Paper Citation Record · LEDGER

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

As of 22 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 7 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 77 of 77 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:17:46.200669Z

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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:54.319938Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:54.499478Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:54.591185Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:54.660908Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:54.731674Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:54.923841Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:55.019857Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:00.103941Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:55.235854Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:55.317481Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:55.388800Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:55.460172Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:55.529646Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:55.649040Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:55.807275Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:55.880736Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:55.971511Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:56.039027Z

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Unavailable: canonical work link unavailable.

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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:56.118892Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:56.305023Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:56.372336Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:56.464514Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:56.572435Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:56.768339Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:56.777115Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:57.094974Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:57.261092Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:57.414092Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:57.555068Z

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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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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

Resolution: verified exact; doi, observed 2026-08-07T14:18:02.853230Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:58.286417Z

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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 2026-08-21T06:32:19.484+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:58.565406Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:58.964633Z

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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

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

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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

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

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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 2026-08-21T06:32:19.484+00:00.

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

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

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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 2026-08-21T06:32:19.484+00:00.

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

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

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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 2026-08-21T06:32:19.484+00:00.

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

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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

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

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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

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

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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

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

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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

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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 2026-08-21T06:32:19.484+00:00.

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 2026-08-21T06:32:19.484+00:00.

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 2026-08-21T06:32:19.484+00:00.

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 2026-08-21T06:32:19.484+00:00.

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 2026-08-21T06:32:19.484+00:00.

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 2026-08-21T06:32:19.484+00:00.

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 2026-08-21T06:32:19.484+00:00.

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

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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 2026-08-21T06:32:19.484+00:00.

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

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

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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.

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.

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.

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.

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.

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 2026-08-21T06:32:19.484+00:00.

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.

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

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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 2026-08-21T06:32:19.484+00:00.

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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 87663caa-4c24-4fc6-9a1b-82ea161d8746 · inbound

Which Decisions Low-Bit Quantization Breaks, and How to Predict Them cites this paper.

Which Decisions Low-Bit Quantization Breaks, and How to Predict Them Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T04:22:45.194076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c046188-ff4f-473b-9a55-4123810ac477 · inbound

Which Decisions Low-Bit Quantization Breaks, and How to Predict Them cites this paper.

Which Decisions Low-Bit Quantization Breaks, and How to Predict Them Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T04:17:46.200669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.