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Paper Citation Record · LEDGER

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

As of 20 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 2026-08-20T06:33:59.587034+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=sha256:6c2d0bc1cd90f903f001bae981b8b2686378612d57f6afd17de0312d004f046f

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=sha256:1ab1708272830462d767377ccd4028ac45d0cfb03ac7bc8c3bfd60f965006d0c

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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source=pdf_text observed=2026-08-14T13:52:06.150249Z digest=sha256:18d1f71077b4f9c306464c2a8799faf0ee4a049d70fe8afcd2e7d6e9db37c550

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=sha256:17663330fe3b5d57248eee0714b5a243e08a52500e7d777fd8c1d66572dc6e80

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=sha256:333c11d1705d7b5ef49ef611a0622f2c575954723261cd7223fbfc2e951091eb

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=sha256:7eb11ed5d0a687c70688120c7035cf2ec626bf7cfcbf77924fbac3ad38631042

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:e8610574edf38af654ad5891c2f76077b311a13215f141445596822d586b9615

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:fa2a486fb0b765216aa1f4ddbef5581349553e1285d33244e47c71b0c07ea0f1

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=sha256:0589778617c13c9518d27c48ce3f9fce02a47a75b0e0d590692148a2d11b9880

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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source=pdf_text observed=2026-08-14T13:52:06.220660Z digest=sha256:27b3f9405f772a77c79ca44b96a86ac21954ec79d11ec1bd9c78bcef3b1406e1

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T13:52:06.225338Z digest=sha256:055c39dd388b63b573b6279191bf9626733d02c6b0df82ec1d711401fe68f427

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=sha256:2ac72d2667c73da2a1b0103ac023daee8d74c28c1b1807d9bc5f9856dbbb9938

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=sha256:6ab49d0b760204f52a77f6c3e130b5e5d04b367650a554c6a4e5cbc8f4e98c8e

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=sha256:581c9885ca821cb888be394c8b68e6608505ea3e0d3a36428aaf36dd78123a4d

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=sha256:60db74da57215523ded833141ba7894d31efe6ff0d174b4e0f8d0ceee9ae3c34

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T13:52:06.248116Z digest=sha256:abe4491c9f889ad2a556d4d66fc9cdb7ffd3e620725f6410bae19e491ed950b9

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T13:52:06.252426Z digest=sha256:d7cbaa2633171439e76496e9503ad743f06feb983949149a448f333f1507366c

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:d30996688340b3ae251fd72163b3ed8a27a16d13efa64397d40b8328f3e7c359

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T13:52:06.261555Z digest=sha256:49f2d236b905eeccb507107e25f7b51d7b5cb02c2ab0839fd6f22c193194e8fc

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T13:52:06.266001Z digest=sha256:102ff7dc04dc724da64213d54567abd79b5ba0ae15ff8fec671bb90d87945d43

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=sha256:7e09b6904dc399eb4c15028aa3b058217625ae201085fdde4ed0968a582d0913

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=sha256:88749bd37e03d5adc14bc9da9ad515c5daa9ca95dbbb1f7031c988b16f5ff27c

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=sha256:0687ff78a9e3bd6dd491b9971313a76983c4b7344ce02c5b0a855b61b6871b33

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T13:52:06.176683Z digest=sha256:3235b33ad748b2ced9348d680f7f0ef6365e74cdd6b0a3074e3e60d9d2eb8047

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:a92a8393480966a801473db5925eeda9fd43db28d181704ebb9cec33eb4a1b93

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=sha256:5474f0e8951d0ba1dc18bc95ec1980ae7eb2df0b44b9cc9f2f1c59857e7fc252

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T13:52:06.145062Z digest=sha256:13f3fad5df1114699c8e6356800b37a1e9e4dd8c265c2dcfd27eae27327a21fc

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=sha256:4b9c0f84dc6a11c12240c37a0c6d549680699c251f16773fc111f513c62927b9

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T13:52:06.197815Z digest=sha256:9626de3080d3c696ac57da2875a8b43b213e9355998dd6be417050ee1587b422

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T13:52:06.134670Z digest=sha256:eecc1f502053509b4fc480ae266e933f7547ffe4ee09c784583c7ed74b7d3b98

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T13:52:06.161001Z digest=sha256:a6f74191fdca27378249ba9f8f5202fce476105b10fbccfe7a8a1eb5e5084eb5

Pith citing papers

No inbound Pith citation observations are available.