TSLAM-4B: Telecom-Adapted 4B LLM

Updated 30 December 2025

Telecom-Adapted Agent (TSLAM-4B) is a 4-billion-parameter LLM designed specifically for telecom applications, featuring domain-specific pretraining and quantization.
It employs multi-stage fine-tuning with adapter layers and reinforcement strategies to achieve low-latency performance and robust safety in network management.
Integrated into multi-agent systems and digital twin pipelines, TSLAM-4B improves troubleshooting accuracy, scalability, and operational efficiency in telecom environments.

Telecom-Adapted Agent (TSLAM-4B) denotes a class of 4-billion-parameter LLMs specifically architected, quantized, and fine-tuned for the demands of real-time telecommunications. TSLAM-4B operates as an enabling component in end-to-end autonomous network management, safe agentic control, low-latency voice assistants, and automated troubleshooting workflows utilizing telecom knowledge graphs, digital twin data, and retrieval-augmented inference pipelines. It has been realized by multiple research groups over architectures derived from the Phi, Qwen, and GPT-style families, with persistent focus on quantization, memory efficiency, domain grounding, and safe-action enforcement (Ethiraj et al., 5 Aug 2025, Shi et al., 1 Nov 2025, Vijay et al., 23 Dec 2025, Ethiraj et al., 10 May 2025).

1. Model Architecture and Quantization

TSLAM-4B is architected as a causal-masked, decoder-only transformer with approximately 4 billion parameters. Concrete architectural implementations diverge slightly by lineage:

Phi-3/Phi-4 Mini Instruct 4B Derivative: Transformer depth varies between 32–33 layers, with hidden size $d_\mathrm{model}$ ≈ 4096–4160, attention heads 32, and inner feed-forward dimensions ≈ 16,000 (Ethiraj et al., 10 May 2025, Vijay et al., 23 Dec 2025).
Qwen-3-4B Variant: Comparable scale, native context window up to 8,000 tokens, with sub-word BPE vocabulary for telecom jargon coverage (Shi et al., 1 Nov 2025).
Adapter Layers: Some variants employ LoRA PEFT adapters in all attention and feed-forward blocks, raising tunable parameter counts by ≈0.5% (Ethiraj et al., 10 May 2025). Other implementations rely exclusively on pretraining and prompt engineering (Vijay et al., 23 Dec 2025).

All productionized instantiations employ 4-bit quantization (per-tensor NF4 or BitsAndBytes), compressing model footprints to enable efficient edge or kubernetes-deployed inference (Ethiraj et al., 5 Aug 2025). The quantization scheme for weight tensor $w$ follows:

$Q(w) = \mathrm{clip}\bigl(\mathrm{round}\bigl((w - z)/s\bigr), -2^{b-1}, 2^{b-1}-1\bigr)$

where $s = (w_{\max} - w_{\min})/(2^b-1)$ and $z = w_{\min}$ , enabling GPU memory reductions by a factor of 4–8 $\times$ compared to FP16 (Ethiraj et al., 5 Aug 2025, Ethiraj et al., 10 May 2025).

Variant	Layer count	Hidden size	Adapter (LoRA)	Context window	Architecture ref
Phi-4 Mini	32/33	4096/4160	Yes	8,192 (base), up to 128k (G-SPEC)	(Ethiraj et al., 10 May 2025, Vijay et al., 23 Dec 2025)
Qwen-3-4B	28–32	~4096	Yes	8,000	(Shi et al., 1 Nov 2025)

A plausible implication is that memory- and latency-optimized 4-bit quantization, combined with parameter-efficient adapters, is now standard for telco-scale LLM deployment.

2. Domain Adaptation and Fine-Tuning

TSLAM-4B is shaped by multi-stage training to align to telecom semantics and operational workflows:

Pretraining: Conducted on a corpus integrating IETF/3GPP RFCs, operator runbooks, network element logs, and digital-twin simulations constructed by SME input (Ethiraj et al., 10 May 2025, Vijay et al., 23 Dec 2025). The size for TSLAM-Mini is 100,000 samples; recommendations for TSLAM-4B target up to 200,000, emphasizing coverage of 5G-Advanced, Open RAN, and rare edge cases.
Fine-Tuning Regimens:
- Supervised Fine-Tuning (SFT): Domain question-answer pairs drawn from troubleshooting manuals, performance metrics, alarms, and SME-annotated scripts; typical splits are 90/10 train/validation, with LoRA ranks of 4–16 (Shi et al., 1 Nov 2025, Ethiraj et al., 10 May 2025).
- Reinforcement Fine-Tuning (RFT): Reward functions amalgamate XML/JSON formatting correctness, domain completeness, relevancy (RAGAS), and groundedness to retrieved support context. Group Relative Policy Optimization (GRPO) is used, optimizing
$J(\theta) = \mathbb{E}_{x \sim D, y \sim \pi_\theta}[R_\mathrm{total}(x, y)],$

with $R_\mathrm{total} = R_\mathrm{fmt} + R_\mathrm{raga}$ (Shi et al., 1 Nov 2025).
Tokenization: Qwen-based variants utilize a 64K subword BPE vocabulary; some deploy custom delimiters (e.g., <RFC>...</RFC>) for RFC-indexed knowledge injection (Ethiraj et al., 5 Aug 2025).

Uniform document chunking (e.g., 512-token segments) is critical to tractable memory usage and convergence in long troubleshooting sequences (Shi et al., 1 Nov 2025).

3. Inference Pipelines and Agentic Integration

TSLAM-4B acts as a reasoning/planning module within complex agentic architectures for telecom operations:

Multi-Agent System (MAS) Workflow (Hypha framework): Agents include an LLM-based orchestrator, data retriever, root-cause analyzer, and a TSLAM-4B “solution planner” operating with retrieval-augmented prompts referencing alarms and performance counters (Shi et al., 1 Nov 2025). HITL validation is supported before execution.
Governance Triad in G-SPEC: TSLAM-4B interacts with a dynamic Network Knowledge Graph (NKG) and SHACL constraints. For every intent, a local subgraph $S_\mathrm{sub}$ is extracted and serialized (JSON), then supplied to TSLAM-4B along with operator intent. Outputs include a “Trace” (chain-of-thought reasoning) and a “Plan” (JSON list of actions), with each plan step simulated and verified against SHACL policies before execution (Vijay et al., 23 Dec 2025).

Inference pipelines for voice agents include streaming ASR, RAG retrieval over telecom documents, LLM question answering, and real-time TTS, with TSLAM-4B’s sub-second latency supporting live IVR and customer-support deployments (Ethiraj et al., 5 Aug 2025).

4. Evaluation, Performance, and Benchmarking

Evaluation encompasses semantic fidelity, domain specificity, latency, and safety:

Automated judge models: Qwen3-235B-A22B scores TSLAM-Mini and extrapolated TSLAM-4B on instruction-following (F̄ ≈ 9.2/10), domain accuracy (Ā ≈ 8.8/10), and telecom use-case benchmarks (telecom average ≈ 92.5%) (Ethiraj et al., 10 May 2025).
MAS troubleshooting: TSLAM-4B planner reduces mean time per node from ≈ 12 min (human) to ≈ 2 min, with accuracy uplift from 82% to 90%. Ablations confirm total reward improvement (base → fine-tuned Qwen-3-4B: 5.08 → 8.17) and show the necessity of RAG/reward shaping (Shi et al., 1 Nov 2025).
G-SPEC safety: TSLAM-4B achieves a 94.1% successful remediation rate, 0.2% hallucination rate, and zero safety violations in 5G network intent planning. Removal of NKG or SHACL validation degrades safety and remediation, while using a generic LLM in place of TSLAM-4B increases hallucination rate by >8× (Vijay et al., 23 Dec 2025).
Voice Agent Latencies: For real-time factor (RTF), $\mathrm{RTF} = \frac{T_\mathrm{ASR} + T_\mathrm{RAG} + T_\mathrm{LLM} + T_\mathrm{TTS}}{T_\mathrm{audio}}$ sub-1.0 is routinely observed. TSLAM-4B-inferred LLM time is ≈ 0.67–2.1 s per interaction, with time-to-first-token ≈ 0.1 s (Ethiraj et al., 5 Aug 2025).

Metric	Value (TSLAM-4B pipeline)	Reference
Mean time per node (MAS)	~2 min (6×↓ vs. human)	(Shi et al., 1 Nov 2025)
Troubleshooting accuracy	90% (vs. 82%, baseline)	(Shi et al., 1 Nov 2025)
Remediation (5G, G-SPEC)	94.1%	(Vijay et al., 23 Dec 2025)
Hallucination rate	0.2%	(Vijay et al., 23 Dec 2025)
Real-Time Factor (ASR→LLM→TTS)	≲ 0.15	(Ethiraj et al., 5 Aug 2025)

5. Prompt Engineering, Action Formatting, and Hallucination Controls

Prompt engineering is tailored to enforce determinism, domain compliance, and actionable outputs:

Retrieval-Augmented Prompts: TSLAM-4B is furnished context windows of alarms, counters, or subgraphs. Prompts specify XML step enumeration (MAS) or JSON Chain-of-Thought templates (G-SPEC) (Shi et al., 1 Nov 2025, Vijay et al., 23 Dec 2025).
Format Enforcement: Reward functions (e.g., regex XML/JSON format checks) are combined with completeness/relevancy scores to penalize malformed outputs (Shi et al., 1 Nov 2025).
Hallucination Rejection: In agentic/MAS/G-SPEC workflows, hallucination is checked downstream by graph validation: action plans referencing non-existent nodes or violating SHACL topology/state/resource constraints are rejected prior to execution (Vijay et al., 23 Dec 2025).

Restriction of context to retrieved passages and enforcing pre-defined formats measurably reduce hallucination and promote consistency across upstream agent calls (Shi et al., 1 Nov 2025, Vijay et al., 23 Dec 2025).

6. Practical Deployments and Limitations

TSLAM-4B is deployed in both edge and core (SMO-layer) environments with a focus on efficient inference and privacy-preserving troubleshooting:

Hardware and Scaling: Kubernetes clusters with 8 vCPU/32GB RAM per node support per-intent latency ≈ 2.2s and linear scaling with node count (Vijay et al., 23 Dec 2025). GPU memory footprints for 4B models remain below 4GB (due to 4-bit quantization) (Ethiraj et al., 5 Aug 2025, Ethiraj et al., 10 May 2025).
Limitations: Deficits persist in handling rare/novel failure modes not present in fine-tuning corpora. Context window limits (8K–128K tokens depending on variant) may be insufficient for the largest vendor documents or NKG snapshots (Shi et al., 1 Nov 2025, Vijay et al., 23 Dec 2025).
Deployment Optimizations: End-to-end pipelines utilize multi-threading, binary serialization, and sentence-level TTS streaming to minimize bottlenecks. Warm-up routines and queue-based buffer management further ensure real-time performance (Ethiraj et al., 5 Aug 2025).
Future Directions: Research targets vocabulary/context window expansion via sparse attention, integration of post-deployment SME feedback for continual learning, use of generative LLMs for synthetic data augmentation, and hardening security through on-premises operation and data encryption (Shi et al., 1 Nov 2025, Ethiraj et al., 10 May 2025).

7. Impact and Position in Telecom AI Ecosystem

TSLAM-4B establishes a robust template for agentic, telecom-specific LLMs, distinct from general-purpose LLMs by virtue of:

Resource and Safety Efficiency: High domain accuracy with ≤4GB model footprints and real-time semantic fidelity, enabling scalable, on-premises, or edge deployment.
Symbolic/Neuro-Symbolic Enforcement: Extension of LLM capabilities via rule-based validation layers (e.g., NKG+SHACL), mitigating stochastic risk and ensuring regulatory/policy adherence (Vijay et al., 23 Dec 2025).
Agentic Autonomy at Scale: MAS deployment demonstrates practical gains—order-of-magnitude reductions in mean time-to-diagnosis, absolute accuracy improvements, and low hallucination rates—across RAN and core troubleshooting, call center automation, and multi-modal IVR (Ethiraj et al., 5 Aug 2025, Shi et al., 1 Nov 2025).

Collectively, the TSLAM-4B line catalyzes intelligent, low-latency, and safe telecom automation, with empirical evidence for substantial acceleration of network management workflows and increased robustness in the presence of complex and heterogeneous network data (Ethiraj et al., 5 Aug 2025, Shi et al., 1 Nov 2025, Ethiraj et al., 10 May 2025, Vijay et al., 23 Dec 2025).