CA-SQL achieves 51.72% execution accuracy on the challenging tier of the BIRD benchmark using GPT-4o-mini by scaling exploration breadth according to estimated task difficulty, evolutionary prompt seeding, and candidate voting.
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A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
19 Pith papers cite this work. Polarity classification is still indexing.
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Structurally rich task descriptions make LLMs robust to prompt under-specification, and under-specification can enhance code correctness by disrupting misleading lexical or structural cues.
LLM-native figures embed provenance and enable direct LLM interaction with scientific visualizations to accelerate discovery and improve reproducibility.
AI coding agents perform vibe architecting by making prompt-driven architectural choices that produce structurally different systems for identical tasks.
LLMs perform in-context learning as trajectories through a structured low-dimensional conceptual belief space, with the structure visible in both behavior and internal representations and causally manipulable via interventions.
Open-weight LLMs reach 81-91% success generating formally verified Dafny code for complex algorithmic problems when given structural signatures and self-healing verifier feedback.
The paper systematizes agentic skills beyond tool use, providing design pattern and representation-scope taxonomies plus security analysis of malicious skill infiltration in agent marketplaces.
Hermes uses multi-agent LLMs to detect 2450 documentation and REST smells across 600 OpenAPI endpoints, demonstrating that structurally valid microservice APIs are often not semantically ready for agent consumption.
LLM-generated code matches human-written code in overall readability but exhibits different issue patterns, and prompt engineering has limited impact on improving it.
LLMs can detect usability content in user reviews with F-scores comparable to humans, though performance depends strongly on prompt design.
LLMs for smart contract security analysis show lexical bias from identifier names causing high false positives, with prompting creating precision-recall trade-offs, positioning them as complements rather than replacements for static analysis tools.
Few-shot prompting with the 32B DeepSeek-R1 model achieves the highest accuracy on a balanced set of 3,200 conventional commits mined from InfluxDB, while chain-of-thought adds no benefit and larger model scale improves results.
CausaDisco integrates Aristotle's Four Causes into LLM prompts to produce more engaging, exploratory, and multifaceted self-learning dialogues, as evidenced by controlled user studies.
STaR-DRO applies momentum-smoothed Tsallis reweighting to focus learning on hard groups in structured prediction, yielding F1 gains on clinical label extraction.
LLM2Manim pipeline generates pedagogy-aware Manim animations for STEM, producing slightly better student post-test scores (83% vs 78%), learning gains (d=0.67), and engagement than PowerPoint in a controlled study.
PICCO is a five-element reference architecture (Persona, Instructions, Context, Constraints, Output) for structuring LLM prompts, derived from synthesizing prior frameworks along with a taxonomy distinguishing prompt concepts.
A multi-agent multimodal system with fact-grounded adjudication and a dynamic two-tier preference graph cuts false positives in content filtering by 74.3% and nearly doubles F1-score versus text-only baselines while supporting user-driven Delta adjustments.
Experts can deliver helpful advice on over half of short 'nanoquestions' about feature-rich software in under one minute.
The survey organizes the shift of LLMs toward deliberate System 2 reasoning, covering model construction techniques, performance on math and coding benchmarks, and future research directions.
citing papers explorer
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CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation
CA-SQL achieves 51.72% execution accuracy on the challenging tier of the BIRD benchmark using GPT-4o-mini by scaling exploration breadth according to estimated task difficulty, evolutionary prompt seeding, and candidate voting.
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When Prompt Under-Specification Improves Code Correctness: An Exploratory Study of Prompt Wording and Structure Effects on LLM-Based Code Generation
Structurally rich task descriptions make LLMs robust to prompt under-specification, and under-specification can enhance code correctness by disrupting misleading lexical or structural cues.
-
Figures as Interfaces: Toward LLM-Native Artifacts for Scientific Discovery
LLM-native figures embed provenance and enable direct LLM interaction with scientific visualizations to accelerate discovery and improve reproducibility.
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Architecture Without Architects: How AI Coding Agents Shape Software Architecture
AI coding agents perform vibe architecting by making prompt-driven architectural choices that produce structurally different systems for identical tasks.
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Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space
LLMs perform in-context learning as trajectories through a structured low-dimensional conceptual belief space, with the structure visible in both behavior and internal representations and causally manipulable via interventions.
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From Natural Language to Verified Code: Toward AI Assisted Problem-to-Code Generation with Dafny-Based Formal Verification
Open-weight LLMs reach 81-91% success generating formally verified Dafny code for complex algorithmic problems when given structural signatures and self-healing verifier feedback.
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SoK: Agentic Skills -- Beyond Tool Use in LLM Agents
The paper systematizes agentic skills beyond tool use, providing design pattern and representation-scope taxonomies plus security analysis of malicious skill infiltration in agent marketplaces.
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Making OpenAPI Documentation Agent-Ready: Detecting Documentation and REST Smells with a Multi-Agent LLM System
Hermes uses multi-agent LLMs to detect 2450 documentation and REST smells across 600 OpenAPI endpoints, demonstrating that structurally valid microservice APIs are often not semantically ready for agent consumption.
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The Readability Spectrum: Patterns, Issues, and Prompt Effects in LLM-Generated Code
LLM-generated code matches human-written code in overall readability but exhibits different issue patterns, and prompt engineering has limited impact on improving it.
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User Reviews as a Source for Usability Requirements: A Precursor Study on Using Large Language Models
LLMs can detect usability content in user reviews with F-scores comparable to humans, though performance depends strongly on prompt design.
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Benchmarking LLM-Based Static Analysis for Secure Smart Contract Development: Reliability, Limitations, and Potential Hybrid Solutions
LLMs for smart contract security analysis show lexical bias from identifier names causing high false positives, with prompting creating precision-recall trade-offs, positioning them as complements rather than replacements for static analysis tools.
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Conventional Commit Classification using Large Language Models and Prompt Engineering
Few-shot prompting with the 32B DeepSeek-R1 model achieves the highest accuracy on a balanced set of 3,200 conventional commits mined from InfluxDB, while chain-of-thought adds no benefit and larger model scale improves results.
-
Enhanced Self-Learning with Epistemologically-Informed LLM Dialogue
CausaDisco integrates Aristotle's Four Causes into LLM prompts to produce more engaging, exploratory, and multifaceted self-learning dialogues, as evidenced by controlled user studies.
-
STaR-DRO: Stateful Tsallis Reweighting for Group-Robust Structured Prediction
STaR-DRO applies momentum-smoothed Tsallis reweighting to focus learning on hard groups in structured prediction, yielding F1 gains on clinical label extraction.
-
LLM2Manim: Pedagogy-Aware AI Generation of STEM Animations
LLM2Manim pipeline generates pedagogy-aware Manim animations for STEM, producing slightly better student post-test scores (83% vs 78%), learning gains (d=0.67), and engagement than PowerPoint in a controlled study.
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The PICCO Framework for Large Language Model Prompting: A Taxonomy and Reference Architecture for Prompt Structure
PICCO is a five-element reference architecture (Persona, Instructions, Context, Constraints, Output) for structuring LLM prompts, derived from synthesizing prior frameworks along with a taxonomy distinguishing prompt concepts.
-
Transparent and Controllable Recommendation Filtering via Multimodal Multi-Agent Collaboration
A multi-agent multimodal system with fact-grounded adjudication and a dynamic two-tier preference graph cuts false positives in content filtering by 74.3% and nearly doubles F1-score versus text-only baselines while supporting user-driven Delta adjustments.
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Nanomentoring: Investigating How Quickly People Can Help People Learn Feature-Rich Software
Experts can deliver helpful advice on over half of short 'nanoquestions' about feature-rich software in under one minute.
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From System 1 to System 2: A Survey of Reasoning Large Language Models
The survey organizes the shift of LLMs toward deliberate System 2 reasoning, covering model construction techniques, performance on math and coding benchmarks, and future research directions.