WebSailor: Navigating Super-human Reasoning for Web Agent
Paper
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2507.02592
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Published
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67
Jaward Sesay
Jaward
AI & ML interests
Building Lectūra AI | CS Grad Student @BIT | AI/ML Research: Autonomous Agents, LLMs | First Paper (AutoAgents: A Framework for Automatic Agent Generation) Accepted @ IJCAI 2024 | Role Model Karpathy
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WebSailor: Navigating Super-human Reasoning for Web Agent
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5 days ago
I played around with the new RXTX paper (XX^T) and was able to train nanogpt with 4x4 RXTX matmuls in both attention layer and optimizer🤕
It just works (well I had to add some guardrails) but still saves 5% of memory usage:
The Patch:
- Computes attention scores with a 4x4 blockwise RXTX matmuls (no pytorch dot prod)
- Handles arbitrary sequence lengths by padding to the nearest multiple of 4.
- An RXTX variant of shampoo with params reshaped into 4x4 blocks during each optimizer step.
- Uses 5% less ops
Code: https://github.com/Jaykef/ai-algorithms/blob/main/nanogpt-rxtx.ipynb
Paper: https://arxiv.org/pdf/2505.09814
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1965
I played around with the new RXTX paper (XX^T) and was able to train nanogpt with 4x4 RXTX matmuls in both attention layer and optimizer🤕
It just works (well I had to add some guardrails) but still saves 5% of memory usage:
The Patch:
- Computes attention scores with a 4x4 blockwise RXTX matmuls (no pytorch dot prod)
- Handles arbitrary sequence lengths by padding to the nearest multiple of 4.
- An RXTX variant of shampoo with params reshaped into 4x4 blocks during each optimizer step.
- Uses 5% less ops
Code: https://github.com/Jaykef/ai-algorithms/blob/main/nanogpt-rxtx.ipynb
Paper: https://arxiv.org/pdf/2505.09814
It just works (well I had to add some guardrails) but still saves 5% of memory usage:
The Patch:
- Computes attention scores with a 4x4 blockwise RXTX matmuls (no pytorch dot prod)
- Handles arbitrary sequence lengths by padding to the nearest multiple of 4.
- An RXTX variant of shampoo with params reshaped into 4x4 blocks during each optimizer step.
- Uses 5% less ops
Code: https://github.com/Jaykef/ai-algorithms/blob/main/nanogpt-rxtx.ipynb
Paper: https://arxiv.org/pdf/2505.09814
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9 days ago
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2289
Mind2Web 2 is out - this time featuring eval and benchmark for deep research🔥
Paper: Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge (2506.21506)
Project: https://osu-nlp-group.github.io/Mind2Web-2/
Paper: Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge (2506.21506)
Project: https://osu-nlp-group.github.io/Mind2Web-2/
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12 days ago
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3406
Awesome intro to LLM course "Language Modeling from Scratch" by stanford. love the aesthetics behind the lecture notes, notes-in-code genius idea👍
Course site: https://stanford-cs336.github.io/spring2025/
Repo: https://github.com/stanford-cs336/spring2025-lectures
Videos: https://www.youtube.com/playlist?list=PLoROMvodv4rOY23Y0BoGoBGgQ1zmU_MT_
Course site: https://stanford-cs336.github.io/spring2025/
Repo: https://github.com/stanford-cs336/spring2025-lectures
Videos: https://www.youtube.com/playlist?list=PLoROMvodv4rOY23Y0BoGoBGgQ1zmU_MT_
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17 days ago
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not sure of what to make of this but solving autonomous/selective reflection seems like a big deal in current agent frameworks. We did hit on this with iterative self-refinement in our AutoAgents framework (https://ijcai.org/proceedings/2024/0003.pdf). Nice read, looking forward to the code.
Paper: Scaling Test-time Compute for LLM Agents (2506.12928)
Paper: Scaling Test-time Compute for LLM Agents (2506.12928)
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will cook a deep dive tutorial on dfms sometime next week, the math is nolonger scary after taking this course:)
https://diffusion.csail.mit.edu/
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an
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22 days ago
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1388
You can now edit operations with a discrete flow model, supercool👍! It's amazing to see the progress on DFM within one year since its introduction - literally my litmus test for how fast the field is progressing:
1st Introduced (2024): https://arxiv.org/abs/2402.04997
Discrete Flow Matching (2024): https://arxiv.org/abs/2407.15595
Edit Discrete Flow (2025): https://arxiv.org/pdf/2506.09018
Looking forward to a SaaS level reach like that of dLLMs e.g Mercury by inception labs 🚀
1st Introduced (2024): https://arxiv.org/abs/2402.04997
Discrete Flow Matching (2024): https://arxiv.org/abs/2407.15595
Edit Discrete Flow (2025): https://arxiv.org/pdf/2506.09018
Looking forward to a SaaS level reach like that of dLLMs e.g Mercury by inception labs 🚀
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bumped into one of the OG reads today!! handwriting generation & synthesis is still my favorite application of RNNs - supper amazed at how such a small model (3.6M params), trained overnight on cpu could reach such peak performance. Huge credit to the data (IAM-OnDB🔥) which was meticulously curated using an infra-red device to track pen position.
Try demo here: https://www.calligrapher.ai/
Code: https://github.com/sjvasquez/handwriting-synthesis
Try demo here: https://www.calligrapher.ai/
Code: https://github.com/sjvasquez/handwriting-synthesis
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