TADI shows that domain-specialized tools orchestrated by an LLM over dual structured and semantic databases can convert heterogeneous wellsite data into evidence-grounded drilling intelligence, with tool design mattering more than model scale.
citation dossier
arXiv preprint arXiv:2406.06608 , year=
17Pith papers citing it
17reference links
cs.AItop field · 5 papers
UNVERDICTEDtop verdict bucket · 15 papers
why this work matters in Pith
Pith has found this work in 17 reviewed papers. Its strongest current cluster is cs.AI (5 papers). The largest review-status bucket among citing papers is UNVERDICTED (15 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
years
2026 17representative citing papers
Vision-language models perform only marginally above random on action quality assessment and retain systematic biases even after targeted prompting and contrastive reformulation.
AI alignment must move beyond assuming users have fully formed goals and instead provide active cognitive support to help form and refine intent over time.
Arbiter-K is a new execution architecture that treats LLMs as probabilistic processors inside a neuro-symbolic kernel with a semantic ISA to enable deterministic security enforcement and unsafe trajectory interdiction in agentic AI.
LLMs improve with detailed code descriptions but remain insufficient to replace human annotators for security-specific qualitative coding.
LLARS is a new integrated platform that combines collaborative prompt authoring, cost-controlled batch generation, and hybrid evaluation to help domain experts and developers jointly build and assess LLM systems.
U-Define improves user control in LLM planning by letting people define hard rules and soft preferences in natural language with matching verification methods, raising usefulness and satisfaction scores.
Eye movements during Holocaust survivor interviews vary by episodic, semantic, affective and temporal memory dimensions, with pre-onset gaze sufficient to predict sentence temporal context.
OOPrompt reifies user intents into structured manipulable artifacts to enable modular and iterative prompting in LLM-based interactive systems.
Agent Mentor analyzes semantic trajectories in agent logs to identify undesired behaviors and derives corrective prompt instructions, yielding measurable accuracy gains on benchmark tasks across three agent setups.
Standard LLM chats produce high perceived understanding but low objective learning in students, while future-self explanations best align confidence with actual gains and guided hints maximize learning with moderate workload.
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.
Refined bottom-up categories of LLM usage in critical thinking homework, labeled by student initiative, are examined for associations with midterm performance across two course offerings.
In a randomized experiment with 97 graduate students, deferred AI assistance produced the highest-quality hints and helped students spot more code mistakes than independent writing or immediate AI help.
Multi-shot prompting raises agreement with humans for Claude Haiku but not DeepSeek-Chat or Gemini 2.5 Flash, with models showing different stability and a consistent bias toward over-labeling negative feedback.
An LLM ensemble reached 80 macro-F1 on 3-class clarity detection and 59 on 9-class evasion detection, with partial layer unfreezing and multilingual ensembles improving encoder results while enriched context helped only LLMs.
MetaRAG is only partially reproducible with lower absolute scores than originally reported, gains substantially from reranking, and shows greater robustness than SIM-RAG under extended retrieval features.
citing papers explorer
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TADI: Tool-Augmented Drilling Intelligence via Agentic LLM Orchestration over Heterogeneous Wellsite Data
TADI shows that domain-specialized tools orchestrated by an LLM over dual structured and semantic databases can convert heterogeneous wellsite data into evidence-grounded drilling intelligence, with tool design mattering more than model scale.
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Can Vision Language Models Judge Action Quality? An Empirical Evaluation
Vision-language models perform only marginally above random on action quality assessment and retain systematic biases even after targeted prompting and contrastive reformulation.
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Alignment has a Fantasia Problem
AI alignment must move beyond assuming users have fully formed goals and instead provide active cognitive support to help form and refine intent over time.
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From Craft to Kernel: A Governance-First Execution Architecture and Semantic ISA for Agentic Computers
Arbiter-K is a new execution architecture that treats LLMs as probabilistic processors inside a neuro-symbolic kernel with a semantic ISA to enable deterministic security enforcement and unsafe trajectory interdiction in agentic AI.
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LLMs for Qualitative Data Analysis Fail on Security-specificComments in Human Experiments
LLMs improve with detailed code descriptions but remain insufficient to replace human annotators for security-specific qualitative coding.
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LLARS: Enabling Domain Expert & Developer Collaboration for LLM Prompting, Generation and Evaluation
LLARS is a new integrated platform that combines collaborative prompt authoring, cost-controlled batch generation, and hybrid evaluation to help domain experts and developers jointly build and assess LLM systems.
-
U-Define: Designing User Workflows for Hard and Soft Constraints in LLM-Based Planning
U-Define improves user control in LLM planning by letting people define hard rules and soft preferences in natural language with matching verification methods, raising usefulness and satisfaction scores.
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Looking Into the Past: Eye Movements Characterize Elements of Autobiographical Recall in Interviews with Holocaust Survivors
Eye movements during Holocaust survivor interviews vary by episodic, semantic, affective and temporal memory dimensions, with pre-onset gaze sufficient to predict sentence temporal context.
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OOPrompt: Reifying Intents into Structured Artifacts for Modular and Iterative Prompting
OOPrompt reifies user intents into structured manipulable artifacts to enable modular and iterative prompting in LLM-based interactive systems.
-
Agent Mentor: Framing Agent Knowledge through Semantic Trajectory Analysis
Agent Mentor analyzes semantic trajectories in agent logs to identify undesired behaviors and derives corrective prompt instructions, yielding measurable accuracy gains on benchmark tasks across three agent setups.
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Confidence Without Competence in AI-Assisted Knowledge Work
Standard LLM chats produce high perceived understanding but low objective learning in students, while future-self explanations best align confidence with actual gains and guided hints maximize learning with moderate workload.
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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.
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Characterizing Students' LLM Usage Behaviors and Their Association with Learning in Critical Thinking Tasks
Refined bottom-up categories of LLM usage in critical thinking homework, labeled by student initiative, are examined for associations with midterm performance across two course offerings.
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Hint-Writing with Deferred AI Assistance: Fostering Critical Engagement in Data Science Education
In a randomized experiment with 97 graduate students, deferred AI assistance produced the highest-quality hints and helped students spot more code mistakes than independent writing or immediate AI help.
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Prompt Engineering Strategies for LLM-based Qualitative Coding of Psychological Safety in Software Engineering Communities: A Controlled Empirical Study
Multi-shot prompting raises agreement with humans for Claude Haiku but not DeepSeek-Chat or Gemini 2.5 Flash, with models showing different stability and a consistent bias toward over-labeling negative feedback.
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CLaC at SemEval-2026 Task 6: Response Clarity Detection in Political Discourse
An LLM ensemble reached 80 macro-F1 on 3-class clarity detection and 59 on 9-class evasion detection, with partial layer unfreezing and multilingual ensembles improving encoder results while enriched context helped only LLMs.
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A Reproducibility Study of Metacognitive Retrieval-Augmented Generation
MetaRAG is only partially reproducible with lower absolute scores than originally reported, gains substantially from reranking, and shows greater robustness than SIM-RAG under extended retrieval features.