Pith. sign in

Paper Citation Record · LEDGER

An Empirical Study on Eliciting and Improving R1-like Reasoning Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 35 inbound Pith citation observations for arXiv:2503.04548.

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

pith.paper-citation-record.v1
2503.04548 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 35 of 35 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:02:23.397708Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T04:27:36.567324Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 58656352-dd46-4280-83b0-75243c5d2710 · inbound

LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation cites this paper.

LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-08T13:02:23.397708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T13:02:23.397708Z digest=sha256:a9c81eaeb4149689f01649bdda96db6631871c1978ec246d36728373759cfa69

Observation f33de9cf-eec2-4453-83db-1f09bebf594e · inbound

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

DAPO: An Open-Source LLM Reinforcement Learning System at Scale An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-22T23:35:13.444477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-22T23:33:10.824995Z digest=sha256:f477c58a074a07b0a0655d5f491dc0c01e09fdd70655ed94e2869e10a363d70e

Observation 06469bab-0143-4fcb-82dd-0f9ab749777c · inbound

OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles cites this paper.

OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-19T06:59:03.369257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-19T06:59:03.112252Z digest=sha256:2d31f897c0852d26fc61b15903a749c5129dbeea2eeeee9f63188278e1636a59

Observation 76b6808a-bca6-4a3f-96b9-3f5d8dfea00a · inbound

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

ReTool: Reinforcement Learning for Strategic Tool Use in LLMs An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-13T18:42:39.082992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T18:42:39.023650Z digest=sha256:2a1d942152895f11bf00d62550c722d96ae580acd52b7a55386fbd8b8537f7b9

Observation 4381c426-e715-40cb-b99e-6afc554e7039 · inbound

Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning cites this paper.

Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-22T18:46:56.774430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-22T18:46:11.571831Z digest=sha256:b483276204f59c476d425d3ad017b5d318016565fe9a6c72281be43126dc5174

Observation 29297c44-ea38-4cf0-8954-a3cb1074da32 · inbound

WebThinker: Empowering Large Reasoning Models with Deep Research Capability cites this paper.

WebThinker: Empowering Large Reasoning Models with Deep Research Capability An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-16T19:14:25.443648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-16T19:14:25.283645Z digest=sha256:7913c9658681b70aefa18e84acefa04243cec8b9534f543848f03bcdbc63bb01

Observation 1026facc-1e92-45cb-be99-89ba8001c0c8 · inbound

DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models cites this paper.

DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T15:42:20.985769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:42:20.985769Z digest=sha256:fb065cc3287bdbc8178c26df3728a007b9c7fa9065c01778b4a432af7ea83b7a

Observation 1b8875ab-b37b-43d1-add0-a48501d7948a · inbound

Prior Prompt Engineering for Reinforcement Fine-Tuning cites this paper.

Prior Prompt Engineering for Reinforcement Fine-Tuning An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 1901

Resolution
unresolved
no resolver link, observed 2026-08-07T15:43:00.380468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:43:00.380468Z digest=sha256:c4f593eba4f2b350176ac6a6862b0d3e65dcef611e98f1d585a94eefba367fb5

Observation a6f741f9-565d-4385-8f11-ea6c15b61ef1 · inbound

GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI Agents cites this paper.

GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI Agents An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T15:16:01.048333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:16:01.048333Z digest=sha256:b6286843a847cb17cc348e01a2343694c86694f6521001b039c98066bd3f71ec

Observation c5f52244-f3d1-4478-92df-621630b3f6d6 · inbound

Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning cites this paper.

Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T15:06:03.057637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:06:03.057637Z digest=sha256:a7520ee907182378e323a6ced5ab7ac4d85c30fae149f78eaaa3094c74a948c3

Observation fe80a669-587b-42f0-83c4-ecb28f4aebd1 · inbound

DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation cites this paper.

DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T14:58:12.795266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:58:12.795266Z digest=sha256:b064168152e809edc2da93dc21d399d094963e686b89405b89c8f55d74e0c9e9

Observation 198ecfa4-6606-4742-af44-4607ce100a41 · inbound

LARES: Latent Reasoning for Sequential Recommendation cites this paper.

LARES: Latent Reasoning for Sequential Recommendation An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T14:59:15.857633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:59:15.857633Z digest=sha256:aef2f1b0c4f06e71ddfbb5aada78895b1aca56ce065bce88a4e7237171218553

Observation 622a08fe-2874-4516-9be6-0009b06f3410 · inbound

Stepwise Reasoning Checkpoint Analysis: A Test Time Scaling Method to Enhance LLMs' Reasoning cites this paper.

Stepwise Reasoning Checkpoint Analysis: A Test Time Scaling Method to Enhance LLMs' Reasoning An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T14:45:16.258476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:45:16.258476Z digest=sha256:670135b9a700457c173f76cbc30de3ece25f45b2fd35408c7f0ade20acaa53ed

Observation 4b20eca8-405a-43c7-b82f-25ee47b0b245 · inbound

How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning cites this paper.

How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T12:32:21.206536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:32:21.206536Z digest=sha256:7f30a1a694fc8cbe95cd1678b6418e48e68754f047550a59d9dfe0c2d4a0ab1b

Observation c1dd7f1c-5a02-496d-920e-2483e7447a1f · inbound

Towards Effective Code-Integrated Reasoning cites this paper.

Towards Effective Code-Integrated Reasoning An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T12:25:26.696704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:25:26.696704Z digest=sha256:2e6d52f8c29314c558d1080648f5aed1308c60809487d64f6f0d70dba5e71da6

Observation 91706d69-cc3c-4652-96bf-4e1f55e08da2 · inbound

ICPC-Eval: Probing the Frontiers of LLM Reasoning with Competitive Programming Contests cites this paper.

ICPC-Eval: Probing the Frontiers of LLM Reasoning with Competitive Programming Contests An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-07T10:35:02.142063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:35:02.142063Z digest=sha256:aac34e674c0bb3ae95dd5f0342a746ebde94f3709058c0c98cc151bc78610393

Observation a14cc001-f48a-4c0f-801b-fff9e23f21f7 · inbound

Wait, We Don't Need to "Wait"! Removing Thinking Tokens Improves Reasoning Efficiency cites this paper.

Wait, We Don't Need to "Wait"! Removing Thinking Tokens Improves Reasoning Efficiency An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T05:19:15.802751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:19:15.802751Z digest=sha256:b53ed0d067f1c855a88d406b51c97cb9dbfd4a1765ccf24e755d5baa02e82160

Observation f7a66c95-c231-4ac5-800a-9fb4f645469d · inbound

Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning cites this paper.

Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T05:09:42.527494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:09:42.527494Z digest=sha256:36ac1e44eeb79dd8c4ae181715f81d2319887f0fff769f68711362c8362ee6e2

Observation 66a3de8d-8fc8-4511-9668-9911376d2011 · inbound

CoRT: Code-integrated Reasoning within Thinking cites this paper.

CoRT: Code-integrated Reasoning within Thinking An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T04:46:20.731461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:46:20.731461Z digest=sha256:da4ab36598941065dbd0385827d759fe6a9459f044e6934b6cf82be77f2a61ae

Observation c297cfb4-f2b0-4ecc-91df-2dc7fee1cf47 · inbound

Act-With-Think: Chunk Auto-Regressive Modeling for Generative Recommendation cites this paper.

Act-With-Think: Chunk Auto-Regressive Modeling for Generative Recommendation An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T21:40:48.214783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:40:48.214783Z digest=sha256:0778c2471a6561dfe74fd8cf187497eb18521cbdb0524ed772518b7b0f948a55

Observation 6d6b4f97-6902-456c-ba54-d30bb5150131 · inbound

Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning cites this paper.

Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-06T14:55:03.899694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:55:03.899694Z digest=sha256:7e0072c7da8d4102af703a28575acd69c38e44047fc82a8780e9a8c3585f61c8

Observation 9a097a0b-09ff-4ebf-8733-eafab4a5893d · inbound

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models cites this paper.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-05T20:21:05.350521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:21:05.350521Z digest=sha256:f6c88ce36cebc1896ea31d54fdbb594b3135cc7b5f84faac4c2367e54a7cacd3

Observation 0f1e3433-c858-49cf-852f-f44ed4fbd56d · inbound

Why Does Reasoning Length Converge? Unveiling the Underfitting-Overfitting Trade-off in Chain-of-Thought cites this paper.

Why Does Reasoning Length Converge? Unveiling the Underfitting-Overfitting Trade-off in Chain-of-Thought An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-05T10:31:37.763822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T10:31:37.763822Z digest=sha256:3e71763bb414a3240569de8d6f4887158dac89bea23589a335139fc7c8d90d34

Observation b17cbcda-a229-4ac6-a328-5428da7d0df3 · inbound

Sticker-TTS: Learn to Utilize Historical Experience with a Sticker-driven Test-Time Scaling Framework cites this paper.

Sticker-TTS: Learn to Utilize Historical Experience with a Sticker-driven Test-Time Scaling Framework An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-05T05:45:02.361736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T05:45:02.361736Z digest=sha256:a753372e4b67286a66bb02997be32fd9bbd33bb1996fb09ad55a337d053f354a

Observation 5c756cda-6e36-440b-b588-c5ad41ddeee5 · inbound

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

A Survey of Reinforcement Learning for Large Reasoning Models An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 82

Resolution
verified exact
arxiv_id, observed 2026-05-18T00:02:25.155358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-18T00:02:24.352947Z digest=sha256:d39b859c6714048ca6cb5ca5e2b0a628107930ff0a8b9298fc882cc7008052eb

Observation 628b4a99-10af-4df0-843a-96075de8680e · inbound

Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards cites this paper.

Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-18T14:26:28.245340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T14:24:48.666197Z digest=sha256:928f583d6c2bfaa86d5adb0b3de578ecfcae08377486701a510712ad70909375

Observation f6ece2d3-1975-4510-b898-b35c9b2453fa · inbound

EEPO: Exploration-Enhanced Policy Optimization via Sample-Then-Forget cites this paper.

EEPO: Exploration-Enhanced Policy Optimization via Sample-Then-Forget An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-18T08:56:08.702299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T08:53:31.803096Z digest=sha256:c9684ed813ece71b2de782a3089a8d60b237d43c60de87be0aa8f9fae3622161

Observation a9173ba0-c924-44b1-b186-7d8790d0b7f9 · inbound

How You Begin is How You Reason: Driving Exploration in RLVR via Prefix-Tuned Priors cites this paper.

How You Begin is How You Reason: Driving Exploration in RLVR via Prefix-Tuned Priors An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-12T03:26:19.275213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-12T03:25:04.955816Z digest=sha256:0b3d3675572aa9f95e0e35aa682bb4626b415ce9c751f55480862832ad66dec6

Observation 6e29e227-7d4b-4b70-bf5f-5f7909f88ea4 · inbound

PruneTIR: Inference-Time Tool Call Pruning for Effective yet Efficient Tool-Integrated Reasoning cites this paper.

PruneTIR: Inference-Time Tool Call Pruning for Effective yet Efficient Tool-Integrated Reasoning An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 40

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T06:01:23.360130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-12T04:42:49.165066Z digest=sha256:ee75f0f6f17df3ff6f37152c1e802df116c36e1f2d7696f1264a848e90095d5d

Observation 6e0ee3e9-a6f6-4d9c-8ee7-5edacf66493d · inbound

TimelineReasoner: Advancing Timeline Summarization with Large Reasoning Models cites this paper.

TimelineReasoner: Advancing Timeline Summarization with Large Reasoning Models An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-14T21:38:01.053722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-14T21:33:48.376967Z digest=sha256:0d1e32b572d43aa0e209166ec202512a642a29f46fa397e9fcc54b07b2a8873f

Observation 28301dbc-933c-438e-98be-bb5d6bead1b3 · inbound

SAGE: Shaping Anchors for Guided Exploration in RLVR of LLMs cites this paper.

SAGE: Shaping Anchors for Guided Exploration in RLVR of LLMs An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 33

Resolution
metadata mismatch
arxiv_id, observed 2026-05-20T20:13:44.094363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-20T20:09:42.841695Z digest=sha256:f37a2e3c114b9947ad0a8ff652eb477c9683f7e19315e5a05aca7b6181714427

Observation 9a52c77f-62d9-4e31-bdcd-bf21981882e7 · inbound

RLVR Datasets and Where to Find Them: Tracing Data Lineage for Better Training Data cites this paper.

RLVR Datasets and Where to Find Them: Tracing Data Lineage for Better Training Data An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-06-29T19:43:55.066083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-29T19:34:12.081362Z digest=sha256:6fb1af2b2f1f6eb44440fdb731a44d67c453851d81fe1a7a6eefd178db045009

Observation 02371df3-e049-457f-a350-f324403fbc61 · inbound

Trust Region On-Policy Distillation cites this paper.

Trust Region On-Policy Distillation An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 289

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T20:56:13.161760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-28T17:38:50.313305Z digest=sha256:9893acee6f2fcc05eae1b49f993e98e9fa0f1a19307148d633134810c347aaac

Observation 6f19636a-2dcb-4fd8-9d09-89c43a4236a4 · inbound

GUI-AC: Enhancing Continual Learning in GUI Agents cites this paper.

GUI-AC: Enhancing Continual Learning in GUI Agents An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-07-03T04:27:36.568792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-27T13:56:09.049753Z digest=sha256:2ddb05e912abfaab376b419404006eeb88c5b344fca9e148e211b3a66e4a528e

Observation 9ea71d90-980c-4f64-9ae2-54906282b8b8 · inbound

GUI-AC: Enhancing Continual Learning in GUI Agents cites this paper.

GUI-AC: Enhancing Continual Learning in GUI Agents An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-07-12T14:27:05.589465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T14:27:05.589465Z digest=sha256:8e1e419a1ebb800caf3092fe1817f5bd141388ad3a98aff525d24774dcd00c34