Agent-ModernColBERT

About

What Agent-ModernColBERT is

[](https://lighton.ai) [](https://www.linkedin.com/company/lighton/) [](https://x.com/LightOnIO) Tl;Dr A few weeks ago, we evaluated Reason-ModernColBERT, a 150M late-interaction model trained on ReasonIR data nearly solved BrowseComp-Plus, reaching 87.56% accuracy with GPT-5 (a +7.59 absolute jump over the previous SOTA) while topping recall and calibration error, while not being trained for agentic retrieval at all (and being one year old). We now present Agent-ModernColBERT, a model specifically fine-tuned for agentic retrieval using the AgentIR dataset released alongside AgentIR. You can find the training boilerplate [here](https://github.com/lightonai/pylate/blob/main/examples/train/agentmoderncolbert.py ). This very lightweight fine-tuning adds increase the performance of Reason-ModernColBERT by another 10%, which allows, when exposing the getdocument function and the GPT-OSS-120B model, to beat the original GPT-5 + Qwen3-8B runs, while using a retriever model 54× smaller and an open source LLM. How it works Before issuing a query, deep research agents generate explicit reasoning traces describing what they're looking for and why. Conventional retrievers throw all of that away. The AgentIR paper introduced Reasoning-Aware Retrieval: instead of discarding those reasoning traces, concatenate them to the query you send to the retriever. To enable this, the authors generated trajectories from a deep-research agent and released them (the Tevatron/AgentIR-data dataset), then trained AgentIR-4B, a 4B dense model that beats much larger baselines including ReasonIR-8B and rerank pipelines. BrowseComp-Plus Results The getdocument column indicates whether the LLM was exposed to the possibility of using the getdocument function that allows, in addition to the usual search function that returns a snippet of the top-5 documents for a given query (the beginning of the document), to request the full content of a document using its id. It allows the model to get more information when needed, but can also add noise to the process if the model reads full documents that are irrelevant, and thus require a precise retrieval signal. A few things worth highlighting: - With the ge

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys agent-moderncolbert for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (agent-moderncolbert below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"agent-moderncolbert","input":"text to embed"}'

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