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feeling." style="position:absolute;left:0;top:0;width:100%;height:100%;max-width:none;max-height:none" loading="lazy"/></picture></noscript></div></div><div class="@/md:d_grid @/md:grid-cols_[minmax(min-content,_calc(50%_+_76px))_minmax(min-content,_1fr)] pb_[40px]"><div class="p_[20px] @/md:p_[49px_106px_92px_20px] pos_relative before:d_block before:@/md:d_none before:content_"" before:pos_absolute before:bg_[inherit] before:w_[72.4%] before:h_[40px] before:top_[-40px] before:left_0 @/md:d_flex @/md:flex_column @/md:items_flex-end @/md:justify_flex-end m_0 @/md:m_[-82px_0_0_0] text_text.primary bg_background"><h1 class="w_100% max-w_[610px]"><span class="d_block textStyle_heroEyebrow smoothing_antialiased w_100% mt_[-7px] @/md:mt_0 mb_[26px] @/md:mb_[50px] @/md:max-w_[610px]">Research</span><span class="sr_true"> - </span><span class="d_block textStyle_heroTitle smoothing_antialiased fs_[5.5625rem] @/md:mb_[-92px] @/md:w_100% @/md:max-w_[610px] text_text.primary">Papers</span></h1></div><div class="[&_img]:w_[unset] [&_img]:h_[unset] [&_a]:text_[currentColor] [&_a]:fw_semiBold [&_a]:text-decor_underline [&_a]:[&:has(img[width])]:d_block [&_a]:[&:has(img[width])]:m_[0_auto] [&_a]:[&:has(img[width])]:w_[fit-content] [&_:first-child]:mt_0 [&_:last-child]:mb_0 textStyle_heroBody smoothing_antialiased p_[0_20px] mt_[20px] @/md:mt_0 mb_0 @/md:max-w_[528px] @/md:pt_[51px] @/md:pl_[2px] @/md:pb_[35px] @/md:self_end"><p>Explore a selection of our published work on a variety of key research challenges in AI.</p></div></div></section><div class="section text_text.primary bg_background pt_0 pb_5 md:pb_[80px]"><div class="max-w_wideContentSize ml_5 @/wideWithPad:ml_auto mr_5 @/wideWithPad:mr_auto"><div class="d_grid grid-cols_1 gap_2"><div class="text_text.primary bg_[white] p_5"><form action="papers" method="GET"><strong class="text_accent.primary fw_medium d_block fs_xl mb_[1rem]">Filter papers</strong><div class="d_flex flex-wrap_wrap 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Scholar">Semantic Scholar</option><option value="Skylight">Skylight</option><option value="Wildlands">Wildlands</option></select></div></div></div><button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_text.secondary text-decor_none fw_medium disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none p_[14px_25px] rounded_pill [&_.label]:d_inline-block [&_.label]:transform_translateY(2px) pos_relative bg_accent.primary mr_[40px] transform_[translateY(-2px)]"><span class="label">Filter</span><span class="d_inline-block pos_absolute top_[50%] transform_translateY(-50%) rounded_[50%] h_[calc(100%_-_4px)] aspect_1 border_[inherit] bg_[inherit] fill_[currentColor] [&_svg]:d_block [&_svg]:box_border-box [&_svg]:p_[12px] [&_svg]:w_100% [&_svg]:h_100% left_[100%]"><svg class="aspect_14_/_9" aria-hidden="true"><use href="#arrow-svg"></use></svg></span></button></div></form></div><div class="text_text.primary bg_[white] p_5 p_5"><h2 class="fw_medium fs_xl m_[0] text_contrast-pink"><a href="https://www.semanticscholar.org/paper/1dbc2cdcae3e17c3d721d12a5a2d98ced727681a">Holodeck: Language Guided Generation of 3D Embodied AI Environments</a></h2><div class="d_flex items_center fs_sm flex-wrap_wrap m_[10px_0]"><span class="mr_[0.5rem] d_block white-space_nowrap">Yue Yang</span><span class="mr_[0.5rem] d_block white-space_nowrap">Fan-Yun Sun</span><span class="mr_[0.5rem] d_block white-space_nowrap">Luca Weihs</span><button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none mr_[0.5rem]"><span class="label">+10 authors</span></button><span class="mr_[0.5rem] d_block white-space_nowrap">Christopher Clark</span></div><div class="d_grid grid-cols_[min-content_1fr] gap_3"><div class="fs_md white-space_nowrap">2025</div><div class="fs_md white-space_nowrap">Computer Vision and Pattern Recognition</div></div><p class="fs_md mt_[1rem]">3D simulated environments play a critical role in Embodied AI, but their creation requires expertise and extensive manual effort, restricting their diversity and scope. To miti-gate this limitation,… <button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none"><span class="label">Expand</span></button></p></div><div class="text_text.primary bg_[white] p_5 p_5"><h2 class="fw_medium fs_xl m_[0] text_contrast-pink"><a href="https://www.semanticscholar.org/paper/445815278d640c594b5591f42046fd70606e58f3">Re-evaluating Automatic LLM System Ranking for Alignment with Human Preference</a></h2><div class="d_flex items_center fs_sm flex-wrap_wrap m_[10px_0]"><span class="mr_[0.5rem] d_block white-space_nowrap">Mingqi Gao</span><span class="mr_[0.5rem] d_block white-space_nowrap">Yixin Liu</span><span class="mr_[0.5rem] d_block white-space_nowrap">Xinyu Hu</span><button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none mr_[0.5rem]"><span class="label">+2 authors</span></button><span class="mr_[0.5rem] d_block white-space_nowrap">Arman Cohan</span></div><div class="d_grid grid-cols_[min-content_1fr] gap_3"><div class="fs_md white-space_nowrap">2025</div><div class="fs_md white-space_nowrap">NAACL</div></div><p class="fs_md mt_[1rem]">Evaluating and ranking the capabilities of different LLMs is crucial for understanding their performance and alignment with human preferences. Due to the high cost and time-consuming nature of human… <button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none"><span class="label">Expand</span></button></p></div><div class="text_text.primary bg_[white] p_5 p_5"><h2 class="fw_medium fs_xl m_[0] text_contrast-pink"><a href="https://www.semanticscholar.org/paper/98aa03d0b541c6512b71fcf3f9bb69dafb9654ab">Social-RAG: Retrieving from Group Interactions to Socially Ground Proactive AI Generation to Group Preferences</a></h2><div class="d_flex items_center fs_sm flex-wrap_wrap m_[10px_0]"><span class="mr_[0.5rem] d_block white-space_nowrap">Ruotong Wang</span><span class="mr_[0.5rem] d_block white-space_nowrap">Xinyi Zhou</span><span class="mr_[0.5rem] d_block white-space_nowrap">Lin Qiu</span><button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none mr_[0.5rem]"><span class="label">+2 authors</span></button><span class="mr_[0.5rem] d_block white-space_nowrap">Amy X. Zhang</span></div><div class="d_grid grid-cols_[min-content_1fr] gap_3"><div class="fs_md white-space_nowrap">2025</div><div class="fs_md white-space_nowrap">CHI</div></div><p class="fs_md mt_[1rem]">AI agents are increasingly tasked with making proactive suggestions in online spaces where groups collaborate, but can be unhelpful or even annoying, due to not fitting the group's preferences or… <button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none"><span class="label">Expand</span></button></p></div><div class="text_text.primary bg_[white] p_5 p_5"><h2 class="fw_medium fs_xl m_[0] text_contrast-pink"><a href="https://openreview.net/forum?id=6NNA0MxhCH">Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questions</a></h2><div class="d_flex items_center fs_sm flex-wrap_wrap m_[10px_0]"><span class="mr_[0.5rem] d_block white-space_nowrap">Sarah Wiegreffe</span><span class="mr_[0.5rem] d_block white-space_nowrap">Oyvind Tafjord</span><span class="mr_[0.5rem] d_block white-space_nowrap">Yonatan Belinkov</span><button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none mr_[0.5rem]"><span class="label">+1 authors</span></button><span class="mr_[0.5rem] d_block white-space_nowrap">Ashish Sabharwal</span></div><div class="d_grid grid-cols_[min-content_1fr] gap_3"><div class="fs_md white-space_nowrap">2025</div><div class="fs_md white-space_nowrap">ICLR</div></div><p class="fs_md mt_[1rem]">Multiple-choice question answering (MCQA) is a key competence of performant transformer language models that is tested by mainstream benchmarks. However, recent evidence shows that models can have… <button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none"><span class="label">Expand</span></button></p></div><div class="text_text.primary bg_[white] p_5 p_5"><h2 class="fw_medium fs_xl m_[0] text_contrast-pink"><a href="https://www.semanticscholar.org/paper/48c83799530dc523ee01e6c1c40ad577d5c10a16">DiscoveryBench: Towards Data-Driven Discovery with Large Language Models</a></h2><div class="d_flex items_center fs_sm flex-wrap_wrap m_[10px_0]"><span class="mr_[0.5rem] d_block white-space_nowrap">Bodhisattwa Prasad Majumder</span><span class="mr_[0.5rem] d_block white-space_nowrap">Harshit Surana</span><span class="mr_[0.5rem] d_block white-space_nowrap">Dhruv Agarwal</span><button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none mr_[0.5rem]"><span class="label">+6 authors</span></button><span class="mr_[0.5rem] d_block white-space_nowrap">Peter Clark</span></div><div class="d_grid grid-cols_[min-content_1fr] gap_3"><div class="fs_md white-space_nowrap">2025</div><div class="fs_md white-space_nowrap">ICLR</div></div><p class="fs_md mt_[1rem]">Can the rapid advances in code generation, function calling, and data analysis using large language models (LLMs) help automate the search and verification of hypotheses purely from a set of… <button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none"><span class="label">Expand</span></button></p></div><div class="text_text.primary bg_[white] p_5 p_5"><h2 class="fw_medium fs_xl m_[0] text_contrast-pink"><a href="https://www.semanticscholar.org/paper/7f0ad3a6aef6bb51b599138ff83437fcbe6d6072">Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Data</a></h2><div class="d_flex items_center fs_sm flex-wrap_wrap m_[10px_0]"><span class="mr_[0.5rem] d_block white-space_nowrap">Antonis Antoniades</span><span class="mr_[0.5rem] d_block white-space_nowrap">Xinyi Wang</span><span class="mr_[0.5rem] d_block white-space_nowrap">Yanai Elazar</span><button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none mr_[0.5rem]"><span class="label">+3 authors</span></button><span class="mr_[0.5rem] d_block white-space_nowrap">W. Wang</span></div><div class="d_grid grid-cols_[min-content_1fr] gap_3"><div class="fs_md white-space_nowrap">2025</div><div class="fs_md white-space_nowrap">ICLR</div></div><p class="fs_md mt_[1rem]">The impressive capabilities of large language models (LLMs) have sparked debate over whether these models genuinely generalize to unseen tasks or predominantly rely on memorizing vast amounts of… <button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none"><span class="label">Expand</span></button></p></div><div class="text_text.primary bg_[white] p_5 p_5"><h2 class="fw_medium fs_xl m_[0] text_contrast-pink"><a href="https://www.semanticscholar.org/paper/860ba78f9789bbfc99c299b18558ca19430d8fea">LLM-SR: Scientific Equation Discovery via Programming with Large Language Models</a></h2><div class="d_flex items_center fs_sm flex-wrap_wrap m_[10px_0]"><span class="mr_[0.5rem] d_block white-space_nowrap">Parshin Shojaee</span><span class="mr_[0.5rem] d_block white-space_nowrap">Kazem Meidani</span><span class="mr_[0.5rem] d_block white-space_nowrap">Shashank Gupta</span><button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none mr_[0.5rem]"><span class="label">+1 authors</span></button><span class="mr_[0.5rem] d_block white-space_nowrap">Chandan K Reddy</span></div><div class="d_grid grid-cols_[min-content_1fr] gap_3"><div class="fs_md white-space_nowrap">2025</div><div class="fs_md white-space_nowrap">ICLR</div></div><p class="fs_md mt_[1rem]">Mathematical equations have been unreasonably effective in describing complex natural phenomena across various scientific disciplines. However, discovering such insightful equations from data… <button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none"><span class="label">Expand</span></button></p></div><div class="text_text.primary bg_[white] p_5 p_5"><h2 class="fw_medium fs_xl m_[0] text_contrast-pink"><a href="https://openreview.net/forum?id=EDoD3DgivF">On Linear Representations and Pretraining Data Frequency in Language Models</a></h2><div class="d_flex items_center fs_sm flex-wrap_wrap m_[10px_0]"><span class="mr_[0.5rem] d_block white-space_nowrap">Jack Merullo</span><span class="mr_[0.5rem] d_block white-space_nowrap">Noah A. Smith</span><span class="mr_[0.5rem] d_block white-space_nowrap">Sarah Wiegreffe</span><span class="mr_[0.5rem] d_block white-space_nowrap">Yanai Elazar</span></div><div class="d_grid grid-cols_[min-content_1fr] gap_3"><div class="fs_md white-space_nowrap">2025</div><div class="fs_md white-space_nowrap">ICLR</div></div><p class="fs_md mt_[1rem]">Pretraining data has a direct impact on the behaviors and quality of language models (LMs), but we only understand the most basic principles of this relationship. While most work focuses on… <button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none"><span class="label">Expand</span></button></p></div><div class="text_text.primary bg_[white] p_5 p_5"><h2 class="fw_medium fs_xl m_[0] text_contrast-pink"><a href="https://www.semanticscholar.org/paper/dd08b1b9bb644fdaaf3eb348ecb0f12d65703401">A Little Depth Goes a Long Way: The Expressive Power of Log-Depth Transformers</a></h2><div class="d_flex items_center fs_sm flex-wrap_wrap m_[10px_0]"><span class="mr_[0.5rem] d_block white-space_nowrap">William Merrill</span><span class="mr_[0.5rem] d_block white-space_nowrap">Ashish Sabharwal</span></div><div class="d_grid grid-cols_[min-content_1fr] gap_3"><div class="fs_md white-space_nowrap">2025</div><div class="fs_md white-space_nowrap">arXiv</div></div><p class="fs_md mt_[1rem]">Recent theoretical results show transformers cannot express sequential reasoning problems over long input lengths, intuitively because their computational depth is bounded. However, prior work… <button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none"><span class="label">Expand</span></button></p></div><div class="text_text.primary bg_[white] p_5 p_5"><h2 class="fw_medium fs_xl m_[0] text_contrast-pink"><a href="https://www.semanticscholar.org/paper/40ac055737ddfc5e91d6231e61f4ea58fec67404">ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning</a></h2><div class="d_flex items_center fs_sm flex-wrap_wrap m_[10px_0]"><span class="mr_[0.5rem] d_block white-space_nowrap">Bill Yuchen Lin</span><span class="mr_[0.5rem] d_block white-space_nowrap">Ronan Le Bras</span><span class="mr_[0.5rem] d_block white-space_nowrap">Kyle Richardson</span><button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none mr_[0.5rem]"><span class="label">+3 authors</span></button><span class="mr_[0.5rem] d_block white-space_nowrap">Yejin Choi</span></div><div class="d_grid grid-cols_[min-content_1fr] gap_3"><div class="fs_md white-space_nowrap">2025</div><div class="fs_md white-space_nowrap">arXiv</div></div><p class="fs_md mt_[1rem]">We investigate the logical reasoning capabilities of large language models (LLMs) and their scalability in complex non-monotonic reasoning. To this end, we introduce ZebraLogic, a comprehensive… <button class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_[currentColor] text-decor_none fw_semiBold disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none"><span class="label">Expand</span></button></p></div></div><div class="d_grid grid-cols_[1fr_max-content_1fr] pt_10 gap_5 place-content_center place-items_center pb_[1px] [@container_(inline-size_<_450px)]:gap_3"><button disabled="" class="btn-no-appearance cursor_pointer d_inline-block leading_1 text_text.secondary text-decor_none fw_medium disabled:cursor_default disabled:opacity_0.6 disabled:pointer-events_none p_[14px_25px] rounded_pill [&_.label]:d_inline-block [&_.label]:transform_translateY(2px) pos_relative bg_accent.primary ml_[40px] justify-self_end"><span class="label">Previous</span><span class="d_inline-block pos_absolute top_[50%] transform_[translate(-100%,_-50%)_scaleX(-1)] rounded_[50%] h_[calc(100%_-_4px)] aspect_1 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simulated environments play a critical role in Embodied AI, but their creation requires expertise and extensive manual effort, restricting their diversity and scope. To miti-gate this limitation, we present Holodeck, a system that generates 3D environments to match a user-supplied prompt fullyautomatedly. Holodeck can generate diverse scenes, e.g., arcades, spas, and museums, adjust the designs for styles, and can capture the semantics of complex queries such as “apartment for a researcher with a cat” and “office of a professor who is a fan of Star Wars”. Holodeck leverages a large language model (i.e., GPT-4) for common sense knowledge about what the scene might look like and uses a large collection of 3D assets from Objaverse to populate the scene with diverse objects. To address the challenge of positioning objects correctly, we prompt GPT-4 to generate spatial relational constraints between objects and then optimize the layout to satisfy those constraints. Our large-scale human evaluation shows that annotators prefer Holodeck over manually designed procedural baselines in residential scenes and that Holode"])</script><script>self.__next_f.push([1,"ck can produce high-quality outputs for diverse scene types. We also demonstrate an exciting application of Holodeck in Embodied AI, training agents to navigate in novel scenes like music rooms and daycares without human-constructed data, which is a significant step forward in developing general-purpose embodied agents.25:T58d,Evaluating and ranking the capabilities of different LLMs is crucial for understanding their performance and alignment with human preferences. Due to the high cost and time-consuming nature of human evaluations, an automatic LLM bencher (i.e., an automatic evaluation framework that aims to rank LLMs based on their alignment with human preferences) is indispensable. An automatic LLM bencher consists of four components: the input set (e.g., a user instruction), the evaluation model (e.g., an LLM), the evaluation type (e.g., pairwise comparison), and the aggregation method (e.g., the ELO rating system). However, previous work has not thoroughly explored how to select these components or how their different combinations influence the results. In this work, through controlled experiments, we provide a series of recommendations on how to choose each component to better automate the evaluation of LLMs. Furthermore, we discovered that when evaluating LLMs with similar performance, the performance of the automatic LLM bencher declines sharply, underscoring the limitations of current benchers and calling for future work. Lastly, we found that the evaluation models' performance at the instance level (e.g., the accuracy of selecting the best output) does not always align with their effectiveness when used as a component of a bencher, highlighting the importance of dedicated system-level evaluation of benchers.26:T41a,AI agents are increasingly tasked with making proactive suggestions in online spaces where groups collaborate, but can be unhelpful or even annoying, due to not fitting the group's preferences or behaving in socially inappropriate ways. Fortunately, group spaces have a rich history of prio"])</script><script>self.__next_f.push([1,"r social interactions and affordances for social feedback to support creating agents that align to a group's interests and norms. We present Social-RAG, a workflow for grounding agents to social information about a group, which retrieves from prior group interactions, selects relevant social signals, and then feeds the context into a large language model to generate messages to the group. We implement this into PaperPing, our system that posts academic paper recommendations in group chat, leveraging social signals determined from formative studies with 39 researchers. From a three-month deployment in 18 channels, we observed PaperPing posted relevant messages in groups without disrupting their existing social practices, fostering group common ground.27:T519,Multiple-choice question answering (MCQA) is a key competence of performant transformer language models that is tested by mainstream benchmarks. However, recent evidence shows that models can have quite a range of performance, particularly when the task format is diversified slightly (such as by shuffling answer choice order). In this work we ask: how do successful models perform formatted MCQA? We employ vocabulary projection and activation patching methods to localize key hidden states that encode relevant information for predicting the correct answer. We find that prediction of a specific answer symbol is causally attributed to a single middle layer, and specifically its multi-head self-attention mechanism. We show that subsequent layers increase the probability of the predicted answer symbol in vocabulary space, and that this probability increase is associated with a sparse set of attention heads with unique roles. We additionally uncover differences in how different models adjust to alternative symbols. Finally, we demonstrate that a synthetic task can disentangle sources of model error to pinpoint when a model has learned formatted MCQA, and show that an inability to separate answer symbol tokens in vocabulary space is a property of models unable to perf"])</script><script>self.__next_f.push([1,"orm formatted MCQA tasks.28:T58e,Can the rapid advances in code generation, function calling, and data analysis using large language models (LLMs) help automate the search and verification of hypotheses purely from a set of provided datasets? To evaluate this question, we present DiscoveryBench, the first comprehensive benchmark that formalizes the multi-step process of data-driven discovery. The benchmark is designed to systematically assess current model capabilities in discovery tasks and provide a useful resource for improving them. Our benchmark contains 264 tasks collected across 6 diverse domains, such as sociology and engineering, by manually deriving discovery workflows from published papers to approximate the real-world challenges faced by researchers, where each task is defined by a dataset, its metadata, and a discovery goal in natural language. We additionally provide 903 synthetic tasks to conduct controlled evaluations across task complexity. Furthermore, our structured formalism of data-driven discovery enables a facet-based evaluation that provides useful insights into different failure modes. We evaluate several popular LLM-based reasoning frameworks using both open and closed LLMs as baselines on DiscoveryBench and find that even the best system scores only 25%. Our benchmark, thus, illustrates the challenges in autonomous data-driven discovery and serves as a valuable resource for the community to make progress.29:T5d1,The impressive capabilities of large language models (LLMs) have sparked debate over whether these models genuinely generalize to unseen tasks or predominantly rely on memorizing vast amounts of pretraining data. To explore this issue, we introduce an extended concept of memorization, distributional memorization, which measures the correlation between the LLM output probabilities and the pretraining data frequency. To effectively capture task-specific pretraining data frequency, we propose a novel task-gram language model, which is built by counting the co-occurrence of semantic"])</script><script>self.__next_f.push([1,"ally related $n$-gram pairs from task inputs and outputs in the pretraining corpus. Using the Pythia models trained on the Pile dataset, we evaluate four distinct tasks: machine translation, factual question answering, world knowledge understanding, and math reasoning. Our findings reveal varying levels of memorization, with the strongest effect observed in factual question answering. Furthermore, while model performance improves across all tasks as LLM size increases, only factual question answering shows an increase in memorization, whereas machine translation and reasoning tasks exhibit greater generalization, producing more novel outputs. This study demonstrates that memorization plays a larger role in simpler, knowledge-intensive tasks, while generalization is the key for harder, reasoning-based tasks, providing a scalable method for analyzing large pretraining corpora in greater depth.2a:T64e,Mathematical equations have been unreasonably effective in describing complex natural phenomena across various scientific disciplines. However, discovering such insightful equations from data presents significant challenges due to the necessity of navigating extremely high-dimensional combinatorial and nonlinear hypothesis spaces. Traditional methods of equation discovery, commonly known as symbolic regression, largely focus on extracting equations from data alone, often neglecting the rich domain-specific prior knowledge that scientists typically depend on. To bridge this gap, we introduce LLM-SR, a novel approach that leverages the extensive scientific knowledge and robust code generation capabilities of Large Language Models (LLMs) to discover scientific equations from data in an efficient manner. Specifically, LLM-SR treats equations as programs with mathematical operators and combines LLMs' scientific priors with evolutionary search over equation programs. The LLM iteratively proposes new equation skeleton hypotheses, drawing from its physical understanding, which are then optimized against data to estimate skelet"])</script><script>self.__next_f.push([1,"on parameters. We demonstrate LLM-SR's effectiveness across three diverse scientific domains, where it discovers physically accurate equations that provide significantly better fits to in-domain and out-of-domain data compared to the well-established symbolic regression baselines. Incorporating scientific prior knowledge also enables LLM-SR to search the equation space more efficiently than baselines. Code is available at: https://github.com/deep-symbolic-mathematics/LLM-SR2b:T7fb,Pretraining data has a direct impact on the behaviors and quality of language models (LMs), but we only understand the most basic principles of this relationship. While most work focuses on pretraining data's effect on downstream task behavior, we investigate its relationship to LM representations. Previous work has discovered that, in language models, some concepts are encoded as ``linear representations'', but what factors cause these representations to form (or not)? We study the connection between differences in pretraining data frequency and differences in trained models' linear representations of factual recall relations. We find evidence that the two are linked, with the formation of linear representations strongly connected to pretraining term frequencies. First, we establish that the presence of linear representations for subject-relation-object (s-r-o) fact triplets is highly correlated with both subject-object co-occurrence frequency and in-context learning accuracy. This is the case across all phases of pretraining, i.e., it is not affected by the model's underlying capability. In OLMo 7B and GPT-J (6B), we discover that a linear representation consistently (but not exclusively) forms when the subjects and objects within a relation co-occur at least 1-2k times, regardless of when these occurrences happen during pretraining. In the OLMo 1B model, consistent linearity only occurs after 4.4k occurrences, suggesting a connection to scale. Finally, we train a regression model on measurements of linear representation quality that "])</script><script>self.__next_f.push([1,"can predict how often a term was seen in pretraining. We show such model achieves low error even for a different model and pretraining dataset, providing a new unsupervised method for exploring possible data sources of closed-source models. We conclude that the presence or absence of linear representations in LMs contains signal about their pretraining corpora that may provide new avenues for controlling and improving model behavior. We release our code to support future work.2c:T5d5,Recent theoretical results show transformers cannot express sequential reasoning problems over long input lengths, intuitively because their computational depth is bounded. However, prior work treats the depth as a constant, leaving it unclear to what degree bounded depth may suffice for solving problems over short inputs, or how increasing the transformer's depth affects its expressive power. We address these questions by analyzing the expressive power of transformers whose depth can grow minimally with context length n. We show even highly uniform transformers with depth Theta(log n) can express two important problems: recognizing regular languages, which captures state tracking abilities, and graph connectivity, which underlies multi-step reasoning. Notably, both of these problems cannot be expressed by fixed-depth transformers under standard complexity conjectures, demonstrating the expressivity benefit of growing depth. Moreover, our theory quantitatively predicts how depth must grow with input length to express these problems, showing that depth scaling is more efficient than scaling width or chain-of-thought steps. Empirically, we find our theoretical depth requirements for regular language recognition match the practical depth requirements of transformers remarkably well. Thus, our results clarify precisely how depth affects transformers' reasoning capabilities, providing potential practical insights for designing models that are better at sequential reasoning.2d:T52a,We investigate the logical reasoning capabilities of large"])</script><script>self.__next_f.push([1," language models (LLMs) and their scalability in complex non-monotonic reasoning. To this end, we introduce ZebraLogic, a comprehensive evaluation framework for assessing LLM reasoning performance on logic grid puzzles derived from constraint satisfaction problems (CSPs). ZebraLogic enables the generation of puzzles with controllable and quantifiable complexity, facilitating a systematic study of the scaling limits of models such as Llama, o1 models, and DeepSeek-R1. By encompassing a broad range of search space complexities and diverse logical constraints, ZebraLogic provides a structured environment to evaluate reasoning under increasing difficulty. Our results reveal a significant decline in accuracy as problem complexity grows -- a phenomenon we term the curse of complexity. This limitation persists even with larger models and increased inference-time computation, suggesting inherent constraints in current LLM reasoning capabilities. Additionally, we explore strategies to enhance logical reasoning, including Best-of-N sampling, backtracking mechanisms, and self-verification prompts. 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