30:41.814039Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:41.814039Z digest=sha256:cf20645d1719f00a71f50dd190f7866adeddbfc27d1ffe09cbb18a7b2711a520

Observation 6cf789c9-e7e9-4eec-b280-72cc02359497 · outbound

This paper cites OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning

Reference 57

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no resolver link, observed 2026-08-03T16:30:41.891719Z

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source=arxiv_source observed=2026-08-03T16:30:41.891719Z digest=sha256:42e62ce83ec5820bb9fb5a9c2d1071135a3ecb21f5628dd8a9043902c36321dd

Observation 77fa8646-034d-48b3-ab9d-0bc73dcad890 · outbound

This paper cites Kimi K2: Open Agentic Intelligence.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Kimi K2: Open Agentic Intelligence

Reference 58

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no resolver link, observed 2026-08-03T16:30:41.973536Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T16:30:41.973536Z digest=sha256:e6fd1f6e76c5545b720d4b0f4b7a1bf9c3e1e89d0511d42d0c6b812a5ade7fbf

Observation 057966dd-cdd8-488b-bf4c-d6d454a361f7 · outbound

This paper cites QwQ-32B: Embracing the power of reinforcement learning.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning QwQ-32B: Embracing the power of reinforcement learning

Reference 59

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:42.055537Z digest=sha256:70e2f43ac8e2c1e8dc4366b5a6bfcf52f44cff14b9911d9a55d706f71eba84a5

Observation 57dcc06c-fa5a-49de-aecd-ed383a1e6acc · outbound

This paper cites Mllm-tool: A multimodal large language model for tool agent learning.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Mllm-tool: A multimodal large language model for tool agent learning

Reference 60

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no resolver link, observed 2026-08-03T16:30:42.089740Z

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source=arxiv_source observed=2026-08-03T16:30:42.089740Z digest=sha256:87b7cd9e896480b134b7ec781c78d7dfa8e31571b3110e8e351e9d966046cb28

Observation 9f29736f-0b7a-451f-8e22-4bbfdb37d26e · outbound

This paper cites RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

Reference 61

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:42.182243Z digest=sha256:feebb75c543815726e5ba274e5e6ac428c2b88ff90328479fb14a3641bde8997

Observation e8b0cb53-9418-4652-bacd-56693c122f4f · outbound

This paper cites V?: Guided visual search as a core mechanism in multimodal llms.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning V?: Guided visual search as a core mechanism in multimodal llms

Reference 62

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no resolver link, observed 2026-08-03T16:30:42.243559Z

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source=arxiv_source observed=2026-08-03T16:30:42.243559Z digest=sha256:72ae8a2ccd19ec65f7a879d8a3265e6e467af5f9efc0c0d29861363e3f08fe30

Observation 3a6d581c-b512-46c4-86cc-86d8438f1a13 · outbound

This paper cites Qwen3 Technical Report.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Qwen3 Technical Report

Reference 63

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no resolver link, observed 2026-08-03T16:30:42.295189Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:42.295189Z digest=sha256:8cf2f6b0afcb157e0c89768bede00d502e7b1b56d561248543c41f90793e4fe0

Observation 4e14f09d-7245-47a2-bb0f-9578b2937891 · outbound

This paper cites HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Reference 64

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no resolver link, observed 2026-08-03T16:30:42.373206Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:42.373206Z digest=sha256:397305cf45b3a061027805a1d88bd95b2b583d58826a685bdd297e482d13fa1e

Observation db4f7ab4-05c9-4861-af96-c9a70d5683b1 · outbound

This paper cites React: Synergizing reasoning and acting in language models.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning React: Synergizing reasoning and acting in language models

Reference 65

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no resolver link, observed 2026-08-03T16:30:42.436544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:42.436544Z digest=sha256:909a8b2648339d28dfd44350b4d84ed3467785a437819f2a48dd4845ab81f5dd

Observation a38f3143-dbaf-44c0-8262-0743699e1567 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 66

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:42.491383Z digest=sha256:33f6789c06112e8000b661b9cf0b9f7383d148201dc31886828d1135677a24e0

Observation bf2c3c28-9aec-4bd2-9627-b59868c6cbbe · outbound

This paper cites Demystifying reinforcement learning in agentic reasoning.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Demystifying reinforcement learning in agentic reasoning

Reference 67

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no resolver link, observed 2026-08-03T16:30:42.547876Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:42.547876Z digest=sha256:fe4e8ef9e49edc0543621ecb71674579039c60ad10bb7f767dba6d60156f991d

Observation 37c209e9-d4c0-4f9d-9176-ed54e8751f04 · outbound

This paper cites CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets

Reference 68

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:42.624466Z digest=sha256:084169f5e2653cd8ad39398418aaca78fccabb4843301f3be52faa95f330e6f2

Observation c7ed6abd-390a-425d-a86c-a2308a01938e · outbound

This paper cites MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning

Reference 69

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no resolver link, observed 2026-08-03T16:30:42.687230Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:42.687230Z digest=sha256:d56274b07a252728d6d35ff273ed791a4985acac51cbb103153284d9daedf837

Observation 682f35c2-e409-4f13-8b23-36a30217c046 · outbound

This paper cites Gvpo: Group variance policy optimization for large language model post-training.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Gvpo: Group variance policy optimization for large language model post-training

Reference 70

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no resolver link, observed 2026-08-03T16:30:42.751463Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:42.751463Z digest=sha256:2b47b47fb4f94989083cc0f18e5d2132218dc9ffb491cc04e328f01f6a8369ab

Observation 866dc050-5700-4f32-9fda-615670171906 · outbound

This paper cites Cumulative Reasoning with Large Language Models.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Cumulative Reasoning with Large Language Models

Reference 71

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source=arxiv_source observed=2026-08-03T16:30:42.820202Z digest=sha256:456d9d7305fb8559741c96e95154e1684303c5bd3976ac8d4f7d758d4b4bb8b6

Observation 49e3122f-5c8d-4a34-a573-e68da4d67bf5 · outbound

This paper cites Geometric-mean policy optimization.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Geometric-mean policy optimization

Reference 72

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:42.897436Z digest=sha256:6df4edebaa11c7b9806eb3e1888240acbe5d818d5da7846aa77cd35e35c02c62

Observation 0415d2eb-aefb-42a9-a98a-299f70ce2bec · outbound

This paper cites Group Sequence Policy Optimization.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Group Sequence Policy Optimization

Reference 73

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no resolver link, observed 2026-08-03T16:30:42.984524Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:42.984524Z digest=sha256:347789655d30edc211745efa0d9d2edc64db476412feffc3a7a4faf7c5ac1f59

Observation 74245e2e-8227-4eca-b6b2-d6d8b9b3b281 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 74

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no resolver link, observed 2026-08-03T16:30:43.045877Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:43.045877Z digest=sha256:e42d2310f985c05b1b427b4b01d2e6fc240051f3047597c2fb96d55ae11ef9e4

Observation 95f8218a-8c78-409c-88e0-df74bcc88c0f · outbound

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

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 75

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source=arxiv_source observed=2026-08-03T16:30:43.135210Z digest=sha256:d7596efd98cedb0ea8d98ed9c02c2ce7023b30828131623698093a9bee77b7bd

Observation 7ee87f31-d9a5-4d1b-95bb-e2cd445ea347 · outbound

This paper cites Tattoo: Tool-grounded thinking prm for test-time scaling in tabular reasoning, 2025.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Tattoo: Tool-grounded thinking prm for test-time scaling in tabular reasoning, 2025

Reference 76

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source=arxiv_source observed=2026-08-03T16:30:43.211918Z digest=sha256:45b88a06b2dac0a7e5096a84322dc3dabe1153054ac64c1718fe3f7ad6f70f37

Observation 77b14374-9e6c-44db-8f70-f4df70f38cdc · outbound

This paper cites @esa (Ref.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning @esa (Ref

Reference 77

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:43.274856Z digest=sha256:f09706a01efd169c54ef2c19b023e4ee7c6fa7a37684d1b3933d12838172a643

Observation e22f285a-0e01-4065-b7ef-425aa1a7b9be · outbound

This paper cites an unresolved cited work.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning Unresolved cited work

Reference 78

Resolution
unresolved
no resolver link, observed 2026-08-03T16:30:43.336238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 88d2dccf-c767-481b-8059-88b1759ca55f · outbound

This paper cites BI PdOmĠddL uoZuZ Z:vr.vi d 0n o^[ ?J.

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning BI PdOmĠddL uoZuZ Z:vr.vi d 0n o^[ ?J

Reference 79

Resolution
malformed identifier
no resolver link, observed 2026-08-03T16:30:43.415659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T16:30:43.415659Z digest=sha256:7e8ee6b93ff29f874fd03a9daefca0e6250ef848f8bc691b779b4d8f18ba35c6

Pith citing papers

Observation fab1e47d-e9c9-4126-852b-162117e34775 · inbound

Agentic Reasoning for Large Language Models cites this paper.

Agentic Reasoning for Large Language Models AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning

Reference 238

Resolution
verified exact
arxiv_id, observed 2026-06-08T02:03:49.128931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation b58698c3-f42c-4544-8a82-c1c2d016c161 · inbound

Security Considerations for Multi-agent Systems cites this paper.

Security Considerations for Multi-agent Systems AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning

Reference 58

Resolution
verified exact
arxiv_id, observed 2026-06-08T02:03:49.128931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation e3516961-6fac-4fd6-90c0-51164d816d0c · inbound

Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering cites this paper.

Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning

Reference 204

Resolution
verified exact
arxiv_id, observed 2026-06-08T02:03:49.128931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 39f0efdf-0e25-4b9c-bd19-fe3f5309d28c · inbound

Code as Agent Harness cites this paper.

Code as Agent Harness AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning

Reference 232

Resolution
verified exact
arxiv_id, observed 2026-06-08T02:03:49.128931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation e4c81d7e-4009-4ba6-990f-b5189311733d · inbound

ToolAnchor: Anchoring Counterfactual Context to Boost Agentic Tool-use Capability cites this paper.

ToolAnchor: Anchoring Counterfactual Context to Boost Agentic Tool-use Capability AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-02T06:36:48.004746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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