Apple AI Chooses Mamba: Better Than Transformers for Agent Tasks
Apple’s AI: A Leap Beyond Transformers with Mamba + Tools
Everyone says Apple’s AI tends to lag “half a step” behind, but this time a new study goes straight for the heart of the Transformer architecture — with promising results.
Key finding: Mamba + Tools shows superior performance in agent-like scenarios.
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Study Overview
In the recent paper To Infinity and Beyond, researchers conclude:
> In long-horizon and multi-interaction, agent-style tasks, models based on the SSM (State Space Model) architecture — such as Mamba — show potential to surpass Transformers in both efficiency and generalization.
Lead author of Mamba explains:
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Understanding Mamba’s Edge
The Problem with Transformers
Transformers feature self-attention — enabling them to connect all tokens in a sequence and understand context deeply.
However, this comes with major drawbacks:
- Quadratic computation cost: Sequence length × sequence length in relationships.
- Example:
- 1,000 tokens → 1 million pairwise computations
- Tens of thousands of tokens → hundreds of millions — a GPU-heavy burden.
Consequences:
- High latency in long-sequence tasks.
- Inefficiency in agent-like tasks that need iterative decision-making and adaptability.
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Mamba’s Lightweight State Space Model
Mamba, an SSM variant, avoids global attention. Instead, it stores and updates an internal state — like keeping a diary of recent events without rereading old entries.
Advantages:
- Linear computation growth with sequence length.
- Streaming processing — handle data as it arrives.
- Stable memory footprint regardless of length.
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Limitation — and Apple’s Improvement
Weakness: Limited Internal Memory
In ultra-long sequences, early context can be overwritten due to capacity constraints.
Apple’s Remedy: External Tool Integration
By adding specialized tools, Mamba can extend its capabilities:
- Math pointer tools: Record numbers, track operations.
- Code debugging tools: View files repeatedly; run and verify code.
Result: Tools act as external memory + interactive interfaces.
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Performance Gains
With tools, Mamba achieves:
- Multi-digit addition:
- Pointer tool usage enables training on 5-digit sums and scaling to 1,000-digit sums with ~100% accuracy.
- Transformers show errors beyond 20-digit sums.
- Code debugging:
- Simulated workflows (view → edit → run → verify) yield better accuracy than Transformers, even on more complex codebases.
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Connecting to Broader AI Trends
Tool-augmented AI aligns with wider development in:
- Structured tool ecosystems
- Multi-modal input handling
- Collaborative agents
Platforms like AiToEarn官网 and AiToEarn开源地址 offer multi-platform publishing and monetization, enabling creators to distribute AI-driven outputs to:
- Douyin
- Kwai
- Bilibili
- Xiaohongshu
- Threads
- YouTube
- X (Twitter)
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Other Example: Tower of Hanoi
In step-by-step logic puzzles:
- Transformers: Intelligent but slow — prone to stalls in repeated adjustments.
- Mamba without tools: Fast reactive but weak memory.
- Mamba + tools: Gains quick reactions and strong recall — effectively handling more complex scenarios.
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Final Takeaway
Transformers excel in global context but struggle with efficiency in adaptive, interactive tasks.
Mamba + Tools combines fast state-based processing with external memory, resulting in superior agent-like performance and greater scalability.
References:
- Paper: https://arxiv.org/pdf/2510.14826
- Author comment: https://x.com/_albertgu/status/1980287154883358864
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For Developers & Creators
As AI agent architectures evolve, pairing reactive models like Mamba with specialized tools could redefine efficiency benchmarks.
Platforms such as AiToEarn官网 help creators:
- Experiment with such AI innovations.
- Publish across multiple platforms.
- Monetize results effectively.
If you’d like, I can also prepare a concise comparison table between Transformer vs Mamba + Tools for quick reference.
Would you like me to add that?
