AI research often requires decisions before future evidence exists: which bottleneck to attack, which direction to pursue, or where a project should be positioned. We introduce ForeSci, a temporally controlled benchmark for evaluating whether LLM agents can make such forward-looking research judgements from historical evidence. </p>\n","updatedAt":"2026-06-05T12:53:05.074Z","author":{"_id":"64c939307dba66c3a7e4d215","avatarUrl":"/avatars/b4c7f43b47db93ca5d7aa30e3d9ef80e.svg","fullname":"BruceLyu","name":"brucelyu","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":1,"isUserFollowing":false}},"numEdits":1,"identifiedLanguage":{"language":"en","probability":0.8598677515983582},"editors":["brucelyu"],"editorAvatarUrls":["/avatars/b4c7f43b47db93ca5d7aa30e3d9ef80e.svg"],"reactions":[],"isReport":false}},{"id":"6a237b95bddb7b055313a7b9","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":362,"isUserFollowing":false},"createdAt":"2026-06-06T01:44:53.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?](https://huggingface.co/papers/2605.19196) (2026)\n* [TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning](https://huggingface.co/papers/2606.01498) (2026)\n* [ProofAgent Harness: Open Infrastructure for Adversarial Evaluation of AI Agents](https://huggingface.co/papers/2605.24134) (2026)\n* [Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?](https://huggingface.co/papers/2604.10547) (2026)\n* [AION: Next-Generation Tasks and Practical Harness for Time Series](https://huggingface.co/papers/2605.25045) (2026)\n* [Harnessing Pre-Resolution Signals for Future Prediction Agents](https://huggingface.co/papers/2604.15719) (2026)\n* [Ambig-DS: A Benchmark for Task-Framing Ambiguity in Data-Science Agents](https://huggingface.co/papers/2605.09698) (2026)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"<p>This is an automated message from the <a href=\"https://huggingface.co/librarian-bots\">Librarian Bot</a>. I found the following papers similar to this paper. </p>\n<p>The following papers were recommended by the Semantic Scholar API </p>\n<ul>\n<li><a href=\"https://huggingface.co/papers/2605.19196\">Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2606.01498\">TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.24134\">ProofAgent Harness: Open Infrastructure for Adversarial Evaluation of AI Agents</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.10547\">Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.25045\">AION: Next-Generation Tasks and Practical Harness for Time Series</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.15719\">Harnessing Pre-Resolution Signals for Future Prediction Agents</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.09698\">Ambig-DS: A Benchmark for Task-Framing Ambiguity in Data-Science Agents</a> (2026)</li>\n</ul>\n<p> Please give a thumbs up to this comment if you found it helpful!</p>\n<p> If you want recommendations for any Paper on Hugging Face checkout <a href=\"https://huggingface.co/spaces/librarian-bots/recommend_similar_papers\">this</a> Space</p>\n<p> You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: <code><span class=\"SVELTE_PARTIAL_HYDRATER contents\" data-target=\"UserMention\" data-props=\"{"user":"librarian-bot"}\"><span class=\"inline-block\"><span class=\"contents\"><a href=\"/librarian-bot\">@<span class=\"underline\">librarian-bot</span></a></span> </span></span> recommend</code></p>\n","updatedAt":"2026-06-06T01:44:53.975Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":362,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.761619508266449},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2606.00644","authors":[{"_id":"6a22c60c76dea4a01ef147d7","name":"Qiuyu Tian","hidden":false},{"_id":"6a22c60c76dea4a01ef147d8","name":"Haojie Yin","hidden":false},{"_id":"6a22c60c76dea4a01ef147d9","name":"Yingce Xia","hidden":false},{"_id":"6a22c60c76dea4a01ef147da","name":"Youyong Kong","hidden":false},{"_id":"6a22c60c76dea4a01ef147db","name":"Zequn Liu","hidden":false}],"publishedAt":"2026-06-04T00:00:00.000Z","submittedOnDailyAt":"2026-06-05T00:00:00.000Z","title":"ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment","submittedOnDailyBy":{"_id":"64c939307dba66c3a7e4d215","avatarUrl":"/avatars/b4c7f43b47db93ca5d7aa30e3d9ef80e.svg","isPro":false,"fullname":"BruceLyu","user":"brucelyu","type":"user","name":"brucelyu"},"summary":"AI research often requires decisions before future evidence exists: which bottleneck to attack, which direction to pursue, or where a project should be positioned. We introduce ForeSci, a temporally controlled benchmark for evaluating whether LLM agents can make such forward-looking research judgements from historical evidence. ForeSci contains 500 tasks across four fast-moving AI domains and four decision families. Each task is paired with a cutoff-aligned offline knowledge base; post-cutoff papers are hidden during generation and used only for validation. To avoid random future-event prediction, tasks are derived from pre-cutoff taxonomy branches and evidence signals, and answer-generation backbones are selected to precede the task cutoffs. We evaluate native LLMs, Hybrid RAG, and three research-agent adaptations across four backbones. Results show that explicit evidence organization improves traceability and factual support, but gains depend strongly on the decision family. Diagnostics reveal a recurring evidence-decision decoupling: agents may cite relevant evidence while forecasting the wrong research object. ForeSci turns forward-looking AI research judgement into a controlled benchmark for evaluating research agents as decision-making systems.","upvotes":0,"discussionId":"6a22c60c76dea4a01ef147dc","ai_summary":"ForeSci is a temporally controlled benchmark that evaluates LLM agents' ability to make forward-looking research decisions from historical evidence across fast-moving AI domains.","ai_keywords":["LLM agents","forward-looking research judgments","temporal control","benchmark evaluation","hybrid RAG","research-agent adaptations","decision-making systems"],"ai_summary_model":"Qwen/Qwen2.5-Coder-32B-Instruct"},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[],"acceptLanguages":["en"],"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2606/2606.00644.md"}">
ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment
Abstract
ForeSci is a temporally controlled benchmark that evaluates LLM agents' ability to make forward-looking research decisions from historical evidence across fast-moving AI domains.
AI research often requires decisions before future evidence exists: which bottleneck to attack, which direction to pursue, or where a project should be positioned. We introduce ForeSci, a temporally controlled benchmark for evaluating whether LLM agents can make such forward-looking research judgements from historical evidence. ForeSci contains 500 tasks across four fast-moving AI domains and four decision families. Each task is paired with a cutoff-aligned offline knowledge base; post-cutoff papers are hidden during generation and used only for validation. To avoid random future-event prediction, tasks are derived from pre-cutoff taxonomy branches and evidence signals, and answer-generation backbones are selected to precede the task cutoffs. We evaluate native LLMs, Hybrid RAG, and three research-agent adaptations across four backbones. Results show that explicit evidence organization improves traceability and factual support, but gains depend strongly on the decision family. Diagnostics reveal a recurring evidence-decision decoupling: agents may cite relevant evidence while forecasting the wrong research object. ForeSci turns forward-looking AI research judgement into a controlled benchmark for evaluating research agents as decision-making systems.
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AI research often requires decisions before future evidence exists: which bottleneck to attack, which direction to pursue, or where a project should be positioned. We introduce ForeSci, a temporally controlled benchmark for evaluating whether LLM agents can make such forward-looking research judgements from historical evidence.
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