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Layer-Parallel Inference Reduces Encrypted Nonlinear Depth in Transformers
Ligong Han, Kai Xu, Hao Wang +3
Fully homomorphic encryption (FHE) enables computation on encrypted data, but practical encrypted Transformer inference is bottlenecked by the sequential composition of many nonlin…
sGPO: Trading Inference FLOPs for Training Efficiency in RLVR
Shivchander Sudalairaj, Kai Xu, Akash Srivastava +1
Standard Reinforcement Learning with Verifiable Rewards (RLVR) training allocates a fixed rollout budget to every query, without regard for what each query's difficulty means for t…
Intrinsic Selection and Particle Resampling for Inference-Time Scaling Beyond Domain Verifiability
Giorgio Giannone, Mustafa Eyceoz, Shabana Baig +5
Inference-Time Scaling (ITS) has largely succeeded in verifiable domains like math and coding, where cheap verification enables scalable output selection. However, extending ITS to…
SNLP: Layer-Parallel Inference via Structured Newton Corrections
Ligong Han, Kai Xu, Hao Wang +1
Autoregressive language models execute Transformer layers sequentially, creating a latency bottleneck that is not removed by conventional tensor or pipeline parallelism. We study w…
GIFT: Bootstrapping Image-to-CAD Program Synthesis via Geometric Feedback
Giorgio Giannone, Anna Clare Doris, Amin Heyrani Nobari +3
Generating executable CAD programs from images requires alignment between visual geometry and symbolic program representations, a capability that current methods fail to learn reli…
Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scaling
Giorgio Giannone, Guangxuan Xu, Nikhil Shivakumar Nayak +4
Inference-Time Scaling (ITS) improves language models by allocating more computation at generation time. Particle Filtering (PF) has emerged as a strong ITS method for complex math…
Inference-Time Scaling of Diffusion Language Models via Trajectory Refinement
Meihua Dang, Jiaqi Han, Minkai Xu +3
Discrete diffusion models have recently emerged as strong alternatives to autoregressive language models, matching their performance through large-scale training. However, inferenc…
Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning
Nikhil Shivakumar Nayak, Krishnateja Killamsetty, Ligong Han +8
Continual learning in large language models (LLMs) is prone to catastrophic forgetting, where adapting to new tasks significantly degrades performance on previously learned ones. E…
SQuat: Subspace-orthogonal KV Cache Quantization
Hao Wang, Ligong Han, Kai Xu +1
The key-value (KV) cache accelerates LLMs decoding by storing KV tensors from previously generated tokens. It reduces redundant computation at the cost of increased memory usage. T…
Differentially Private Synthetic Data Generation for Relational Databases
Kaveh Alimohammadi, Hao Wang, Ojas Gulati +2
Existing differentially private (DP) synthetic data generation mechanisms typically assume a single-source table. In practice, data is often distributed across multiple tables with…