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20202026
most citedControlled LLM Decoding via Discrete Auto-regressive Biasing

1 citations · 2 across the 26 of their papers we have counts for

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11 papers · 1 filter

cs.LG2026

Masked Diffusion for Travel Diaries Synthesis

Pascal Jutras-Dubé, Raziq Zaman, Fabian Bastin +2

Because household travel surveys combine household characteristics, variable-length person rosters, vehicle rosters, and trip chains, existing synthesis pipelines split them across…

cs.CV2026

How Can Driving World Models Do Counterfactual Prediction?

Jiaru Zhang, Can Cui, Yi Xu +3

Driving world models are often interpreted as counterfactual simulators for observed driving episodes: given a factual driving log, they are asked what would have happened under an…

cs.LG2026

SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups

Yikun Bai, Binghang Lu, Yikai Liu +7

Generative modeling of protein backbones promises the de novo design of proteins with prescribed structural and functional properties. Existing diffusion and flow-matching models p…

cs.LG2026

Sticky Jump Diffusions: A Unifying View of Masked, Continuous, and Hybrid Diffusion

Pascal Jutras-Dubé, Patrick Pynadath, Jeremy Lu +2

We introduce Sticky Jump Diffusions (SJDs), continuous-time Markov processes on whose discrete anchors are token embeddings. In forward time, anchors release their ma…

cs.LG2026

Addressing Performance Saturation for LLM RL via Precise Entropy Curve Control

Bolian Li, Yifan Wang, Yi Ding +3

Reinforcement learning (RL) has enabled complex reasoning abilities in large language models (LLMs). However, most RL algorithms suffer from performance saturation, preventing cont…

cs.LG2026

Uniform-Correct Policy Optimization: Breaking RLVR's Indifference to Diversity

Anamika Lochab, Bolian Li, Ruqi Zhang

Reinforcement Learning with Verifiable Rewards (RLVR) has achieved substantial gains in single-attempt accuracy (Pass@1) on reasoning tasks, yet often suffers from reduced multi-sa…

cs.LG2026

Analytical Correction for Subsampling Bias in Drifting Models

Jiaru Zhang, Zeyun Deng, Juanwu Lu +2

Drifting models are capable one-step generative models trained to follow a drifting field. The field combines attractive and repulsive softmax-weighted centroids over the data and…

cs.SE2026

Learning From Developers: Towards Reliable Patch Validation at Scale for Linux

Chih-En Lin, Attreyee Mukherjee, Ajay Rawat +2

Patch reviewing is critical for software development, especially in distributed open-source development, which highly depends on voluntary work, such as Linux. This paper studies t…

cs.SE2026

Who Tests the Testers? Systematic Enumeration and Coverage Audit of LLM Agent Tool Call Safety

Xuan Chen, Lu Yan, Ruqi Zhang +1

Large Language Model (LLM) agents increasingly act through external tools, making their safety contingent on tool-call workflows rather than text generation alone. While recent ben…

cs.CV2026

Efficient and Explainable End-to-End Autonomous Driving via Masked Vision-Language-Action Diffusion

Jiaru Zhang, Manav Gagvani, Can Cui +3

Large Language Models (LLMs) and Vision-Language Models (VLMs) have emerged as promising candidates for end-to-end autonomous driving. However, these models typically face challeng…

cs.LG2026

Why Any-Order Autoregressive Models Need Two-Stream Attention: A Structural-Semantic Tradeoff

Patrick Pynadath, Ruqi Zhang

Any-order autoregressive models (AO-ARMs) offer a promising path toward efficient masked diffusion by enabling native key-value caching, but competitive performance has so far requ…