12 ms·

> For example, in a variant of environment TR87, Opus 4.6 scores 0.0% with no harness and 97.1% with the Duke harness (12), yet in environment BP35, Opus 4.6 s

> For example, in a variant of environment TR87, Opus 4.6 scores 0.0% with no harness and 97.1% with the Duke harness (12), yet in environment BP35, Opus 4.6 scores 0.0% under both configuration This is with a harness that has been designed to tackle "a small set of public environments: ls20, ft09, and vc33" (of the arc-agi-3 challenge), yet it looks like it does not solve the full arc-agi-3 benchmark, just some of them.

famouswaffles 6mo ago
The harness was designed with the preview, but no it was still tested on the full public set in that environment. You can run the benchmark in different 'environments' though it's unclear what the difference between them is. >We then tested the harnesses on the full public set (which researchers did not have access to at the time)
- daveguy 6mo ago
  It may have been tested on the full set, but the score you quote is for a single game environment. Not the full public set. That fact is verbatim in what you responded to and vbarrielle quoted. It scored 97% in one game, and 0% in another game. The full prelude to what vbarrielle quoted, the last sentence of which you left out, was: > We then tested the harnesses on the full public set (which researchers did not have access to at the time). We found extreme bimodal performance across the two sets, controlling for the same frontier model... The harness only transfers to like-environments and the intelligence for those specific games is baked into the harness by the humans who coded it for this specific challenge. The point of ARC-AGI is to test the intelligence of AI systems in novel, but simple, environments. Having a human give it more powerful tools in a harness defeats the purpose. You should go back and read the original ARC-AGI paper to see what this is about+. Are you upset about the benchmark because frontier LLM models do so poorly exhibiting the ability to generalize when the benchmarks are released? + https://arxiv.org/abs/1911.01547 https://arxiv.org/abs/1911.01547
  - fc417fc802 6mo ago
    > intelligence for those specific games is baked into the harness This is your claim but the other commenter claims the harness consists only of generic tools. What's the reality? I also encountered confusion about this exact issue in another subthread. I had thought that generic tooling was allowed but others believed the benchmark to be limited to ingesting the raw text directly from the API without access to any agent environment however generic it might be.
    - deleted 6mo ago
      [deleted]
    - daveguy 6mo ago
      1) Pointing out what tools to use is part of the intelligence that LLMs aren't great at. 2) one of the tools is a path finding algorithm. A big improvement/crutch over a regular LLM that has no such capability. You'd think if LLMs are intelligent they'd be able to determine that a path finding algorithm is necessary and have a sub agent code it up real quick. But apparently they just can't do that without humans stepping in to make it a standard tool for them. Here's the paper on what they did for the Duke Harness: https://blog.alexisfox.dev/arcagi3 https://blog.alexisfox.dev/arcagi3
      - famouswaffles 6mo ago
        >You'd think if LLMs are intelligent they'd be able to determine that a path finding algorithm is necessary and have a sub agent code it up real quick. ARC 3 doesn't allow that so. >Here's the paper on what they did for the Duke Harness: https://blog.alexisfox.dev/arcagi3 https://blog.alexisfox.dev/arcagi3 Yeah, and the tools are general, not 'baked into the harness by the humans who coded it for this specific challenge.'
        daveguy 6mo ago
        Adding a path finding algorithm and environment transform tools to a supposed "AGI", sure does seem like cheating to me. Sad part is, it's a cheat that only works on environments where pathfinding is a major part. And when it doesn't have those clues it bombs on everything. I guess you really want to love the current SOTA LLMs. It's a shame they're dumb af. Have a great day.

famouswaffles 6mo ago

The harness was designed with the preview, but no it was still tested on the full public set in that environment. You can run the benchmark in different 'environments' though it's unclear what the difference between them is. >We then tested the harnesses on the full public set (which researchers did not have access to at the time)

daveguy 6mo ago
It may have been tested on the full set, but the score you quote is for a single game environment. Not the full public set. That fact is verbatim in what you responded to and vbarrielle quoted. It scored 97% in one game, and 0% in another game. The full prelude to what vbarrielle quoted, the last sentence of which you left out, was: > We then tested the harnesses on the full public set (which researchers did not have access to at the time). We found extreme bimodal performance across the two sets, controlling for the same frontier model... The harness only transfers to like-environments and the intelligence for those specific games is baked into the harness by the humans who coded it for this specific challenge. The point of ARC-AGI is to test the intelligence of AI systems in novel, but simple, environments. Having a human give it more powerful tools in a harness defeats the purpose. You should go back and read the original ARC-AGI paper to see what this is about+. Are you upset about the benchmark because frontier LLM models do so poorly exhibiting the ability to generalize when the benchmarks are released? + https://arxiv.org/abs/1911.01547 https://arxiv.org/abs/1911.01547
- fc417fc802 6mo ago
  > intelligence for those specific games is baked into the harness This is your claim but the other commenter claims the harness consists only of generic tools. What's the reality? I also encountered confusion about this exact issue in another subthread. I had thought that generic tooling was allowed but others believed the benchmark to be limited to ingesting the raw text directly from the API without access to any agent environment however generic it might be.
  - deleted 6mo ago
    [deleted]
  - daveguy 6mo ago
    1) Pointing out what tools to use is part of the intelligence that LLMs aren't great at. 2) one of the tools is a path finding algorithm. A big improvement/crutch over a regular LLM that has no such capability. You'd think if LLMs are intelligent they'd be able to determine that a path finding algorithm is necessary and have a sub agent code it up real quick. But apparently they just can't do that without humans stepping in to make it a standard tool for them. Here's the paper on what they did for the Duke Harness: https://blog.alexisfox.dev/arcagi3 https://blog.alexisfox.dev/arcagi3
    - famouswaffles 6mo ago
      >You'd think if LLMs are intelligent they'd be able to determine that a path finding algorithm is necessary and have a sub agent code it up real quick. ARC 3 doesn't allow that so. >Here's the paper on what they did for the Duke Harness: https://blog.alexisfox.dev/arcagi3 https://blog.alexisfox.dev/arcagi3 Yeah, and the tools are general, not 'baked into the harness by the humans who coded it for this specific challenge.'
      - daveguy 6mo ago
        Adding a path finding algorithm and environment transform tools to a supposed "AGI", sure does seem like cheating to me. Sad part is, it's a cheat that only works on environments where pathfinding is a major part. And when it doesn't have those clues it bombs on everything. I guess you really want to love the current SOTA LLMs. It's a shame they're dumb af. Have a great day.