Context Engineering

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12 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Beyond Surface Alignment: Grounding the Dynamics of Situational Understanding and Generative Control in LLMs
Aug 30, 2026Chenghao YangContext Engineering LLM Grounding
DEPARTMENT OF COMPUTER SCIENCE THE UNIVERSITY OF CHICAGO CHICAGO, ILLINOIS
AgenticRepair: Multi-Faceted Program Context Engineering for Agentic Vulnerability Repair
Jul 31, 2026Michael Fu, Qiyue Mei, Patanamon Thongtanunam +1Automated Program Repair Agentic Code Generation
the School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia. · Kla Tantithamthavorn is with the Faculty of Information Technology, Monash University, Melbourne, Australia.
AI Agents Do Not Fail Alone:The Context Fails First
Jul 15, 2026Fouad BousetouaneAI Agent Reliability Agent Reliability
The University of Chicago, USA
Rule Taxonomy and Evolution in AI IDEs: A Mining and Survey Study
Jun 10, 2026Guangzong Cai, Ruiyin Li, Peng Liang +2Software Engineering Context Engineering
School of Computer Science, Wuhan University, China · School of Computer Science, Central China Normal University, China · School of Computing Technologies, RMIT University, Australia
X-SYNTH: Beyond Retrieval -- Enterprise Context Synthesis from Observed Digital Human Attention
May 15, 2026Guruprasad Raghavan, George Nychis, Rohan Narayana MurthyUser Modeling Context Engineering
Workfabric AI
Mise en Place for Agentic Coding: Deliberate Preparation as Context Engineering Methodology
May 6, 2026Andrew ZiglerLLM Planning Agentic Code Generation
LinearB Los Angeles, USA
Context Recycling for Long-Horizon LLM Inference
May 1, 2026Derek ThomasLLM Inference Efficiency Long-Context Language Model Inference
Independent Researcher · contextforge
Context Engineering: A Practitioner Methodology for Structured Human-AI Collaboration
Apr 5, 2026Elias CalboreanuContext Engineering LLM Context Management
Swift North AI Lab, The Swift Group, LLC, Maryland, USA.
The Root Theorem of Context Engineering
Mar 29, 2026Borja Odriozola SchickContext Engineering LLM Context Management
Independent Researcher
Loosely-Structured Software: Engineering Context, Structure, and Evolution Entropy in Runtime-Rewired Multi-Agent Systems
Mar 16, 2026Weihao Zhang, Yitong Zhou, Huanyu Qu +1Context Engineering Software Engineering
The Hong Kong University of Science and Technology, Hong Kong SAR, China · AI Chip Center for Emerging Smart Systems, Hong Kong SAR, China · University of Macau, Macau SAR, China +1
Context Engineering: From Prompts to Corporate Multi-Agent Architecture
Mar 10, 2026Vera V. VishnyakovaContext Engineering LLM Context Management
Director, Center for Transfer and Management of Socio-Economic Information, HSE University, Moscow
PARCER as an Operational Contract to Reduce Variance, Cost, and Risk in LLM Systems
Mar 1, 2026Elzo Brito dos Santos FilhoContext Engineering LLM Reliability

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Beyond Surface Alignment: Grounding the Dynamics of Situational Understanding and Generative Control in LLMs

Chenghao Yang

DEPARTMENT OF COMPUTER SCIENCE THE UNIVERSITY OF CHICAGO CHICAGO, ILLINOIS

Context Engineering LLM Grounding LLM Alignment Controllable Text Generation

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The current alignment tuning paradigm for Large Language Models (LLMs) prioritizes surface-level behaviors -- fluency, safety, and tonal consistency. While effective for casual chat, this thesis argues that such surface alignment masks a lack of grounding, creating models that are stylistically confident but situationally brittle. We propose a framework of Grounded Alignment, analyzing how models process context (Input) and structure generation (Output), then aligning these grounded behaviors to human needs. First, we evaluate failures in Situational Grounding. SitTest shows that despite large context windows, state-of-the-art models struggle to maintain a consistent "mental model" of a changing environment. ReCode further shows that models rely on surface heuristics rather than deep syntactic dependencies: they "read" extensive histories without truly "understanding" the evolving situation. Second, we evaluate Generative Grounding. We introduce the Branching Factor (BF) to map LLM generation, finding that standard alignment tuning constricts this landscape into premature stylistic collapse. Hindsight further shows that models often fail to understand their own generations. Finally, we propose Dynamic Control for grounded interaction. AI Realtor demonstrates context engineering to compensate for poor situational grounding. Base-Aligned Model Collaboration decouples exploration from stylistic constraints. We also present Annealed Sampling for verifiable reinforcement learning and apply these ideas to Addiction Support, where model-generated rationalization offers a communication interface for high-stakes domains. Collectively, this work moves beyond surface alignment toward agents anchored in both context and generation.