On (2), I agree with you for local models. BUT, there are also the open source Chinese models accessible via open-router. Your argument ("don't hold a candle to SOTA models") does not hold if the comparison is between those.

On (1), I agree more with the grandparent than with your assessment. Yes, OpenAI and Anthropic are killing it for now, but the time horizon is very short. I use codex and claude daily, but it's also clear to me that open source is catching up quickly, both w.r.t. the models and the agentic harnesses.

My current approach to IP is trade secrets. If we publish, we are careful to avoid details that would make the techniques easy to productionize.

Comments URL: https://news.ycombinator.com/item?id=47271374

Points: 2

# Comments: 0

We support both commercial APIs and self-hosted options:

  - Cohere (rerank-english-v3.0, etc.)
  - Voyage AI (rerank-2.5)
  - Jina AI (jina-reranker-v3)

  - TEI - https://github.com/huggingface/text-embeddings-inference
  - vLLM - https://docs.vllm.ai/en/v0.8.1/serving/openai_compatible_server.html#rerank-api

  # Cohere
  goodmem reranker create \
    --display-name "Cohere" \
    --provider-type COHERE \
    --endpoint-url "https://api.cohere.com" \
    --model-identifier "rerank-english-v3.0" \
    --cred-api-key "YOUR_API_KEY"

  # Self-hosted TEI (e.g., BAAI/bge-reranker-v2-m3)
  goodmem reranker create \
    --display-name "TEI Local" \
    --provider-type TEI \
    --endpoint-url "http://localhost:8081" \
    --model-identifier "BAAI/bge-reranker-v2-m3"

  goodmem memory retrieve \
    --space-id  \
    --post-processor-interactive \
    "your query"

It's built on Postgres, which I know you said you left behind, but one of the cool features it supports is hybrid search over multiple vector representations of a passage, so you can do a dense (e.g. nomic) and sparse (e.g. splade) search. Reranking is also built in, although it lacks automatic caching (since, in general, the corpus changes over time)

It also deploys to fly.io/railway and costs a few bucks a month to run if you're willing to use cloud-hosted embedding models (otherwise, you can run TEI/vLLM on CPU or GPU for the setup you described).

I hope it's helpful to someone.

https://dorrit.pairsys.ai/

> This benchmark evaluates the ability of multimodal language models to interpret handwritten editorial corrections in printed text. Using annotated scans from Charles Dickens' "Little Dorrit," we challenge models to accurately capture human editing intentions.

Curious to hear thoughts from others working on similar problems.

Comments URL: https://news.ycombinator.com/item?id=43641115

Points: 1

# Comments: 2

But to your point, note that in 2020 neuroscientists introduced the Tolman-Eichenbaum Machine (TEM) [1], a mathematical model of the hippocampus that bears a striking resemblance to transformer architecture.

Artem Kirsanov has a very nice piece on TEM, "Can we Build an Artificial Hippocampus?" [2] The link is directly to the spot where he makes the connection to transformers, although you should watch the whole video for context.

Because I wasn't clear on the chronology, I went back and asked one of the "Attention" authors whether mathematical models of the hippocampus inspired their paper? His answer was "no". If TEM was developed without pre-knowledge of transformers, then it's a very deep result IMHO.

[1] https://www.sciencedirect.com/science/article/pii/S009286742...

[2] https://www.youtube.com/watch?v=cufOEzoVMVA&t=1254s

As others have pointed out, self-attention was already a known concept in the research community. They don't claim to have invented that. Rather, the authors began by looking at how to improve the power of feed-forward neural networks using a combination of techniques, obtained some exciting results, and then, in the course of ablation studies, discovered that attention was really all you needed!

The title is a play on the Beatles song, "All You Need Is Love".

In terms of expository style, the paper that was most helpful for me was [Formal Algorithms for Transformers](https://arxiv.org/abs/2207.09238) by Phuong and Hutter. Written for clarity and with an emphasis on precision, the motivation section (Section 2) of the paper does a great job of explaining deficiencies in the original paper and subsequent ones.

BM25 presents a challenging cross-domain benchmark, and it wasn't till ~2022 that neural methods overtook it. If memory serves, it was the sparse neural methods like Splade, although recent dense models can also beat it.

The caveat is that BEIR is suffering from overfitting at this point.

In my experience, HNSW indexes are very expensive to build, relative to indexes like IVF. They also have a larger memory footprint. IVF, on the other hand, is pretty trivial to parallelize across multiple machines, and while I'm aware there are techniques for doing that with HNSW, I don't know the details well enough.

Also, if you review papers like "SOAR: Improved Quantization for Approximate Nearest Neighbor Search", they hint at some of the throughput barriers faced by graph-based methods like HNSW.

"However, fine-tuning on relevant, high quality, knowledge-rich question/answer pairs seems dominant, when such examples are available or can be generated."

How does one solve the problem of access-controlled data, if not through RAG? Do you imagine a separate version of the LLM for every user, reflecting their unique permissions on the data?

Also, in scenarios where the data is being updated regularly, RAG provides much lower latency to the new information. Deletes also present a challenge for a pure-LLM approach.

Disclosure: I am one of the founders of Vectara and head research there.

On the other hand, GTR-XXL is an example of a research model that biases in favor of search relevance, at the expense of latency. It's not really practical to deploy in production environments as a result.

However, instead of simply being a post-processing step at the end of an IR pipeline, LLMs will eventually sandwhich the IR system, along the lines of the [Demonstrate, Search, Predict framework](https://arxiv.org/abs/2212.14024) by Khattab et al.

Great work, and thank you for your contributions.

I know that Cruikshank was the original illustrator of many of Dickens's novels, but I prefer the artwork of James Mahoney. As a point of comparison, the same scene by both artists:

1. "Oliver Rather Astonishes Noah", https://imgur.com/a/DWeblXT, as illustrated by James Mahoney.

2. "Oliver Plucks up a Spirit", https://www.charlesdickensillustration.org/oliver-twist?pgid..., as illustrated by George Cruikshank.

I scanned and vectorized all the artwork for "Oliver Twist" and typeset it at http://ahmadsoft.org/downloads/Oliver%20Twist,%20or,%20The%2... (warning, it's a large PDF due to the high resolution vector imagery), but then I started a company in 2020, and haven't had time to finish "Little Dorit", which, compared to "Oliver Twist", I was able to scan at a much higher resolution and get better quality.