Institution profile

Singapore Institute of Manufacturing Technology

Academic institutionasia · sg
Official website
Research library24linked papers
Opportunities0open roles
Selected work

Representative Papers

Should Missing Modalities Always Be Necessary to Repair for Multi-modal Sentiment Analysis?

Jul 19, 2026

This work challenges the prevailing assumption in multimodal sentiment analysis that all missing modalities must be reconstructed under partial observability. To address this, the authors propose SIEVE, a novel framework featuring a sample-adaptive modality reconstruction mechanism. SIEVE employs a dual-branch architecture to directly compare the losses of reconstructing versus not reconstructing missing modalities, generating an empirical sufficiency signal. By integrating evidential deep learning with cognitive uncertainty modeling, it implements an evidence-gated mechanism that dynamically decides, on a per-sample basis, whether reconstruction is necessary. Notably, SIEVE operates as a plug-and-play module without requiring modifications to underlying reconstruction components. Experiments on CMU-MOSI and IEMOCAP demonstrate consistent and significant performance gains across diverse backbone models, approaching the theoretical sample-level optimum.

0 citationsRead paper

Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs

May 31, 2026

This work addresses the inefficiency of large language models (LLMs) in reasoning, where excessive computation—often termed “overthinking”—leads to wasted resources. Existing approaches apply uniform compression strategies that neglect variations in reasoning complexity both across problems and within individual reasoning steps. To overcome this limitation, the authors propose an “Economical Reasoning” framework featuring a hierarchical adaptive budgeting mechanism: at the problem level, it predicts the optimal reasoning depth; at the step level, it dynamically allocates token budgets via perplexity-based comparisons and Pareto optimization, while leveraging Fisher information pruning to guide the generator toward efficient reasoning patterns. This approach achieves the first dual-granularity, fine-grained resource allocation scheme, explicitly modeling the quality–efficiency trade-off as a locally adaptive objective. Experiments on GSM8K and MATH500 demonstrate simultaneous improvements in accuracy and reductions in token consumption, significantly outperforming standard chain-of-thought and other baselines.

0 citationsRead paper
Recent publications

Latest Papers

Should Missing Modalities Always Be Necessary to Repair for Multi-modal Sentiment Analysis?

Jul 19, 2026

This work challenges the prevailing assumption in multimodal sentiment analysis that all missing modalities must be reconstructed under partial observability. To address this, the authors propose SIEVE, a novel framework featuring a sample-adaptive modality reconstruction mechanism. SIEVE employs a dual-branch architecture to directly compare the losses of reconstructing versus not reconstructing missing modalities, generating an empirical sufficiency signal. By integrating evidential deep learning with cognitive uncertainty modeling, it implements an evidence-gated mechanism that dynamically decides, on a per-sample basis, whether reconstruction is necessary. Notably, SIEVE operates as a plug-and-play module without requiring modifications to underlying reconstruction components. Experiments on CMU-MOSI and IEMOCAP demonstrate consistent and significant performance gains across diverse backbone models, approaching the theoretical sample-level optimum.

0 citationsRead paper

Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs

May 31, 2026

This work addresses the inefficiency of large language models (LLMs) in reasoning, where excessive computation—often termed “overthinking”—leads to wasted resources. Existing approaches apply uniform compression strategies that neglect variations in reasoning complexity both across problems and within individual reasoning steps. To overcome this limitation, the authors propose an “Economical Reasoning” framework featuring a hierarchical adaptive budgeting mechanism: at the problem level, it predicts the optimal reasoning depth; at the step level, it dynamically allocates token budgets via perplexity-based comparisons and Pareto optimization, while leveraging Fisher information pruning to guide the generator toward efficient reasoning patterns. This approach achieves the first dual-granularity, fine-grained resource allocation scheme, explicitly modeling the quality–efficiency trade-off as a locally adaptive objective. Experiments on GSM8K and MATH500 demonstrate simultaneous improvements in accuracy and reductions in token consumption, significantly outperforming standard chain-of-thought and other baselines.

0 citationsRead paper