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WonderfulClaire/README.md

Claire — AI Research & Engineering. LLM post-training, tool-using agents, speech and audio.

Academic homepage  ·  ORCID  ·  Email  ·  X

I'm an M.S. student at Tsinghua University, based in Shenzhen. I work on LLM post-training and tool-using agents, with a background in multimodal learning and speech/audio processing.

I like turning ideas into systems you can run, inspect, and improve—from learning objectives and agent interactions to training code and experiment reports.

Current research focus

  • Generative recommendation: studying unified generation, candidate attribution, and the boundary between model, strategy, ranking, and feedback gains.
  • Speech enhancement: studying content-preserving enhancement under nonstationary noise and competing speakers.
  • Verifiable post-training: building evaluation loops where rewards, trajectories, and held-out behavior can be inspected independently.

I keep public claims tied to runnable protocols, measured outcomes, and explicit evidence boundaries.

Featured study · Foundation models

Kimi K3 · Deep Dive & Lab is my focused study of foundation model mechanisms, from KDA memory and AttnRes to MoE routing. I connect source-grounded derivations with runnable NumPy experiments, a pre-training and systems guide, and an agentic post-training lab.

Learning route · Numerical report · Pre-training & systems · Agent Lab

Selected work

Agents & post-training Multimodal learning
5G Diagnostic Agent — multi-turn diagnosis with evidence-guided tools BLM Multimodal Audit — visual learning, GRPO, and reviewed data production
Multi-turn diagnosis with evidence-guided tools, LoRA-SFT/GRPO, and trajectory-level evaluation. Visual representation learning, distributed contrastive training, and a reviewed data flywheel.
Learning loop · Experiments Data flywheel · Distributed training
Speech & spatial audio Reinforcement learning
HearWeave — spatial-audio building blocks for smart wearables RL From Scratch — mathematical derivations, code, and experiments
Microphone-array simulation, beamforming, localization, and reproducible wearable-audio experiments. Derivations and NumPy/PyTorch implementations, from Bellman equations to PPO, GRPO, and agentic post-training.
Tutorial · BeamBench Learning path · Agentic post-training

A connected research toolkit

Foundations: RL From Scratch and Go agent exercises. Learning loops: harnesses, trajectories, verifiers, SFT and GRPO. Applications: 5G diagnosis, multimodal learning, and spatial audio.

The common thread is learning from interaction: build an environment, follow the signal through the update, and measure what changes.

Explore more: Go agent exercises · JobNebula · Research training guide · Learning hub

Pinned Loading

  1. kimi-k3-deep-dive kimi-k3-deep-dive Public

    Kimi K3 深度解读与基模实验:KDA、AttnRes、MoE 的公式推导、NumPy 实现、数值验证,以及预训练系统与 Agent 后训练。

    Python 1

  2. rl-from-scratch rl-from-scratch Public

    强化学习算法全景库:数学推导 + 从零实现(numpy/PyTorch) + 可运行验证。11 章覆盖表格方法到 RLHF 与量化交易。

    Python 2

  3. HearWeave HearWeave Public

    Smart wearable microphone-array toolkit for beamforming, binaural enhancement, simulation, and localization.

    Python

  4. 5G-Diagnostic-Agent 5G-Diagnostic-Agent Public

    5G multi-turn diagnosis agent with evidence-gated tools, step rewards, LoRA-GRPO configuration and layered evaluation

    Python

  5. agent-hard-way agent-hard-way Public

    逐章手写 Learn Agent the Hard Way(Go):从一次 API 调用到生产级 harness,每章 go build 验证

    Go

  6. BLM-Multimodal-Audit BLM-Multimodal-Audit Public

    Multimodal audit: dynamic visual encoding, fine-grained alignment, reviewed data production and GRPO training

    Python