Pith. sign in

REVIEW 4 major objections 5 minor 70 references

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

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Compression damages LLM agents unevenly: tool use survives, real-world tasks lose 10-15%.

desk verdict ACBench is a useful new resource, but the paper's headline tradeoff numbers don't survive a close look at its own tables. read the letter →

arxiv 2505.19433 v2 pith:ZTBNYP2X submitted 2025-05-26 cs.LG

Peijie Dong , Zhenheng Tang , Xiang Liu , Lujun Li , Xiaowen Chu , Bo Li This is my paper
classification cs.LG
keywords LLMcompressionquantizationpruningagenticcapabilitiestooluselong-contextunderstandingworkflowgenerationbenchmark
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that standard compression benchmarks—perplexity and GLUE-style accuracy—miss what matters when compressed models are deployed as agents: the ability to plan, call tools, follow long contexts, and complete real-world tasks. It introduces ACBench, a benchmark of 12 tasks across four agentic capabilities, and evaluates 15 models under GPTQ, AWQ, Wanda, and SparseGPT compression. The central empirical claim is that 4-bit quantization is nearly lossless for workflow generation and tool use (1%-3% drop) but degrades real-world application accuracy by 10%-15%. Compression also hits structured JSON output harder than plain text, and quantized models retain tool-use ability better than pruned ones. If these results hold, deployment choices need agentic benchmarks, not perplexity or GLUE alone.

What carries the argument

The load-bearing object is ACBench, a benchmark spanning 12 tasks across four capabilities: action execution (T-Eval tool and function use), workflow generation (WorfBench), long-context understanding (LongBench, LongGenBench, Needle-in-the-Haystack), and real-world applications (AgentBoard). Three analysis metrics carry the interpretation layer: ERank, the effective rank of weight matrices; Top-K ranking correlation, a Jaccard-style overlap of top-k token sets between compressed and uncompressed models; and energy-based logit analysis, which compares the free energy of output distributions. The benchmark supplies the numbers; the three metrics are meant to explain why compression breaks some agent skills and not others.

What would settle it

Rerun the LongBench and AgentBoard evaluations for the same models and compression methods with fixed checkpoints, fixed temperature, and multiple seeds, then compare against the reported baselines; if the compressed models no longer surpass the uncompressed baselines on tasks like NrtvQA and HotpotQA, the paper's tradeoff numbers do not survive.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that compression damage is capability-specific: the same 4-bit quantization that barely moves workflow generation and tool use can cost ten to fifteen percentage points on real-world agent applications. The authors find that quantization (especially AWQ and GPTQ) preserves tool use better than sparsification, that compressed models lose more accuracy when generating JSON-structured outputs than free-form strings, and that DeepSeek-R1-distilled reasoning models underperform their undistilled counterparts on agentic tasks. They propose that the mechanism behind these losses is visible in logit statistics: quantization lowers effective rank, disturbs top-k token ranking, and shifts logit energy distributions early in decoding. The implication is that agentic capability should be treated as a separate axis in compression evaluation rather than assumed to track language-modeling metrics.

Load-bearing premise

The headline tradeoff numbers assume the uncompressed baseline scores are correct; a few table rows show compressed models far outperforming their uncompressed baselines, so if those baselines are artifacts the 1%-3% and 10%-15% claims collapse.

Editorial extensions

If this is right

  • Deployment teams can choose 4-bit quantization when the task is workflow planning or tool calling and expect only a few points of loss.
  • Real-world agent tasks such as embodied control and game interaction need larger safety margins, since 10%-15% accuracy loss is typical under 4-bit quantization.
  • Quantization methods (GPTQ, AWQ) should be preferred over sparsification when tool use matters; Wanda with unstructured sparsity is the closest pruning competitor.
  • Models distilled for reasoning cannot be assumed to keep agentic skills; the distilled DeepSeek-R1 series underperforms the undistilled Qwen2.5 models on agent benchmarks.
  • Compression evaluation for agents should include structured-output tasks, because JSON generation degrades more than string generation under the same compression.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, if the capability-specific pattern generalizes, compression-aware agent training could target the fragile skills—structured output and long-context retrieval—instead of average accuracy.
  • Some table rows show compressed models scoring far above their uncompressed baselines; whether those are real gains or evaluation noise is a question the paper leaves open and one that affects the headline tradeoff.
  • Beyond the paper, a testable extension is to compare ACBench scores with perplexity at matched compression levels; the paper's thesis predicts weak correlation on real-world tasks, which would make perplexity-only reporting misleading for agent deployment.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces ACBench, a benchmark for evaluating post-training compression (GPTQ, AWQ, SparseGPT, Wanda, and magnitude pruning) on agentic capabilities: action execution via T-Eval, workflow generation via WorfBench, long-context understanding via LongBench/LongGenBench/Needle-in-the-Haystack, and real-world applications via AgentBoard. It evaluates 15 models spanning small, standard, and distilled-reasoning families, and proposes three descriptive diagnostics (ERank, top-k ranking correlation, energy) for compression-induced distribution shifts. The central claim is the abstract's tradeoff statement: 4-bit quantization preserves workflow generation and tool use with a 1%-3% drop, while degrading real-world application accuracy by 10%-15%, motivating the use of agentic benchmarks in compression evaluation.

Significance. If the headline result were fully supported by the tables, ACBench would be a useful community resource: the task coverage (12 tasks across 4 capability groups), the breadth of models, and the use of external benchmarks with standard calibration settings (128 Pile samples, sequence length 512, temperature 0) are genuine strengths. The three statistical metrics are descriptive and are not fitted to the downstream scores they explain, so I see no circularity in the analysis. The code release and the explicit limitation that only vLLM-compatible PTQ methods are used are also commendable. However, the significance of the paper rests on the quantitative tradeoff claims, and those claims are not currently supported by the manuscript's own tables; the benchmark's deployment recommendations therefore require re-analysis before they can be accepted.

major comments (4)
  1. [Abstract and Section 7, Table 9] The abstract's claim that 4-bit quantization degrades real-world application accuracy by 10%-15% is not supported by Table 9. Averaging the five progress-rate columns for Qwen2.5-7B gives base 26.5%, AWQ 26.6%, GPTQ 24.0%, and for InternLM2.5-7B base 17.0%, AWQ 16.1%, GPTQ 17.8%; no reasonable aggregation of these columns yields a uniform 10%-15% drop for both models under either AWQ or GPTQ. The term "real-world application accuracy" is never defined, and Section 7 does not specify the aggregation formula behind the claimed figure, so the paper's central deployment conclusion is not verifiable from the presented evidence.
  2. [Appendix C.4, Table 10] Table 10 cannot support the claimed 1%-3% workflow-generation drop because the table is missing explicit uncompressed baseline rows for the first Qwen2.5-7B block, and the block labeled Qwen2.5-3B contains two AWQ(INT4) rows plus a GPTQ(INT4) row with average F1 values of 0.69-0.71, which are higher than the rows above it. This suggests model-group mislabeling or missing rows, and it makes the workflow-generation degradation numbers in Section 5.2 and the abstract uncomputable. Please relabel every block, add explicit base-model rows for each model group, and recompute the stated drops.
  3. [Section 5.2 and Appendix C.4, Table 10] The claim in Section 5.2 that larger models like Qwen2.5-32B maintain performance under quantization is directly contradicted by Table 10: for Qwen2.5-32B, the base average F1 is 0.72, while GPTQ(INT4) gives 0.36, a roughly 50% relative drop. Since the abstract's 1%-3% workflow-generation preservation claim is the key positive result of the paper, this internal contradiction must be resolved by either correcting the table, correcting the text, or substantially revising the claim.
  4. [Section 6, Table 3] Table 3 reports compression-induced improvements that are implausible without explanation: InternLM2.5-7B NrtvQA goes from 0 to 25.08 under AWQ, Qwen2.5-7B HotpotQA goes from 10.78 to 54.53 under GPTQ, and several other quantized rows exceed the uncompressed baseline. If these baselines or evaluation runs are artifacts, then the uncompressed-versus-compressed comparisons throughout LongBench, and the long-context conclusions in Section 6.2, are not reliable. These entries need to be verified, corrected, or removed, and the evaluation harness should be described in sufficient detail to rule out scoring inconsistencies.
minor comments (5)
  1. [Section 2.3] The numbered list of statistical metrics uses the label "(2)" twice, for both Top-K Ranking Consistency and Energy-based Analysis; renumber the list.
  2. [Section 6.2] "Merge-3B" is a typo for "Megrez-3B" in the LongBench analysis paragraph.
  3. [Tables 5 and 7] Model naming is inconsistent (e.g., "Qwen-3b" and "Qwen-1.5b" in Table 5 versus "Qwen2.5-3B" elsewhere), and Table 7 reports MMLU = -1.00 for Phi-3.5, which is not a valid accuracy score; clarify or correct these entries.
  4. [Section 5.2] The text says "most compression methods maintain model performance within a 5% degradation margin," but the abstract claims a 1%-3% drop and Table 10 shows magnitude pruning collapsing to zero; these three statements should be made consistent.
  5. [Appendix C.5, Figures 11-13] The needle-in-the-haystack figures are described as attention heatmaps, but in the printed text they are not legible enough to verify the claims about attention-pattern degradation; provide higher-resolution figures or numeric summaries for the 32K-token boundary claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an empirical benchmark evaluation with external benchmarks, standard compression methods, and post-hoc descriptive statistics rather than a derivation whose outputs are built into its inputs.

full rationale

This paper does not present a derivation chain in which a predicted quantity is defined in terms of, or fitted to, the target result. The central claims are empirical comparisons between uncompressed and compressed models on external benchmarks (T-Eval, WorfBench, LongBench, LongGenBench, AgentBoard), using standard compression implementations (GPTQ, AWQ, SparseGPT, Wanda) with shared calibration data from the Pile validation set. The statistical tools introduced in Section 2.3 (ERank, Top-K Ranking Consistency, Energy) are descriptive diagnostics computed from logits and singular values; they are not fitted to the downstream accuracy numbers they are used to interpret, and the paper makes no claim that these metrics generate the benchmark scores. The benchmark tasks, model families, and compression configurations are all external to the paper's own conclusions, so the headline tradeoff statements in the abstract are observations over measured data rather than quantities fixed by construction. The paper does contain self-citations to prior work by the same authors, but these appear in related-work and background contexts and are not load-bearing for the empirical evaluation; no uniqueness theorem or ansatz is imported from those citations to force the paper's choices. Concerns that the abstract's 10%-15% real-world degradation figure is not clearly derivable from Table 9, or that Table 10 has a missing baseline row, are correctness or reproducibility issues about how the reported numbers were aggregated, not instances of the paper's conclusions being equivalent to its inputs. The evaluation is self-contained against external benchmarks, so the appropriate circularity finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No fitted constants are introduced for the benchmark conclusions. The analysis relies on the external validity of the selected agentic benchmarks, the standard calibration-data practice from LLMC, and the assumption that post-training compression effects can be attributed to compression configuration despite model-family confounds.

assumptions (4)
  • domain assumption Selected benchmarks (T-Eval, WorfBench, LongBench/LongGenBench/NIAH, AgentBoard) are valid and sufficient proxies for the four agentic capabilities.
    The capability taxonomy and all conclusions inherit the validity and cleanliness of these external benchmarks; contamination or evaluation-format issues would change the findings.
  • domain assumption Calibrating compression methods on 128 sequences of length 512 from the Pile validation set (LLMC setting) transfers to agentic tasks.
    Compression quality depends on calibration data. Agentic prompts differ from Pile text, and no sensitivity analysis is provided.
  • domain assumption Within-family comparisons attribute performance differences to compression, while cross-family comparisons are treated as architectural effects.
    The paper compares different base models across compression methods, so training data and architecture confounds are not controlled.
  • domain assumption ERank, Top-k Jaccard rank overlap, and logit-energy distance reflect decision-relevant changes induced by compression.
    These are descriptive statistics whose explanatory link to agentic performance is assumed rather than demonstrated.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression." pith.science (2026). https://pith.science/paper/ZTBNYP2X

@misc{pith2026250519433,
  author       = {Pith},
  title        = {Pith review of: Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZTBNYP2X}},
  note         = {Machine review of arXiv:2505.19433}
}
read the original abstract

Post-training compression reduces the computational and memory costs of large language models (LLMs), enabling resource-efficient deployment. However, existing compression benchmarks only focus on language modeling (e.g., perplexity) and natural language understanding tasks (e.g., GLUE accuracy), ignoring the agentic capabilities - workflow, tool use/function call, long-context understanding and real-world application. We introduce the Agent Compression Benchmark (ACBench), the first comprehensive benchmark for evaluating how compression impacts LLMs' agentic abilities. ACBench spans (1) 12 tasks across 4 capabilities (e.g., WorfBench for workflow generation, Needle-in-Haystack for long-context retrieval), (2) quantization (GPTQ, AWQ) and pruning (Wanda, SparseGPT), and (3) 15 models, including small (Gemma-2B), standard (Qwen2.5 7B-32B), and distilled reasoning LLMs (DeepSeek-R1-Distill). Our experiments reveal compression tradeoffs: 4-bit quantization preserves workflow generation and tool use (1%-3% drop) but degrades real-world application accuracy by 10%-15%. We introduce ERank, Top-k Ranking Correlation and Energy to systematize analysis. ACBench provides actionable insights for optimizing LLM compression in agentic scenarios. The code can be found in https://github.com/pprp/ACBench.

Figures

Figures reproduced from arXiv: 2505.19433 by the authors.

Figure 1
Figure 1. Overview of Agent Compression Benchmarks and Methods for Large Language Models (LLMs). (a) Benchmark [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. ERank analysis difference analysis for quantized LLaMA-2-7B (left) and Mistral-7B (right) models [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Comparison of format performance differences [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (11 more)
Figure 5
Figure 5. Figure 5: Spearman correlation analysis between perplexity [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Performance Evaluation of InternLM-2.5-7B and Qwen-2.5-7B Models Across Different Sparsification (left) and [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Comparison of average F1 scores across different model sizes and configurations. The models are evaluated based [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Performance Comparison between DeepSeek-R1-Qwen2.5-7B and Qwen2.5-7B across four evaluation benchmarks. [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: Performance comparison of sparse compression methods on tool use tasks [PITH_FULL_IMAGE:figures/full_fig_p026_9.png]
Figure 10
Figure 10. Figure 10: Performance comparison of quantization methods on tool use tasks [PITH_FULL_IMAGE:figures/full_fig_p027_10.png]
Figure 11
Figure 11. Figure 11: Visualization of attention patterns in needle-in-haystack tests across different compression methods for InternLM [PITH_FULL_IMAGE:figures/full_fig_p030_11.png]
Figure 12
Figure 12. Figure 12: Comparative analysis of attention patterns in Qwen-2.5-7B under different compression schemes. The visualiza [PITH_FULL_IMAGE:figures/full_fig_p031_12.png]
Figure 13
Figure 13. Figure 13: Attention pattern analysis for smaller and distilled models across 40k context length. The visualizations [PITH_FULL_IMAGE:figures/full_fig_p031_13.png]
Figure 14
Figure 14. Figure 14: Logits distribution visualization across different sequence positions in compressed models [PITH_FULL_IMAGE:figures/full_fig_p034_14.png]
Figure 15
Figure 15. Figure 15: Energy distribution analysis at various sequence positions in compressed models [PITH_FULL_IMAGE:figures/full_fig_p034_15.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

70 extracted references · 21 canonical work pages

  1. [1]

    A., Awan, A

    Abdin, M., Jacobs, S. A., Awan, A. A., et al. Phi-3 technical report: A highly capable language model locally on your phone.arXiv, abs/2404.14219,

    arXiv

  2. [3]

    L., Li, B., Jaggi, M., Alistarh, D., Hoefler, T., and Hensman, J

    Ashkboos, S., Mohtashami, A., Croci, M. L., Li, B., Jaggi, M., Alistarh, D., Hoefler, T., and Hensman, J. Quarot: Outlier-free 4-bit inference in rotated llms.arXiv preprint arXiv:2404.00456, abs/2404.00456,

    arXiv

  3. [4]

    R., Ezzeldin, Y

    Babakniya, S., Elkordy, A. R., Ezzeldin, Y . H., Liu, Q., Song, K.-B., El-Khamy, M., and Avestimehr, S. Slora: Federated parameter efficient fine-tuning of language models.arXiv preprint arXiv:2308.06522, abs/2308.06522,

    arXiv

  4. [5]

    doi: 10.18653/v1/2024.acl-long.172

    Association for Computational Linguistics. doi: 10.18653/v1/2024.acl-long.172. Besta, M., Blach, N., Kubicek, A., Gerstenberger, R., Pod- stawski, M., Gianinazzi, L., Gajda, J., Lehmann, T., Niewiadomski, H., Nyczyk, P., et al. Graph of thoughts: Solving elaborate problems with large language models. InAAAI, pp. 17682–17690,

    doi:10.18653/v1/2024.acl-long.172 2024

  5. [6]

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

    Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. Language models are few-shot learners. NeurIPS, 33:1877–1901,

    1901

  6. [8]

    Scaling syn- thetic data creation with 1,000,000,000 personas.arXiv preprint arXiv:2406.20094,

    10 Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Chan, X., Wang, X., Yu, D., Mi, H., and Yu, D. Scaling syn- thetic data creation with 1,000,000,000 personas.arXiv preprint arXiv:2406.20094,

    arXiv

  7. [9]

    P., and Richardson, K

    Chen, J., Yuan, S., Ye, R., Majumder, B. P., and Richardson, K. Put your money where your mouth is: Evaluating strategic planning and execution of llm agents in an auc- tion arena.arXiv preprint arXiv:2310.05746, 2023a. Chen, J., Wang, X., Xu, R., Yuan, S., Zhang, Y ., Shi, W., Xie, J., Li, S., Yang, R., Zhu, T., et al. From persona to personalization: A ...

    arXiv

  8. [10]

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

    14778/3626292.3626303. Xiao, G., Lin, J., Seznec, M., Wu, H., Demouth, J., and Han, S. Smoothquant: Accurate and efficient post-training quantization for large language models. In Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S., and 16 Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Scarlett...

    arXiv

Show all 70 references
  1. [12]

    Lpzero: Language model zero-cost proxy search from zero

    Dong, P., Li, L., Liu, X., Tang, Z., Liu, X., Wang, Q., and Chu, X. Lpzero: Language model zero-cost proxy search from zero. InEMNLP, 2024a. Dong, P., Li, L., Tang, Z., Liu, X., Pan, X., Wang, Q., and Chu, X. Pruner-zero: Evolving symbolic pruning metric from scratch for large...

    arXiv

  2. [13]

    and Alistarh, D

    Frantar, E. and Alistarh, D. Sparsegpt: Massive language models can be accurately pruned in one-shot.arXiv, abs/2301.00774, 2023a. Frantar, E. and Alistarh, D. Sparsegpt: Massive language models can be accurately pruned in one-shot. In Krause, A., Brunskill, E., Cho, K., Engel...

    2023 arXiv

  3. [14]

    The pile: An 800gb dataset of diverse text for language modeling.arXiv preprint, abs/2101.00027,

    Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., Presser, S., and Leahy, C. The pile: An 800gb dataset of diverse text for language modeling.arXiv preprint, abs/2101.00027,

    arXiv

  4. [15]

    Retrieval-augmented generation for large language models: A survey.arXiv preprint arXiv:2312.10997, 2023a

    Gao, Y ., Xiong, Y ., Gao, X., Jia, K., Pan, J., Bi, Y ., Dai, Y ., Sun, J., and Wang, H. Retrieval-augmented generation for large language models: A survey.arXiv preprint arXiv:2312.10997, 2023a. Gao, Y ., Xiong, Y ., Gao, X., Jia, K., Pan, J., Bi, Y ., Dai, Y ., Sun, J., Wan...

    arXiv

  5. [16]

    Gehman, S., Gururangan, S., Sap, M., Choi, Y ., and Smith, N. A. Realtoxicityprompts: Evaluating neural toxic degeneration in language models.arXiv preprint arXiv:2009.11462,

    2009 arXiv

  6. [17]

    Delta decompression for moe-based llms compression.ArXiv, abs/2502.17298,

    Gu, H., Li, W., Li, L., Zhu, Q., Lee, M., Sun, S., Xue, W., and Guo, Y .-T. Delta decompression for moe-based llms compression.ArXiv, abs/2502.17298,

    arXiv

  7. [19]

    E., Vinyals, O., and Dean, J

    Hinton, G. E., Vinyals, O., and Dean, J. Distilling the knowledge in a neural network.arXiv.org, 2015a. Hinton, G. E., Vinyals, O., and Dean, J. Distilling the knowledge in a neural network.ArXiv, abs/1503.02531, 2015b. Hong, S., Zheng, X., Chen, J., Cheng, Y ., Wang, J., Zhan...

    arXiv

  8. [20]

    L., Zhang, K., Wang, C., Yao, Y ., Zhao, C., Zhou, J., Cai, J., Zhai, Z., Ding, N., Jia, C., Zeng, G., Li, D., Liu, Z., and Sun, M

    Hu, S., Tu, Y ., Han, X., He, C., Cui, G., Long, X., Zheng, Z., Fang, Y ., Huang, Y ., Zhao, W., Zhang, X., Thai, Z. L., Zhang, K., Wang, C., Yao, Y ., Zhao, C., Zhou, J., Cai, J., Zhai, Z., Ding, N., Jia, C., Zeng, G., Li, D., Liu, Z., and Sun, M. Minicpm: Unveiling the poten...

    arXiv

  9. [21]

    War and peace (waragent): Large language model-based multi-agent simulation of world wars.arXiv preprint arXiv:2311.17227,

    Hua, W., Fan, L., Li, L., Mei, K., Ji, J., Ge, Y ., Hemphill, L., and Zhang, Y . War and peace (waragent): Large language model-based multi-agent simulation of world wars.arXiv preprint arXiv:2311.17227,

    arXiv

  10. [22]

    M., Luck, M., and Cui, H

    Huang, D., Bu, Q., Zhang, J. M., Luck, M., and Cui, H. Agentcoder: Multi-agent-based code generation with iterative testing and optimisation.arXiv preprint arXiv:2312.13010,

    arXiv

  11. [23]

    An empirical study of llm-as-a-judge for llm evaluation: Fine- tuned judge models are task-specific classifiers.arXiv preprint arXiv:2403.02839, 2024a

    Huang, H., Qu, Y ., Liu, J., Yang, M., and Zhao, T. An empirical study of llm-as-a-judge for llm evaluation: Fine- tuned judge models are task-specific classifiers.arXiv preprint arXiv:2403.02839, 2024a. Huang, Y ., Zhong, W., Shi, E., Yang, M., Chen, J., Li, H., Ma, Y ., Wang...

    arXiv

  12. [25]

    Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D

    Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., de Las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L. R., Lachaux, M.-A., Stock, P., Scao, T. L., Lavril, T., Wang, T., Lacroix, T., and Sayed, W. E. Mistral 7b.arXiv preprint,...

    arXiv

  13. [26]

    W., and Keutzer, K

    Kim, S., Hooper, C., Gholami, A., Dong, Z., Li, X., Shen, S., Mahoney, M. W., and Keutzer, K. Squeezellm: Dense- and-sparse quantization.CoRR, abs/2306.07629,

    arXiv

  14. [27]

    Koa, JL, K., Ma, Y ., Ng, R., and Chua, T.-S

    doi: 10.48550/ARXIV .2306.07629. Koa, JL, K., Ma, Y ., Ng, R., and Chua, T.-S. Learning to gen- erate explainable stock predictions using self-reflective 12 Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression large language models....

    doi arXiv

  15. [28]

    doi: 10.1609/AAAI.V38I12. 29237. Lee, D. and Tiwari, M. Prompt infection: Llm-to-llm prompt injection within multi-agent systems.arXiv preprint arXiv:2410.07283,

    arXiv

  16. [30]

    Liu, J., Li, L., Xiang, T., Wang, B., and Qian, Y

    doi: 10.48550/arXiv.2306.00978. Liu, J., Li, L., Xiang, T., Wang, B., and Qian, Y . Tcra- llm: Token compression retrieval augmented large lan- guage model for inference cost reduction.arXiv preprint arXiv:2310.15556, 2023a. Liu, S., Cheng, H., Liu, H., Zhang, H., Li, F., Ren,...

    doi arXiv

  17. [31]

    Norm: Knowledge distillation via n-to-one representation matching, 2023c

    Liu, X., Li, L., Li, C., and Yao, A. Norm: Knowledge distillation via n-to-one representation matching, 2023c. URLhttps://arxiv.org/abs/2305.13803. Liu, X., Dong, P., Hu, X., and Chu, X. Longgenbench: Long- context generation benchmark.ArXiv, abs/2410.04199, 2024a. 13 Can Comp...

    arXiv

  18. [33]

    Refiner: Reasoning feedback on intermediate representations.arXiv preprint arXiv:2304.01904,

    Paul, D., Ismayilzada, M., Peyrard, M., Borges, B., Bosse- lut, A., West, R., and Faltings, B. Refiner: Reasoning feedback on intermediate representations.arXiv preprint arXiv:2304.01904,

    arXiv

  19. [34]

    and Alami, G

    Pipitone, N. and Alami, G. H. Legalbench-rag: A bench- mark for retrieval-augmented generation in the legal do- main.arXiv preprint arXiv:2408.10343,

    arXiv

  20. [35]

    Benchmarking agentic workflow generation.arXiv, abs/2410.07869,

    Qiao, S., Fang, R., Qiu, Z., Wang, X., Zhang, N., Jiang, Y ., Xie, P., Huang, F., and Chen, H. Benchmarking agentic workflow generation.arXiv, abs/2410.07869,

    arXiv

  21. [36]

    Qin, Y ., Hu, S., Lin, Y ., Chen, W., Ding, N., Cui, G., Zeng, Z., Huang, Y ., Xiao, C., Han, C., Fung, Y . R., Su, Y ., Wang, H., Qian, C., Tian, R., Zhu, K., Liang, S., Shen, X., Xu, B., Zhang, Z., Ye, Y ., Li, B., Tang, Z., Yi, J., Zhu, Y ., Dai, Z., Yan, L., Cong, X., Lu, ...

    arXiv

  22. [38]

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

    doi: 10.1109/ ICASSP48485.2024.10445737. Shi, Z., Gao, S., Chen, X., Feng, Y ., Yan, L., Shi, H., Yin, D., Ren, P., Verberne, S., and Ren, Z. Learning to use tools via cooperative and interactive agents. In Al-Onaizan, Y ., Bansal, M., and Chen, Y .-N. (eds.),Find- ings of EMN...

    2024

  23. [39]

    doi: 10.18653/v1/2024.findings-emnlp.624

    Association for Computational Linguis- tics. doi: 10.18653/v1/2024.findings-emnlp.624. Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K., and Yao, S. Reflexion: Language agents with verbal reinforce- ment learning.NeurIPS, 36,

    2024 doi

  24. [40]

    Fine tuning vs

    Soudani, H., Kanoulas, E., and Hasibi, F. Fine tuning vs. re- trieval augmented generation for less popular knowledge. arXiv preprint arXiv:2403.01432,

    arXiv

  25. [41]

    ISBN 9781450366717

    Association for Comput- ing Machinery. ISBN 9781450366717. doi: 10.1145/ 3307772.3328315. Tang, Zhenheng, Shi, Shaohuai, Chu, Xiaowen, Wang, Wei, Li, and Bo. Communication-efficient distributed deep learning: A comprehensive survey.arXiv preprint arXiv:2003.06307,

  26. [42]

    Toolalpaca: Generalized tool learning for language models with 3000 simulated cases, 2023a

    Tang, Q., Deng, Z., Lin, H., Han, X., Liang, Q., Cao, B., and Sun, L. Toolalpaca: Generalized tool learning for language models with 3000 simulated cases, 2023a. Tang, Q., Deng, Z., Lin, H., Han, X., Liang, Q., and Sun, L. Toolalpaca: Generalized tool learning for language mod...

    arXiv

  27. [43]

    M., Zaman, S

    Tonmoy, S. M., Zaman, S. M., Jain, V ., Rani, A., Rawte, V ., Chadha, A., and Das, A. A comprehensive survey of hallucination mitigation techniques in large language models.arXiv preprint arXiv:2401.01313,

    arXiv

  28. [44]

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

    Valmeekam, K., Olmo, A., Sreedharan, S., and Kambham- pati, S. Large language models still can’t plan (a bench- mark for llms on planning and reasoning about change). InNeurIPS 2022 Foundation Models for Decision Mak- ing Workshop,

    2022

  29. [45]

    V oyager: An open-ended embodied agent with large language models.Transac- tions on Machine Learning Research, 2023a

    15 Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Wang, G., Xie, Y ., Jiang, Y ., Mandlekar, A., Xiao, C., Zhu, Y ., Fan, L., and Anandkumar, A. V oyager: An open-ended embodied agent with large language models.Transac- tions ...

    2023 arXiv

  30. [46]

    V ., Chi, E

    Wang, X., Wei, J., Schuurmans, D., Le, Q. V ., Chi, E. H., Narang, S., Chowdhery, A., and Zhou, D. Self- consistency improves chain of thought reasoning in lan- guage models. InThe Eleventh International Confer- ence on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5,

    2023

  31. [47]

    Wang, X., Yang, Q., Qiu, Y ., Liang, J., He, Q., Gu, Z., Xiao, Y ., and Wang, W

    OpenReview.net, 2023c. Wang, X., Yang, Q., Qiu, Y ., Liang, J., He, Q., Gu, Z., Xiao, Y ., and Wang, W. Knowledgpt: Enhancing large language models with retrieval and storage access on knowledge bases.arXiv preprint arXiv:2308.11761, 2023d. Wang, X., Zheng, Y ., Wan, Z., and Z...

    arXiv

  32. [48]

    Perception of knowledge boundary for large language models through semi-open-ended question answering.ArXiv, abs/2405.14383,

    Wen, Z., Tian, Z., Jian, Z., Huang, Z., Ke, P., Gao, Y ., Huang, M., and Li, D. Perception of knowledge boundary for large language models through semi-open-ended question answering.ArXiv, abs/2405.14383,

    arXiv

  33. [49]

    Coral: Collaborative retrieval-augmented large language models improve long-tail recommendation

    Wu, J., Chang, C.-C., Yu, T., He, Z., Wang, J., Hou, Y ., and McAuley, J. Coral: Collaborative retrieval-augmented large language models improve long-tail recommendation. InACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 3391–3401, 2024a. Wu, Q., Bansal, G., ...

    arXiv

  34. [50]

    Autogen: Enabling next-gen llm applications via multi-agent conversation

    Wu, Q., Bansal, G., Zhang, J., Wu, Y ., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J., et al. Autogen: Enabling next-gen llm applications via multi-agent conversation. In ICLR 2024 Workshop on Large Language Model (LLM) Agents, 2024b. Xia, H., Zheng, Z., Li, Y ., Z...

    2024

  35. [52]

    Are large language models really good logical reasoners? a comprehensive evaluation from deductive, inductive and abductive views.arXiv preprint arXiv:2306.09841,

    Xu, F., Lin, Q., Han, J., Zhao, T., Liu, J., and Cambria, E. Are large language models really good logical reasoners? a comprehensive evaluation from deductive, inductive and abductive views.arXiv preprint arXiv:2306.09841,

    arXiv

  36. [53]

    Llmcbench: Benchmarking large language model compression for efficient deployment

    Yang, G., He, C., Guo, J., Wu, J., Ding, Y ., Liu, A., Qin, H., Ji, P., and Liu, X. Llmcbench: Benchmarking large language model compression for efficient deployment. ArXiv, abs/2410.21352, 2024a. Yang, H., Liu, X.-Y ., and Wang, C. D. Fingpt: Open-source financial large langu...

    arXiv

  37. [54]

    Rankrag: Unifying con- text ranking with retrieval-augmented generation in llms

    Yu, Y ., Ping, W., Liu, Z., Wang, B., You, J., Zhang, C., Shoeybi, M., and Catanzaro, B. Rankrag: Unifying con- text ranking with retrieval-augmented generation in llms. arXiv preprint arXiv:2407.02485,

    arXiv

  38. [55]

    Asvd: Activation-aware singular value decompo- sition for compressing large language models.ArXiv, abs/2312.05821,

    Yuan, Z., Shang, Y ., Song, Y ., Wu, Q., Yan, Y ., and Sun, G. Asvd: Activation-aware singular value decompo- sition for compressing large language models.ArXiv, abs/2312.05821,

    arXiv

  39. [56]

    Toolcoder: Teach code generation models to use api search tools.arXiv preprint arXiv:2305.04032, 2023a

    Zhang, Kechi, Zhang, Huangzhao, Li, Ge, Li, Jia, Li, Zhuo, Jin, and Zhi. Toolcoder: Teach code generation models to use api search tools.arXiv preprint arXiv:2305.04032, 2023a. Zhang, F., Chen, B., Zhang, Y ., Keung, J., Liu, J., Zan, D., Mao, Y ., Lou, J.-G., and Chen, W. Rep...

    arXiv

  40. [57]

    19 A.2 LLM-based Agents

    17 Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Appendix A More Related Works 19 A.1 LLM Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 A.2 LLM-based Agents . . . ....

    2024

  41. [59]

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

    methods adjust the sparsity level during training or inference, allowing for more flexible compression tailored to specific deployment needs. Research has shown that judicious pruning can maintain or even sometimes improve model generalization by removing overfitting parameter...

    2024

  42. [60]

    is a process where a smaller, student model is trained to replicate the behavior of a larger, teacher model. By transferring the knowledge from the teacher to the student, the resulting compressed model retains much of the performance and capabilities of the original while bei...

    2023

  43. [61]

    in LLMs refer to their ability to process information logically, make inferences, and arrive at conclusions through structured thought processes (Jiang et al., 2023b; Sun et al., 2023; Zhou et al., 2023; Han et al., 2022; Sun et al., 2024a). This encompasses various forms of r...

    2023

  44. [62]

    in LLMs allows them to apply established rules or premises to derive specific outcomes. For example, given the premises ”All humans are mortal” and ”Socrates is a human,” an LLM can deduce that ”Socrates is mortal.” Inductive reasoning (Sun et al., 2024a; Xu et al., 2024

    2024

  45. [63]

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

    enables LLMs to make generalized statements 20 Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression based on specific instances, such as inferring that ”The sun will rise tomorrow” based on past observations. Analogical reasoning (X...

    2024

  46. [64]

    involves drawing parallels between different scenarios, aiding LLMs in transferring knowledge from one domain to another, which is particularly useful in problem-solving and creative tasks. Recent research has demonstrated that LLMs can showcase sophisticated reasoning abiliti...

    2024

  47. [65]

    checking (Koa et al., 2024; Shinn et al., 2024; Madaan et al., 2024; Li et al., 2024g; 2023c), another technique, involves generating multiple reasoning paths (Leblond et al., 2021; Chakraborty et al.,

    2024

  48. [66]

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

    and selecting the most consistent or probable one, thereby mitigating errors and improving reliability. However, challenges persist in ensuring that LLMs maintain reliable and consistent logical reasoning across different contexts and domains. Factors such as ambiguous inputs ...

    2024

  49. [67]

    can impact the quality of reasoning. To address these issues, ongoing research focuses on integrating structured reasoning frameworks (Sun et al., 2023; Luo et al., 2023; Jiang et al., 2023b), enhancing model architectures to better capture logical relationships (Xu et al., 20...

    2023

  50. [68]

    Moreover, the interpretability of reasoning in LLMs is a critical area of interest

    that provide factual accuracy and context. Moreover, the interpretability of reasoning in LLMs is a critical area of interest. Developing methods to visualize and understand the internal reasoning processes of these models can lead to better insights into their decision-making...

    2023

  51. [69]

    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

    allow LLMs to explore different branches of possible actions and evaluate their outcomes, fostering more informed decision-making (Xie et al., 2024). Recursive planning enables models to refine and adapt their plans based on intermediate results or feedback, enhancing their ab...

    2024

  52. [70]

    framework for unified quantization and aligned the settings in LLMC. Sparsification MethodsWe investigated both structured and unstructured pruning approaches: (1) Magnitude Pruning, both unstructured (Mag(Un)) and structured 2:4 (Mag(2:4)) variants, which remove weights based...

    2024

  53. [1998]

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

    Jeong, Soyeong, Baek, Jinheon, Cho, Sukmin, Hwang, Ju, S., Park, and C, J. Adaptive-rag: Learning to adapt retrieval-augmented large language models through ques- tion complexity. InProceedings of the 2024 Conference of the North American Chapter of the Association for Computa...

    2024

  54. [2007]

    E., Adi, Y ., Liu, J., Remez, T., Rapin, J., et al

    Roziere, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y ., Liu, J., Remez, T., Rapin, J., et al. Code llama: Open foundation models for code.arXiv preprint arXiv:2308.12950,

    arXiv

  55. [2015]

    Folio: Natural language reasoning with first-order logic.arXiv preprint arXiv:2209.00840,

    Han, S., Schoelkopf, H., Zhao, Y ., Qi, Z., Riddell, M., Zhou, W., Coady, J., Peng, D., Qiao, Y ., Benson, L., et al. Folio: Natural language reasoning with first-order logic.arXiv preprint arXiv:2209.00840,

    arXiv

  56. [2018]

    Camel: Communicative agents for ”mind” exploration of large language model society.NeurIPS, 36:51991– 52008, 2023a

    Li, G., Hammoud, H., Itani, H., Khizbullin, D., and Ghanem, B. Camel: Communicative agents for ”mind” exploration of large language model society.NeurIPS, 36:51991– 52008, 2023a. Li, J., Chen, J., Ren, R., Cheng, X., Zhao, X., Nie, J.-Y ., and Wen, J.-R. The dawn after the dar...

    arXiv

  57. [2019]

    doi: 10.1109/CVPR.2019.01152. OpenAI. Gpt-4 technical report,

    2019

  58. [2020]

    Internlm2 technical report.ArXiv, abs/2403.17297,

    Cai, Z., Cao, M., Chen, H., Chen, K., et al. Internlm2 technical report.ArXiv, abs/2403.17297,

    arXiv

  59. [2022]

    T-eval: Evaluating the tool utilization capability step by step

    Chen, Z., Du, W., Zhang, W., Liu, K., Liu, J., Zheng, M., Zhuo, J., Zhang, S., Lin, D., Chen, K., et al. T-eval: Evaluating the tool utilization capability step by step. arXiv preprint arXiv:2312.14033, 2023c. Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y ., Wu, Z., Zhang, H., Zhen...

    2023 arXiv

  60. [2023]

    Cost-effective distillation of large language models

    Dasgupta, S., Cohn, T., and Baldwin, T. Cost-effective distillation of large language models. In Rogers, A., Boyd-Graber, J. L., and Okazaki, N. (eds.),ACL 2023 Findings, pp. 7346–7354. Association for Computational Linguistics,

    2023

  61. [2024]

    Andriushchenko, M., Souly, A., Dziemian, M., Duenas, D., Lin, M., Wang, J., Hendrycks, D., Zou, A., Kolter, Z., Fredrikson, M., et al

    ISBN 9798400704369. Andriushchenko, M., Souly, A., Dziemian, M., Duenas, D., Lin, M., Wang, J., Hendrycks, D., Zou, A., Kolter, Z., Fredrikson, M., et al. Agentharm: A benchmark for measuring harmfulness of LLM agents.arXiv preprint arXiv:2410.09024,

    arXiv

  62. [2025]

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

    involves systematically removing less important parameters or connections within a large language model (LLM) to reduce its size and computational requirements. This technique helps enhance the model’s efficiency without significantly compromising its performance by eliminatin...

    2024

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.