SGI Deep Research

SGI Deep Research (arxiv) — “Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows”; AgentCompass uses the SGI Deep Research subset. It evaluates a deep-research agent on scientific-research questions that require multi-step web retrieval and evidence gathering to produce a detailed research answer, which an LLM judge then grades as correct or not against the ground truth.

Like BrowseComp, SGI Deep Research uses single-sided judging. The judge only compares the agent-under-test’s answer against the ground truth, without comparing to any baseline. Both inference and judging run in the local process (host_process) — the harness first drives the model under test through the search loop to produce a final answer, then the judge model grades it.

How it works

An SGI Deep Research run has two stages — inference and judging.

Inference and judging

• Inference. The model under test acts as a research agent and, driven by the harness (default naive_search_agent), completes multi-turn tool loops such as search / visit per task, gathering evidence across sources to produce a detailed natural-language research answer.
• Judging. The judge model (judge_model) receives “question + ground truth + answer under test” and grades it with the built-in A/B/C protocol. The judge compares only the final answer, ignoring reasoning and formatting differences; equivalent expressions are accepted. The judge and the model under test are two separate endpoints; judge_model must be specified explicitly.

The A/B/C verdict

The judge returns exactly one verdict, and only A counts as correct:

• A — CORRECT: the answer semantically matches the ground truth (equivalent expressions and formatting allowed).
• B — INCORRECT: any deviation from the ground truth.
• C — INCOMPLETE / REPETITIVE / REFUSAL: an invalid answer (cut off mid-sentence, looping repetition, or an explicit refusal).

Parameters

Pass a JSON object via --benchmark-params '{...}', or a benchmark.params block in the YAML given to --config; the CLI wins on shared keys. See the Benchmark overview for merge precedence.

Parameter reference

Parameter	Type	Default	Choices / values	Description
judge_model	dict	null	{id, base_url, api_key, api_protocol, params}	Judge model spec, required (see Judge model spec). It decides grading, and is not the CLI —model-*.
category	string / list	”all"	"all”, life, earth, material, physics, mathematics, neuroscience, information, astronomy, chemistry, energy	Filter tasks by category; “all” = no filter, a list takes the union. Task counts by category — life (87), earth (54), material (38), physics (32), mathematics (25), neuroscience (24), information (20), astronomy (17), chemistry (11), energy (10); 318 in total.

Shared parameters such as k, avgk, and sample_ids follow the conventions in Benchmark Parameters.

Judge model spec

judge_model is passed as a dict: {"id","base_url","api_key","api_protocol","params"}, pointing to the judge model’s own endpoint, with inference parameters under params.

We recommend fixing a single judge across all models under test. Grading directly decides the scores, so switching judges makes scores no longer comparable across models; likewise, the model under test should not serve as its own judge, as that is neither fair nor comparable. The judge need not be especially strong — the A/B/C criterion (semantic match) is relatively objective, so a mid-sized model suffices. AgentCompass recommends Qwen3.6-35B-A3B.

Run examples

The SGI Deep Research run command takes the form agentcompass run sgi_deep_research <harness> <model>, whose three positional arguments are:

• sgi_deep_research — the benchmark id;
• <harness> — the harness that drives the model under test through the search loop, defaulting to naive_search_agent; its own configuration is passed via --harness-params;
• <model> — the model under test, i.e. the agent that performs retrieval and answering; its access credentials are passed via --model-base-url / --model-api-key.

Run configuration is split into two JSON blocks: --benchmark-params carries benchmark-level configuration (judge model, data filtering; see the Parameter reference above), and --harness-params carries the naive_search_agent harness’s own configuration (enabled tools, Serper / Jina keys, iterations, timeout, etc.; see the full list in NaiveSearchAgent harness). Both can also be written into the benchmark.params / harness.params blocks of --config, with the CLI winning on shared keys.

In the examples below, --harness-params always passes the Serper and Jina keys required for retrieval directly via serper_api_key / jina_api_key (the search / visit tools of naive_search_agent depend on them); the examples differ only in --benchmark-params.

Smoke test (single task end-to-end)

Use sample_ids to evaluate a single task, verifying that the end-to-end inference and judging flow works; defaults for the rest.

agentcompass run \
  sgi_deep_research \
  naive_search_agent \
  "$MODEL_NAME" \
  --env host_process \
  --benchmark-params '{
    "judge_model": {"id": "Qwen3.6-35B-A3B", "base_url": "https://your-judge-endpoint/v1", "api_key": "sk-…"},
    "sample_ids": ["1"]
  }' \
  --harness-params '{
    "serper_api_key": "your-serper-key",
    "jina_api_key": "your-jina-key"
  }' \
  --model-base-url "$MODEL_BASE_URL" \
  --model-api-key "$MODEL_API_KEY" \
  --model-api-protocol openai-chat

Custom parameters

Evaluate only a subset of categories to focus analysis on a specific domain; also demonstrates narrowing the toolset and iterations in --harness-params.

agentcompass run \
  sgi_deep_research \
  naive_search_agent \
  "$MODEL_NAME" \
  --env host_process \
  --benchmark-params '{
    "judge_model": {"id": "Qwen3.6-35B-A3B", "base_url": "https://your-judge-endpoint/v1", "api_key": "sk-…"},
    "category": ["physics", "biology"]
  }' \
  --harness-params '{
    "tools": ["search", "visit"],
    "max_iterations": 40,
    "serper_api_key": "your-serper-key",
    "jina_api_key": "your-jina-key"
  }' \
  --model-base-url "$MODEL_BASE_URL" \
  --model-api-key "$MODEL_API_KEY" \
  --model-api-protocol openai-chat \
  --task-concurrency 16

AgentCompass recommended config

Evaluate the full task set. --benchmark-params only needs the judge model judge_model; use --task-concurrency to raise cross-task concurrency.

agentcompass run \
  sgi_deep_research \
  naive_search_agent \
  "$MODEL_NAME" \
  --env host_process \
  --benchmark-params '{
    "judge_model": {"id": "Qwen3.6-35B-A3B", "base_url": "https://your-judge-endpoint/v1", "api_key": "sk-…"}
  }' \
  --harness-params '{
    "serper_api_key": "your-serper-key",
    "jina_api_key": "your-jina-key"
  }' \
  --model-base-url "$MODEL_BASE_URL" \
  --model-api-key "$MODEL_API_KEY" \
  --model-api-protocol openai-chat \
  --task-concurrency 16

Outputs

A run produces two kinds of results, both under results/sgi_deep_research/<model>/<run>/: aggregate metrics (summary.md, overall performance) and per-task details (details/, per-task grading).

Aggregate metrics (summary.md)

summary.md summarizes the overall performance of the run, in two parts — a run overview and the metrics.

Run overview

Field	Meaning
Model	The model-under-test id
Total	The total number of loaded tasks
Evaluated	The number of tasks evaluated (should normally equal Total)
Error	The number of tasks that errored during running or judging (RUN_ERROR); a value greater than 0 means those tasks produced no valid grading and need investigation

Metrics

There is a single headline metric, accuracy: the share of tasks judged correct. A task counts as correct (scored 1, otherwise 0) if and only if the judge returns verdict A; accuracy is the average over all tasks.

Per-task details (details/)

Each task has one JSON file, in which the judge’s grading for the task is recorded under the extra.scoring field:

Field	Meaning
evaluation_type	Fixed as llm_judge
correct	Whether the task is finally judged correct (judge verdict A)
model_answer	The final answer produced by the model under test
ground_truth	The reference answer

Only the parsed verdict is persisted here; the answer under test and ground truth are kept for tracing, while the full trajectory is written alongside in the same task file.