Search papers, labs, and topics across Lattice.
100 papers published across 4 labs.
BAS-OPD is proposed, a budget-aware selective OPD framework that allocates teacher supervision under limited query budgets that achieves strong performance while substantially reducing teacher supervision costs, highlighting the effectiveness of selective OPD under constrained budgets.
A diagnostic framework that measures point accuracy alongside dispersion retention, the ratio of predicted to human standard deviation, on 10,000 respondent--question pairs from the World Values Survey (WVS) spanning twelve countries and six continents is developed.
NSFT (Neural Sub-expert Fine-Tuning), a fine-grained PEFT framework that refines MoE adaptation from experts to sub-experts, is proposed and suggests that sub-expert-level adaptation is a more precise and efficient PEFT paradigm for MoE LLMs.
A progressive four-stage framework that addresses three clinically critical gaps in TCM prescription generation and substantially improves prescription quality over zero-shot baselines is proposed, with a 7B model (Mistral-7B) surpassing zero-shot GPT-5 on all three TCM evaluation metrics.
This work recast black-box black-box LLM steganography as a sequential communication problem with causal, noiseless feedback: every generated token is observed by both parties and can guide subsequent embedding.
A framework for evaluating LLM-generated RTL beyond compilation, which is named GRADE-RTL, provides a practical basis for comparing LLM-generated hardware descriptions by separating structural validity, behavioral correctness, and implementation quality.
The findings to date suggest that significant abrupt changes in climate-change stance do occur in this simple model, and the need to prevent LLM biases from interfering with the conversational dynamics and, more generally, to maintain agent personality and episodic memories of interactions in the face of such biases is reported.
A comprehensive taxonomy is proposed that maps physical, network, and application architectural IoT layers against the consumer, commercial, industrial, and infrastructure IoT domains and identifies key gaps in current research and outline future directions for context-aware and adaptive trust management in edge-enabled IoT.
Control experiments on three cybersecurity datasets covering MITRE ATT&CK network traffic, cyber threat intelligence (CTI), and CVE vulnerability analysis show that reasoning topology substantially affects performance, and Graph reasoning achieves the highest overall accuracy.
