T-Tech

company

https://t-tech.dev

AI & ML interests

Scientific research; Natural language processing: speech analytics, search engines, dialogue systems; A family of LLMs; Speech technologies; Fraud prevention technologies; Computer vision; Recommender systems; Time series analysis

Recent Activity

kenkaneki published a model 2 days ago

t-tech/T-Search-NVFP4

kenkaneki published a dataset 2 days ago

t-tech/SynthComp

kenkaneki published a dataset 2 days ago

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Papers

Rank-Then-Act: Reward-Free Control from Frame-Order Progress

Unstable Features, Reproducible Subspaces: Understanding Seed Dependence in Sparse Autoencoders

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t-tech 's papers 23

Submitted by

Natyren

Rank-Then-Act: Reward-Free Control from Frame-Order Progress

t-tech

Submitted by

Nikita Balagansky

Unstable Features, Reproducible Subspaces: Understanding Seed Dependence in Sparse Autoencoders

t-tech

2

Submitted by

Nikita Balagansky

Interpreting and Steering a Text-to-Speech Language Model with Sparse Autoencoders

t-tech

2

Submitted by

Alexey Gorbatovski

Trust-Region Behavior Blending for On-Policy Distillation

t-tech

4

Learning Selective LLM Autonomy from Copilot Feedback in Enterprise Customer Support Workflows

t-tech

Submitted by

Natyren

Next Embedding Prediction Makes World Models Stronger

t-tech

Submitted by

Alexey Gorbatovski

F-GRPO: Don't Let Your Policy Learn the Obvious and Forget the Rare

t-tech

3

Submitted by

Olga Tsymboi

T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground

t-tech

4

Small Vectors, Big Effects: A Mechanistic Study of RL-Induced Reasoning via Steering Vectors

t-tech

Submitted by

Daniil Gavrilov

Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success

t-tech

Submitted by

Nikita Balagansky

Teach Old SAEs New Domain Tricks with Boosting

t-tech

1

ESSA: Evolutionary Strategies for Scalable Alignment

t-tech

Train One Sparse Autoencoder Across Multiple Sparsity Budgets to Preserve Interpretability and Accuracy

t-tech

Submitted by

Daniil Laptev

Train Sparse Autoencoders Efficiently by Utilizing Features Correlation

t-tech

2

Steering LLM Reasoning Through Bias-Only Adaptation

t-tech

Submitted by

Yaroslav Aksenov

You Do Not Fully Utilize Transformer's Representation Capacity

t-tech

Submitted by

Daniil Gavrilov

Analyze Feature Flow to Enhance Interpretation and Steering in Language Models

t-tech

2

Submitted by

Alexey Gorbatovski

The Differences Between Direct Alignment Algorithms are a Blur

t-tech

3

Submitted by

Daniil Gavrilov

Mechanistic Permutability: Match Features Across Layers

t-tech

2

Submitted by

AK

Learn Your Reference Model for Real Good Alignment

t-tech

Submitted by

AK

Linear Transformers with Learnable Kernel Functions are Better In-Context Models

t-tech

Classifiers are Better Experts for Controllable Text Generation

t-tech

PALBERT: Teaching ALBERT to Ponder

t-tech