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Oleg Maslov
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{"instance_id": "locomo_conv-43_q155", "cell": "agentic", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 23121, "prompt_tokens": 457, "completion_tokens": 791, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F2\",\n \"rationale\": \"The model's answer 'great' reflects part of John's sentiment ('It was great!') but omits the required ground-truth fact that it was 'a huge success,' so it is incomplete.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model's answer 'great' reflects part of John's sentiment ('It was great!') but omits the required ground-truth fact that it was 'a huge success,' so it is incomplete.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
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{"instance_id": "locomo_conv-50_q015", "cell": "agentic", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 29466, "prompt_tokens": 556, "completion_tokens": 1024, "raw_text": "We are asked: \"Why does Dave regularly visit parks?\" Ground truth: \"because it relaxes and calms him\". Model's answer: \"to relax and recharge\". We need to determine if correct. The model's answer essentially says to relax and recharge. Ground truth says relaxes and calms him. \"Recharge\" is similar to calm? Recharge implies restore energy, not necessarily calm. The context says parks are calming, peaceful, relax. \"Recharge\" might be a slight addition but is it factually correct? The context: \"I've been spending lots of time at this beautiful park lately - so calming.\" and \"I've been exploring some parks on the weekends to relax - it's so peaceful\". The ground truth explicitly says \"because it relaxes and calms him\". The model adds \"recharge\" which is not directly in the ground truth, but does that make it incorrect? \"Recharge\" might be considered synonymous with relax/restore energy, but it's not exactly the same as \"calms\". The ground truth says \"relaxes and calms\". If the model says \"relax and recharge\", is that a partial fact? It includes relax, which is correct, but \"recharge\" is an extra claim that may not be present. The question: is \"recharge\" factually correct based on the context? The context mentions relaxing, calming, peaceful. Recharge is not mentioned. However, it's a plausible inference. The rubric: \"Extra detail is acceptable ONLY if it is factually correct.\" So if \"recharge\" is not in the ground truth supporting context, is it factually correct? The ground truth supporting context does not say anything about recharging. But is it a false claim? The model might be adding something that is not supported. It's not false, but it's an extrapolation. The instruction: \"correct\" means model's answer contains all required facts from ground truth, with no additional incorrect claims. So we need to see if \"to relax and recharge\" contains the required fact: the reason is to relax and calm. The model missed \"calms\" and added \"recharge\". So it doesn't contain all required facts (missing \"calms\") and it has an extra claim \"recharge\" which is not in ground truth. But is \"recharge\" incorrect? Not necessarily incorrect, but if it's not in ground truth, it's an extra detail that might be considered a fabrication if it's not supported by context. The context doesn't mention recharge, so the model is claiming something not in the context. That might be hallucination. However, \"recharge\" could be seen as synonymous with \"relax\" in common usage. The decision tree: if model relies on entities, names, dates, or claims that do NOT appear in the ground-truth context (fabrication) -> F4. The claim \"recharge\" is a claim that does not appear in the ground-truth context. So it might be F4. But it also missed \"calms\", so it could be partial (F2) if it states some required facts but misses others, without stating any incorrect facts. But here it added \"recharge\", which is not a required fact. If \"recharge\" is considered incorrect, then it's F3. But is \"recharge\" incorrect? Not necessarily. It's a minor synonym for relaxation, maybe. But the ground truth says \"relaxes and calms him\". The model says \"relax and recharge\". It didn't mention calm. So it missed a fact. So it's partial: it correctly states relax, but omits calms, and adds recharge. But F2 requires \"model correctly state SOME required facts but miss others, without stating any incorrect facts.\" If recharge is not incorrect, then it qualifies for F2. If recharge is incorrect (i.e., not supported and might be considered a claim not in context), then it might be F4. But F4 says \"rely on entities, names, dates, or claims that do NOT appear in the ground-truth context\". Recharge is a claim not in context. So that's F4. The tree: first F1 no, F5 off-topic no, F4 fabrication? Yes, it uses a claim \"recharge\" that is not in the context. But is it a fabrication? The model might be summarizing the idea of relaxing, and \"recharge\" is a common associated word. Usually, if it's a minor wording variation and still essentially correct, we'd mark correct. The ground truth is a short phrase: \"because it relaxes and calms him\". The model answer \"to relax and recharge\" is very similar. \"Recharge\" might be considered a synonym for rejuvenate, but the ground truth emphasizes calm. Is the model answer correct? I think many would consider \"to relax and recharge\" as capturing the same idea. The context says \"relax", "parsed_verdict": null, "parsed_failure_mode": null, "parsed_rationale": null, "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
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{"instance_id": "locomo_conv-43_q155", "cell": "full-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 15408, "prompt_tokens": 457, "completion_tokens": 523, "raw_text": "{\n \"verdict\": \"correct\",\n \"failure_mode\": null,\n \"rationale\": \"The model's answer 'great' accurately reflects John's positive feelings from the context, where he states 'It was great!'\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'great' accurately reflects John's positive feelings from the context, where he states 'It was great!'", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
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{"instance_id": "locomo_conv-50_q015", "cell": "full-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 11517, "prompt_tokens": 554, "completion_tokens": 387, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F2\",\n \"rationale\": \"The model states that Dave visits parks to relax, but misses the ground-truth detail that it also calms him.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model states that Dave visits parks to relax, but misses the ground-truth detail that it also calms him.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
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{"instance_id": "locomo_conv-43_q155", "cell": "oracle-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 29140, "prompt_tokens": 457, "completion_tokens": 1024, "raw_text": "We are asked: \"How did John feel after being able to jog without pain?\" Ground truth: \"It was a huge success.\" That is from the context: \"John: It was great! After being out for so long, jogging without any pain was a huge success.\" So the feeling is \"great\" and it was a huge success. Model answer: \"Great\". That captures the feeling \"great\". The ground truth answer only says \"It was a huge success.\" But the question is \"How did John feel?\" In the context, John says \"It was great! ... a huge success.\" So the feeling is \"great\". The model's answer \"Great\" is a synonym for \"great\". So it's correct. However, the ground truth answer is \"It was a huge success.\" That might be a paraphrase, but the question is about feeling. The ground-truth answer might be incomplete since it doesn't mention \"great\". But our task is to compare model's answer to ground truth. The ground truth provided is \"It was a huge success.\" That's from the excerpt. But the model's answer \"Great\" is directly from John's words \"It was great!\" Is \"Great\" a correct answer given the ground truth? The ground truth answer is \"It was a huge success.\" That's not exactly \"great\" but both indicate positive feeling. Could this be considered a partial? The ground truth answer is a single sentence. The model says \"Great\". Does it contain all required facts from ground truth? The ground truth says \"It was a huge success.\" The model says \"Great\". They are different. But the question is \"How did John feel?\" The ground truth answer is a rephrasing of his feeling? Actually, the supporting context says: \"It was great! ... jogging without any pain was a huge success.\" So John felt great, and it was a success. The ground truth answer is \"It was a huge success.\" That might be seen as an indirect answer: he felt it was a huge success, i.e., he felt successful, great. The model's answer \"Great\" captures the direct emotional descriptor. It might be considered equivalent? However, the ground truth is specifically \"It was a huge success.\" If the ground truth answer is that, then the model's answer \"Great\" might not contain the phrase \"huge success\". But the question asks \"How did John feel?\" The answer could be \"Great\" or \"It was a huge success\" both being acceptable summaries. But to be \"correct\", the model's answer must contain all required facts from ground truth. Here, ground truth fact: \"It was a huge success.\" Model says \"Great\". Is \"Great\" the same fact? Not exactly. The facts: John felt it was a huge success. The model says he felt great. Both are positive, but are they interchangeable? In the context, he said \"It was great!\" So \"great\" is literally his feeling. The ground truth answer might be a poor selection; but we are to evaluate if model's answer matches the ground truth. Since the ground truth answer is \"It was a huge success,\" the model's answer \"Great\" is not a paraphrase of that. It's a different statement. It doesn't include the notion of success. So it might be considered partial: it gives some of the feeling (great) but misses the explicit \"huge success\" part. However, the ground truth is just one sentence. If the model's answer is \"Great\", it doesn't contain the fact that it was a huge success. So is that a partial omission? The question: \"How did John feel?\" The ground truth says \"It was a huge success.\" That is a statement about the event, not a feeling. But they use that as the answer. If we strictly compare, the model's answer does not contain the ground truth fact. However, \"It was a huge success\" implies feeling successful, which is similar to great. But we need to apply the decision tree. The model is not refusing (F1), not off-topic (F5). Does it fabricate? No, \"Great\" is in the context. So F4 not. Does it correctly state SOME required facts but miss others, without stating any incorrect facts? It states he felt \"Great\". Does it miss the \"huge success\"? That might be missing the fact that he considered it a huge success. But the ground truth answer is just \"It was a huge success.\" The model answer is \"Great\". They are different facts. The model's answer doesn't contain the ground truth fact. Therefore, it might be incorrect (F3) if it gets the fact wrong. But it doesn't get it wrong; it just gives a different correct fact. The question is about feeling, the context says \"It was great!\" and \"a huge success.\" Both describe his feeling/reaction. So if the ground truth answer only extracted \"It was a huge", "parsed_verdict": null, "parsed_failure_mode": null, "parsed_rationale": null, "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
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{"instance_id": "locomo_conv-47_q092", "cell": "oracle-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 21200, "prompt_tokens": 460, "completion_tokens": 767, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F2\",\n \"rationale\": \"The model correctly identifies that John created an application, but omits the required detail that it is a computer application on smartphones, as specified in the ground truth.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identifies that John created an application, but omits the required detail that it is a computer application on smartphones, as specified in the ground truth.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
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{"instance_id": "locomo_conv-50_q015", "cell": "oracle-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 7473, "prompt_tokens": 554, "completion_tokens": 198, "raw_text": "{\n \"verdict\": \"correct\",\n \"failure_mode\": null,\n \"rationale\": \"The model's answer 'To relax' is semantically equivalent to the ground-truth reason that visiting parks relaxes and calms him.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'To relax' is semantically equivalent to the ground-truth reason that visiting parks relaxes and calms him.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
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