arxiv: 2605.09929 · v1 · submitted 2026-05-11 · 💻 cs.LG · cs.SE

Recognition: no theorem link

TeleResilienceBench: Quantifying Resilience for LLM Reasoning in Telecommunications

Pranshav Gajjar , Emmanuel Ojo , Vijay K Shah

Authors on Pith no claims yet

Pith reviewed 2026-05-12 04:09 UTC · model grok-4.3

classification 💻 cs.LG cs.SE

keywords LLM resiliencetelecommunicationsreasoning benchmarkerror recoverypartial reasoningCorrect Flip Ratemodel evaluationGSMA

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The pith

Telecom LLMs recover from already-wrong partial reasoning only 29 percent of the time on average.

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

This paper presents TeleResilienceBench to test how well language models can pick up and fix reasoning chains that have started incorrectly in telecommunications tasks. The benchmark creates examples by running a weak model until it fails, cutting the trace in the middle, and challenging a target model to continue correctly. Results indicate that the highest-scoring model reaches only 29.1 percent success on average across seven domains, larger models do not always outperform smaller ones in the same series, and a 4-billion parameter model leads the pack. This capability matters for real systems where reasoning passes between steps or agents and early errors must be caught and reversed. The work also finds that standard difficulty ratings in telecom tests track specific facts more than the depth of reasoning required.

Core claim

The central discovery is that reasoning resilience, defined as the ability to correct inherited errors in ongoing reasoning traces, remains low even for strong models. TeleResilienceBench generates its test set from midpoint-truncated failures of a weak generator and scores models using the Correct Flip Rate, the fraction of cases where the model successfully flips the trajectory to a correct conclusion. Across Qwen, Gemma, and Nemotron families the macro-average CFR peaks at 29.1 percent, scale provides no consistent gain inside a family, and the smallest Nemotron model achieves the highest score while also leading on a numerical math auxiliary task.

What carries the argument

TeleResilienceBench, a dataset of truncated flawed reasoning traces drawn from telecom sub-domains that forces models to recover from errors already present in the middle of a solution; paired with the Correct Flip Rate metric that directly counts successful corrections of the partial mistake.

If this is right

Real-world telecom LLM pipelines that chain multiple reasoning steps will suffer repeated failures unless models gain better error-recovery skills.
Model families should be evaluated separately for resilience rather than assuming larger size improves performance.
Current telecom benchmark difficulty labels may not capture reasoning depth and instead mainly test knowledge of specific facts.
Smaller models can offer better value when resilience per compute cost is the priority.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Training objectives could be redesigned to reward detection and correction of errors in partial outputs rather than only final accuracy.
Similar resilience benchmarks could be built for other high-stakes domains such as healthcare or autonomous systems where partial plans are common.
Collecting real error traces from production LLM deployments would provide a stronger test of whether the synthetic failures here match practical conditions.
Companies deploying these models might shift toward hybrid systems that include explicit error-checking modules alongside the LLM.

Load-bearing premise

That the partial errors produced by truncating traces from one weak model accurately represent the kinds of flawed reasoning that models will receive when continuing tasks in actual telecommunications deployments.

What would settle it

Collecting a dataset of genuine partial reasoning traces from live telecom LLM applications, truncating them similarly, and measuring whether model recovery rates match the CFR values reported on the synthetic benchmark.

Figures

Figures reproduced from arXiv: 2605.09929 by Emmanuel Ojo, Pranshav Gajjar, Vijay K Shah.

**Figure 1.** Figure 1: Overview of TeleResilienceBench. A target model then receives the original question, answer options, and inherited flawed trace, and is evaluated on whether it recovers the correct answer (CFR), repeats the original wrong answer (NFR), or flips to another wrong answer (WFR), along with efficiency measures such as output tokens and peak VRAM. syntactic integrity. Let ri denote the full reasoning trace for i… view at source ↗

**Figure 2.** Figure 2: Sub-benchmark distribution The benchmark is intentionally asymmetric. The target model does not observe y orig explicitly; it only sees the partial rationale r˜ that led toward it. As a result, success requires more than simply disagreeing with a prior prediction. The model must infer from the inherited trace itself that the reasoning path is defective and must be corrected. B. Benchmark Statistics The res… view at source ↗

**Figure 3.** Figure 3: Correct Flip Rate (CFR) comparison across different benchmarks. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: CFR vs. Mean Output Tokens for various models. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: CFR vs. VRAM usage for various models. CFR across all models on this subset, ranging from 3.3% to 16.7%, is consistent with this interpretation. The three higher-resilience sub-benchmarks share a more compositional structure, drawing on interface specifications, protocol relationships, and network-level design principles that a continuation model can approach through inference even when the inherited trace… view at source ↗

**Figure 7.** Figure 7: Correct Flip Rate stratified by source-task difficulty for ORANBench [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗

read the original abstract

Deploying large language models in telecommunications requires more than task accuracy. In realistic workflows, a model may inherit partially completed reasoning from a prior step, an upstream agent, or its own earlier generation, and must continue that reasoning even when it is already going wrong. We introduce TeleResilienceBench, a benchmark that quantifies this capability, which we term reasoning resilience, across seven telecom sub-domains drawn from the GSMA Open-Telco LLM suite. Instances are constructed by collecting failures from a weak generator model, truncating the flawed reasoning trace at its midpoint, and asking a target model to continue and correct it. We propose the Correct Flip Rate (CFR) as a direct measure of successful recovery and evaluate eight models spanning the Qwen3.5, Gemma4, and Nemotron-3 families. Our results show that even the strongest model achieves a macro-average CFR of only 29.1%, and scale does not reliably improve resilience within families. Nemotron-3-nano 4b outperforms all Qwen3.5 variants including the 27b model and leads the auxiliary TeleMath numerical evaluation at 23.4% CR%, offering the best resilience-to-cost ratio in the set. A difficulty-stratified analysis further reveals that existing telecom benchmark difficulty labels reflect factual specificity rather than reasoning depth, suggesting that current evaluations measure knowledge coverage more than reasoning ability.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper introduces TeleResilienceBench by harvesting failures from one weak model, truncating traces, and scoring recovery via Correct Flip Rate, with results showing low resilience overall and a small Nemotron model beating larger ones.

read the letter

The core new piece is the benchmark construction: run a weak generator on GSMA telecom tasks, keep only its failures, cut each trace at the midpoint, and ask target models to continue and fix it. They pair this with the Correct Flip Rate metric to quantify successful recovery instead of just final accuracy. The evaluation covers seven sub-domains and eight models from Qwen3.5, Gemma4, and Nemotron-3 families, plus an auxiliary TeleMath check. The headline numbers are concrete—top macro CFR at 29.1 percent, scale not reliably helping within families, and Nemotron-3-nano 4b outperforming the 27b Qwen variant. The side finding that existing difficulty labels track factual specificity more than reasoning depth is also useful and worth noting. These elements give a practical new axis for testing chained reasoning in a domain where partial errors are common. The main soft spot is exactly the one the stress-test note flags. Failures collected from a single weak generator and truncated at midpoint may not produce the same mix of error types or recoverability profiles that arise when a stronger model or human hands off partial work in real telecom workflows. The paper reports the CFR results clearly but does not appear to include direct checks that the generated partial states match the depth or category of errors the evaluated models would actually encounter. Without that, the low resilience claim stays tied to this specific construction rather than generalizing cleanly. Details on failure filtering and statistical controls would also help readers assess robustness. This work is aimed at researchers and engineers building or evaluating LLM agents for critical infrastructure who already care about robustness beyond accuracy. It deserves peer review because the empirical setup is straightforward, the numbers are reported, and the benchmark idea fills a gap, even though the realism question will need attention in revision.

Referee Report

2 major / 2 minor

Summary. The paper introduces TeleResilienceBench to quantify reasoning resilience in LLMs for telecommunications tasks drawn from the GSMA Open-Telco suite. Instances are built by running a weak generator model, retaining only its failures, truncating each flawed trace at the midpoint, and prompting target models to continue and correct the partial reasoning. The Correct Flip Rate (CFR) is defined as the primary success metric. Evaluation of eight models from the Qwen3.5, Gemma4, and Nemotron-3 families yields a maximum macro-average CFR of 29.1%, with the finding that scale does not reliably improve resilience within families and that Nemotron-3-nano 4b outperforms larger Qwen3.5 variants; an auxiliary TeleMath evaluation and a difficulty-stratified analysis are also reported.

Significance. If the benchmark instances are representative of real partial-reasoning errors, the results would demonstrate a substantial gap in current LLMs' ability to recover from inherited errors in telecom workflows and would usefully highlight that parameter count alone is not a reliable predictor of resilience. The concrete cross-model CFR numbers and the observation that existing difficulty labels track factual specificity more than reasoning depth could inform both deployment decisions and future benchmark design in the domain.

major comments (2)

[§3] §3 (Benchmark Construction): The pipeline collects failures exclusively from a single weak generator model and truncates traces at their midpoint, yet the manuscript supplies no validation, error-category analysis, or comparison showing that the resulting partial states and error profiles are statistically similar to those arising when a production telecom agent inherits reasoning from a stronger upstream model, prior turn, or human. This assumption is load-bearing for the claim that CFR measures general reasoning resilience rather than recovery from one specific class of shallow failures.
[§4] §4 (Evaluation and Results): The reported macro-average CFR of 29.1% and the within-family scale comparisons are presented without details on the number of instances per sub-domain, truncation criteria, failure-collection statistics, or any statistical controls (e.g., variance, prompt sensitivity, or multiple-generator ablation). These omissions make it difficult to assess whether the headline result that Nemotron-3-nano 4b outperforms Qwen3.5-27b is robust or sensitive to the particular generator chosen.

minor comments (2)

[Abstract] Abstract: The auxiliary TeleMath result is reported as 23.4% CR% while the main metric is CFR; a brief definition or note clarifying whether CR% is identical to CFR or a distinct quantity would prevent reader confusion.
[§5] The difficulty-stratified analysis is summarized in the abstract but the manuscript does not include an explicit table or figure linking the stratification method to the main CFR results, making it hard to evaluate the claim that current labels reflect factual specificity rather than reasoning depth.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the thoughtful and constructive report. The comments highlight important aspects of benchmark validity and reporting transparency that we address below. We maintain that TeleResilienceBench offers a useful initial quantification of reasoning resilience in telecom tasks, while agreeing that additional clarifications and discussions will strengthen the manuscript.

read point-by-point responses

Referee: [§3] §3 (Benchmark Construction): The pipeline collects failures exclusively from a single weak generator model and truncates traces at their midpoint, yet the manuscript supplies no validation, error-category analysis, or comparison showing that the resulting partial states and error profiles are statistically similar to those arising when a production telecom agent inherits reasoning from a stronger upstream model, prior turn, or human. This assumption is load-bearing for the claim that CFR measures general reasoning resilience rather than recovery from one specific class of shallow failures.

Authors: We agree that explicit validation against errors from stronger models or human traces would provide stronger support for generalizability. The weak-generator approach was chosen deliberately to produce a high density of failures from the same GSMA Open-Telco task distribution, ensuring the benchmark contains genuine mid-trace errors rather than artificial ones. We will add an error-category breakdown of the collected failures (e.g., factual vs. logical vs. domain-specific) and a dedicated limitations paragraph discussing the single-generator assumption, along with a call for future multi-generator and human-in-the-loop ablations. These additions constitute a partial revision; a full comparative study lies beyond the scope of the current work. revision: partial
Referee: [§4] §4 (Evaluation and Results): The reported macro-average CFR of 29.1% and the within-family scale comparisons are presented without details on the number of instances per sub-domain, truncation criteria, failure-collection statistics, or any statistical controls (e.g., variance, prompt sensitivity, or multiple-generator ablation). These omissions make it difficult to assess whether the headline result that Nemotron-3-nano 4b outperforms Qwen3.5-27b is robust or sensitive to the particular generator chosen.

Authors: We will expand §4 and the appendix with the requested statistics: exact instance counts per sub-domain, the precise midpoint truncation rule (token or step count), failure-collection yield (e.g., number of traces generated to obtain the final set), and any available run-to-run variance. Prompt sensitivity was controlled via fixed templates; we will report this explicitly. The Nemotron-3-nano result is presented as an empirical observation rather than a universal claim, and we will qualify it accordingly. These are straightforward reporting improvements that we will implement in the revision. revision: yes

standing simulated objections not resolved

A comprehensive statistical comparison of error profiles against those produced by stronger upstream models or human operators would require new data collection and experiments not performed in the original study.

Circularity Check

0 steps flagged

No circularity: purely empirical benchmark construction and evaluation

full rationale

The paper defines TeleResilienceBench by running a fixed weak generator on GSMA tasks, retaining its failures, truncating traces at midpoint, and measuring target-model recovery via the directly defined Correct Flip Rate (CFR). No equations, predictions, or first-principles claims are present; all reported numbers (macro CFR 29.1 %, per-model rankings, difficulty stratification) are raw empirical counts on the constructed instances. No self-citations, fitted parameters renamed as predictions, or ansatzes appear in the derivation chain. The work is self-contained against external benchmarks and contains no load-bearing reductions to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claim rests on the assumption that mid-trace error continuation is a meaningful and measurable proxy for real-world telecom LLM usage; no free parameters are fitted in the reported results, but the benchmark itself introduces new entities and domain assumptions.

axioms (1)

domain assumption Reasoning resilience (ability to continue and correct partially flawed traces) is a distinct and practically important capability for LLM deployment in telecommunications.
Invoked in the motivation and benchmark design; treated as self-evident for the target application.

invented entities (2)

reasoning resilience no independent evidence
purpose: New capability to be measured, distinct from task accuracy.
Introduced as the core quantity the benchmark quantifies.
Correct Flip Rate (CFR) no independent evidence
purpose: Direct metric of successful recovery from flawed partial reasoning.
Proposed as the primary evaluation score.

pith-pipeline@v0.9.0 · 5552 in / 1472 out tokens · 27207 ms · 2026-05-12T04:09:48.152655+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

28 extracted references · 28 canonical work pages

[1]

NVIDIA and Nokia to pioneer the AI platform for 6G: Powering America’s return to telecommunications leadership

NVIDIA Corporation, “NVIDIA and Nokia to pioneer the AI platform for 6G: Powering America’s return to telecommunications leadership.” NVIDIA Newsroom, 2025

work page 2025
[2]

NVIDIA and partners show that software-defined AI-RAN is the next wireless generation

NVIDIA Corporation, “NVIDIA and partners show that software-defined AI-RAN is the next wireless generation.” NVIDIA Blog, Mar. 2026

work page 2026
[3]

Beyond connectivity: An open architecture for ai-ran convergence in 6g,

M. Polese, N. Mohamadi, S. D’Oro, L. Bonati, and T. Melodia, “Beyond connectivity: An open architecture for ai-ran convergence in 6g,” 2025

work page 2025
[4]

Ai-native o-ran architectures for 6g: Towards real-time adaptation, conflict resolution, and efficient resource management,

S. Salmi, M. A. Ouameur, M. Bagaa, G. C. Alexandropou- los, A. Tahenni, D. Massicotte, and A. Ksentini, “Ai-native o-ran architectures for 6g: Towards real-time adaptation, conflict resolution, and efficient resource management,” IEEE Transactions on Network and Service Management, pp. 1–1, 2026

work page 2026
[5]

Teleqna: A benchmark dataset to assess large language models telecommunica- tions knowledge,

A. Maatouk, F. Ayed, N. Piovesan, A. D. Domenico, M. Debbah, and Z.-Q. Luo, “Teleqna: A benchmark dataset to assess large language models telecommunica- tions knowledge,”IEEE Network, vol. 40, no. 2, pp. 253– 260, 2026

work page 2026
[6]

Oran-bench-13k: An open source benchmark for assessing llms in open radio access networks,

P. Gajjar and V . K. Shah, “Oran-bench-13k: An open source benchmark for assessing llms in open radio access networks,” in2025 IEEE 22nd Consumer Communica- tions & Networking Conference (CCNC), pp. 1–4, 2025

work page 2025
[7]

Teletables: A benchmark for large language models in telecom table interpretation,

A. Ezzakri, N. Piovesan, M. Sana, A. D. Domenico, F. Ayed, and H. Zhang, “Teletables: A benchmark for large language models in telecom table interpretation,” 2025

work page 2025
[8]

Reasoning language models for root cause analysis in 5g wireless networks,

M. Sana, N. Piovesan, A. D. Domenico, Y . Kang, H. Zhang, M. Debbah, and F. Ayed, “Reasoning language models for root cause analysis in 5g wireless networks,” 2025

work page 2025
[9]

6g-bench: An open benchmark for semantic communication and network-level reasoning with foundation models in ai- native 6g networks,

M. A. Ferrag, A. Lakas, and M. Debbah, “6g-bench: An open benchmark for semantic communication and network-level reasoning with foundation models in ai- native 6g networks,”IEEE Open Journal of the Commu- nications Society, vol. 7, pp. 3305–3330, 2026

work page 2026
[10]

Telcoagent-bench: A multilingual benchmark for telecom ai agents,

L. Bariah, B. Mefgouda, F. Tavakkoli, E. Molero, L. Pow- ell, and M. Debbah, “Telcoagent-bench: A multilingual benchmark for telecom ai agents,” Mar. 2026

work page 2026
[11]

Chain-of-thought prompting elicits reasoning in large language models,

J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” 2023

work page 2023
[12]

Language models don't always say what they think: Unfaithful explanations in chain-of-thought prompting,

M. Turpin, J. Michael, E. Perez, and S. Bowman, “Language models don't always say what they think: Unfaithful explanations in chain-of-thought prompting,” inAdvances in Neural Information Processing Systems (A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, eds.), vol. 36, pp. 74952–74965, Curran Associates, Inc., 2023

work page 2023
[13]

Large language models cannot self-correct reasoning yet,

J. Huang, X. Chen, S. Mishra, H. S. Zheng, A. W. Yu, X. Song, and D. Zhou, “Large language models cannot self-correct reasoning yet,” 2024

work page 2024
[14]

Self-refine: iterative refinement with self-feedback,

A. Madaan, N. Tandon, P. Gupta, S. Hallinan, L. Gao, S. Wiegreffe, U. Alon, N. Dziri, S. Prabhumoye, Y . Yang, S. Gupta, B. P. Majumder, K. Hermann, S. Welleck, A. Yazdanbakhsh, and P. Clark, “Self-refine: iterative refinement with self-feedback,” inProceedings of the 37th International Conference on Neural Information Processing Systems, NIPS ’23, (Red H...

work page 2023
[15]

Reflexion: language agents with verbal rein- forcement learning,

N. Shinn, F. Cassano, A. Gopinath, K. Narasimhan, and S. Yao, “Reflexion: language agents with verbal rein- forcement learning,” inAdvances in Neural Information Processing Systems(A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, eds.), vol. 36, pp. 8634–8652, Curran Associates, Inc., 2023

work page 2023
[16]

Tspec-llm: An open-source dataset for llm understanding of 3gpp specifications,

R. Nikbakht, M. Benzaghta, and G. Geraci, “Tspec-llm: An open-source dataset for llm understanding of 3gpp specifications,” 2024

work page 2024
[17]

Self-consistency improves chain of thought reasoning in language models,

X. Wang, J. Wei, D. Schuurmans, Q. Le, E. Chi, S. Narang, A. Chowdhery, and D. Zhou, “Self-consistency improves chain of thought reasoning in language models,” 2023

work page 2023
[18]

Star: self-taught reasoner bootstrapping reasoning with reasoning,

E. Zelikman, Y . Wu, J. Mu, and N. D. Goodman, “Star: self-taught reasoner bootstrapping reasoning with reasoning,” NIPS ’22, (Red Hook, NY , USA), Curran Associates Inc., 2022

work page 2022
[19]

A chain-of-thought is as strong as its weakest link: A benchmark for verifiers of reasoning chains,

A. Jacovi, Y . Bitton, B. Bohnet, J. Herzig, O. Honovich, M. Tseng, M. Collins, R. Aharoni, and M. Geva, “A chain-of-thought is as strong as its weakest link: A benchmark for verifiers of reasoning chains,” inPro- ceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (L.-W. Ku, A. Martins, and V . Sri...

work page 2024
[20]

Let’s verify step by step,

H. Lightman, V . Kosaraju, Y . Burda, H. Edwards, B. Baker, T. Lee, J. Leike, J. Schulman, I. Sutskever, and K. Cobbe, “Let’s verify step by step,” 2023

work page 2023
[21]

Math-shepherd: Verify and reinforce LLMs step-by-step without human annotations,

P. Wang, L. Li, Z. Shao, R. Xu, D. Dai, Y . Li, D. Chen, Y . Wu, and Z. Sui, “Math-shepherd: Verify and reinforce LLMs step-by-step without human annotations,” inPro- ceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (L.-W. Ku, A. Martins, and V . Srikumar, eds.), (Bangkok, Thailand), pp. 9426–943...

work page 2024
[22]

Lost at the beginning of reasoning,

B. Liao, X. Chen, S. Rajaee, Y . Xu, C. Herold, A. Søgaard, M. de Rijke, and C. Monz, “Lost at the beginning of reasoning,” 2025

work page 2025
[23]

[Online; accessed 2026- 05-08]

“Ollama.” https://ollama.com/. [Online; accessed 2026- 05-08]

work page 2026
[24]

Nemotron 3 nano: Open, efficient mixture- of-experts hybrid mamba-transformer model for agentic reasoning,

NVIDIA, :, A. Blakeman, A. Grattafiori, A. Basant, A. Gupta, A. Khattar, A. Renduchintala, A. Vavre, A. Shukla, A. Bercovich, A. Ficek, A. Shaposhnikov, A. Kondratenko, A. Bukharin, A. Milesi, A. Taghibakhshi, A. Liu, A. Barton, A. S. Mahabaleshwarkar, A. Klein, A. Zuker, A. Geifman, A. Shen, A. Bhiwandiwalla, A. Tao, A. Guan, A. Mandarwal, A. Mehta, A. A...

work page 2025
[25]

Qwen3.5: Accelerating productivity with native multimodal agents,

Q. Team, “Qwen3.5: Accelerating productivity with native multimodal agents,” February 2026

work page 2026
[26]

Gemma 4: Byte for byte, the most capable open models,

C. Farabet, “Gemma 4: Byte for byte, the most capable open models,” 04 2026. APPENDIX A. Prompt Template for Evaluation We use a single continuation-style prompt template across the main sub-benchmarks: the model is provided the question, the answer options, and a partial (half) reasoning trace, and is instructed to continue the reasoning and output exact...

work page 2026
[27]

Final Answer: <n>

**Measurement Data**: We have a set of measurements (RSRP, RSRQ, SINR) associated with different cells. 3. **... Flip outcome:Corrected to gold (pred=3: C3, gold=C3). 11 TeleTables Model:gemma4:31b Sample ID:ot-lite:TeleTables:21 Question (truncated):What is the maximum SNR difference between FRC G-FR1-A3-33A and G-FR1-A4-29A in HST Scenario 1-NR500? Corr...

work page 2000
[28]

SA4", "RAN2

* **Text Content:** The text mentions "SA4", "RAN2", "SA4 is able to address this...", "SA4 kindly asks RAN2". * **Reasoning:** The text explicitly discusses SA4 (Service Access Architectur... Observed error propagation:pred=4: CT6, gold=SA5. TeleLogs Model:qwen3.5:27b Sample ID:ot-lite:TeleLogs:66 Question (truncated):Analyze the 5G wireless network driv...

work page