3 citations · 3 across the 15 of their papers we have counts for
20 papers
Direct Pass@ Optimization for Thought Pruning: Balancing Quality and Diversity in Test-Time Reasoning
Mahdi Salmani, Adel Javanmard, Devansh Gupta +1
Test-time scaling has emerged as a key inference strategy for reasoning models, where a model generates a large number of candidate solutions in the hope of finding at least one co…
BRANCH-MoE: Balance-Aware Tree Routing for Large Embedding Models
Gang Fu, Adel Javanmard, MohammadHossein Bateni +1
Mixture-of-experts (MoE) layers increase model capacity without a proportional increase in per-example computation. However, conventional flat routers can yield imbalanced expert u…
A Proof of the Most Informative Boolean Function Conjecture
Zijie Chen, Amin Gohari, Adel Javanmard +4
Let be uniform on , let be obtained by passing its coordinates independently through a binary symmetric channel with crossover probability , and let $g:\{-1,…
SSTQ:Privacy-Preserving Vector Quantization via Subsampled Stochastic TurboQuant
Adel Javanmard, David P. Woodruff, Vahab Mirrokni
Achieving local differential privacy in distributed optimization while maintaining low communication cost remains challenging. Existing vector quantization methods, such as vqSGD,…
Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data
Kareem Amin, Rudrajit Das, Alessandro Epasto +4
The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alternative to sensitive real-world datasets. Ho…
Geometric Signatures of Reasoning: A Spectral Perspective on Task Hardness
Aria Masoomi, Mahsa Bazzaz, Adel Javanmard +1
Chain-of-thought (CoT) reasoning enables large language models (LLMs) to solve complex problems by generating intermediate reasoning steps. While much attention has been paid to th…
Aletheia tackles FirstProof autonomously
Tony Feng, Junehyuk Jung, Sang-hyun Kim +14
We report the performance of Aletheia (Feng et al., 2026b), a mathematics research agent powered by Gemini 3 Deep Think, on the inaugural FirstProof challenge. Within the allowed t…
Accelerating Scientific Research with Gemini: Case Studies and Common Techniques
David P. Woodruff, Vincent Cohen-Addad, Lalit Jain +33
Recent advances in large language models (LLMs) have opened new avenues for accelerating scientific research. While models are increasingly capable of assisting with routine tasks,…
Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models
Adel Javanmard, Baharan Mirzasoleiman, Vahab Mirrokni
Large Language Models (LLMs) are pretrained on massive datasets and later instruction-tuned via supervised fine-tuning (SFT) or reinforcement learning (RL). Best practices emphasiz…
Progress on the Courtade-Kumar Conjecture: Optimal High-Noise Entropy Bounds and Generalized Coordinate-wise Mutual Information
Adel Javanmard, David P. Woodruff
The Courtade-Kumar conjecture posits that dictatorship functions maximize the mutual information between the function's output and a noisy version of its input over the Boolean hyp…
Understanding the Role of Training Data in Test-Time Scaling
Adel Javanmard, Baharan Mirzasoleiman, Vahab Mirrokni
Test-time scaling improves the reasoning capabilities of large language models (LLMs) by allocating extra compute to generate longer Chains-of-Thoughts (CoTs). This enables models…
Differentially Private Model-X Knockoffs via Johnson-Lindenstrauss Transform
Yuxuan Tao, Adel Javanmard
We introduce a novel privatization framework for high-dimensional controlled variable selection. Our framework enables rigorous False Discovery Rate (FDR) control under differentia…
Differentially Private Synthetic Data Release for Topics API Outputs
Travis Dick, Alessandro Epasto, Adel Javanmard +5
The analysis of the privacy properties of Privacy-Preserving Ads APIs is an area of research that has received strong interest from academics, industry, and regulators. Despite thi…
Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing
Adel Javanmard, Rudrajit Das, Alessandro Epasto +1
Retraining a model using its own predictions together with the original, potentially noisy labels is a well-known strategy for improving the model performance. While prior works ha…
DeepCrossAttention: Supercharging Transformer Residual Connections
Mike Heddes, Adel Javanmard, Kyriakos Axiotis +3
Transformer networks have achieved remarkable success across diverse domains, leveraging a variety of architectural innovations, including residual connections. However, traditiona…
Retraining with Predicted Hard Labels Provably Increases Model Accuracy
Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto +5
The performance of a model trained with noisy labels is often improved by simply \textit{retraining} the model with its \textit{own predicted hard labels} (i.e., 1/0 labels). Yet,…
Optimistic Rates for Learning from Label Proportions
Gene Li, Lin Chen, Adel Javanmard +1
We consider a weakly supervised learning problem called Learning from Label Proportions (LLP), where examples are grouped into ``bags'' and only the average label within each bag i…
PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses
Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni
This work studies algorithms for learning from aggregate responses. We focus on the construction of aggregation sets (called bags in the literature) for event-level loss functions.…
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
Adel Javanmard, Lin Chen, Vahab Mirrokni +2
Due to the rise of privacy concerns, in many practical applications the training data is aggregated before being shared with the learner, in order to protect privacy of users' sens…
Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization
Adel Javanmard, Vahab Mirrokni
While personalized recommendations systems have become increasingly popular, ensuring user data protection remains a top concern in the development of these learning systems. A com…