Jul 2026· International Conference on Generative Artificial Intelligence and Image Processing· Vol 14292, pp. 1429207 - 1429207-6· 0 citations· 8 references
Engineering
TL;DR
Lite-CoNER is proposed, a lightweight NER framework that achieves an effective balance between recognition accuracy and inference efficiency and provides a transparent view of the decision-making process, proving that lightweight models can effectively inherit complex logic through structured distillation.
Abstract
While large language model based approaches for Named Entity Recognition have advanced through fine-tuning and in-context learning, they often suffer from mediocre performance and a significant lack of interpretability. Although reasoning models employing Chain-of-Thought mechanisms demonstrate superior inferential capabilities, their practical deployment is hindered by high computational overhead and logical redundancy. To address these limitations, this paper proposes Lite- CoNER, a lightweight NER framework that achieves an effective balance between recognition accuracy and inference efficiency. By leveraging knowledge distillation and reasoning simplification, the framework transfers the deep reasoning expertise of teacher models—specifically in entity boundary determination and category logic—to lightweight student models. We first generate high-quality reasoning data through prompt engineering and introduce an LLM-based evaluator to prune logical redundancy and compress reasoning trajectories. Finally, we apply LoRA for parameter-efficient finetuning, enabling the student model to internalize the distilled reasoning capabilities. Experimental results across multiple benchmark datasets demonstrate that Lite-CoNER significantly outperforms traditional fine-tuning methods while maintaining high inference speeds. Furthermore, the simplified reasoning trajectories provide a transparent view of the decision-making process, proving that lightweight models can effectively inherit complex logic through structured distillation.
Treating supervision format as a first-class hyperparameter for multi-task reasoning SFT in large language models—at least in this benchmark-and-model setting—rather than a mere rendering detail is supported.
Nhat Thanh Vu, M. Rashid, Fariza Sabrina· Electronics· 0 citations
This framework proposes a context-editing framework that performs selective abstraction over entities that appear in both the context and the question, establishing symbolic abstraction as a highly cost-efficient solution for ensuring context fidelity in LLMs.
Rounak Sharma, Debabrata Mahapatra, S. Saini· Annual International ACM SIG...· 0 citations
This work investigates a neuro-symbolic agentic framework to enhance the reasoning capabilities of SLMs, specifically Gemma 3 (1B, 4B) and Llama 3.2 (3B), using the CLUTRR kinship benchmark.
Dimitrios Kelesis, K. Bougiatiotis, Georgios Paliouras· 0 citations
Large language models (LLMs) have demonstrated increasingly strong reasoning capabilities, achieving remarkable progress in knowledge graph question answering (KGQA). However, a key challenge in such systems is non-deterministic reasoning, where the model indecisively activates multiple semantically related knowledge graph edges for a given query, frequently leading to incorrect answers. To address this issue, we propose D iagnosing and R emedying Representation Deficiencies for D eterministic R easoning in KGQA (DR 2 ). DR 2 identifies and localizes non-deterministic reasoning behaviors, uncovering the underlying semantic representation deficiencies in LLMs. Building on this diagnosis, we design abductive reasoning-based preference learning, which promotes fine-grained semantic discrimination and mitigates non-deterministic reasoning errors. Experimental results demonstrate that the proposed DR 2 significantly outperforms several strong baselines, achieving state-of-the-art performance on the widely used WebQSP and CWQ benchmarks.Our code and data is available at https://github.com/HITlgw/DR2.
Ge Liang, Mufan Xu, Kehai Chen et al.· Annual Meeting of the Associ...· 0 citations
Large Language Models (LLMs) trained using Chain-of-Thought (CoT) supervision have achieved state-of-the-art performance on complex reasoning tasks. However, the generation of long reasoning chains introduces substantial computational overhead during inference, limiting their deployment in low-latency and resource-constrained environments. This paper proposes AdaptiReason, a novel framework that dynamically compresses intermediate reasoning steps based on task difficulty and model confidence without requiring retraining of the underlying base model. AdaptiReason employs a lightweight difficulty estimator to determine the appropriate reasoning depth for each input, followed by a learned token-pruning policy that eliminates redundant or low-information reasoning steps. Experimental evaluation on the MATH, GSM8K, and ARC-Challenge benchmarks demonstrates that AdaptiReason reduces the average number of generated tokens by 3.7× while preserving 98.2% of the baseline reasoning accuracy. Furthermore, the proposed framework is model-agnostic and can be seamlessly integrated with instruction-tuned LLMs without requiring access to model parameters, relying solely on output logits for adaptive reasoning compression. The results demonstrate that AdaptiReason significantly improves inference efficiency while maintaining high reasoning performance, making it suitable for real-time and resource-constrained LLM applications.
V. A, Mithaguru, Amrita Kundu et al.· 2026 4th International Confe...· 0 citations
WhisperRec compresses teacher-generated CoT into learnable latent reasoning tokens, enabling a Latent-Reason-then-Answer paradigm that performs reasoning in latent space without producing verbose rationales, and achieves over 10x higher online inference throughput.
Hao Jiang, Pei Du, Pengfei Yao et al.· 0 citations