1 citations · 1 across the 18 of their papers we have counts for
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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…
Turning Intent into Specifications: A Benchmark and an Interactive User-Assistant Agent
Hao Wang, Ligong Han, Kai Xu +1
Today's agents are highly effective at implementing well-scoped software design plans, but user intent is often vague and admits multiple equally valid solutions. In this paper, we…
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…
Few-Step Diffusion Language Models via Trajectory Self-Distillation
Tunyu Zhang, Xinxi Zhang, Ligong Han +9
Diffusion large language models (DLLMs) have emerged as powerful generative models with the promise of fast text generation through parallel decoding. However, realizing this poten…