GIANTS-4B, trained with RL on a new 17k-example benchmark of parent-to-child paper insights, achieves 34% relative improvement over gemini-3-pro in LM-judge similarity and is rated higher-impact by a citation predictor.
Chenglei Si, Diyi Yang, and Tatsunori Hashimoto
11 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
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2026 11roles
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LLM-generated research ideas cluster more around bridge-like opportunities and synthesis methods than the broader distribution seen in human papers.
A new benchmark and clean-room harness show frontier AI agents reach only 0.337 factual F1 when synthesizing conclusions from scientific evidence.
The Divergent Remote Association Test (DRAT) is the first creativity test that significantly predicts LLMs' scientific ideation ability, unlike prior tests such as DAT or RAT.
A knowledge-first approach to LLM-driven automatic heuristic design in combinatorial optimization yields better discovery efficiency, transfer, and generalization than code-centric baselines by formalizing a distortion-compression trade-off.
SoundnessBench shows frontier LLMs exhibit pervasive optimism bias when rating the soundness of ML research proposals, frequently calling low-soundness ideas sound under standard prompts.
Intern-Atlas constructs a methodological evolution graph with 9.4 million edges from 1.03 million AI papers to capture how methods emerge, adapt, and transition, enabling better idea evaluation and generation for AI-driven research.
Small LMs reach 77.1% accuracy at comparative forecasting of research idea success on benchmarks after supervised fine-tuning, with RLVR yielding interpretable reasoning at 71.35%.
LLMs generate kitsch due to their training process, causing outputs to be perceived as kitschier than human-created works in controlled reader studies.
AI writing support reduces ownership most at drafting and least at planning, tracking how much text and ideas the AI contributes, while more AI help raises essay quality.
AI can generate research artifacts faster than it can verify them, so across all eight lifecycle stages the credible deployment mode is human-governed collaboration rather than full autonomy.
citing papers explorer
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GIANTS: Generative Insight Anticipation from Scientific Literature
GIANTS-4B, trained with RL on a new 17k-example benchmark of parent-to-child paper insights, achieves 34% relative improvement over gemini-3-pro in LM-judge similarity and is rated higher-impact by a citation predictor.
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Measuring the Gap Between Human and LLM Research Ideas
LLM-generated research ideas cluster more around bridge-like opportunities and synthesis methods than the broader distribution seen in human papers.
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Can AI Agents Synthesize Scientific Conclusions?
A new benchmark and clean-room harness show frontier AI agents reach only 0.337 factual F1 when synthesizing conclusions from scientific evidence.
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Assessing the Creativity of Large Language Models: Testing, Limits, and New Frontiers
The Divergent Remote Association Test (DRAT) is the first creativity test that significantly predicts LLMs' scientific ideation ability, unlike prior tests such as DAT or RAT.
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Back to the Beginning of Heuristic Design: Bridging Code and Knowledge with LLMs
A knowledge-first approach to LLM-driven automatic heuristic design in combinatorial optimization yields better discovery efficiency, transfer, and generalization than code-centric baselines by formalizing a distortion-compression trade-off.
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SoundnessBench: Can Your AI Scientist Really Tell Good Research Ideas from Bad Ones?
SoundnessBench shows frontier LLMs exhibit pervasive optimism bias when rating the soundness of ML research proposals, frequently calling low-soundness ideas sound under standard prompts.
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Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists
Intern-Atlas constructs a methodological evolution graph with 9.4 million edges from 1.03 million AI papers to capture how methods emerge, adapt, and transition, enabling better idea evaluation and generation for AI-driven research.
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Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation
Small LMs reach 77.1% accuracy at comparative forecasting of research idea success on benchmarks after supervised fine-tuning, with RLVR yielding interpretable reasoning at 71.35%.
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LLMs Generate Kitsch
LLMs generate kitsch due to their training process, causing outputs to be perceived as kitschier than human-created works in controlled reader studies.
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From Planning to Revision: How AI Writing Support at Different Stages Alters Ownership
AI writing support reduces ownership most at drafting and least at planning, tracking how much text and ideas the AI contributes, while more AI help raises essay quality.
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AI for Auto-Research: Roadmap & User Guide
AI can generate research artifacts faster than it can verify them, so across all eight lifecycle stages the credible deployment mode is human-governed collaboration rather than full autonomy.