Agent-uibk

-72.41-3987e5)

About Agent-UIBK-v1

A deep-research agent does not search with a user's question. It searches with short queries it writes itself, in the middle of a long chain of reasoning, and the reasoning says what the agent is actually looking for. Agent-UIBK-v1 is a retriever for that setting. It reads the agent's current reasoning together with its search query, in the format introduced by AgentIR:

Reasoning:

Query:

Agent-UIBK-v1 is UIBK-Embedding-v1 fine-tuned on agent search data. It keeps that model's design: one encoder holds a late-interaction (ColBERT) retriever and a dense retriever, and both are stored in one list of 128-dimensional vectors, so a plain MaxSim adds the two scores. It loads as a PyLate ColBERT model and works with any PLAID index.

Notably it:

• Reaches 72.41% accuracy on BrowseComp-Plus as the search tool of gpt-oss-120b (reasoning effort high) with search and get_document. That is on par with Agent-ModernColBERT (72.53, one question of 830 apart), the strongest gpt-oss-120b entry on the leaderboard.
• Reaches 64.10% with the search tool only, above Agent-ModernColBERT (63.86) and behind only AgentIR-4B (66.99), a model 22 times larger.
• Leads the retrieval-only BrowseComp-Plus leaderboard on 6 of its 8 metrics, with evidence nDCG@10 of 24.78 against 20.26 for Qwen3-Embedding-8B.
• Reads long inputs: queries (reasoning plus query) and documents up to 2048 tokens.

Results

BrowseComp-Plus: deep-research agent (gpt-oss-120b, reasoning effort high)

BrowseComp-Plus asks 830 hard questions over a fixed corpus of 100,195 web documents. The agent searches the corpus with the retriever (top 5 results, 512-token snippets) and, in the get_document setting, can also read a full document by its id. Accuracy is judged by Qwen3-32B.

The numbers of the other models are those of the BrowseComp-Plus leaderboard (October 2026). The UIBK rows are our own runs; the UIBK-Embedding-v1 row uses the tool-call repairs below but plain queries, as that model was not trained on reasoning.

How to read the comparison.

• Each row is a single run, and agent runs vary. Between two of our own fine-tunes with nearly equal quality, about 160 of the 830 questions were solved by one run and not by the other. Differences of one to three points between rows are therefore within run-to-run variation. The difference between Agent-UIBK-v1 and Agent-ModernColBERT with get_document is one question.

• Our runs use the benchmark's own agent client with two changes:

  • Reasoning-aware queries. The retriever receives "Reasoning: ...\n\nQuery: ..." as in AgentIR. This is the input the model was trained on.
  • Tool-call repairs. gpt-oss under vLLM 0.30 sometimes formats a tool call in a way the server does not parse. The client then executes such a message as the tool call, removes stray format tokens from tool names, and counts reasoning-only responses towards the iteration limit.

  The prompt, the tools, top-k, the snippet length and the judge are the benchmark's. AgentIR's own client patches similar vLLM issues. The repairs are not specific to this model: with UIBK-Embedding-v1 they moved accuracy from 56.57 (829 of 830 queries finished) to 59.04.

• Being able to read full documents matters more than any retriever difference here: get_document adds 8.3 points to Agent-UIBK-v1.

BrowseComp-Plus: retrieval only

Here the model ranks the corpus for the 830 plain questions, with no agent and no reasoning. Evidence documents are those needed to answer a question; gold documents also contain the answer.