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Paper Citation Record · LEDGER

Agentic Reasoning for Large Language Models

As of 14 August 2026, this Paper Citation Record lists 100 of 300 outbound references and 42 inbound Pith citation observations for arXiv:2601.12538.

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

pith.paper-citation-record.v1
2601.12538 v1

Coverage vector

measured 100 of 300 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-17T15:14:25.558878Z

measured 142 of 142 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 42 of 42 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:55:21.083222Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

100 of 300 outbound references displayed

  • verified exact63
  • verified fuzzy34
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch3

External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 1a524457-23fd-4ea9-a51f-27b1339fa111 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837.

Agentic Reasoning for Large Language Models Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.878297Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:0416f9c40f4f84101c45c92d4215666b16df9e6bb705e8cb7cc728aef42068a2

Observation b78b3f6f-ada9-4f43-be2c-a58458945612 · outbound

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

Agentic Reasoning for Large Language Models Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.775220Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:69cc28c7646722928561022921e9a6f63b8b828d839cbd50bbd30a3b38f4e698

Observation a7d85888-507e-4ffa-838f-4c88edeff19b · outbound

This paper cites Pal: Program-aided language models.

Agentic Reasoning for Large Language Models Pal: Program-aided language models

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.975301Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:d16e2225668fa6ea5e22f7bfeeffe531b05d9b78a737cae2bbb4477094edc54e

Observation 3b4cf975-6e6a-4860-9a41-d78ec900194b · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822.

Agentic Reasoning for Large Language Models Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.952446Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:8e9dca6366fcdf67288045feff6e3ba66f0ed7e0a410fac42730328354009f9c

Observation a62a3308-4609-4059-ab03-69de37509e7b · outbound

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

Agentic Reasoning for Large Language Models React: Synergizing reasoning and acting in language models

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.936492Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:63be58d59d50d91471ac5228bd53083d976ce1c74e5fff983c7fb1bc764fa7c7

Observation 75c91e70-9e68-419f-9921-e54eed6b5246 · outbound

This paper cites Toolformer: Language models can teach themselves to use tools.Advances in Neural Information Processing Systems, 36:68539–68551.

Agentic Reasoning for Large Language Models Toolformer: Language models can teach themselves to use tools.Advances in Neural Information Processing Systems, 36:68539–68551

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.931708Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:7a848b308ff3da363d6e71d4e0df14106ffa72c88ecc63762d2349e64f83d70f

Observation 515f29bb-ce7f-457f-be84-ee8056af87a8 · outbound

This paper cites Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face.Advances in Neural Information Processing Systems, 36:38154–38180.

Agentic Reasoning for Large Language Models Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face.Advances in Neural Information Processing Systems, 36:38154–38180

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.940992Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:b016a16f71c43acd1c6f74e737a379ead448e0f61c5ff4325c1a2b67c998c9bc

Observation 145aa674-e0c6-4d79-8803-8a0f58f4f736 · outbound

This paper cites A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345.

Agentic Reasoning for Large Language Models A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.918247Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:4ceb5863d2f0960c82064066486ae512ec54afc16037901dbbce482b0044d164

Observation b7d6ee4e-bb8e-498c-a010-ad7ed1150149 · outbound

This paper cites Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG.

Agentic Reasoning for Large Language Models Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.781204Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:2acd63655c1528ae990d9885a61d39f2699040eaf400aec053ba9221157abf0c

Observation dfccf5fa-8ffa-4326-8ae9-b4d1fd8dca03 · outbound

This paper cites A Survey on Retrieval-Augmented Text Generation for Large Language Models.

Agentic Reasoning for Large Language Models A Survey on Retrieval-Augmented Text Generation for Large Language Models

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.786152Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:40a8ee28e9aa9616c538cebdec2ca8af2c0a00949bff69af9b6ce5cc26b2e2ca

Observation c2140d99-5dea-4122-97d1-80715db500f6 · outbound

This paper cites OpenHands: An Open Platform for AI Software Developers as Generalist Agents.

Agentic Reasoning for Large Language Models OpenHands: An Open Platform for AI Software Developers as Generalist Agents

Reference 11

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.790886Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:4d5c0d0466a88e402c561dce661c44f1e8c8f0816796ecd7ef5178a34419f277

Observation 07f7bc4d-b567-4667-918c-c5b22f13a148 · outbound

This paper cites Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory.

Agentic Reasoning for Large Language Models Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.796564Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:0f1dd94b71a88e160ab87bd3853822c7c5622e95cdaa6f203d1394320eab8dad

Observation 2b3968af-c110-44cb-8789-f5fb48c91e66 · outbound

This paper cites MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models.

Agentic Reasoning for Large Language Models MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.802079Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:30da8297ae7569b4609f9180d27915468021a7cde3b24cb1a1e4a991dd6a892d

Observation 8e226316-265a-4f1e-9b7f-4e966b57804e · outbound

This paper cites Reflexion: Language agents with verbal reinforcement learning.Advances in Neural Information Processing Systems, 36:8634–8652.

Agentic Reasoning for Large Language Models Reflexion: Language agents with verbal reinforcement learning.Advances in Neural Information Processing Systems, 36:8634–8652

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.891293Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:51d24c2f07cb08fe3023e51ab9449ac0e2b01dbc3da7130d88d89de117917494

Observation ac9f726e-2a40-4108-b002-f56f004108a8 · outbound

This paper cites Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning.

Agentic Reasoning for Large Language Models Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning

Reference 15

Resolution
metadata mismatch
local_arxiv, observed 2026-05-17T15:14:25.806772Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:4d3e12619f35731197ff3d1b3ec794a83d87dcc6ff6919251f5a7d09bee8bc2e

Observation a6df40a5-c79a-4190-abdc-eddb92f37813 · outbound

This paper cites AutoAgents: A Framework for Automatic Agent Generation.

Agentic Reasoning for Large Language Models AutoAgents: A Framework for Automatic Agent Generation

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.812027Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:855ced39b314cd1e7a323f0bd38c64a3057035097e699e1d499b7c9e3d013314

Observation e95802d1-9c22-4be3-a3ed-e76aeafbabcd · outbound

This paper cites MetaGPT: Meta programming for a multi-agent collaborative framework.

Agentic Reasoning for Large Language Models MetaGPT: Meta programming for a multi-agent collaborative framework

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.886489Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:dae6f7146fc2eb37fa8f1590133ba006ddbec32dca6880264ae8057d83778903

Observation ca004b1e-a999-4177-a674-515a2a266b72 · outbound

This paper cites Unleashing cogni- tive synergy in large language models: A task-solving agent through multi-persona self-collaboration.

Agentic Reasoning for Large Language Models Unleashing cogni- tive synergy in large language models: A task-solving agent through multi-persona self-collaboration

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.872834Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:a6b2307a612b280649a204a7f29faf768513b3a6d150e7b77efb5492dfb333d5

Observation 8810323f-30ad-4717-974d-a0aca85c4436 · outbound

This paper cites BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems.

Agentic Reasoning for Large Language Models BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.817356Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:213ca28d4bf0e35b61e3dbaa9eb71a205f6da13a49ee70eb009c2a341f8d3fa1

Observation fa9d5eb8-628c-489b-8b13-8012c571c1be · outbound

This paper cites AgentBench: Evaluating LLMs as Agents.

Agentic Reasoning for Large Language Models AgentBench: Evaluating LLMs as Agents

Reference 20

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.741724Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:c2754f75722ade94ea53d9e106a4595583b5c0e9489e1ea0037efdee2597a539

Observation 8d35b3e5-4ab4-4ab1-a102-a5fa0cdcab1c · outbound

This paper cites MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents.

Agentic Reasoning for Large Language Models MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.748174Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:abbb5a252ec9c44c3419f512564a7aba3ccf456ff9ab8e1f0bdeb67ebad1cf33

Observation 7b11fd33-dd63-407c-971f-735c63c09150 · outbound

This paper cites Tree-of-code: A self-growing tree framework for end-to-end code generation and execution in complex tasks.

Agentic Reasoning for Large Language Models Tree-of-code: A self-growing tree framework for end-to-end code generation and execution in complex tasks

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.944895Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:919bead25abba3bff7836f8ac1d17d148083eea69c5fb790e27afb88b3c6c802

Observation d4c96ae9-d7e9-412b-abf5-8f4e99820d8c · outbound

This paper cites Search-o1: Agentic Search-Enhanced Large Reasoning Models.

Agentic Reasoning for Large Language Models Search-o1: Agentic Search-Enhanced Large Reasoning Models

Reference 23

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.753232Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:25c40204ad76054e3c081eca3484b8e139b690bb2d359ba13bf48538292be345

Observation 626783b4-e981-4e01-8e31-a9baed1a5d9e · outbound

This paper cites A-MEM: Agentic Memory for LLM Agents.

Agentic Reasoning for Large Language Models A-MEM: Agentic Memory for LLM Agents

Reference 24

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.759591Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:ed1245f152710c8c7daa65085e69617a963115fbc330197476dc64900b1fd482

Observation 0837b2ba-1b6a-4925-a54d-0c4e790718f1 · outbound

This paper cites Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory.

Agentic Reasoning for Large Language Models Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory

Reference 25

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.765131Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:40ec55e7ed37fca1a7b147c7fe2e35321cf98151f2b777861fb807b0f5d2b26c

Observation 71e1554f-af61-4307-913e-2794387c455f · outbound

This paper cites Coevolving with the other you: Fine-tuning llm with sequential cooperative multi-agent reinforcement learning.

Agentic Reasoning for Large Language Models Coevolving with the other you: Fine-tuning llm with sequential cooperative multi-agent reinforcement learning

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:26.545520Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:c723c35383bbeac0dc62726a8407a7c5a0568ec76ddf91cfbf71d9d14bf38411

Observation fbcc1997-e7d8-466d-aa09-e63ffe3eb9c9 · outbound

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

Agentic Reasoning for Large Language Models Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 27

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:26.443702Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:427479ab7ab09ddef64f22744497410cd9a04faf26bd5e36d8bd2f22dc17e649

Observation d2f6f48d-bad7-4196-93ca-305b0456ae3a · outbound

This paper cites Webagent-r1: Training web agents via end-to-end multi-turn reinforcement learning.

Agentic Reasoning for Large Language Models Webagent-r1: Training web agents via end-to-end multi-turn reinforcement learning

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.448890Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:c9a29c1eb0cac5b526f2bd17161e49162789980d5a0230e4f682cc51d8a0e8fa

Observation 047bb391-d216-422c-ab31-b302aeec5613 · outbound

This paper cites Solving olympiad geometry without human demonstrations.Nature, 625(7995):476–482.

Agentic Reasoning for Large Language Models Solving olympiad geometry without human demonstrations.Nature, 625(7995):476–482

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:26.576879Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:25ef53bcb7735a37d39eb9868f0398d3efe3e4695fc540dd4a13d7f031e4815b

Observation a1320889-7ad9-4be6-80c1-b7a2b5e249e6 · outbound

This paper cites Mathematical discoveries from program search with large language models.Nature, 625(7995): 468–475.

Agentic Reasoning for Large Language Models Mathematical discoveries from program search with large language models.Nature, 625(7995): 468–475

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:26.580896Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:5af1f7fd71fff173fc5f1f1e171aeeea91bd33ffbc1a6a5b9c853881888baed7

Observation 7ea8910f-9596-4cde-ab36-d20d521dc005 · outbound

This paper cites Vibe coding vs.

Agentic Reasoning for Large Language Models Vibe coding vs

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:26.584358Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:212383fe5238ecba65392f4cd7f5271cde478bd1e7a5c8cb1d8168688ed551a1

Observation a85b3329-4a03-4478-bbb2-a9a00578e630 · outbound

This paper cites Vibe coding — wikipedia.https://en.wikipedia.org/wiki/Vibe_coding.

Agentic Reasoning for Large Language Models Vibe coding — wikipedia.https://en.wikipedia.org/wiki/Vibe_coding

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:26.587582Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:dfc7ebd4bb111d44c72d71e1b351e8ffb3ed628ecf1767deda2d4609d47f1203

Observation 98bc61c5-e7e9-4bda-9cc3-64bcde81b545 · outbound

This paper cites ChemCrow: Augmenting large-language models with chemistry tools.

Agentic Reasoning for Large Language Models ChemCrow: Augmenting large-language models with chemistry tools

Reference 33

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:26.453637Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:37cd18cad42985c3a13660d73374596eb81f79a9d5794923c3c5f64cb804d4ed

Observation 289eb546-fde2-47b2-a8c6-9eb4cb888de0 · outbound

This paper cites Physical AI Agents: Integrating Cognitive Intelligence with Real-World Action.

Agentic Reasoning for Large Language Models Physical AI Agents: Integrating Cognitive Intelligence with Real-World Action

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.458987Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:3ea8ab84e3fc4ffccfc81e519e3443d0fcf122a39436967eb1eb62b619f9346d

Observation 3ed8c7e5-dca0-4884-8282-5196d78b4d22 · outbound

This paper cites MatExpert: Decomposing Materials Discovery by Mimicking Human Experts.

Agentic Reasoning for Large Language Models MatExpert: Decomposing Materials Discovery by Mimicking Human Experts

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.464103Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:8218cd8c365dca9f787ab7cad1ae106be53d3cd6f6cb350e5c5251eebfa2d97d

Observation 2dbaa7fa-fb88-49b7-b030-fa458bd25f92 · outbound

This paper cites Voyager: An Open-Ended Embodied Agent with Large Language Models.

Agentic Reasoning for Large Language Models Voyager: An Open-Ended Embodied Agent with Large Language Models

Reference 36

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:26.469234Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:9e4cd01f80a8912f3393ce698763de420c562bd5b3c23d19aeca78830aa5081a

Observation 8a0975d9-af2e-44fa-a7ce-9ecbe8bf1ab6 · outbound

This paper cites EmbodiedRAG: Dynamic 3D Scene Graph Retrieval for Efficient and Scalable Robot Task Planning.

Agentic Reasoning for Large Language Models EmbodiedRAG: Dynamic 3D Scene Graph Retrieval for Efficient and Scalable Robot Task Planning

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.474243Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:332d102bd6631ca364e229f4afcb82aec804be7432f2bb98010b100be3dc1fac

Observation 0ee42fed-5050-4d55-bee3-a03fc5841317 · outbound

This paper cites Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement Learning.

Agentic Reasoning for Large Language Models Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement Learning

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.479628Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:ceeeb85512ead72f219ed40481b4f56216be210b7b09e35d1185c3ea7c21542f

Observation 952e444e-bda9-4d1e-b574-25ff45dea72b · outbound

This paper cites MMedAgent: Learning to Use Medical Tools with Multi-modal Agent.

Agentic Reasoning for Large Language Models MMedAgent: Learning to Use Medical Tools with Multi-modal Agent

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.484089Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:6d2ce21632090e17c3b03d7cedfa61c8de1ddb8b2b1f022715ff30d768a9f907

Observation a046da27-6517-40c2-b3c6-a1c8efe73d08 · outbound

This paper cites Biomni: A general-purpose biomedical ai agent.biorxiv.

Agentic Reasoning for Large Language Models Biomni: A general-purpose biomedical ai agent.biorxiv

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:26.613015Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:b9801ef6b64902e4054e464a116eae18f89418c4906aa665f945fbd9d4e6387a

Observation 6f72f640-5da7-411a-8b93-0bb0027215af · outbound

This paper cites WebSailor: Navigating Super-human Reasoning for Web Agent.

Agentic Reasoning for Large Language Models WebSailor: Navigating Super-human Reasoning for Web Agent

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:37:09.773663Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:3e2acaa17c3145b3290882f336b1392356666d1fa4d6b283716b80e9b1c89b9e

Observation 52f1a5c1-0577-4794-9968-9a2f9f95dafc · outbound

This paper cites SkillWeaver: Web Agents can Self-Improve by Discovering and Honing Skills.

Agentic Reasoning for Large Language Models SkillWeaver: Web Agents can Self-Improve by Discovering and Honing Skills

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:26.493464Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:94a7827f20f11141bd36e6d5e3d303be43e2c2abd48d7360878df1735075771e

Observation 69e75777-505b-4652-a24d-eb0e4f638139 · outbound

This paper cites Agentic AI: A Conceptual Taxonomy, Applications and Challenges.

Agentic Reasoning for Large Language Models Agentic AI: A Conceptual Taxonomy, Applications and Challenges

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.498363Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:2d0d0cb596684a5f3dc370988210d5aa27d8b874ff3ab5fbbe1c50a85e7751bb

Observation 693421ce-ab21-4433-9987-f8f82d6112a9 · outbound

This paper cites A dynamic llm-powered agent network for task-oriented agent collaboration.

Agentic Reasoning for Large Language Models A dynamic llm-powered agent network for task-oriented agent collaboration

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:26.626273Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:9ab26acb72b152e9d7b0b1ee697c13ff5d6dd610d2451e0ecfec52142ca58d9b

Observation 9c4344bf-7e94-423d-9564-26146fd6f3d1 · outbound

This paper cites WebArena: A Realistic Web Environment for Building Autonomous Agents.

Agentic Reasoning for Large Language Models WebArena: A Realistic Web Environment for Building Autonomous Agents

Reference 45

Resolution
metadata mismatch
local_arxiv, observed 2026-05-17T15:14:26.502931Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:d6bc767f5e624b8a86c178d3b1a12b970a9b67109f63c3da354dc6b6f32be306

Observation 50dfcc78-15e6-4bc6-92d0-6151d5be9b6d · outbound

This paper cites VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks.

Agentic Reasoning for Large Language Models VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:20:36.653799Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:bafd38906ed8ea05768b76dbf1ccc6ec2c67092a933c64fbf1dbd796533dae8e

Observation fb86df3c-d0de-4b97-96e5-67fa66b42386 · outbound

This paper cites VideoWebArena: Evaluating Long Context Multimodal Agents with Video Understanding Web Tasks.

Agentic Reasoning for Large Language Models VideoWebArena: Evaluating Long Context Multimodal Agents with Video Understanding Web Tasks

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.512642Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:fad9236ebdea113d7dda3e048b24a7d0fcc1f0007b18df77fbd639c8d5f4240e

Observation 37c84e52-5ebe-44a8-b1e7-236473722070 · outbound

This paper cites ALFWorld: Aligning Text and Embodied Environments for Interactive Learning.

Agentic Reasoning for Large Language Models ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

Reference 48

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:26.517220Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:afe56063b94d9b32b667e4c3b0f3a0a3604c0c3c63959eff4640a3b36af63c94

Observation ea59da53-e623-48e7-8b86-03b02a080e3f · outbound

This paper cites Mind2web: Towards a generalist agent for the web.Advances in Neural Information Processing Systems, 36:28091–28114.

Agentic Reasoning for Large Language Models Mind2web: Towards a generalist agent for the web.Advances in Neural Information Processing Systems, 36:28091–28114

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:26.644293Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:77305ae55551bb8e636de1db5161d8e2d561975a84d3b8b1b87b13fdfe966df2

Observation d3bb3d08-19c1-465f-90d4-006f1ecf22dc · outbound

This paper cites Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge.

Agentic Reasoning for Large Language Models Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.522389Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:e0b528df3ad23c81b9e72be9d13e3be0cd2a2455e8da2492a554003636906f65

Observation fa71ba02-ece4-4ccd-af36-69ee2b4b4b21 · outbound

This paper cites Towards Reasoning in Large Language Models: A Survey.

Agentic Reasoning for Large Language Models Towards Reasoning in Large Language Models: A Survey

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-05-18T13:22:30.236738Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:385034a6eb1d6fd2f609346e1a6a79a86f8b0e1a211bca0bbec3e2862ceb6d28

Observation df077d39-810c-4bca-8793-78114fcd31fe · outbound

This paper cites Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models.

Agentic Reasoning for Large Language Models Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

Reference 52

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:26.531951Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:0b4192ec98ac75515312b2c1c5a147c8b8de7a39542247866b60ef1d368ee194

Observation 1989dfcf-bbe4-46fc-9813-b7fadf92f9ad · outbound

This paper cites Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models.

Agentic Reasoning for Large Language Models Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models

Reference 53

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:26.536535Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:c414936eaf3265cb720a785620fb06bf2abe3f9793e80dbfc41c10fb334a5fb5

Observation 05061b00-6a5b-4860-918e-c500b3ba3e1e · outbound

This paper cites https://arxiv.org/abs/2504.09037.

Agentic Reasoning for Large Language Models https://arxiv.org/abs/2504.09037

Reference 54

Resolution
metadata mismatch
arxiv_id, observed 2026-05-17T15:14:26.541446Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:fff4c918b140349e3213201f17b7adb0c0423d2287d6be0f014cc64b9d0e17b2

Observation 2dd15c0e-5e12-409b-8f38-7b1e4bdb921f · outbound

This paper cites A Survey of Reinforcement Learning for Large Reasoning Models.

Agentic Reasoning for Large Language Models A Survey of Reinforcement Learning for Large Reasoning Models

Reference 55

Resolution
verified exact
arxiv_id, observed 2026-05-18T00:02:25.511734Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:406ab02ebf1a88379afffbbe54aba8ff65d2553d8f932b428d3804e4fccbaf05

Observation c1ee493e-32f3-4ecb-b41d-38c9b4c1c565 · outbound

This paper cites The Landscape of Agentic Reinforcement Learning for LLMs: A Survey.

Agentic Reasoning for Large Language Models The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

Reference 56

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.827351Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:837b71ebb65dccf2b161bcee3b96f29f75648464376a4fb735f746fa2ff2e73c

Observation e9a8cef4-ded1-4b46-84d3-68c83d2ed222 · outbound

This paper cites A comprehensive survey on reinforcement learning-based agentic search: Foundations, roles, optimizations, evaluations, and applications.

Agentic Reasoning for Large Language Models A comprehensive survey on reinforcement learning-based agentic search: Foundations, roles, optimizations, evaluations, and applications

Reference 57

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.832715Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:c608b3019ca78bf25746918d82f6871b848924cfcf20c5c4ec054f24628e45b4

Observation 24a1e75f-a958-4516-83bf-87a36c4af8e1 · outbound

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

Agentic Reasoning for Large Language Models A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems

Reference 58

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.837390Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:120b0ed12c45bb187b64d5270eb61c90f2b43f552a0a88670cacaad206d88ef4

Observation 6d44f76b-1c64-484b-a754-c9b71fde98d3 · outbound

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

Agentic Reasoning for Large Language Models A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence

Reference 59

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.842301Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:695220053b0bc5b41efb02e9d9fc92beb8ccb4ea0b39b71f9f785e704028e32e

Observation 477bbfe5-1f93-448f-ba75-d3a1c7a94d9d · outbound

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

Agentic Reasoning for Large Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 60

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.846688Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:2d8d647c4f6939bdadc521462892c8eda9beb8d279180154062438887a0594d3

Observation 8101ce10-2b7b-4ad2-ad1d-c76b4a8d9052 · outbound

This paper cites DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning.

Agentic Reasoning for Large Language Models DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 61

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.851932Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:892568075244e8f0d1af18a5453bcd841e583feaf91a1e6b2a4cea72a79f64ce

Observation 9a7a8c2b-eb0a-48ff-b07b-90f8923253eb · outbound

This paper cites Proximal Policy Optimization Algorithms.

Agentic Reasoning for Large Language Models Proximal Policy Optimization Algorithms

Reference 62

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.856335Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:374b62fcf7c4da5b1d232bb385145210cd137f8898ea323c9ff38c641d8b27f1

Observation afbc1ba9-375a-493f-a97f-b73bc8066d54 · outbound

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

Agentic Reasoning for Large Language Models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 63

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.860967Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:c50008074056bace1d5a6118876dacc91dac3ca5568a3987fcf69eb2dcee6f32

Observation f7a6fa83-59c1-42f8-bd82-ecc76dcd26e5 · outbound

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

Agentic Reasoning for Large Language Models ARPO:End-to-End Policy Optimization for GUI Agents with Experience Replay

Reference 64

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.866371Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:7f371bba189accac3ff68d510773b6e47dc204972455a004f8cf0238700d5728

Observation ba1dfd94-6f78-48b2-b413-002fc0373170 · outbound

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

Agentic Reasoning for Large Language Models DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 65

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.871086Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:5710af226622649a1eafe8d9935686b8abbc325fee2f417338428372a7207c51

Observation 5f76d5ae-0e2c-49c5-b801-037720520a9a · outbound

This paper cites Autogen: Enabling next-gen LLM applications via multi-agent conversations.

Agentic Reasoning for Large Language Models Autogen: Enabling next-gen LLM applications via multi-agent conversations

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.854238Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:9682814552d4dfa29cd2456e7e7f48bf22123c61728658a51044995f977989f6

Observation be8d73e8-9c1c-434a-8201-d5c22116ffcf · outbound

This paper cites Camel: Commu- nicative agents for" mind" exploration of large language model society.Advances in Neural Information Processing Systems, 36:51991–52008.

Agentic Reasoning for Large Language Models Camel: Commu- nicative agents for" mind" exploration of large language model society.Advances in Neural Information Processing Systems, 36:51991–52008

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.948485Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:f998c9c9d1e089212ba1328f61e93bbd938e45e0900b106822add90dea71de45

Observation bd123ca3-d994-4899-9791-ac1a8ea08215 · outbound

This paper cites Gptswarm: Language agents as optimizable graphs.

Agentic Reasoning for Large Language Models Gptswarm: Language agents as optimizable graphs

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.868550Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:0d4a8d661c8c71b7969a6441fcc39f026aecc20786265e5184737c9f22378451

Observation 6f6ad5c8-fc98-48a8-a888-f626652e95e2 · outbound

This paper cites Multi-agent deep research: Training multi-agent systems with m-grpo.

Agentic Reasoning for Large Language Models Multi-agent deep research: Training multi-agent systems with m-grpo

Reference 69

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.876357Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:2a260603996e8d68dc8a7259d7b134a4fcbebeb086f9198396c7f30742ecc552

Observation 9bed2f78-786f-454d-b966-77901a08d2b9 · outbound

This paper cites AlphaEvolve: A coding agent for scientific and algorithmic discovery.

Agentic Reasoning for Large Language Models AlphaEvolve: A coding agent for scientific and algorithmic discovery

Reference 70

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.880979Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:6b7e9d115b6ca79ef58eef5bb2b30166bbadda61aced4d6075d2a558185ac1fa

Observation 4d966776-fbca-43c1-83e8-a03402af783b · outbound

This paper cites ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models.

Agentic Reasoning for Large Language Models ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models

Reference 71

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.885711Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:e7240a3c1ccca00bb8d00eae6d2f9e9e3ba2a4668e8bc19dd7f82b05bc8a2a73

Observation 5f6e0fb6-ca51-492b-8080-3ba94ae9539e · outbound

This paper cites LLM+P: Empowering Large Language Models with Optimal Planning Proficiency.

Agentic Reasoning for Large Language Models LLM+P: Empowering Large Language Models with Optimal Planning Proficiency

Reference 72

Resolution
verified exact
local_arxiv, observed 2026-05-17T15:14:25.890806Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:db2144ccd43c2e719e6d7ed164dd6a38f6058c2c71745839c068cf08ad5060b2

Observation 2310e2d4-d8ac-4f5a-b380-bea53fc7e5ef · outbound

This paper cites On the planning abilities of large language models: A critical investigation.Advances in Neural Information Processing Systems, 36:75993–76005.

Agentic Reasoning for Large Language Models On the planning abilities of large language models: A critical investigation.Advances in Neural Information Processing Systems, 36:75993–76005

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.913734Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:23dcac0d304b13ea5f1c7a76e6c89cc1bd8e68702b9af3b4431a6771c5f34951

Observation d53421ae-8df2-400e-b137-b129b8ad2c0c · outbound

This paper cites Graph of thoughts: Solving elaborate problems with large language models.

Agentic Reasoning for Large Language Models Graph of thoughts: Solving elaborate problems with large language models

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.896117Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:f02683ea93fadad10d24a313d92e718b3246bb83ac8a405b71bc8aa0575fcd50

Observation f06c75d5-4931-4885-811a-12234061a29d · outbound

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

Agentic Reasoning for Large Language Models Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models

Reference 75

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.896262Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:487a0a49844bd6793c152ee3c27c5aa6998d4b471c39230f8aba0825970d8286

Observation 52167bd2-dda9-484d-b306-17b7197c2efc · outbound

This paper cites HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking.

Agentic Reasoning for Large Language Models HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking

Reference 76

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.901737Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:6068e9c3f1dfffe50132d2c720f55d24cb2b38a61cd322baafc3e1d6472790dd

Observation 26f71b9d-ad91-4084-834d-b3d50183cc9f · outbound

This paper cites Reflect-then-Plan: Offline Model-Based Planning through a Doubly Bayesian Lens.

Agentic Reasoning for Large Language Models Reflect-then-Plan: Offline Model-Based Planning through a Doubly Bayesian Lens

Reference 77

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.907040Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:1f8493b19898a3ebf3fe71793e20429976ed3ac5c0aa8d3293aa57002d631128

Observation 18213d82-ffa2-43a2-a4c2-8aafbc5d062d · outbound

This paper cites Gorilla: Large language model connected with massive apis.Advances in Neural Information Processing Systems, 37:126544–126565.

Agentic Reasoning for Large Language Models Gorilla: Large language model connected with massive apis.Advances in Neural Information Processing Systems, 37:126544–126565

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.963497Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:52a42c98126d7f6cf3eabd1fca51b522aa4744986e60afa99cd1f3f2d35b7859

Observation 7a2fd0e1-fe33-4992-8742-dda74e6b9906 · outbound

This paper cites CodeNav: Beyond tool-use to using real-world codebases with LLM agents.

Agentic Reasoning for Large Language Models CodeNav: Beyond tool-use to using real-world codebases with LLM agents

Reference 79

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.912092Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:6d6556390134702ae025d8599aac8b8a5b28d377b7735b74a401405d421b25d5

Observation 1eb18c7f-c0c5-42c5-a7a3-c2a9b2ff0c44 · outbound

This paper cites Plan-on-graph: Self-correcting adaptive planning of large language model on knowledge graphs.Advances in Neural Information Processing Systems, 37:37665–37691.

Agentic Reasoning for Large Language Models Plan-on-graph: Self-correcting adaptive planning of large language model on knowledge graphs.Advances in Neural Information Processing Systems, 37:37665–37691

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.864069Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:f87642c11c97323ef1c31400e6a55abd657f89f3d8a935d1c2d0cdfd08352524

Observation f82ad0f7-6aa5-416b-89a6-2006546c9daf · outbound

This paper cites Tool-planner: Task planning with clusters across multiple tools.

Agentic Reasoning for Large Language Models Tool-planner: Task planning with clusters across multiple tools

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.922136Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:9b5d72ab3b2aa95fe0aebe6bf60f9a818d5f69912a008597bf69f24bd79b1ab8

Observation a2864974-fbe4-46ca-934b-f2acc5a7231e · outbound

This paper cites VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning.

Agentic Reasoning for Large Language Models VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning

Reference 82

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.917300Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:f2cd426ffdad228a7948e3feeb4104cf6e1199e8f4ad7b3916e51be25545d0cc

Observation 253944fa-3654-44a3-9783-dc5df7709ecd · outbound

This paper cites Llm- planner: Few-shot grounded planning for embodied agents with large language models.

Agentic Reasoning for Large Language Models Llm- planner: Few-shot grounded planning for embodied agents with large language models

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.859003Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:e8490e41d65de304acf9b7e420cec6e45c2e86ec8cc8082d542706b808f48f68

Observation 604738bc-dc2e-4590-9e01-dfba63ecdc01 · outbound

This paper cites Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems.

Agentic Reasoning for Large Language Models Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems

Reference 84

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.922405Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:646a7a13cea510ffde8382cd82688278936b95d9d69ce65a3ee02b8d95eb9c42

Observation 2b1f6599-68bb-426f-891e-47838a7f4d51 · outbound

This paper cites Agent S: An Open Agentic Framework that Uses Computers Like a Human.

Agentic Reasoning for Large Language Models Agent S: An Open Agentic Framework that Uses Computers Like a Human

Reference 85

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.928252Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:6da7d69afbe597b2355788bfe6ab2276190c3c0a2e6f74503bab6d74994b52af

Observation 1f98031f-e9ff-428d-a091-b46156355964 · outbound

This paper cites Exploratory retrieval-augmented planning for continual embodied instruction following.Advances in Neural Information Processing Systems, 37: 67034–67060.

Agentic Reasoning for Large Language Models Exploratory retrieval-augmented planning for continual embodied instruction following.Advances in Neural Information Processing Systems, 37: 67034–67060

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.967392Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:44ae86a330f36a447ec7167804f2064a1ae5f84f1193f7f5a69042449c98032c

Observation ba8a187b-d962-4c33-bd57-c75e0b1cabd3 · outbound

This paper cites Real-Time Anomaly Detection and Reactive Planning with Large Language Models.

Agentic Reasoning for Large Language Models Real-Time Anomaly Detection and Reactive Planning with Large Language Models

Reference 87

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.933475Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:d7af468268983fc13788b7dbe21dd256a153badf67c62c5f7b58e8a47ea99797

Observation 9fd8e3de-5812-4aa6-a2e6-6f7a2723143e · outbound

This paper cites Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification.

Agentic Reasoning for Large Language Models Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification

Reference 88

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.938596Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:6326c29ee6c097400def9361804cdd6bb191a8339d590cc9582b8e6b0fee5330

Observation 21ff2849-e615-4eb8-bdc8-a6583e912f48 · outbound

This paper cites Behaviorgpt: Smart agent simulation for autonomous driving with next-patch prediction.Advances in Neural Information Processing Systems, 37:79597–79617.

Agentic Reasoning for Large Language Models Behaviorgpt: Smart agent simulation for autonomous driving with next-patch prediction.Advances in Neural Information Processing Systems, 37:79597–79617

Reference 89

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.882609Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:497de1b78853486e41dac44653a29c25f89361e853fe37b24016948fdd19f2b6

Observation fa16941f-6e13-4fd2-8176-26774831562f · outbound

This paper cites DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning.

Agentic Reasoning for Large Language Models DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning

Reference 90

Resolution
verified exact
arxiv_id, observed 2026-05-17T16:06:10.055251Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:4d20982fc9413a261c5b6091abd7ef1d89df4b6e1e632133d3a30c8096dfdd83

Observation 88bad030-cef4-49a2-8c5c-521d4c29ca65 · outbound

This paper cites FLIP: Flow-Centric Generative Planning as General-Purpose Manipulation World Model.

Agentic Reasoning for Large Language Models FLIP: Flow-Centric Generative Planning as General-Purpose Manipulation World Model

Reference 91

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.948880Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:41080a7749bd313a5b4b5605197406026343597be95e7816eabe23279fdb882d

Observation 6f9d87b1-3ff6-4748-b7ec-2b9c192057a0 · outbound

This paper cites LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models.

Agentic Reasoning for Large Language Models LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models

Reference 92

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.954129Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:5833483b062a079e90520a90f8a1845b3c8a5d03cec28781e2827493cc1ddf7a

Observation 32f98ada-9a9b-42ac-9d26-b22d0dbdb373 · outbound

This paper cites Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models.

Agentic Reasoning for Large Language Models Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models

Reference 93

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.904762Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:cfd23f4c197bcd536f1022e83db474f0cdacad4b66724562f2a98115cba93d1d

Observation eb52c38c-3bab-4c56-a8d6-6064878a67aa · outbound

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

Agentic Reasoning for Large Language Models Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts

Reference 94

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.958624Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:e331af19fea9afebe3c7ec7d2a35f1d6afaf40fcf23b3b1a1d2c336a5d8430bc

Observation 8c271e9b-acc5-405d-840c-61878138eddf · outbound

This paper cites Peria: Perceive, reason, imagine, act via holistic language and vision planning for manipulation.Advances in Neural Information Processing Systems, 37:17541–17571.

Agentic Reasoning for Large Language Models Peria: Perceive, reason, imagine, act via holistic language and vision planning for manipulation.Advances in Neural Information Processing Systems, 37:17541–17571

Reference 95

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.909471Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:1078129c0a3bd28fe40105dd72cd65a6963248c6f00dcd7296c81e81d275948d

Observation bb6d3f26-ab5b-49d9-8a92-f0acd88ef44a · outbound

This paper cites Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks.

Agentic Reasoning for Large Language Models Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks

Reference 96

Resolution
verified exact
arxiv_id, observed 2026-05-17T21:32:18.834213Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:d693a54e202027fca236fcc0d2a2e927b1988a6415c0ed39f228e0911f9dba44

Observation 6726f689-4f00-4e27-864b-e56de61f09e2 · outbound

This paper cites Codeplan: Unlocking reasoning potential in large language models by scaling code-form planning.

Agentic Reasoning for Large Language Models Codeplan: Unlocking reasoning potential in large language models by scaling code-form planning

Reference 97

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.841241Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:b422c0ab1db62f96243569129c4a0c6a5b98361cc96fddeff29783e46fb08416

Observation e771ed09-0473-4a9a-84a3-0f653f27080b · outbound

This paper cites WILBUR: Adaptive In-Context Learning for Robust and Accurate Web Agents.

Agentic Reasoning for Large Language Models WILBUR: Adaptive In-Context Learning for Robust and Accurate Web Agents

Reference 98

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.968735Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:603d625c5fdbbf42495da9ac2d6fd2bb3740c830860cb71f74244168bad631d9

Observation 095cafb2-e7e4-4b15-9bbf-2f43dcf2ba3d · outbound

This paper cites Executable code actions elicit better llm agents.

Agentic Reasoning for Large Language Models Executable code actions elicit better llm agents

Reference 99

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T15:14:48.900833Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:cc8ea1bd919e7e8060f127ae36befcb3ea5451cae15351e60bf9a12ab37ed536

Observation 8972082d-5a09-49e5-b6a5-ae53779d5a6e · outbound

This paper cites MARCO: Multi-Agent Code Optimization with Real-Time Knowledge Integration for High-Performance Computing.

Agentic Reasoning for Large Language Models MARCO: Multi-Agent Code Optimization with Real-Time Knowledge Integration for High-Performance Computing

Reference 100

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:25.973783Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T15:14:25.558878Z digest=sha256:835ee5fe8b0541556c224599611455660363ded7a7a2c0d524a1de502cd739c8

Pith citing papers

Observation 4f49b958-1d8d-4e41-b015-7a0e10e19c9b · inbound

A Dual-Helix Governance Approach Towards Reliable Agentic Artificial Intelligence for WebGIS Development cites this paper.

A Dual-Helix Governance Approach Towards Reliable Agentic Artificial Intelligence for WebGIS Development Agentic Reasoning for Large Language Models

Reference 1937

Resolution
unresolved
no resolver link, observed 2026-08-02T18:53:59.134929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T18:53:59.134929Z digest=sha256:41e0fc7f2f6dd615e355963ab694d80ac16056bce811a5207e977aa3e0cc43a0

Observation 53339e83-66e6-4fe9-8922-9c8dd67d19b3 · inbound

Exploring Robust Multi-Agent Workflows for Environmental Data Management cites this paper.

Exploring Robust Multi-Agent Workflows for Environmental Data Management Agentic Reasoning for Large Language Models

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-02T16:57:53.530022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T16:57:53.530022Z digest=sha256:81408fdc042f7888b941b92dc710863c8db67af3ac78ef3d5cd5090b1ee79ee5

Observation a2f988d0-3f96-4ab1-9b6b-1699e9b9689b · inbound

ActionNex: A Virtual Outage Manager for Cloud Computing cites this paper.

ActionNex: A Virtual Outage Manager for Cloud Computing Agentic Reasoning for Large Language Models

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-13T19:18:39.881155Z digest=sha256:aa5cec731026876fc3c4afc8b7439b413bd31a9a899257e15615c69da7501507

Observation ff82e728-c313-46d9-8e04-4dc9ac9c72f7 · inbound

Hackers or Hallucinators? A Comprehensive Analysis of LLM-Based Automated Penetration Testing cites this paper.

Hackers or Hallucinators? A Comprehensive Analysis of LLM-Based Automated Penetration Testing Agentic Reasoning for Large Language Models

Reference 114

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-10T18:52:57.225878Z digest=sha256:180931499607e6b353de56e403b3b13be0533de4e42ee40363ab696e9b789cb8

Observation 3e47f441-76e1-4bc3-88c8-53f6321f3a77 · inbound

Agentic Frameworks for Reasoning Tasks: An Empirical Study cites this paper.

Agentic Frameworks for Reasoning Tasks: An Empirical Study Agentic Reasoning for Large Language Models

Reference 6

Resolution
metadata mismatch
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-10T08:24:51.573913Z digest=sha256:66da91e853b922739a3caf1cef23ba5c9aba203abcb41bd52d264baa173e3656

Observation fdc29134-1963-424d-9e55-cd9ec5366838 · inbound

WebUncertainty: Dual-Level Uncertainty Driven Planning and Reasoning For Autonomous Web Agent cites this paper.

WebUncertainty: Dual-Level Uncertainty Driven Planning and Reasoning For Autonomous Web Agent Agentic Reasoning for Large Language Models

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-05-10T04:51:36.374917Z digest=sha256:8055d91ee59925c2422d2e732503d4c9c0c567b6a2bfdc3cdf5f958ec313488a

Observation 1c7722e7-e88b-4fbf-8294-51a027b1eb80 · inbound

Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation cites this paper.

Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation Agentic Reasoning for Large Language Models

Reference 117

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-15T17:30:44.919870Z digest=sha256:f60e6d97f59b6ff4176a5d47a573fcf0f6e22706dad72e74470fd134d2ba4c77

Observation 2a1246a2-3b8d-4926-ad7d-4e7eb7f09020 · inbound

HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering cites this paper.

HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering Agentic Reasoning for Large Language Models

Reference 78

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-05-09T23:51:47.724033Z digest=sha256:e5bc7066c862b7e950d181fdd5ee6545b07b5ba073fc1bad21c152a694c2afa2

Observation 7dbd6ff7-4504-4382-9358-c94d569d3658 · inbound

TDD Governance for Multi-Agent Code Generation via Prompt Engineering cites this paper.

TDD Governance for Multi-Agent Code Generation via Prompt Engineering Agentic Reasoning for Large Language Models

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-07T11:40:59.915105Z digest=sha256:e1c5194ce5035eac01b057e07dde77c8d5f20b5998e7b753fc93a3ca23712130

Observation c8b9410e-d4ee-4ae0-9b14-f723cfa5c527 · inbound

Heterogeneous Scientific Foundation Model Collaboration cites this paper.

Heterogeneous Scientific Foundation Model Collaboration Agentic Reasoning for Large Language Models

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-07T08:50:05.980191Z digest=sha256:016d566e2fe9b2cf7ef31f1c16183317b6c75f7bc2101c0982bd5c54c183483f

Observation 0be6aae9-97f1-4146-9838-b293b87430c4 · inbound

Confidence Estimation in Automatic Short Answer Grading with LLMs cites this paper.

Confidence Estimation in Automatic Short Answer Grading with LLMs Agentic Reasoning for Large Language Models

Reference 34

Resolution
metadata mismatch
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-09T20:22:34.911547Z digest=sha256:e6672b904b907c1c3bee3b6cc9ea5a5539c4664880b73126dce5971f3ed3892e

Observation 5dfe8596-c97a-4bdf-98f4-59205aa5596c · inbound

Confidence Estimation in Automatic Short Answer Grading with LLMs cites this paper.

Confidence Estimation in Automatic Short Answer Grading with LLMs Agentic Reasoning for Large Language Models

Reference 34

Resolution
metadata mismatch
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-14T20:59:33.337511Z digest=sha256:e36e1238ec00dc504bac7deca350cb2b3c568815cb8bbb9d3609151d01101512

Observation dae53552-c71f-4f20-bfb1-26a45aeb51a8 · inbound

Reinforcement Learning for LLM-based Multi-Agent Systems through Orchestration Traces cites this paper.

Reinforcement Learning for LLM-based Multi-Agent Systems through Orchestration Traces Agentic Reasoning for Large Language Models

Reference 65

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-08T18:44:27.685266Z digest=sha256:aafc5ddb614dc3e348f01d9e33a5921b9bd492fbcbf5d47e3ec2fcc28af4dd6b

Observation d475e552-e1fd-4b97-8430-55d90e8a1ed2 · inbound

Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning cites this paper.

Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning Agentic Reasoning for Large Language Models

Reference 53

Resolution
metadata mismatch
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-05-08T10:23:52.522238Z digest=sha256:55802c2689a4621a6b87364697c5ce7d5cced3259e7abee0b3cb4484bf100978

Observation 1b37489f-0f23-4811-b543-67d0f5ab7ceb · inbound

Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning cites this paper.

Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning Agentic Reasoning for Large Language Models

Reference 53

Resolution
metadata mismatch
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-05-11T02:00:00.663355Z digest=sha256:46e8b1f0875753c78923b7ed5194e3a394c1e7e138c404b49fd1d618062f3191

Observation e8928110-a631-42d4-8476-23f031968c5b · inbound

Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning cites this paper.

Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning Agentic Reasoning for Large Language Models

Reference 53

Resolution
metadata mismatch
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-05-13T07:17:13.708752Z digest=sha256:6c44311b50e630f73a2beb8e88d4c4fc5c5b3d51e097c7cd757a4bb0ef396e34

Observation 23910d93-2be7-4bcc-8951-44d01598a565 · inbound

M2A: Synergizing Mathematical and Agentic Reasoning in Large Language Models cites this paper.

M2A: Synergizing Mathematical and Agentic Reasoning in Large Language Models Agentic Reasoning for Large Language Models

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-12T04:43:50.384980Z digest=sha256:98718901b5450a6c745a0252df33258df2896b41fcb8fa98eb091e8b2ecdfcc9

Observation 29c2203f-6d93-45a5-a241-3e46ea130189 · inbound

The Bystander Effect in Multi-Agent Reasoning: Quantifying Cognitive Loafing in Collaborative Interactions cites this paper.

The Bystander Effect in Multi-Agent Reasoning: Quantifying Cognitive Loafing in Collaborative Interactions Agentic Reasoning for Large Language Models

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-05-12T04:59:15.063642Z digest=sha256:fc6dcf8bcb22e756629d0b86d402f15b80fdf451d7af3d865abd1185f4786ba2

Observation f9d25609-d38b-42b2-8332-d678f8004c54 · inbound

Learning Agentic Policy from Action Guidance cites this paper.

Learning Agentic Policy from Action Guidance Agentic Reasoning for Large Language Models

Reference 63

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-13T05:02:49.206053Z digest=sha256:9438bf1a64f7ceed13bd5291081bbdff2d5f0e251f6b2cb8a03e5b4c5f37577a

Observation ebd37ab4-27f5-4588-8463-a80945ff8ec8 · inbound

MAP: A Map-then-Act Paradigm for Long-Horizon Interactive Agent Reasoning cites this paper.

MAP: A Map-then-Act Paradigm for Long-Horizon Interactive Agent Reasoning Agentic Reasoning for Large Language Models

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-14T19:48:53.213389Z digest=sha256:91367872d4c0f0cba4baa5f4610cd97eab1079147646617c4cf4ac882faf6440

Observation c121c2f6-f30d-4d24-905e-76e16eba5c72 · inbound

ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents cites this paper.

ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents Agentic Reasoning for Large Language Models

Reference 100

Resolution
metadata mismatch
arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-05-15T05:08:15.750558Z digest=sha256:7351ccd08985a84d22181d73acafda97ff36fafcd1556b89ca5f6de6ce23cb07

Observation 0959e00a-2a79-4d39-bbed-43218083d110 · inbound

ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents cites this paper.

ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents Agentic Reasoning for Large Language Models

Reference 100

Resolution
metadata mismatch
local_arxiv, observed 2026-05-20T20:23:43.249036Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-05-20T20:19:21.824216Z digest=sha256:80426590b10ffc95af0f17d4235c1e1016193dac6aea0893a01b23ef994117d2

Observation d50181a9-f33e-475c-9d4a-b1f5c2de89f2 · inbound

SEAL: Synergistic Co-Evolution of Agents and Learning Environments cites this paper.

SEAL: Synergistic Co-Evolution of Agents and Learning Environments Agentic Reasoning for Large Language Models

Reference 7

Resolution
verified exact
local_arxiv, observed 2026-06-30T13:44:40.887501Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-30T13:38:10.466713Z digest=sha256:abbe43e812823b7c39d343163b93a2ded91f384ab5061dc9a235caea36f894d0

Observation d5f34bee-a990-4340-adb6-07abe80f2334 · inbound

AVTrack: Audio-Visual Tracking in Human-centric Complex Scenes cites this paper.

AVTrack: Audio-Visual Tracking in Human-centric Complex Scenes Agentic Reasoning for Large Language Models

Reference 70

Resolution
metadata mismatch
local_arxiv, observed 2026-07-01T22:46:20.136495Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-06-28T15:00:41.632699Z digest=sha256:772f4ffe57c299d9d8d16823f34060f4e21fcfe983cb75bf003692e12ade27fb

Observation eea68090-2d47-4bb0-9f6f-09a8aa07611c · inbound

Adaptive Latent Agentic Reasoning cites this paper.

Adaptive Latent Agentic Reasoning Agentic Reasoning for Large Language Models

Reference 10

Resolution
metadata mismatch
local_arxiv, observed 2026-07-01T23:26:22.276111Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-06-28T14:24:22.855486Z digest=sha256:6e9a26ca63a4313feadbb1c207588a1e2dd5eb37aed7a3af4bb7ee67096bf479

Observation 8a5ad477-2db1-4763-8d54-b820dcc3868e · inbound

TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning cites this paper.

TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning Agentic Reasoning for Large Language Models

Reference 41

Resolution
metadata mismatch
local_arxiv, observed 2026-07-02T03:36:29.884087Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-06-28T09:51:59.586487Z digest=sha256:8e2ef3d02bb80d9857c265bc87f7c1cf387970c2293ecaddbcb102c9bce9e1e7

Observation 8a601860-9683-447d-acd8-68456e319a80 · inbound

Rethinking Continual Experience Internalization for Self-Evolving LLM Agents cites this paper.

Rethinking Continual Experience Internalization for Self-Evolving LLM Agents Agentic Reasoning for Large Language Models

Reference 60

Resolution
metadata mismatch
local_arxiv, observed 2026-07-02T07:56:47.744780Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-06-28T06:29:11.398007Z digest=sha256:027c095c198f2f3aa10c43ddbc65f3d8f948b24c2961df53840b3157d5f92d57

Observation 8be65db4-28ea-48bf-982b-fca614b9bafc · inbound

Co-Evolving Skill Generation and Policy Optimization cites this paper.

Co-Evolving Skill Generation and Policy Optimization Agentic Reasoning for Large Language Models

Reference 8

Resolution
verified exact
local_arxiv, observed 2026-07-02T22:47:26.432494Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-27T18:37:00.015083Z digest=sha256:c6c5365a88a981264523860d1c3d300bd6a8199f18253eda1de7b5e4839c3b34

Observation 000b949f-56ad-4a42-b27f-8dc7114f7f00 · inbound

Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application cites this paper.

Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application Agentic Reasoning for Large Language Models

Reference 26

Resolution
verified exact
local_arxiv, observed 2026-06-27T09:50:48.417435Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-27T09:46:30.702256Z digest=sha256:467c64e07a57a0187b1f21bec4f4ba0d68139df88bab8db14f3cd238ba059121

Observation 463236a3-0639-412c-93b7-7363b3de8256 · inbound

From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI cites this paper.

From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI Agentic Reasoning for Large Language Models

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-02T11:29:20.280274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T11:29:20.280274Z digest=sha256:7bf6d04bc265860b93c76941128fc60c007d8b9e2e4a9593db632bdb53701fc3

Observation 18dce63b-2f32-4b4b-89b1-647f753d5064 · inbound

Critique of Agent Model cites this paper.

Critique of Agent Model Agentic Reasoning for Large Language Models

Reference 82

Resolution
verified exact
local_arxiv, observed 2026-07-04T11:29:51.078377Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-26T07:57:20.830605Z digest=sha256:0e5d99bbd05d43ee9b2fc1c6c74274a8606df855d74adb0dc95c8364de5ddec2

Observation 0baad184-0cc3-4624-b9a2-34fcc0900a47 · inbound

Towards Self-Evolving Agents: A Human-Inspired Adaptive Exploration-Exploitation Framework for Genetic Network Programming cites this paper.

Towards Self-Evolving Agents: A Human-Inspired Adaptive Exploration-Exploitation Framework for Genetic Network Programming Agentic Reasoning for Large Language Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-07-15T10:12:25.518134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-15T10:12:25.518134Z digest=sha256:c0aafe9af2184b3f4e1c3afa0bc8584ed058c685fb96e443684fc8a94689f815

Observation dcdc4444-cc68-481c-b4a2-62a4d8ededa6 · inbound

Orchestrating Power Grid Studies with Multi-Agent AI and MCP Servers cites this paper.

Orchestrating Power Grid Studies with Multi-Agent AI and MCP Servers Agentic Reasoning for Large Language Models

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-02T05:42:25.080053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T05:42:25.080053Z digest=sha256:3f06acf7e9326dacb006a5aa8dd82c3330aef86c466a7ef02fe52bcedb7b184c

Observation e67fc772-ec5e-4d18-aaff-22a570ddd1bc · inbound

C-PTQ: Fisher-weighted Channel-wise Sensitivity for Post-training Quantization of MLLMs cites this paper.

C-PTQ: Fisher-weighted Channel-wise Sensitivity for Post-training Quantization of MLLMs Agentic Reasoning for Large Language Models

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-01T08:35:52.722378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T08:35:52.722378Z digest=sha256:67395773e6af466bba3e5793240bd24bde5af06f13cddee586cba0a04a665924

Observation 771c193b-97fe-4540-b906-4d3511c68ff6 · inbound

PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning cites this paper.

PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning Agentic Reasoning for Large Language Models

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-01T07:34:38.402726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T07:34:38.402726Z digest=sha256:3173e5da44a3c9c03fc6799e6beabd38cf99215d9fd46b5823937ed7cf894cf0

Observation c871051c-eac8-464e-be0f-4efce5de4c16 · inbound

ODYSSE: Episode-wise Policy Optimization for Personalized Agentic Reasoning cites this paper.

ODYSSE: Episode-wise Policy Optimization for Personalized Agentic Reasoning Agentic Reasoning for Large Language Models

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-01T02:44:32.776319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T02:44:32.776319Z digest=sha256:30b89cb865118e9ebf6985e4395b4b12d80cb9803c0b495f7dcc59655a43455d

Observation e7984be7-cde4-425b-84f7-62d238b8de45 · inbound

From Scoring to Acting: Outcome-Verified Comparative Self-Distillation for LLM Agents cites this paper.

From Scoring to Acting: Outcome-Verified Comparative Self-Distillation for LLM Agents Agentic Reasoning for Large Language Models

Reference 51

Resolution
unresolved
no resolver link, observed 2026-07-31T22:51:51.068384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-31T22:51:51.068384Z digest=sha256:d25484716660c297f253d55ae7afa5e5960b2f480179be706a71f31bf64d1a91

Observation eb919e86-b822-483c-8c31-fbac9a3df7f0 · inbound

OpenClaw and Ollama in Agentic AI: Toward Fully Autonomous and Scalable AI Agent Systems cites this paper.

OpenClaw and Ollama in Agentic AI: Toward Fully Autonomous and Scalable AI Agent Systems Agentic Reasoning for Large Language Models

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-03T00:55:44.590655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T00:55:44.590655Z digest=sha256:739cf1646d387aa5753af4339f4c0119aeacde2c03d6e668842a99c48f9310a5

Observation d797120a-9437-4edf-b3ab-6a5f31c708a0 · inbound

The Formalism Trap: Are LLM-as-a-Judge Evaluators Blinded by Consensus Mimicry under Social Load? cites this paper.

The Formalism Trap: Are LLM-as-a-Judge Evaluators Blinded by Consensus Mimicry under Social Load? Agentic Reasoning for Large Language Models

Reference 36

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no resolver link, observed 2026-08-03T00:53:53.673264Z

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source=arxiv_source observed=2026-08-03T00:53:53.673264Z digest=sha256:02d0a3aa12121922f4be8419e98ae46b7bc28ccbf636332ac3021106ee8f9a71

Observation 55881379-5aca-4698-bd96-58c9b832e468 · inbound

VC-Tooler: Learning Compositional and Adaptive Visual Tool Use cites this paper.

VC-Tooler: Learning Compositional and Adaptive Visual Tool Use Agentic Reasoning for Large Language Models

Reference 3

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no resolver link, observed 2026-08-04T11:21:30.797160Z

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source=arxiv_source observed=2026-08-04T11:21:30.797160Z digest=sha256:80b1e6a3d34212e5592e8add55435f7f8c768eab6ca3550e65096e37bb248901

Observation a09f823f-d825-4bc1-b20e-4133bb9a0468 · inbound

Towards a Risk Assessment of Malicious Skill Files in Coding Agents cites this paper.

Towards a Risk Assessment of Malicious Skill Files in Coding Agents Agentic Reasoning for Large Language Models

Reference 43

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no resolver link, observed 2026-08-08T17:55:21.083222Z

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source=pdf_text observed=2026-08-08T17:55:21.083222Z digest=sha256:ef5b2f55a8703a858616eca0e4acb0c68b608aba773cbb6a1fad6ad39cf29eac

Observation 65e31065-a41c-4184-bace-b033067308db · inbound

Certifying Collective Reasoning in Multi-Agent Systems via Koopman Spectral Analysis cites this paper.

Certifying Collective Reasoning in Multi-Agent Systems via Koopman Spectral Analysis Agentic Reasoning for Large Language Models

Reference 4

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no resolver link, observed 2026-08-07T20:28:41.780001Z

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source=pdf_text observed=2026-08-07T20:28:41.780001Z digest=sha256:d07f208d5d79e3b34a1b31631747683bd9a89a9bf8e40110211a7ae58a4c2f1a