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Paper Citation Record · LEDGER

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems

As of 15 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2608.13317.

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

pith.paper-citation-record.v1
2608.13317 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:52:06.274552Z

measured 31 of 31 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

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  • verified fuzzy12
  • unresolved17
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation adb721f3-7838-4d0b-b54b-cb5e3b9e4a11 · outbound

This paper cites Progressive Depth Up-scaling via Optimal Transport.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Progressive Depth Up-scaling via Optimal Transport

Reference 1

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source=pdf_text observed=2026-08-14T13:52:06.128962Z digest=sha256:a14ba8dc3fe3b255e7fe277b11c398568a8c3f8a55d012c38a1612de6c0573bc

Observation f3246a52-430a-44a6-ab11-a4fa3c74ed1b · outbound

This paper cites Evaluating Large Language Models Trained on Code.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Evaluating Large Language Models Trained on Code

Reference 3

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source=pdf_text observed=2026-08-14T13:52:06.139606Z digest=sha256:46ebbaaabb41f7837dc98e6093894208552823abdef366cd4827bac5829199e0

Observation 3214ba92-ab8f-4af7-a23d-e688cb1ec75d · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 5

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Observation 5a5906cc-ae26-4907-91e0-cf005b4def52 · outbound

This paper cites Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks

Reference 9

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source=pdf_text observed=2026-08-14T13:52:06.171076Z digest=sha256:54058f5558be5452024eed077fa3b78e57e006b15e34781a67ec5c00a18ce5a5

Observation 82d418bf-2469-480d-8e73-b82270941697 · outbound

This paper cites 10 Published as a conference paper at COLM 2026 Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems 10 Published as a conference paper at COLM 2026 Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem

Reference 11

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source=pdf_text observed=2026-08-14T13:52:06.181337Z digest=sha256:dea116fbf83218ccfb1df9d69f59ee3405bc1e922ea37762d158f7f5bbf4964d

Observation 106e156f-f3cc-472a-a012-ee4881e26330 · outbound

This paper cites acl-long.353.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems acl-long.353

Reference 12

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source=pdf_text observed=2026-08-14T13:52:06.186778Z digest=sha256:00dfc5058affa7b44a7494089e6599fc6dbc97bb6ae2b44983a8a2b895daedf0

Observation f5503b29-be8c-4549-8137-59aa46a975e5 · outbound

This paper cites Wei Tao, Yucheng Zhou, Yanlin Wang, Wenqiang Zhang, Hongyu Zhang, and Yu Cheng.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Wei Tao, Yucheng Zhou, Yanlin Wang, Wenqiang Zhang, Hongyu Zhang, and Yu Cheng

Reference 16

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source=pdf_text observed=2026-08-14T13:52:06.206749Z digest=sha256:c4c016fe342f107b314c2c4434e5effe8032cbe1038a0d0318c6870aca4cb285

Observation 197242c6-f816-49b9-a2bc-54bc541597f7 · outbound

This paper cites Olmo 3.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Olmo 3

Reference 17

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source=pdf_text observed=2026-08-14T13:52:06.211224Z digest=sha256:c1a3f3de8461a2aa114c730f6eadd458d6a2f705cb5ff1fa5531510cd4da419b

Observation b3bee6ef-8675-4e63-a83e-471646efa1dd · outbound

This paper cites Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems

Reference 18

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source=pdf_text observed=2026-08-14T13:52:06.215894Z digest=sha256:8c4de3a195a7428d3c6a11bd63e6a607dce6cb9c4d58bdc31b300b4b9bdeff4b

Observation c4594f8c-4d58-4866-8943-c2497d76309b · outbound

This paper cites Talk to Right Specialists: Iterative Routing in Multi-agent Systems for Question Answering.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Talk to Right Specialists: Iterative Routing in Multi-agent Systems for Question Answering

Reference 19

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

source=pdf_text observed=2026-08-14T13:52:06.220660Z digest=sha256:e16989a2c97bc57fa5c0ef362ac7f14a3e2cb96d3678b9d9dc08839f1e0e68fe

Observation f7e7db9d-deb5-44a6-9f54-f158ace5291c · outbound

This paper cites Autogen: Enabling next-gen llm applica- tions via multi-agent conversations.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Autogen: Enabling next-gen llm applica- tions via multi-agent conversations

Reference 20

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

source=pdf_text observed=2026-08-14T13:52:06.225338Z digest=sha256:c7808ee516a0156e4d26dbbec0d554a29a6399159553f8f2e24bfdceb46ae894

Observation c527ce0d-06f9-4026-b8ce-20d5bbe2bb27 · outbound

This paper cites Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems

Reference 21

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source=pdf_text observed=2026-08-14T13:52:06.229824Z digest=sha256:0b47ea9903ddb551de1e6f3c1ffbd189809eb2297b00ca1a276ef24319861a45

Observation 513e17e5-0ce7-4d16-b54d-32119e512d8c · outbound

This paper cites Qwen3 Technical Report.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Qwen3 Technical Report

Reference 22

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source=pdf_text observed=2026-08-14T13:52:06.234459Z digest=sha256:4d7897c48bf27f3c0929047a9379c9dd6f9533df2dccd8d2e3639aeabc7ed55a

Observation b19b445d-48d4-4fe2-b5b5-07798f426630 · outbound

This paper cites LLM-based Multi-Agent Systems: Techniques and Business Perspectives.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems LLM-based Multi-Agent Systems: Techniques and Business Perspectives

Reference 23

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source=pdf_text observed=2026-08-14T13:52:06.239030Z digest=sha256:1ad77514cf9ee1b856a007a2b3707470157814de288fe7d0019d3e3a9b16c68b

Observation 008c1a12-8fcd-421a-93bf-dd53cadcf76d · outbound

This paper cites Mobile-Agent-v3: Fundamental Agents for GUI Automation.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Mobile-Agent-v3: Fundamental Agents for GUI Automation

Reference 24

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source=pdf_text observed=2026-08-14T13:52:06.243532Z digest=sha256:605107b7e98b6a0d3f26e4d9046065b4ac8bb27150d433b6b0e5244a7b003005

Observation a7251c39-4750-4f43-aaf7-2aa6adb07902 · outbound

This paper cites Large language model-brained gui agents: A survey.Transactions on Machine Learning Research, 2025a.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Large language model-brained gui agents: A survey.Transactions on Machine Learning Research, 2025a

Reference 25

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

source=pdf_text observed=2026-08-14T13:52:06.248116Z digest=sha256:68454722c685f3a01d4f255177af7693432e267955bd70aaa5a45e7969e674a1

Observation a0d73eac-387a-40f4-b1a0-3428fe590c76 · outbound

This paper cites We first formalize the information bottleneck in text communication, then contrast the two approaches at the representation level.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems We first formalize the information bottleneck in text communication, then contrast the two approaches at the representation level

Reference 26

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

source=pdf_text observed=2026-08-14T13:52:06.252426Z digest=sha256:f2ef775e2fffe0c7160732f7151f36631e7b0cd68d1d051247e458ad64f4f775

Observation cd35c53d-9760-4e19-a052-06c26178ca55 · outbound

This paper cites For intermediate γ, the S-dependent coefficients in Hγ still encode continuous variation within span(R), but directions outside this subspace remain inaccessible.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems For intermediate γ, the S-dependent coefficients in Hγ still encode continuous variation within span(R), but directions outside this subspace remain inaccessible

Reference 27

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source=pdf_text observed=2026-08-14T13:52:06.257232Z digest=sha256:f08e0afdad37fb888ae497e13c0b493a19d09d51b7a62b57d91601df18a526b0

Observation 3ead1352-1988-49d4-84ae-12702f781d65 · outbound

This paper cites Problems span algebra, geometry, number theory, and combinatorics, and require precise numeric answers.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Problems span algebra, geometry, number theory, and combinatorics, and require precise numeric answers

Reference 28

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

source=pdf_text observed=2026-08-14T13:52:06.261555Z digest=sha256:d542ef9e7b1d665e4fc6f4e0d8674aa2b0907cdcb0bf8efa7896ea23eea0b649

Observation 114a1b79-3349-49d9-a444-d08cd525fd40 · outbound

This paper cites Compared with AIME24, this set includes more multi-phase deriva- tions and intricate combinatorial constructions, offering a complementary test of mathematical reasoning.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Compared with AIME24, this set includes more multi-phase deriva- tions and intricate combinatorial constructions, offering a complementary test of mathematical reasoning

Reference 29

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

source=pdf_text observed=2026-08-14T13:52:06.266001Z digest=sha256:e959b7be0580905a732e2271ba14c06b5e0070661a4e5ffc133f9955d790a570

Observation 585dc091-dfd1-45b5-877c-a2d40454e06c · outbound

This paper cites The dataset emphasizes conceptual depth and cross-disciplinary reasoning.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems The dataset emphasizes conceptual depth and cross-disciplinary reasoning

Reference 30

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source=pdf_text observed=2026-08-14T13:52:06.270191Z digest=sha256:7c04459bd55ca670b336875eb776598cfbc7cf6562e2ae720efde1c6c712686a

Observation 414b5472-218e-4207-82b3-cbcc0815e1dc · outbound

This paper cites text format.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems text format

Reference 31

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source=pdf_text observed=2026-08-14T13:52:06.274552Z digest=sha256:4818d013cd70b5d1d81981f483384c0e81f9406b8285621c646960f7adfe3f52

Observation cffc47d1-a22a-45c0-a24c-b571d7203b67 · outbound

This paper cites Zhi Rui Tam, Cheng-Kuang Wu, Yi-Lin Tsai, Chieh-Yen Lin, Hung-yi Lee, and Yun-Nung Chen.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Zhi Rui Tam, Cheng-Kuang Wu, Yi-Lin Tsai, Chieh-Yen Lin, Hung-yi Lee, and Yun-Nung Chen

Reference 1966

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source=pdf_text observed=2026-08-14T13:52:06.202201Z digest=sha256:cbf2b494fb88c5aa971b5d91423039046217551d529909294c90a18a7d442857

Observation d78e074d-b749-4516-88f4-2e5932ae7614 · outbound

This paper cites Chawla, Olaf Wiest, and Xiangliang Zhang.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Chawla, Olaf Wiest, and Xiangliang Zhang

Reference 2013

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

source=pdf_text observed=2026-08-14T13:52:06.176683Z digest=sha256:a6efc355cdc0cb459ca28619f5bc208abbae59463a006f8e27d61d74d207e782

Observation 2235de64-a6f1-44bb-8e26-e741a4c97b8d · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Training Verifiers to Solve Math Word Problems

Reference 2018

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source=pdf_text observed=2026-08-14T13:52:06.155534Z digest=sha256:dcdf8b736ded18dd171554e3a7e468066ee047de5afd6d3bf9a54d1b3d43e852

Observation b9e37886-6c1a-4b6d-96fb-28a937aa8acb · outbound

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

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems

Reference 2019

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source=pdf_text observed=2026-08-14T13:52:06.166082Z digest=sha256:a3e74011fcdc8ddae50fd6ce65bd465fcd800efb35d91bc568c54d2da1023523

Observation 68ca35bb-4e2b-418d-9ff3-6f273cfe8b67 · outbound

This paper cites Optima: Optimizing effectiveness and efficiency for llm-based multi-agent system.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Optima: Optimizing effectiveness and efficiency for llm-based multi-agent system

Reference 2021

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raw_fallback, observed 2026-08-14T13:52:06.860129Z

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

source=pdf_text observed=2026-08-14T13:52:06.145062Z digest=sha256:513eee33596ee6f5c5a2f873f4d8d405f00bc77e7a88a65a260eb90fa4823157

Observation b4b05471-2c26-4d6a-b8db-5449ed21029b · outbound

This paper cites AIME 2024 dataset.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems AIME 2024 dataset

Reference 2023

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source=pdf_text observed=2026-08-14T13:52:06.193272Z digest=sha256:d337e9ef47a6a64b87ef836156629c9afe57189eebef54b3025fed44f027ea0a

Observation df80b6b0-22d8-4336-84cc-2abf44d14acd · outbound

This paper cites Predicting multi-agent specialization via task paralleliz- ability.arXiv preprint arXiv:2503.15703,.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Predicting multi-agent specialization via task paralleliz- ability.arXiv preprint arXiv:2503.15703,

Reference 2024

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

source=pdf_text observed=2026-08-14T13:52:06.197815Z digest=sha256:5149ffea0e30d1e871cb2f691f7b191f3d83581c5ebe9d62588f74c4aea74d82

Observation 1aaa5e80-1f3a-41e3-af57-e527ac270999 · outbound

This paper cites Why do multi-agent llm systems fail? InAdvances in Neural Information Processing Systems (NeurIPS 2025, Datasets and Benchmarks Track),.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems Why do multi-agent llm systems fail? InAdvances in Neural Information Processing Systems (NeurIPS 2025, Datasets and Benchmarks Track),

Reference 2025

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

source=pdf_text observed=2026-08-14T13:52:06.134670Z digest=sha256:80489b86c1e3031017b0184a5ce1292857871f167a5e0b70a1692b5eaff6b7a0

Observation 72a1fa1d-7dd2-46ef-b9e7-e0caca768477 · outbound

This paper cites How contextual are contextualized word representations? comparing the geometry of BERT, ELMo, and GPT-2 embeddings.

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems How contextual are contextualized word representations? comparing the geometry of BERT, ELMo, and GPT-2 embeddings

Reference 2026

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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=2026-08-14T13:52:06.161001Z digest=sha256:af133d659d932e9b598594330834041347d2e75f609526803b23c1d149c0c604

Pith citing papers

No inbound Pith citation observations are available.