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Paper Citation Record · LEDGER

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression

As of 8 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 5 inbound Pith citation observations for arXiv:2505.19433.

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

pith.paper-citation-record.v1
2505.19433 v2

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:18:02.566935Z

measured 75 of 75 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T20:30:27.324529Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T20:33:43.363632Z

Reference resolution

70 of 70 outbound references displayed

  • verified exact2
  • verified fuzzy11
  • unresolved55
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6cee5c19-3dac-4a76-b0c6-42209d3e4f47 · outbound

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

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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source=pdf_text observed=2026-08-07T14:17:54.319938Z digest=sha256:b7c3e82a860b544a39173505e127f6f94ea6968d1a49e7a31c760de4e0471c1f

Observation c5bdf1af-0b93-47e2-83b3-2c782a3f180e · outbound

This paper cites QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs

Reference 3

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source=pdf_text observed=2026-08-07T14:17:54.499478Z digest=sha256:8a72e22bf169e13c2843ee3a40dc0b059c35d9fe118415941c901f44f95d552d

Observation 5369c5a2-2c56-4249-9171-9ae594a1a5c4 · outbound

This paper cites SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models

Reference 4

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source=pdf_text observed=2026-08-07T14:17:54.591185Z digest=sha256:7efadc8a2dc0b8d27baafb0a8cbd337b35eb846ef34bec0ab1bc161d5df63412

Observation 691c0636-8cbb-42df-b4a0-45013af04bc4 · outbound

This paper cites doi: 10.18653/v1/2024.acl-long.172.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression doi: 10.18653/v1/2024.acl-long.172

Reference 5

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source=pdf_text observed=2026-08-07T14:17:54.660908Z digest=sha256:846da555b62c0a421d7a233f57509f90a0c1d508b15660f719de364445b3b48a

Observation 51e5eb65-e61c-4a80-9738-b3a0ee6b7f16 · outbound

This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 6

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source=pdf_text observed=2026-08-07T14:17:54.731674Z digest=sha256:0b95b3b3462bf52d10317ef7bd074676c59c7f982f587f67d04a98d213811461

Observation 492e7414-f19d-4253-949e-7e5fa85649f1 · outbound

This paper cites Scaling Synthetic Data Creation with 1,000,000,000 Personas.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Scaling Synthetic Data Creation with 1,000,000,000 Personas

Reference 8

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source=pdf_text observed=2026-08-07T14:17:54.923841Z digest=sha256:4133101db6305d7feaa397d2ff0aae6afda96c9b38fd18a0b4067d615b54ff48

Observation 93801daf-29f5-447b-8669-2b8cf24997c9 · outbound

This paper cites Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena

Reference 9

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source=pdf_text observed=2026-08-07T14:17:55.019857Z digest=sha256:8a99524bae684b1010d512985942c5768b704c672f776fbdbb51a811088bd7d0

Observation 2b0a5ace-e407-4015-8992-0197b38424fc · outbound

This paper cites Xiao, G., Lin, J., Seznec, M., Wu, H., Demouth, J., and Han, S.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Xiao, G., Lin, J., Seznec, M., Wu, H., Demouth, J., and Han, S

Reference 10

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source=pdf_text observed=2026-08-07T14:18:00.103941Z digest=sha256:2bae34871a69bf52f63795e0e38aa7b2de251fd657f200fbd8f4fd46659579f6

Observation 90417c47-7f34-4e1a-bcb9-5743b96231d0 · outbound

This paper cites STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs

Reference 12

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Observation 197c24af-69c7-4b92-b1fc-8c86a00dff87 · outbound

This paper cites SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot

Reference 13

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source=pdf_text observed=2026-08-07T14:17:55.317481Z digest=sha256:78b1054c4851d4f6233fda5340d8c1d78d012a3b82b7e61de3c2235b4beca4a4

Observation 692a8ae2-60e6-49aa-b882-9790e1acccb6 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 14

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source=pdf_text observed=2026-08-07T14:17:55.388800Z digest=sha256:bfbdcea17a9792019031316ff8cb40cfbe8fb444cbf00f48c26bc183a9c80f89

Observation c50b340a-bc4d-4a1b-bf1d-d4ce6b583d45 · outbound

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

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 15

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source=pdf_text observed=2026-08-07T14:17:55.460172Z digest=sha256:610e44eecc8b9940283faf971fd7f91c8924759af24aa106304fba0d96aed0c9

Observation d4a8b403-151f-430f-9039-fb73ba2df074 · outbound

This paper cites RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models

Reference 16

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source=pdf_text observed=2026-08-07T14:17:55.529646Z digest=sha256:a43f4bbf2cad94e1c48395db4894a4345d67d350463669146725d726a6183de6

Observation 7471b494-bebc-44b2-ac98-e8fb7b70cc5d · outbound

This paper cites Delta Decompression for MoE-based LLMs Compression.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Delta Decompression for MoE-based LLMs Compression

Reference 17

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source=pdf_text observed=2026-08-07T14:17:55.649040Z digest=sha256:40c20bd15d4b0eb91f3534eac3eda04a2934851e32149bb7df7a25db218ffbe1

Observation e66a4f55-bdec-4f43-987a-555e5cd73698 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Distilling the Knowledge in a Neural Network

Reference 19

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source=pdf_text observed=2026-08-07T14:17:55.807275Z digest=sha256:6e07de59e5184f29fbd7555c447374dafaec20268773c25a13f3ac5c542b676d

Observation 6330433b-669b-4c9a-b609-c283b106b549 · outbound

This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 20

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source=pdf_text observed=2026-08-07T14:17:55.880736Z digest=sha256:b59f510e9c2a75a6aed9ca9ef83b83cc1c2d1a51e59f0bc5a3c14b3db2a0f97a

Observation 0c7d62d5-ebd1-4d9e-883b-457726bf070d · outbound

This paper cites War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars

Reference 21

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source=pdf_text observed=2026-08-07T14:17:55.971511Z digest=sha256:de810c3e076d29b8b81eb4cf65d69de9dbf3867b331a57a650e38feaebeeece6

Observation c3dbe264-b354-48be-b14b-70e8f88c3056 · outbound

This paper cites AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation

Reference 22

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source=pdf_text observed=2026-08-07T14:17:56.039027Z digest=sha256:d7ebbda08c9ba5bbb0ba2679fa7d8db80f2c48216f85dfa5a9b5c6f8fdf98655

Observation eddbdcaf-db0e-4888-8247-92ef29f2589b · outbound

This paper cites An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4

Reference 23

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source=pdf_text observed=2026-08-07T14:17:56.118892Z digest=sha256:0950bd89969e2d485a0a0fc46cdc9628e23c91ce719cbcbeca97e4c607962824

Observation 898c8ecb-688b-470c-9ee3-2243b209b0b2 · outbound

This paper cites Mistral 7B.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Mistral 7B

Reference 25

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source=pdf_text observed=2026-08-07T14:17:56.305023Z digest=sha256:67e673cc5815c899fc387607ed409bf6118842ce4aec93e76dcf3c12ef232b73

Observation 522070b8-6cf0-4e0d-a01a-26bae17ff451 · outbound

This paper cites SqueezeLLM: Dense-and-Sparse Quantization.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression SqueezeLLM: Dense-and-Sparse Quantization

Reference 26

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source=pdf_text observed=2026-08-07T14:17:56.372336Z digest=sha256:fcdeb48fd043531f476502e9992588bedf1d6e3d069121aa3718ed625f135b26

Observation d2d1cc3b-7958-4a42-afa3-9fa22aa93fd5 · outbound

This paper cites SqueezeLLM: Dense-and-Sparse Quantization.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression SqueezeLLM: Dense-and-Sparse Quantization

Reference 27

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source=pdf_text observed=2026-08-07T14:17:56.464514Z digest=sha256:774937403e432703519e3f2d1d4c5a937851112a3efcefd01eff6f5736d6de1f

Observation 1c8b0502-8017-4db4-8842-b6ee59c0859a · outbound

This paper cites Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems

Reference 28

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source=pdf_text observed=2026-08-07T14:17:56.572435Z digest=sha256:6da2091137dbc505992e8e8248a4027f13f40e8152c30bd4e41ee08233893148

Observation 92fadf4b-aaa0-4402-b130-63f5424c5421 · outbound

This paper cites AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 30

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source=pdf_text observed=2026-08-07T14:17:56.768339Z digest=sha256:0b02f012a20f20a7b145cc1b5fce048c9437a646b6e036f303dd81847bd81684

Observation 75267578-9fc3-4a72-8b25-7862fbaf6f36 · outbound

This paper cites NORM: Knowledge Distillation via N-to-One Representation Matching.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression NORM: Knowledge Distillation via N-to-One Representation Matching

Reference 31

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source=pdf_text observed=2026-08-07T14:17:56.777115Z digest=sha256:40c765fe8d29cba710b65f0f97b5dbb4c0d32ffa299ccf8f8a7f893be833992f

Observation 787420ba-b6d5-439a-9c92-1643ad5b01fe · outbound

This paper cites REFINER: Reasoning Feedback on Intermediate Representations.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression REFINER: Reasoning Feedback on Intermediate Representations

Reference 33

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source=pdf_text observed=2026-08-07T14:17:57.094974Z digest=sha256:2752eb6d2949eb5216a5beea63eb5592644408bd7ed581f38207db7d5d80936e

Observation 0361649b-351f-4512-9fa8-13d79663f0ef · outbound

This paper cites LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain

Reference 34

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source=pdf_text observed=2026-08-07T14:17:57.261092Z digest=sha256:e494e4d03ff4d7493ebcc5466eabac53f38d85deefc62425ef971c357abcabd8

Observation f3be9658-980e-4ce6-bd07-9e2786c763eb · outbound

This paper cites Benchmarking Agentic Workflow Generation.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Benchmarking Agentic Workflow Generation

Reference 35

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source=pdf_text observed=2026-08-07T14:17:57.414092Z digest=sha256:519561543aeb852569947f3b945e6187b03992e60420eaac2b1cba13c0e8c0d1

Observation 15a386df-577f-45f7-b05b-b61cef34f7da · outbound

This paper cites ConceptAgent: LLM-Driven Precondition Grounding and Tree Search for Robust Task Planning and Execution.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression ConceptAgent: LLM-Driven Precondition Grounding and Tree Search for Robust Task Planning and Execution

Reference 36

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source=pdf_text observed=2026-08-07T14:17:57.555068Z digest=sha256:bb37a2eed163735c51f90997978bc8c1c9cd87f7e86c9db6b146517241d7cce7

Observation f8eb5b40-89af-4621-a864-e73b114b4d24 · outbound

This paper cites Shi, Z., Gao, S., Chen, X., Feng, Y ., Yan, L., Shi, H., Yin, D., Ren, P., Verberne, S., and Ren, Z.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Shi, Z., Gao, S., Chen, X., Feng, Y ., Yan, L., Shi, H., Yin, D., Ren, P., Verberne, S., and Ren, Z

Reference 38

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source=pdf_text observed=2026-08-07T14:17:57.933597Z digest=sha256:e0c5c7c7c5051f76a3554c025bf2929c852e098bcbdf04fdf5ee874537fc376d

Observation b0d047bb-6420-4995-901c-b02ba3e123af · outbound

This paper cites doi: 10.18653/v1/2024.findings-emnlp.624.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression doi: 10.18653/v1/2024.findings-emnlp.624

Reference 39

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doi, observed 2026-08-07T14:18:02.853230Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:17:58.089613Z digest=sha256:78dcc046cbdeb924c08c295632bd62fa581c483e29684738ff43026879a079db

Observation bfee7631-30d6-44bd-af46-accee6c6df5a · outbound

This paper cites Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge

Reference 40

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source=pdf_text observed=2026-08-07T14:17:58.286417Z digest=sha256:e3f7a95e7a0d4bf6d19ba603cc68e9a98c652c56397a21e0c11b99521c92d96a

Observation c1b6bbc5-de06-46b5-9a50-c5b9b5c2d81f · outbound

This paper cites ISBN 9781450366717.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression ISBN 9781450366717

Reference 41

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:17:58.441353Z digest=sha256:facb4d222c0e5241faafcd60aca43bec00d4cd825b26f2759f90b350688ea2f3

Observation 2a3ab882-04a3-4ee4-b894-ab4ddc3e1afc · outbound

This paper cites DeepSeek-V3 Technical Report.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression DeepSeek-V3 Technical Report

Reference 42

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source=pdf_text observed=2026-08-07T14:17:58.565406Z digest=sha256:492cd460ccab52290a682e4af0ac0241104d4489f1b01c417907c79a0e34227f

Observation a225bcd7-ded9-4023-8d11-e5f357c5a78b · outbound

This paper cites A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models

Reference 43

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:58.964633Z digest=sha256:60d922501bd8f14c928d4707e7b2465ed85d7bcfdfb7d4bb1469f4e856510af7

Observation b453e984-84f8-497d-ad85-e82c939a1ab4 · outbound

This paper cites Large language models still can’t plan (a bench- mark for llms on planning and reasoning about change).

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Large language models still can’t plan (a bench- mark for llms on planning and reasoning about change)

Reference 44

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no resolver link, observed 2026-08-07T14:17:59.040961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:59.040961Z digest=sha256:2052327955f77437c2af97c25c7d86f7bf6b18cfbd9b83562218421be2873bcf

Observation e176186f-f702-44c8-b2d7-122c0cb5e6cd · outbound

This paper cites Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?

Reference 45

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no resolver link, observed 2026-08-07T14:17:59.191769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:59.191769Z digest=sha256:538197d1378370244ce0caf7add9fa0b7aa895eb484c3987e61c83cb11e7e208

Observation 5eea831c-aee0-4620-abe8-aff25b9365f8 · outbound

This paper cites V ., Chi, E.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression V ., Chi, E

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:18:08.852040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:17:59.351138Z digest=sha256:b58391de9b4ffbd54f10d7d7195fb9495e9b4b3670c0f4ab6d1121bb0874a346

Observation 15890b72-13b0-4d97-9d44-4acf05f6c3ff · outbound

This paper cites KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases

Reference 47

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no resolver link, observed 2026-08-07T14:17:59.469327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:59.469327Z digest=sha256:e014b8bac5c202686c42858e31317355c9999999a6e4595d6cae5311b2339a6c

Observation 3614b5fe-43e9-4e2d-b2e5-abc96bba1354 · outbound

This paper cites Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering

Reference 48

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:18:03.559965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:17:59.603609Z digest=sha256:20034f9ff445efda524473571866b5d6601652a65736eaec56b087f0c2481a6f

Observation 94459fa9-47c0-4758-8e63-61ba5fabebfb · outbound

This paper cites AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Reference 49

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no resolver link, observed 2026-08-07T14:17:59.785660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:59.785660Z digest=sha256:441e7ed1a7b7b5b54fb5adb31e114ec8a9fdebe5b44aa9b0aebdc1d43fdafe7d

Observation 82f21725-7061-446a-a1d3-13cc09a97a05 · outbound

This paper cites Autogen: Enabling next-gen llm applications via multi-agent conversation.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Autogen: Enabling next-gen llm applications via multi-agent conversation

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:18:08.639380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:17:59.926287Z digest=sha256:ecb6a784500f6f3e9fddb6da6f58ae401d6da46984c0b92a222d2faa07dcd32a

Observation e93d957f-447a-4409-9691-a9071dc102d5 · outbound

This paper cites Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-07T14:18:00.236516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:18:00.236516Z digest=sha256:0e5e9c21ba2861bd04a4e7b324b4ae899dc10aac8dc824d0fdc4f171438fc50d

Observation 2bc95866-01f3-46fe-b86c-6e67c75a4723 · outbound

This paper cites LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment

Reference 53

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no resolver link, observed 2026-08-07T14:18:00.343574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:18:00.343574Z digest=sha256:0b328e33a2b67d8e787bf6b871ba1eb5a6499f8b453f6ea505a03572c31bc77a

Observation 2bd67bee-7d97-4f9c-afc4-7fc764644665 · outbound

This paper cites RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs

Reference 54

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unresolved
no resolver link, observed 2026-08-07T14:18:00.482428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:18:00.482428Z digest=sha256:ebdbf52638309b3515584729a1236d61b68b9ab7d742d02d2eb8b6ae68d9d6e1

Observation 95d87b76-3d0c-4569-a1a3-ad0ca151f38a · outbound

This paper cites ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-07T14:18:00.636779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:18:00.636779Z digest=sha256:ba5e79769daae36a580ba9f3c9d736e6209f09ffe1d1ff7059fe5177839e6018

Observation 16d633e4-d643-4a08-9ff6-8cb7589d3c5d · outbound

This paper cites ToolCoder: Teach Code Generation Models to use API search tools.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression ToolCoder: Teach Code Generation Models to use API search tools

Reference 56

Resolution
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no resolver link, observed 2026-08-07T14:18:00.757002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:18:00.757002Z digest=sha256:9e72778b9d7c2f3f46631cf78035dfa2d5455aee27c06fd05b6843ae3011974f

Observation c87138db-b4cd-4088-a0b7-fd3a07ec14e5 · outbound

This paper cites 19 A.2 LLM-based Agents.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression 19 A.2 LLM-based Agents

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:18:08.416263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:18:00.933387Z digest=sha256:e9ddca431537ba8353527b5469437ee4b28af1c7bf580e6993ca5b9741eeebe0

Observation 6dd87e3b-5872-4755-a2ce-455c23faa03d · outbound

This paper cites Research has shown that judicious pruning can maintain or even sometimes improve model generalization by removing overfitting parameters.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Research has shown that judicious pruning can maintain or even sometimes improve model generalization by removing overfitting parameters

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:18:08.017714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:18:01.207080Z digest=sha256:7fc5adb880654132d4c60b50e128d8a001b0f1829641347807b6358bf3b8e943

Observation 1ba4a34f-300b-40e1-8d52-7f604f09305e · outbound

This paper cites an unresolved cited work.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Unresolved cited work

Reference 60

Resolution
unresolved
raw_fallback, observed 2026-08-07T14:18:07.869058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:18:01.336083Z digest=sha256:a5f120f4cf576ad4a01b2e3d59c99ea97109e1524fbdddb1619489d827fa5ca9

Observation 0cbc1d02-871c-43ff-b0f9-72ccd43dc050 · outbound

This paper cites an unresolved cited work.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Unresolved cited work

Reference 61

Resolution
unresolved
raw_fallback, observed 2026-08-07T14:18:07.682569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:18:01.513475Z digest=sha256:aaff0204afe6b1ffd522ce038b2e9bc48d72b8f887819ed9456846eb0c293ee8

Observation da2b33f5-aca6-45fb-af63-ea7a2c0e5601 · outbound

This paper cites an unresolved cited work.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Unresolved cited work

Reference 62

Resolution
unresolved
raw_fallback, observed 2026-08-07T14:18:07.522623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:18:01.658178Z digest=sha256:4d9768df55e5d6391e932299799289001c27cbdc566d4b27847879c6c9af621e

Observation 6781b5ee-c899-4d24-953f-51190cc4458e · outbound

This paper cites Analogical reasoning (Xu et al., 2024; 2023; Amirizaniani et al.,.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Analogical reasoning (Xu et al., 2024; 2023; Amirizaniani et al.,

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:18:07.331768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:18:01.793143Z digest=sha256:e440eed3597c24268e3bb880b9a9b24fd0160caef9536a9710f4cf2c7e27870a

Observation 5cdd7199-c5f1-4f5a-ad92-1eb721f503d4 · outbound

This paper cites an unresolved cited work.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Unresolved cited work

Reference 64

Resolution
unresolved
raw_fallback, observed 2026-08-07T14:18:07.106776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:18:01.907174Z digest=sha256:0604e4527510d3decdb670de60b388da88c2669742c61c0862dab213dd0176de

Observation 224ee3f7-901e-4a2e-bf9b-518b863c79c6 · outbound

This paper cites an unresolved cited work.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Unresolved cited work

Reference 65

Resolution
unresolved
raw_fallback, observed 2026-08-07T14:18:06.947558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:18:02.026399Z digest=sha256:d1d9117be0907ef97cc6bdc41fa60671bcaf0133af527d7d3d30d7df4112883e

Observation 834e9b74-d0f7-4f4c-8dce-dc5067ae692f · outbound

This paper cites However, challenges persist in ensuring that LLMs maintain reliable and consistent logical reasoning across different contexts and domains.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression However, challenges persist in ensuring that LLMs maintain reliable and consistent logical reasoning across different contexts and domains

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:18:06.760202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:18:02.121094Z digest=sha256:3983757090d878bc3a784ef54159bc2e7550b706606b4b1d74db835d04d47d81

Observation 93a4b384-2804-411a-8c60-be7b13ef00fe · outbound

This paper cites an unresolved cited work.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Unresolved cited work

Reference 67

Resolution
unresolved
raw_fallback, observed 2026-08-07T14:18:06.504203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:18:02.232970Z digest=sha256:25e43d0c25a408f8eaca8f4e0a7ce0e5a2226b038aa106bce71502d6775f988d

Observation fe7291c7-3b8e-436e-a8a0-60071abc9582 · outbound

This paper cites Moreover, the interpretability of reasoning in LLMs is a critical area of interest.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Moreover, the interpretability of reasoning in LLMs is a critical area of interest

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:18:06.181528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:18:02.339903Z digest=sha256:ba1393195706dbf28b4bebb6a2f18a15a3334dcd49cb737a1c46381985d70c51

Observation 70233d0f-1ac9-4c80-900c-772af2fc5a6c · outbound

This paper cites Recursive planning enables models to refine and adapt their plans based on intermediate results or feedback, enhancing their ability to handle dynamic and uncertain environments.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Recursive planning enables models to refine and adapt their plans based on intermediate results or feedback, enhancing their ability to handle dynamic and uncertain environments

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:18:05.901759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:18:02.439333Z digest=sha256:c442cdee0da5790f150db7ed96a97af14f868d104f578d3d61e04c08d15b9210

Observation 162b9970-6d7d-4038-a11c-e2f56a78cbe7 · outbound

This paper cites an unresolved cited work.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Unresolved cited work

Reference 70

Resolution
unresolved
raw_fallback, observed 2026-08-07T14:18:05.639959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:18:02.566935Z digest=sha256:2ddcc52e2f9dbfd9cb335463cbaab6c027e7e5e8ea662294206d6462475ebba8

Observation 2a6fab10-3cab-4e1d-9263-07ba0ab542db · outbound

This paper cites Adaptive-rag: Learning to adapt retrieval-augmented large language models through ques- tion complexity.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Adaptive-rag: Learning to adapt retrieval-augmented large language models through ques- tion complexity

Reference 1998

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:18:09.116070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:17:56.216862Z digest=sha256:38172caf389b866f0e225dcac21ce352e36b92cf0e97ea4cd979410f4d39ca68

Observation af51b1ce-ab44-4b20-97fb-cd00aa49f04a · outbound

This paper cites Code Llama: Open Foundation Models for Code.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Code Llama: Open Foundation Models for Code

Reference 2007

Resolution
unresolved
no resolver link, observed 2026-08-07T14:17:57.795627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:57.795627Z digest=sha256:29e956b322e498d757cc8e38c7d90c764ff0e0ec6c54da7c131baecf591db9b2

Observation dcd2eb07-fe73-4d35-8e7d-c89e2c8ca88c · outbound

This paper cites FOLIO: Natural Language Reasoning with First-Order Logic.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression FOLIO: Natural Language Reasoning with First-Order Logic

Reference 2015

Resolution
unresolved
no resolver link, observed 2026-08-07T14:17:55.716989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:55.716989Z digest=sha256:76e3c28182db5c2656704a42d1cfdeef31b2596aa09ac91145900a18620175b8

Observation b1e3aa44-a09b-4f60-8de3-237f2c527142 · outbound

This paper cites Self-Alignment with Instruction Backtranslation.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Self-Alignment with Instruction Backtranslation

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-07T14:17:56.663474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:56.663474Z digest=sha256:edc1c7812de21f9ca36266c2f7e4d550c8317d0b3e471baad20d9c518316b0fb

Observation c4db01bd-2d38-4421-9757-14c4049b848a · outbound

This paper cites an unresolved cited work.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Unresolved cited work

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-07T14:17:56.940492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:56.940492Z digest=sha256:fd4a60ce31c3015c6717b4807ec50c8155ec019e1c0b70fe3857ad6a034caf18

Observation 36e134b1-7bfc-4210-986d-5e7f0620129c · outbound

This paper cites InternLM2 Technical Report.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression InternLM2 Technical Report

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-07T14:17:54.810445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:54.810445Z digest=sha256:51f82fc5795804d394d0ba0b993fd016d82346cf8d0bcae31ad3a9e6aa0fbb53

Observation 76754a3d-83af-4802-939b-52f565b2c30e · outbound

This paper cites T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-07T14:17:55.081232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:55.081232Z digest=sha256:df21094099cb8a1cbe0c6fa07a648ba480b1a584731d0f951922719c9278f9c6

Observation 2620662b-3197-48a7-836b-0e93d6325f24 · outbound

This paper cites Cost-effective distillation of large language models.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Cost-effective distillation of large language models

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:18:09.490082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:17:55.160851Z digest=sha256:e3f6c7fa509e362ed886b82069044634e6e0fa114048eb894749963e39ef452e

Observation b4304f88-dc7f-4630-8380-a2db1290803e · outbound

This paper cites AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T14:17:54.377732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:54.377732Z digest=sha256:ffceb154aa2981df35d8aa9c005320109dc024e79a5a9752b851fad5b379e410

Observation 951707d8-5e1a-4231-bd1a-bd36c26e4556 · outbound

This paper cites This technique helps enhance the model’s efficiency without significantly compromising its performance by eliminating redundancies.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression This technique helps enhance the model’s efficiency without significantly compromising its performance by eliminating redundancies

Reference 2025

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:18:08.198671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:18:01.073418Z digest=sha256:34117b6a32319b25e2ae03263bfd7a745dfdf1bbd0b74a8b140732cec7399de4

Pith citing papers

Observation ca3a1bc3-e11b-43a7-ab7c-bd15d736f5fe · inbound

A Survey of Context Engineering for Large Language Models cites this paper.

A Survey of Context Engineering for Large Language Models Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression

Reference 235

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T20:58:45.334271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-13T20:58:45.060041Z digest=sha256:65c4efdc1b144359196caa785d53bc20e1f546bbe95c916b602f68fc3e8cdbae

Observation 18faa33c-8217-43d6-a09f-c98790368b8c · inbound

Model Compression vs. Adversarial Robustness: An Empirical Study on Language Models for Code cites this paper.

Model Compression vs. Adversarial Robustness: An Empirical Study on Language Models for Code Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression

Reference 52

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T00:01:55.782214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T00:01:42.228190Z digest=sha256:dad8c0a3d34cdda04d83e4d891c7b9861ef3fd91ef87320423510a2c469a5e5e

Observation 75f76512-7608-42e0-a49c-58703aae2ddd · inbound

QuantClaw: Precision Where It Matters for OpenClaw cites this paper.

QuantClaw: Precision Where It Matters for OpenClaw Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression

Reference 27

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T19:31:07.507721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-08T11:51:07.657988Z digest=sha256:586adeb624ebc13bb9a3503649d212ac63d858c3c22e4591e174afa42d1dba70

Observation b1569d95-b7d3-46cd-a1f4-4c604567c0b5 · inbound

Position: LLM Inference Should Be Evaluated as Energy-to-Token Production cites this paper.

Position: LLM Inference Should Be Evaluated as Energy-to-Token Production Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression

Reference 65

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T05:27:19.388471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-13T05:17:24.147248Z digest=sha256:eb7c93e839da326040ad60bf0d2cfb652803e8d2ba83a9004ca931ca24981fe8

Observation b32a6b1f-5589-4ba5-853b-0705e8db92e9 · inbound

Evaluating Memory Condensation Strategies for Coding Agents in Data-Driven Scientific Discovery cites this paper.

Evaluating Memory Condensation Strategies for Coding Agents in Data-Driven Scientific Discovery Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-20T20:33:43.365593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-20T20:30:27.324529Z digest=sha256:99388f8f5af2a96abe4b899fa80614d15e430833fc392c9dd5c395dd69d3ad76