Tootfinder

Conversational Search: From Fundamentals to Frontiers in the LLM Era
Fengran Mo, Chuan Meng, Mohammad Aliannejadi, Jian-Yun Nie
https://arxiv.org/abs/2506.10635

Conversational Search: From Fundamentals to Frontiers in the LLM Era
Conversational search enables multi-turn interactions between users and systems to fulfill users' complex information needs. During this interaction, the system should understand the users' search intent within the conversational context and then return the relevant information through a flexible, dialogue-based interface. The recent powerful large language models (LLMs) with capacities of instruction following, content generation, and reasoning, attract significant attention and advancements, …

Growing a Modular Framework for Modal Systems- HOLMS: a HOL Light Library
Antonella Bilotta
https://arxiv.org/abs/2506.10048 https://…

Growing a Modular Framework for Modal Systems- HOLMS: a HOL Light Library
The present dissertation introduces the research project on HOLMS (\textbf{HOL} Light Library for \textbf{M}odal \textbf{S}ystems), a growing modular framework for modal reasoning within the HOL Light proof assistant. To provide an accessible introduction to the library, the fundamentals of modal logic are outlined first, followed by a concise manual for the proof assistant itself. The core contribution of this work on HOLMS is the development of a unified and modular strategy for proving adequ…

Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning
Xiangning Yu, Zhuohan Wang, Linyi Yang, Haoxuan Li, Anjie Liu, Xiao Xue, Jun Wang, Mengyue Yang
https://arxiv.org/abs/2506.09853

Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning
Chain-of-Thought (CoT) prompting plays an indispensable role in endowing large language models (LLMs) with complex reasoning capabilities. However, CoT currently faces two fundamental challenges: (1) Sufficiency, which ensures that the generated intermediate inference steps comprehensively cover and substantiate the final conclusion; and (2) Necessity, which identifies the inference steps that are truly indispensable for the soundness of the resulting answer. We propose a causal framework that …

AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions
Polina Kirichenko, Mark Ibrahim, Kamalika Chaudhuri, Samuel J. Bell
https://arxiv.org/abs/2506.09038

AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions
For Large Language Models (LLMs) to be reliably deployed in both everyday and high-stakes domains, knowing when not to answer is equally critical as answering correctly. Real-world user queries, which can be underspecified, ill-posed, or fundamentally unanswerable, require LLMs to reason about uncertainty and selectively abstain -- i.e., refuse to answer definitively. However, abstention remains understudied, without a systematic evaluation framework for modern LLMs. In this work, we introduce …

This https://arxiv.org/abs/2505.21177 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCG_…

SOLIDGEO: Measuring Multimodal Spatial Math Reasoning in Solid Geometry
Geometry is a fundamental branch of mathematics and plays a crucial role in evaluating the reasoning capabilities of multimodal large language models (MLLMs). However, existing multimodal mathematics benchmarks mainly focus on plane geometry and largely ignore solid geometry, which requires spatial reasoning and is more challenging than plane geometry. To address this critical gap, we introduce SolidGeo, the first large-scale benchmark specifically designed to evaluate the performance of MLLMs …

This https://arxiv.org/abs/2506.04907 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Verbose ListOps (VLO): Beyond Long Context -- Unmasking LLM's Reasoning Blind Spots
Large Language Models (LLMs), whilst great at extracting facts from text, struggle with nested narrative reasoning. Existing long context and multi-hop QA benchmarks inadequately test this, lacking realistic distractors or failing to decouple context length from reasoning complexity, masking a fundamental LLM limitation. We introduce Verbose ListOps, a novel benchmark that programmatically transposes ListOps computations into lengthy, coherent stories. This uniquely forces internal computation …

ScaleRTL: Scaling LLMs with Reasoning Data and Test-Time Compute for Accurate RTL Code Generation
Chenhui Deng, Yun-Da Tsai, Guan-Ting Liu, Zhongzhi Yu, Haoxing Ren
https://arxiv.org/abs/2506.05566

ScaleRTL: Scaling LLMs with Reasoning Data and Test-Time Compute for Accurate RTL Code Generation
Recent advances in large language models (LLMs) have enabled near-human performance on software coding benchmarks, but their effectiveness in RTL code generation remains limited due to the scarcity of high-quality training data. While prior efforts have fine-tuned LLMs for RTL tasks, they do not fundamentally overcome the data bottleneck and lack support for test-time scaling due to their non-reasoning nature. In this work, we introduce ScaleRTL, the first reasoning LLM for RTL coding that scal…

Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Era
Shuo Jiang, Min Xie, Frank Youhua Chen, Jian Ma, Jianxi Luo
https://arxiv.org/abs/2506.09755

Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Era
Research and practice in Intelligent Design (ID) have significantly enhanced engineering innovation, efficiency, quality, and productivity over recent decades, fundamentally reshaping how engineering designers think, behave, and interact with design processes. The recent emergence of Foundation Models (FMs), particularly Large Language Models (LLMs), has demonstrated general knowledge-based reasoning capabilities, and open new paths and avenues for further transformation in engineering design. …

This https://arxiv.org/abs/2503.23630 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Finding Interest Needle in Popularity Haystack: Improving Retrieval by Modeling Item Exposure
Recommender systems operate in closed feedback loops, where user interactions reinforce popularity bias, leading to over-recommendation of already popular items while under-exposing niche or novel content. Existing bias mitigation methods, such as Inverse Propensity Scoring (IPS) and Off-Policy Correction (OPC), primarily operate at the ranking stage or during training, lacking explicit real-time control over exposure dynamics. In this work, we introduce an exposure-aware retrieval scoring appr…

This https://arxiv.org/abs/2506.03437 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Quake: Adaptive Indexing for Vector Search
Vector search, the task of finding the k-nearest neighbors of high-dimensional vectors, underpins many machine learning applications, including recommendation systems and information retrieval. However, existing approximate nearest neighbor (ANN) methods perform poorly under dynamic, skewed workloads where data distributions evolve. We introduce Quake, an adaptive indexing system that maintains low latency and high recall in such environments. Quake employs a hierarchical partitioning scheme th…