Cheng Qian

Personalized memory gating unlocks more relevant long-term recall by learning from individual user interaction patterns, outperforming generic policies.

A novel two-axis framework unifies the study of multimodal agent memory, revealing distinct tiers of memory adaptation from static reuse to self-evolving policies.

LLM agents can improve their decision-making by learning from their own diagnosed failures, with first-proposal corrections boosting success rates by over 30 percentage points.

Translating learned strategic principles into an external runtime harness yields a 12-point performance boost over directly feeding those same principles into an agent's context window.

While long-tail knowledge is notoriously hard for LLMs to retain, structurally popular facts suffer the worst collateral damage during knowledge updates and act as super-spreaders of downstream hallucinations.

Complex scoring algorithms for KV cache compression are largely unnecessary: protecting the initial prompt and dropping reasoning tokens completely at random matches state-of-the-art accuracy with up to 43% higher serving throughput.

On-policy distillation is massively data-overfed: a single training prompt recovers most full-dataset performance gains, while just 16 prompts saturate 98.9% of reachable state space to match full-data distillation.

Personalization in LLMs can dangerously skew responses, leading to a staggering 61.7% increase in sycophantic bias.

Test-time harnesses can nearly double the performance of weaker models, transforming how we think about capability transfer in AI.