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Paper Citation Record · LEDGER

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness

As of 20 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2504.21773.

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

pith.paper-citation-record.v1
2504.21773 v4

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:59:38.882647Z

measured 40 of 40 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.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

  • verified exact3
  • verified fuzzy0
  • unresolved37
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1571e4f6-9df4-4a3f-91d0-dd437cfd62a5 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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source=arxiv_source observed=2026-08-16T04:59:38.681581Z digest=sha256:c4b177984198954f1a8b555f5170de73dec0ce914658410f116156e099de5d96

Observation c385bbfb-ad41-44ef-b645-6f04ce20805a · outbound

This paper cites Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals

Reference 2

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source=arxiv_source observed=2026-08-16T04:59:38.688865Z digest=sha256:1d0c8611df03cbd4cb1a4f681f174113e40c94afd39ba8741058e7a3a8ff335d

Observation abf0785e-5a98-441a-94e9-2964b63a4c3a · outbound

This paper cites an unresolved cited work.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Unresolved cited work

Reference 3

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source=arxiv_source observed=2026-08-16T04:59:38.693947Z digest=sha256:e6f66c020cbffc1bb4a21b8fae9ed0a205cdaf98f582d58a705bfcf472e6aad0

Observation 8e5d7203-d8d7-4261-bead-9b48073b291f · outbound

This paper cites an unresolved cited work.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Unresolved cited work

Reference 4

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source=arxiv_source observed=2026-08-16T04:59:38.699210Z digest=sha256:dae65ea338ec5663a7e2538ff3801fd6f79aa466a442d3912885b13eb3f2f981

Observation 74ed7e44-47f1-4bc6-ae90-1edde4585d15 · outbound

This paper cites Batch Prompting: Efficient Inference with Large Language Model APIs.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Batch Prompting: Efficient Inference with Large Language Model APIs

Reference 5

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source=arxiv_source observed=2026-08-16T04:59:38.704939Z digest=sha256:8c62a92781948fe39723d88e72e65019621f951ea11dd5fa03bdc7a81aa15b86

Observation 2d6c66b9-2338-4b1e-bf32-ac8290b6fc0e · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Training Verifiers to Solve Math Word Problems

Reference 6

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no resolver link, observed 2026-08-16T04:59:38.710133Z

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source=arxiv_source observed=2026-08-16T04:59:38.710133Z digest=sha256:c85860777fae36e3c07f703e4a187b0a9385b790be14f5e3f73c010a7b1921a2

Observation 4d0533c4-c046-4567-878f-fb19540789bd · outbound

This paper cites Improving Factuality and Reasoning in Language Models through Multiagent Debate.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Improving Factuality and Reasoning in Language Models through Multiagent Debate

Reference 7

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source=arxiv_source observed=2026-08-16T04:59:38.715621Z digest=sha256:439c40ab6637877644bdf53c3b1f0422e3dff8e1b6cb8f335b4edcc91ac3c7b5

Observation 8f6cd06c-0379-4b25-a0ad-190646b01ce9 · outbound

This paper cites The Llama 3 Herd of Models.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness The Llama 3 Herd of Models

Reference 8

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source=arxiv_source observed=2026-08-16T04:59:38.721550Z digest=sha256:47aac30b8521ae3572b83a0f7bd6691139d84487a6d43b58f26008dc6db6d048

Observation c7628847-0ef6-4532-9bad-c6a07c5c471e · outbound

This paper cites Measuring and Improving Consistency in Pretrained Language Models.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Measuring and Improving Consistency in Pretrained Language Models

Reference 9

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local_arxiv, observed 2026-08-16T04:59:39.401802Z

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=arxiv_source observed=2026-08-16T04:59:38.726854Z digest=sha256:0d20650e726d5b3f09b1f952faaea53261bc08c4f80b8f80e99dfe4f9efdef3f

Observation 572f9c50-b0d2-4f05-a491-3248a7409f04 · outbound

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

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 10

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source=arxiv_source observed=2026-08-16T04:59:38.732333Z digest=sha256:4e1a2c582a052e3e31444fb3fb46613093a29eab26b0c844cbb64fd6cc284c56

Observation 85f37ca5-e53e-4e06-adf1-4a7c76b6f813 · outbound

This paper cites an unresolved cited work.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Unresolved cited work

Reference 11

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source=arxiv_source observed=2026-08-16T04:59:38.736941Z digest=sha256:42c66d813185b24a7da06f2282d06ee1c03402e0b86307824b0f78a1fe9cb839

Observation a88d1664-ad15-4f79-908a-f09ea27ace84 · outbound

This paper cites an unresolved cited work.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Unresolved cited work

Reference 12

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source=arxiv_source observed=2026-08-16T04:59:38.745736Z digest=sha256:262ad7804e0ab3f3155c7140810cc49e4d74d802a49aee9ded9581d47aeac76b

Observation ebcafd77-8367-447b-abcc-4c1ddc89a4cf · outbound

This paper cites AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation

Reference 13

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source=arxiv_source observed=2026-08-16T04:59:38.750700Z digest=sha256:25b23d77efaf45db336c17de9a40b49af409ef01909a867a0331ee41d17f9c69

Observation a67a5bee-dc0c-45df-9431-16c41038e0ff · outbound

This paper cites Advancing Language Multi-Agent Learning with Credit Re-Assignment for Interactive Environment Generalization.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Advancing Language Multi-Agent Learning with Credit Re-Assignment for Interactive Environment Generalization

Reference 14

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source=arxiv_source observed=2026-08-16T04:59:38.756027Z digest=sha256:0f33ee1c8e6f7309a7d63358fea4ad502ced0915a2fd723ed6c2ca880f59eb0f

Observation 1724996d-a25d-4c98-8a72-ba5310689128 · outbound

This paper cites MATP-BENCH: Can MLLM Be a Good Automated Theorem Prover for Multimodal Problems?.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness MATP-BENCH: Can MLLM Be a Good Automated Theorem Prover for Multimodal Problems?

Reference 15

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source=arxiv_source observed=2026-08-16T04:59:38.760845Z digest=sha256:e70d4385af4faf9cc6c2528900d3f733ff9e734896188efbb6c7bb38d2a5c5fd

Observation c21bd77c-df04-4c17-af26-26e6b0a4b6e6 · outbound

This paper cites MMBoundary: Advancing MLLM Knowledge Boundary Awareness through Reasoning Step Confidence Calibration.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness MMBoundary: Advancing MLLM Knowledge Boundary Awareness through Reasoning Step Confidence Calibration

Reference 16

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source=arxiv_source observed=2026-08-16T04:59:38.765328Z digest=sha256:346859a83b2309f657479766e6a82ea177898db90fdf6cbf94445dfc6553994f

Observation 9a415598-9514-4dbd-9528-46a7f12aa924 · outbound

This paper cites an unresolved cited work.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Unresolved cited work

Reference 17

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source=arxiv_source observed=2026-08-16T04:59:38.769981Z digest=sha256:7baabd2a538923e80d5fed883ae0cc5353412552dcc64cba53a0fae1bc38372c

Observation 7f06af92-287f-4fd0-a733-74f370452ef0 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness LoRA: Low-Rank Adaptation of Large Language Models

Reference 18

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source=arxiv_source observed=2026-08-16T04:59:38.774476Z digest=sha256:d0841f55be0876a220b302640c882bcb28abd33bd53f55d029e2aa55aded88de

Observation d568a92f-9bb2-4461-83c5-10c13a471e5a · outbound

This paper cites an unresolved cited work.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Unresolved cited work

Reference 19

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source=arxiv_source observed=2026-08-16T04:59:38.779371Z digest=sha256:6a506c3c5c6ce6d0f9f3d121493876fbb59e6939892b402924d726de681f6977

Observation e7c050b6-9d3d-4db3-8317-6f4acced1790 · outbound

This paper cites CultureCLIP: Empowering CLIP with Cultural Awareness through Synthetic Images and Contextualized Captions.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness CultureCLIP: Empowering CLIP with Cultural Awareness through Synthetic Images and Contextualized Captions

Reference 20

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local_arxiv, observed 2026-08-16T04:59:39.284908Z

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=arxiv_source observed=2026-08-16T04:59:38.784279Z digest=sha256:bbbc4e4a367978a3eaf07081829ba8fe7aca76684d9867d94350b9d7864da413

Observation 382dd3c9-6ca9-49a2-a3e2-3f72803dd847 · outbound

This paper cites an unresolved cited work.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Unresolved cited work

Reference 21

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source=arxiv_source observed=2026-08-16T04:59:38.789063Z digest=sha256:129b348220e02556a8dabafdb83a2addf81b6f86160069c0cf7b9b8777912e75

Observation 0c5f7df0-f60d-496c-bbfd-171e6c38c44a · outbound

This paper cites RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models

Reference 22

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source=arxiv_source observed=2026-08-16T04:59:38.793662Z digest=sha256:d7f24f3cfca6fcf684556452e4298a7fc5f9829206e402b29183dff6dc3af89f

Observation d05871aa-93ec-402f-ae13-8b42000b262c · outbound

This paper cites Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning

Reference 23

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source=arxiv_source observed=2026-08-16T04:59:38.798407Z digest=sha256:6094fed20a157e1036cd20e460a7bc6d5164194ed842c98257f8d9af596673e3

Observation 5e5bee79-6738-4771-9bc1-08bd179d5b6b · outbound

This paper cites an unresolved cited work.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Unresolved cited work

Reference 24

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raw_fallback, observed 2026-08-16T04:59:39.542802Z

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

source=arxiv_source observed=2026-08-16T04:59:38.803170Z digest=sha256:8e72a71e000ddfff783ff54e5b159dd086e999b5a9c3e6c199c9d85297897446

Observation 46bb6413-e2a7-49bf-bdce-8052781b3f33 · outbound

This paper cites an unresolved cited work.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Unresolved cited work

Reference 25

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source=arxiv_source observed=2026-08-16T04:59:38.807582Z digest=sha256:88f681d0754114ae35143d6da8ad30787bdee2f811738976db64b1f832441e70

Observation 5a14e150-44f3-42ef-84ea-006472352f12 · outbound

This paper cites BatchPrompt: Accomplish more with less.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness BatchPrompt: Accomplish more with less

Reference 26

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verified exact
local_arxiv, observed 2026-08-16T04:59:39.229757Z

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

source=arxiv_source observed=2026-08-16T04:59:38.812138Z digest=sha256:9699615320babf8a3f23a5189001a6fd1a17049e0e55ad55cd565c68c54273f6

Observation 793ba71f-687e-46c4-a70d-8ba8da7826b4 · outbound

This paper cites ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models

Reference 27

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source=arxiv_source observed=2026-08-16T04:59:38.816974Z digest=sha256:566cf5393ab10315d24b4f5a0b5b4c9d7233f7ff4dbc90432f2571e2d6c437c2

Observation 63a4d3c5-8b39-481d-9ab8-e6bfe95caf63 · outbound

This paper cites an unresolved cited work.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Unresolved cited work

Reference 28

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source=arxiv_source observed=2026-08-16T04:59:38.823031Z digest=sha256:a8dd054d032b6452c00d9c2f43b1fd9f49f4cb617c18032d764ee33253032459

Observation 51f92100-2489-4ad5-acc8-dcd08f4a5843 · outbound

This paper cites an unresolved cited work.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Unresolved cited work

Reference 29

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no resolver link, observed 2026-08-16T04:59:38.827641Z

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source=arxiv_source observed=2026-08-16T04:59:38.827641Z digest=sha256:3fe61930c20f4f01c1114ec22b57c79e5f358569431f46a9da47a6b96b401a06

Observation 68498c01-203e-4018-953d-453732c7f3f5 · outbound

This paper cites Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback

Reference 30

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source=arxiv_source observed=2026-08-16T04:59:38.832181Z digest=sha256:86ad84787c62b0f4cf65b7e1379e0438d5f65e5f31e88b21d6094ab9b5ec9367

Observation 8359fdaa-382e-4acd-a9e2-5225cd47d8c0 · outbound

This paper cites Fung, Weizhu Chen, Minhao Cheng, and Furu Wei.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Fung, Weizhu Chen, Minhao Cheng, and Furu Wei

Reference 31

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source=arxiv_source observed=2026-08-16T04:59:38.836879Z digest=sha256:4017b97c8aa7873ec0eefba8eb84549fbf05575954c0c9921d5ba844ed299e19

Observation abe941e6-2172-4df4-824e-d3485c13f3d4 · outbound

This paper cites an unresolved cited work.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Unresolved cited work

Reference 32

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source=arxiv_source observed=2026-08-16T04:59:38.841348Z digest=sha256:683f1a09753b7e1369fa355f632e2e3cd647d2b5de09311b2a78a07eb7940247

Observation 0be8f77a-9c7c-4fc8-9b12-5eccd0d67f01 · outbound

This paper cites TOME: A Two-stage Approach for Model-based Retrieval.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness TOME: A Two-stage Approach for Model-based Retrieval

Reference 33

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source=arxiv_source observed=2026-08-16T04:59:38.846365Z digest=sha256:f3399e0dcb6c171711c6193c1b2985ff0066b51008b380f565df15781644f61f

Observation 862669f0-52f5-4aa8-92ec-15abe79a065d · outbound

This paper cites Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?

Reference 34

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source=arxiv_source observed=2026-08-16T04:59:38.851388Z digest=sha256:5d7d1e01b21f9ece64be7a0da0454bd88223cb167f58dfd5a09ef5c8678763d9

Observation 45e255ea-0640-4669-b75b-e666a4312b25 · outbound

This paper cites Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision

Reference 35

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source=arxiv_source observed=2026-08-16T04:59:38.857297Z digest=sha256:d94e28f3e0fe95bbe20cefff16aff6c9e31a1e51066b85c354c7a5aecc08ef41

Observation f7d6bca0-f4d1-4014-85e6-8da8518083e7 · outbound

This paper cites Evaluating LLMs with Multiple Problems at once.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Evaluating LLMs with Multiple Problems at once

Reference 36

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source=arxiv_source observed=2026-08-16T04:59:38.862438Z digest=sha256:5048c549ddf324e88eddb5b18874631340b287f27ab71ecd8be59fd45f8e158f

Observation 8c544f18-1bcd-43d2-9aab-f3527790954f · outbound

This paper cites Qwen2 Technical Report.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Qwen2 Technical Report

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-16T04:59:38.867384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:59:38.867384Z digest=sha256:9b966318d877b25e6dab46b63c65974892904e664cb567dc552b6c61619787f5

Observation 620468a2-a3e1-4ea4-97af-546ef7e71963 · outbound

This paper cites an unresolved cited work.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Unresolved cited work

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-16T04:59:38.872412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:59:38.872412Z digest=sha256:e38ee7b05b4f2cfc2cfd65e89c5be3b3c9df0244c63f5ee5527d35a63149e7d7

Observation a4bf5e0c-8608-4863-8b5b-a061a351c03a · outbound

This paper cites URL: " 'urlintro :=.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness URL: " 'urlintro :=

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-16T04:59:38.877085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:59:38.877085Z digest=sha256:b5cdeaa5a630cff4ed8b126905a4009f9de8ea6e2bc50b4813b59ec324f68877

Observation c8943b35-4b05-41de-91b6-90a7b2e310b2 · outbound

This paper cites write newline.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness write newline

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-16T04:59:38.882647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:59:38.882647Z digest=sha256:4fb3ea6c2374dc1e413bd670c3e759d0d9d305d0b6486d2035a63fcad938b174

Pith citing papers

No inbound Pith citation observations are available.