See-Kiong Ng

Noise in multi-behavior recommendation can be effectively mitigated through a novel spectral filtering approach that enhances representation purity and reliability.

FineVerify boosts GPT-5-mini's accuracy by 8.2 points with just four sampled trajectories, outperforming standard scaling methods.

LLM-based recommendation systems can now dynamically adjust the granularity of knowledge graph retrieval, boosting performance by adapting to the complexity of user queries.

Weak-to-strong reward models can ace the test but still fail in the real world, revealing a hidden brittleness in current preference learning approaches.

The fragmented field of world modeling can now be unified under a "levels x laws" taxonomy, revealing critical gaps in autonomous model revision and decision-centric evaluation.

LLMs struggle to maintain context and avoid distraction when reasoning about causality, leading to a significant performance drop as tasks increase in complexity.

Forget hand-coded strategies: METRO uses LLMs to automatically learn dialogue strategies from expert transcripts, achieving state-of-the-art results in non-collaborative dialogue.

Customer service chatbots can be transformed from reactive support tools into proactive business intelligence engines by strategically probing users for information.

A cleverly shaped and rendered adversarial patch can reliably spoof palmprint recognition, even across different models and datasets.