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Paper Citation Record · LEDGER

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation

As of 29 July 2026, this Paper Citation Record lists 60 of 60 outbound references and 4 inbound Pith citation observations for arXiv:2603.23234.

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

pith.paper-citation-record.v1
2603.23234 v2

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-13T19:45:45.674967Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-28T06:31:03.373048+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T22:19:48.698285Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-06-29T12:23:24.247119Z

Reference resolution

60 of 60 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved58
  • parse uncertain1
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2f2bbfb3-de2e-4f5d-a087-e4b32f99e44d · outbound

This paper cites Effective context engineering for ai agents, September 2025.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Effective context engineering for ai agents, September 2025

Reference 1

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:8658c8a1e46fa077b6a5b736824575231b98c7d6e52d4caa8d3811c91664f3c9

Observation ac7e0abe-07f3-46c6-b4e7-4a75a8b8a287 · outbound

This paper cites Program Synthesis with Large Language Models.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Program Synthesis with Large Language Models

Reference 2

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:9a0680f37d9b52711a41fd6bc23fcac1132c781a09835270f062c8f07590306d

Observation 748a5ef3-35d2-43c0-8b0b-371007ecb754 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Evaluating Large Language Models Trained on Code

Reference 3

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:f6aab1709f60a55c134d8af71c1d7a4768769d47ca934d6ccf6c5693889d855b

Observation 275eed90-37e1-4a97-8bb0-41309878f894 · outbound

This paper cites Dense x retrieval: What retrieval granularity should we use? In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 15159–15177, 2024.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Dense x retrieval: What retrieval granularity should we use? In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 15159–15177, 2024

Reference 4

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:3561824673df0b29faff3be143638111657f42735ec23bf666351a7023295f05

Observation 69a788c3-8098-4f61-bbf0-d2523e41df3c · outbound

This paper cites Lift yourself up: Retrieval-augmented text generation with self-memory.Advances in Neural Information Processing Systems, 36:43780–43799, 2023.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Lift yourself up: Retrieval-augmented text generation with self-memory.Advances in Neural Information Processing Systems, 36:43780–43799, 2023

Reference 5

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:cb5bae188517dacf3971fc3c84021d4146fd487639a7f4355e9f731a1725b8f2

Observation 21952fc4-5b64-402b-be2f-d384af218b4c · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Training Verifiers to Solve Math Word Problems

Reference 6

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:b9b020e35cfcb7c49cce597a8b9c6e5b39bcc1f9f54ddecfd155651c5dc5e8a7

Observation 750d2c3b-07c9-465f-ab71-56911a864275 · outbound

This paper cites From Local to Global: A Graph RAG Approach to Query-Focused Summarization.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Reference 7

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:3341e14e3e2b0e3e1118cc63432d6685c9679f9ea44a94f93023bba9b695b137

Observation d74b2453-f1c6-42a1-89db-251c581c7d5f · outbound

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

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 8

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:f0086b609b5cf041c9444b5b62c300a5eff8c9f89c0687aadb23729703706ca8

Observation 4a80a35c-1f31-4b5b-be83-7994c4809c70 · outbound

This paper cites Iseeq: Information seeking question generation using dynamic meta-information retrieval and knowledge graphs.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Iseeq: Information seeking question generation using dynamic meta-information retrieval and knowledge graphs

Reference 9

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:46e3786bdd34b06a11f304d7c0534fab4fca91c59975ac793949e704b95861b3

Observation 78c428c7-ec9f-4959-9a13-17506cbfdfaf · outbound

This paper cites The Llama 3 Herd of Models.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation The Llama 3 Herd of Models

Reference 10

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:06e43f16884a696eb2c1a08811615ea5f9ac3d32b9a87522555a4486206ada48

Observation 76ed8576-585b-4e95-b478-0ea6499e3a45 · outbound

This paper cites G-retriever: Retrieval-augmented generation for textual graph understanding and question answering.Advances in Neural Information Processing Systems, 37:132876–132907, 2024.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation G-retriever: Retrieval-augmented generation for textual graph understanding and question answering.Advances in Neural Information Processing Systems, 37:132876–132907, 2024

Reference 11

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:145b2bcf9cf9db7eb1a7c7b8d20c12312f431795e8d4d7451ef754bd49ecb181

Observation 82467a78-64fb-4d4f-b246-89603e8f3e77 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Measuring Mathematical Problem Solving With the MATH Dataset

Reference 12

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:db05cb393f9d3cf391e1406d8e8f53aa7386ac1f910900e6c747f262fcb26ee8

Observation 4e77af6d-75b7-4f33-ab83-d9d2c3e84e83 · outbound

This paper cites ACON: Optimizing Context Compression for Long-horizon LLM Agents.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation ACON: Optimizing Context Compression for Long-horizon LLM Agents

Reference 13

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:7c9c041a1a3245a710c7dbcb10d384ffe0f11b4a4f07f936ec5a38810067dcc6

Observation 77ce644a-1c9e-4a13-b49d-e7d254f4e75b · outbound

This paper cites Distilling llm agent into small models with retrieval and code tools.arXiv preprint arXiv:2505.17612, 2025.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Distilling llm agent into small models with retrieval and code tools.arXiv preprint arXiv:2505.17612, 2025

Reference 14

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:bfa6f5d43d1cbb7167d7178778ea2ac925709c88d9eb3c2b7a2fbbd90b62cfb1

Observation 4b567e72-e99d-411c-bd05-a97a06a68542 · outbound

This paper cites LLMs Get Lost In Multi-Turn Conversation.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation LLMs Get Lost In Multi-Turn Conversation

Reference 15

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:8b92ff0e9d2a5ea9d03d341a90badd2941b3eac57237fa0cbdb0184550e2ab2b

Observation 79dacf38-48e8-489e-b224-2821e8d9fa78 · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in neural information processing systems, 33:9459–9474, 2020.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in neural information processing systems, 33:9459–9474, 2020

Reference 16

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:9af400b37e24823e391414ffd2d40d1670845ec0b9d9f45c9a4a2be929286535

Observation c5199600-cd59-4f1f-a984-9669ff68f926 · outbound

This paper cites From Classification to Generation: Insights into Crosslingual Retrieval Augmented ICL.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation From Classification to Generation: Insights into Crosslingual Retrieval Augmented ICL

Reference 17

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:77afdc24192032adeb70000a1c0ca38611ef87f42e7497800635341ac80ad3d2

Observation f1ad410b-9438-490f-8286-5d0ae979aeff · outbound

This paper cites Let’s verify step by step.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Let’s verify step by step

Reference 18

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:d4796c3cd3e3a385acc6625a96d2b60143ed70292162b954f568ed05e97ccbbb

Observation bb51c875-0e88-4fab-9232-287f8dfe4060 · outbound

This paper cites An efficient framework for learning sentence representations.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation An efficient framework for learning sentence representations

Reference 19

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:c69cca8f491467e1b6e5a77c6d64ced15323d4583904c0fcfb0da1cf1441a752

Observation 5a8c7731-3a42-4cdd-aade-f38f652fd263 · outbound

This paper cites Augmented Large Language Models with Parametric Knowledge Guiding.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Augmented Large Language Models with Parametric Knowledge Guiding

Reference 20

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:8dc3f05c0b26422892fe48869f5a60ad8b2d8a4d05715dae839261075953eb2f

Observation 66b977a4-7bfa-45f7-82c2-d1da47f33722 · outbound

This paper cites Enhancing intelligent agents with episodic memory.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Enhancing intelligent agents with episodic memory

Reference 21

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:80f0cb7625ad6a28fd46b224e1cd993f95a06f2bb419d18ffdb101152afe122b

Observation 14daccfd-bb7b-475a-99fc-3a953f960a20 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Representation Learning with Contrastive Predictive Coding

Reference 22

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:4a3f603da2a777b44c5e165c3ea5dc75c455d1af3cfa6ccb224c50b49ce94ed1

Observation 04420cf3-35d2-45a4-97b8-8b0457d8655c · outbound

This paper cites ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory

Reference 23

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:6a8dc60cb8e4b1c97a3cc9397aa8d043fb61adff925bc0823ddec3376d549b32

Observation fc886ef0-1db2-4142-a261-8ee8e7ea1117 · outbound

This paper cites Memgpt: Towards llms as operating systems.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Memgpt: Towards llms as operating systems

Reference 24

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:83b4a0ff9f122cf18306c392cb43c10743dad4a0caab6189d4ac38a4c61b3e34

Observation 7d88d616-6a8a-458d-b465-ed5fbf2efb3f · outbound

This paper cites Position: Episodic Memory is the Missing Piece for Long-Term LLM Agents.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Position: Episodic Memory is the Missing Piece for Long-Term LLM Agents

Reference 25

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:047210a82eccd75c0bb3ff63cfb3703934b632e440e918904b651e0f1b503e1d

Observation 3febb551-2575-44d6-9c4f-56bf294fdd9f · outbound

This paper cites A theoretical analysis of contrastive unsupervised representation learning.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation A theoretical analysis of contrastive unsupervised representation learning

Reference 26

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:f5aec6ed6e62deda30d064914184d78909956eac1cebf2331afa41c2dc360a4b

Observation 9174d805-5789-424e-a32b-bce3cb741422 · outbound

This paper cites Large language models can be easily distracted by irrelevant context.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Large language models can be easily distracted by irrelevant context

Reference 27

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:9ef8ec5f2f1c54584a494bc145026552b8fde493139a5f7adc947ce8e55e258a

Observation 48ed72c4-fe32-4e48-902b-fb6809cbff8c · outbound

This paper cites Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory

Reference 28

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:80479a3f69b402bff70c109a61f7c9ff97b7fad6c20629bba422054c22dcade7

Observation 75afdc77-c20f-4d55-abac-5c9f6c091863 · outbound

This paper cites Qwen2 Technical Report.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Qwen2 Technical Report

Reference 29

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:4cce135765c5d6e3a4bae05da16d55ff951faacd987fe36e8ec7d02d983b70f6

Observation 10b247c5-11a6-496e-9a05-b15000762200 · outbound

This paper cites Appworld: A controllable world of apps and people for benchmarking interactive coding agents.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Appworld: A controllable world of apps and people for benchmarking interactive coding agents

Reference 30

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:03ccf3843e47b42e4be05d6796c10005a81827e0e57c641e7b2d3746e6ec4f94

Observation 688f78a3-b719-4380-afb7-73a75456f0e6 · outbound

This paper cites Learning to retrieve in-context examples for large language models.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Learning to retrieve in-context examples for large language models

Reference 31

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:fd8e2e61a65afcd8c2f39bbee958c42892900d0cf35d11d9a060a2773516e43b

Observation b541e8d0-a1a0-4187-840d-a282deb2bf4d · outbound

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

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation A-MEM: Agentic Memory for LLM Agents

Reference 32

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:c08b9386c5a71f38ddec2a06b9e29399c462dcb2675d193928f743034ca74a6a

Observation 466fc1e4-6e95-47f0-889d-18e02326c2b0 · outbound

This paper cites Corrective retrieval augmented generation.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Corrective retrieval augmented generation

Reference 33

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:662619914ad9699625f2b00683755e3e9d5c78e239e4b1c32fe28065bb77f07d

Observation 65ca5fe7-e6f9-465c-8677-eb0d04f7b938 · outbound

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

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning

Reference 34

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:0829034df92499cdbb7e99009a6612661cdf98ed9f1a45e91321bed2f9c5a4e9

Observation 02803c80-031d-42ce-94dc-4a831de863ee · outbound

This paper cites Buffer of thoughts: Thought-augmented reasoning with large language models.Advances in Neural Information Processing Systems, 37:113519–113544, 2024.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Buffer of thoughts: Thought-augmented reasoning with large language models.Advances in Neural Information Processing Systems, 37:113519–113544, 2024

Reference 35

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:fa89fd054ec4fef274e99194d44362774435f7db391ed4590bfa58db5618e454

Observation 1fe4c723-a5f5-45ce-9755-d51b6a0ec872 · outbound

This paper cites ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates

Reference 36

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:cb5d3de0e73ee7393e8c134810377d8884d3d23e854920279691e30640add8c7

Observation f270c882-97cb-4d7c-82d0-a34cfcdf9a3d · outbound

This paper cites Generate rather than Retrieve: Large Language Models are Strong Context Generators.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 37

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:2bb6f13b0243b1aaaeea5a54dcdd40f63e0357f41fe11ce30e7d5e4ad432bec2

Observation 12770833-bb3e-46cf-8546-3664dea38226 · outbound

This paper cites Chain-of-note: Enhancing robustness in retrieval-augmented language models.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Chain-of-note: Enhancing robustness in retrieval-augmented language models

Reference 38

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:7f36019ecb44a48be58545c5a99a12785eb6d2a66d251e3c0b0b54d6f1d9e9f8

Observation 67472a1a-77e5-435a-944c-bd370f49f0de · outbound

This paper cites TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT

Reference 39

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:ea85bc260afa75a2ebc991ac87b248a13da42dca6535d24fd6c1f3a28c0b2cbc

Observation 9d230b5a-6b44-4037-9473-d018ac14e02e · outbound

This paper cites A survey on the memory mechanism of large language model-based agents.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation A survey on the memory mechanism of large language model-based agents

Reference 40

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:586077c356fc66907e3302491392ad71b25d1afc99426e390dd6fa83a004f03e

Observation 2619dcee-52dd-47a6-845e-96ff0a0bc814 · outbound

This paper cites Memorybank: Enhancing large language models with long-term memory.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Memorybank: Enhancing large language models with long-term memory

Reference 41

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:c75baaa46c3eaa67091d829bcf3b6ae55c07aa3a939e66f6aa85d75c235c965c

Observation baad0e77-6921-462e-b2d2-17f564ea1fff · outbound

This paper cites MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents

Reference 42

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:929ecbcda84214be2adc72b0cf4044a710970b7dfc7d0c589836ce4f23cf7210

Observation 087d5af1-b20f-42e8-94f4-81655c0a559f · outbound

This paper cites an unresolved cited work.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Unresolved cited work

Reference 43

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:7b8a2218c85a4b022bb633860c857f7387c4198127be023ff9f3ce2659858ca1

Observation 9646226b-a69d-4386-a010-06771ff42950 · outbound

This paper cites an unresolved cited work.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Unresolved cited work

Reference 44

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:a5b4faa6a124c722fc55b3a50ee0d38701c4a0fd3bc64af139f34e98d36372bd

Observation a3757f76-ff70-4eb2-bde8-abe605915218 · outbound

This paper cites For programming problems, provide base classes or interfaces that can be instantiated to solve specific instances.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation For programming problems, provide base classes or interfaces that can be instantiated to solve specific instances

Reference 45

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:00c506ca0914d401b258348e2eb11aa1bcca90a9a806c3e6ee2681fe6625c24e

Observation 9b20e79e-2414-47ea-b26b-3529b0273e20 · outbound

This paper cites an unresolved cited work.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Unresolved cited work

Reference 46

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:937c7c7f5b3056e0e0467d9aab7d1f707af976b47e91f9fdefec5bbd24247e86

Observation b3592621-a4c0-40a8-ba4b-e0a21fc30ace · outbound

This paper cites an unresolved cited work.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Unresolved cited work

Reference 47

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:88a2524a480ecd9692f5acf1274eecca640c026b63b9f5091e36d97c8e0dc744

Observation 2cdb6443-5e21-4a33-a1df-1e6166f40640 · outbound

This paper cites an unresolved cited work.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Unresolved cited work

Reference 48

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:7b25bbd6e7dbbfee50e5ec81e6c8720698305e28866f558be7d1ab3283eb8175

Observation 1c66b283-498e-46c6-a99b-2b21973c5520 · outbound

This paper cites an unresolved cited work.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Unresolved cited work

Reference 49

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:f242cf01f5eb5cc953d8695ee5880539f8b843a84a2769281fec2151af1993f6

Observation 873f439d-089e-4bd4-9afe-4fd4069956fe · outbound

This paper cites an unresolved cited work.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Unresolved cited work

Reference 50

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:32b23db3339418cec0d46e35218eee3598efdd64749757faa99fe5e30199c9e6

Observation d40bc341-634a-4a79-9b87-dcfcff7a45c5 · outbound

This paper cites Table 17: Prompt template for single-model reasoning extraction.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Table 17: Prompt template for single-model reasoning extraction

Reference 51

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:1173a2c45d087ee0aaac82fb6272b98c440b6ec2193bb830245b063c5e090695

Observation 0a7bbccd-db06-43cf-bb82-0cc2c7232ba8 · outbound

This paper cites an unresolved cited work.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Unresolved cited work

Reference 52

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:8360286c0a25769aa84eff51a26149b5f827279b0266988ae535123b3f455a67

Observation 276dba8a-b880-400d-b27d-f1c8672ed0a9 · outbound

This paper cites Each extracted strategy must combine: - a trigger (when this strategy should be considered), and - an enforcement rule (what must be enforced or avoided).

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Each extracted strategy must combine: - a trigger (when this strategy should be considered), and - an enforcement rule (what must be enforced or avoided)

Reference 53

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:52c565c20ab3baac6f2a500b3d65c408b8e8ada3a61f3226e44f93b04ae7d3e9

Observation 41f7be21-d241-4b8c-927d-909041e2de81 · outbound

This paper cites an unresolved cited work.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Unresolved cited work

Reference 54

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:73268a610ff9ac528f37d40a76a1586752c486d759f11e7046b71415ceeecaaa

Observation 18f42023-14d9-47e3-8833-854811a7bdc2 · outbound

This paper cites an unresolved cited work.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Unresolved cited work

Reference 55

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:90c194163badc864ab7c043a8cad921d1d95dbbaf8edada02f39d193aab5a99b

Observation dc367e29-3855-4998-96e2-5b388dd2410f · outbound

This paper cites Your output must be exactly one subcategory from the allowed list.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Your output must be exactly one subcategory from the allowed list

Reference 56

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:0d147df227e112e5ad253c2508ca945bd1e6e36be18ea4a8d9370c6abe8f8394

Observation d32576b1-ce21-433e-ae5a-10997f917d09 · outbound

This paper cites You may NOT output “None”.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation You may NOT output “None”

Reference 57

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:c9922eb2d70f144d7940dad6427c3d889f1e801a5c61ad8725f06834d7e25f04

Observation 10e0ebbc-9d95-46c7-84aa-0dfaed8d3d22 · outbound

This paper cites an unresolved cited work.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Unresolved cited work

Reference 58

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:0e21ff9dea8895bb0d649a89794187ef300618ae1e787c967084774a567cf29b

Observation 273f6882-6467-461b-adc9-d70e0f1b3a99 · outbound

This paper cites an unresolved cited work.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Unresolved cited work

Reference 59

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Source-reported events for the cited work

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:56d93ad79fccfc8e66416adec1d04e4314e780cd258e379012a803b1b92b06e4

Observation 31d52a82-c866-4689-94ca-c338d3741f3b · outbound

This paper cites These rules override all ambiguity.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation These rules override all ambiguity

Reference 60

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source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:7b584c0b9b3db74c67e4ab46cbc59ae5759fd16a34f1678743f1a5ee2e6b5e9d

Pith citing papers

Observation 9e08c8a6-31d4-4268-91a2-633be7bc6341 · inbound

System Identification of Lithium-Ion Battery Equivalent Circuit Models Using Ensemble Kalman Inversion cites this paper.

System Identification of Lithium-Ion Battery Equivalent Circuit Models Using Ensemble Kalman Inversion MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation

Reference 9

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no resolver link, observed 2026-07-12T22:19:48.698285Z

Source-reported events for the cited work

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source=pdf_text observed=2026-07-12T22:19:48.698285Z digest=sha256:08daade686e291cf497cd67d3f76abb35f693043c10527dc73aa1a1242a8b24a

Observation 140eabc1-fc91-407a-9295-fee7862f50a5 · inbound

MeloTune: On-Device Arousal Learning and Peer-to-Peer Mood Coupling for Proactive Music Curation cites this paper.

MeloTune: On-Device Arousal Learning and Peer-to-Peer Mood Coupling for Proactive Music Curation MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation

Reference 4

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verified exact
arxiv_id, observed 2026-05-29T02:05:04.056600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

source=arxiv_source observed=2026-05-10T15:02:36.245968Z digest=sha256:76e667efb0b9774cdb41b897e76633843614290a727ea0f397de448ea3d03b8a

Observation c387b817-be1a-41e6-90e7-bef9efcf5cb5 · inbound

Ace-Skill: Bootstrapping Multimodal Agents with Prioritized and Clustered Evolution cites this paper.

Ace-Skill: Bootstrapping Multimodal Agents with Prioritized and Clustered Evolution MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation

Reference 17

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verified exact
arxiv_id, observed 2026-05-29T02:05:04.056600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

source=pdf_text observed=2026-05-12T01:26:16.300353Z digest=sha256:e02a1887a48086d3332bcb904036631f6341fa3dccb2d32b053b50d3121c0854

Observation ce5d4d22-76f4-41e9-bb08-624d12b663e4 · inbound

When Does Memory Help Multi-Trajectory Inference for Tool-Use LLM Agents? cites this paper.

When Does Memory Help Multi-Trajectory Inference for Tool-Use LLM Agents? MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation

Reference 1

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metadata mismatch
local_arxiv, observed 2026-06-29T12:23:24.248323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

source=pdf_text observed=2026-06-29T12:21:02.155471Z digest=sha256:6e1d10377e19b59fef80d1926ad3b981b8983079ca45528f1c002cf40e2c758b