Run step
Step 2: tool results
get_claim
Input
{
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"claim_id": "846a4d41-3752-45ed-9210-b4177d75b5e9"
}{"claim":{"id":"846a4d41-3752-45ed-9210-b4177d75b5e9","kind":"claim","canonical_form":"In API-only model diffing, an improved LLM baseline performs comparably to SAE-based methods and surfaces more abstract behavioral differences.","claim_type":"empirical_derived","state":"active","decomposition_status":"complete","importance":0.5,"contestation":0.45,"domains":[],"tags":[{"slug":"mechanistic-interpretability","name":"Mechanistic Interpretability"},{"slug":"large-language-models","name":"Large language models"},{"slug":"sparse-autoencoders","name":"Sparse Autoencoders"}],"steward_state":"running","stewarded_at":"2026-11-14T20:43:11.264Z","canonical_direction_note":"The affirmative form states the source's comparative finding about an improved API-only LLM baseline and SAE-based model-diffing methods.","created_at":"2026-11-13T22:50:23.580Z","updated_at":"2026-11-14T20:39:57.467Z","page_url":"https://minerval.ai/claims/846a4d41-3752-45ed-9210-b4177d75b5e9"},"assessment":null,"coverage":{"examined":false,"on":[],"note":"No judgment of this node is in force. That is not a finding either way."},"quantity":null,"threshold_of":null,"question":null,"formalization":null,"formalization_pending":null,"formalization_history":[],"verification":null,"lean_checks":[],"lean_checks_total":0,"waiting_messages":[{"trigger":"structure_and_assess","from":"the system","sender_node_id":null,"sent_at":"2026-11-13T22:50:25.216Z","paid":false,"summary":"New claim onboarded. Give it the structure and assessment its importance warrants; a minor or settled claim may need no decomposition."},{"trigger":"mandate_plan","from":"a mandate's plan","sender_node_id":null,"sent_at":"2026-11-14T20:39:57.468Z","paid":true,"summary":"First assessment. Read the model-diffing comparison behind this claim and the SAE-based method it is compared with. Establish: which model pairs, how differences were scored and by whom, what the impr…"}],"requests":[],"requests_total":0,"subclaim_count":0,"instance_count":1,"source_map":null,"subclaims":[],"subclaims_assessed":0,"arguments":[],"related":[],"unplaced_argument_passages":[{"instance_id":"0bffbda7-9cdc-424b-ae78-a76d5a0296e2","source":{"id":"a5178d56-97c3-499f-9ce3-1d7fe1dad10e","url":"https://arxiv.org/pdf/2602.10371","title":"Simple LLM Baselines are Competitive for Model Diffing"},"verbatim_text":"Across all three experiments, accuracy and frequency are comparable between the diffing meth-\nods (Figure 2), but the LLM-based approach consistently produces more abstract hypotheses.","context":"Results summary across the three experimental setups","stance":"advances","direction":"for"},{"instance_id":"c0f0f8e8-0dc5-48fa-90fa-399cd2787b7a","source":{"id":"a5178d56-97c3-499f-9ce3-1d7fe1dad10e","url":"https://arxiv.org/pdf/2602.10371","title":"Simple LLM Baselines are Competitive for Model Diffing"},"verbatim_text":"LLM distributions are concentrated at high abstraction levels\n(means between 3.8 and 4.3) with low variance, while SAE distributions are lower (means between\n2.4 and 3.5) with higher variance.","context":"Abstraction-level distributions across Qwen, Gemma, and Gemini experiments","stance":"advances","direction":"for"}],"unplaced_argument_passages_total":2}get_claim_instances
Input
{
"claim_id": "846a4d41-3752-45ed-9210-b4177d75b5e9"
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"verbatim_text": "Our results show that an improved\nLLM-based baseline performs comparably to the SAE-based method while typi-\ncally surfacing more abstract behavioral differences.",
"context": "Abstract and intro, summarising a comparison of API-only model diffing methods across three setups (Qwen emergent misalignment, Gemma hidden user-gender assumption, Gemini 2.5 Flash Lite revisions).",
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"source_title": "Simple LLM Baselines are Competitive for Model Diffing",
"source_type": "preprint",
"source_url": "https://arxiv.org/pdf/2602.10371",
"source_published_date": "2026-02-10",
"source_authors": [
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"Schrodi, Simon",
"Cywiński, Bartosz",
"Brox, Thomas",
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