Concept · 40 episode(s)

Agent Scaffolding

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Definition

Agent scaffolding is the control flow wrapped around a language model that turns it into an agent: the prompt structure, tool-call loop, retry logic, planning steps, and memory plumbing. Two agents built on the same base model can perform very differently depending on scaffolding, which makes it a major confound in capability evaluations.

Episodes covering this

  1. 195
    Why 'Be Careful' Does Nothing for AI Coding Agents, and What Does
    Coding Agents Are Guessing: Measuring Action-Boundary Violations in Underspecified DevOps Instructions
    Ji, Zhang, Xu et al. · Hong Kong University of Science and Technology·15 min·Jul 03, 2026
  2. 192
    A 32B Open Model Matched Frontier Systems By Learning to Take Notes
    AutoMem: Automated Learning of Memory as a Cognitive Skill
    Wu, Zhu, Zhang et al. · Stanford University·22 min·Jul 02, 2026
  3. 188
    A Coding Agent Found a Hole in a Peer-Reviewed STOC Proof for Five Dollars
    Beyond the Library: An Agentic Framework for Autoformalizing Research Mathematics
    Moakhar, Gholami, Springer et al. · University of Maryland·20 min·Jul 02, 2026
  4. 181
    How to Backpropagate Blame Through a Team of Chatbots — And When It Backfires
    GBC: Gradient-Based Connections for Optimizing Multi-Agent Systems
    Yang, Alrabah, Hakkani-Tür et al. · University of Illinois Urbana-Champaign·20 min·Jun 29, 2026
  5. 166
    A Router That Beats the Frontier Models It Calls
    Sakana Fugu Technical Report
    Tang, Cetin, Xu et al. · Sakana AI·26 min·Jun 23, 2026
  6. 164
    The Summarizer That Quietly Deletes Your Agent's Safety Rules
    Governance Decay: How Context Compaction Silently Erases Safety Constraints in Long-Horizon LLM Agents
    Chen · Beijing Institute of Technology·28 min·Jun 23, 2026
  7. 157
    When an AI Coding Agent Drives a Phone Through the Terminal, No Screen Needed
    Beyond the GUI Paradigm: Do Mobile Agents Need the Phone Screen?
    Gu, Jiang, Guo et al. · Mila–Québec AI Institute / Concordia University·24 min·Jun 19, 2026
  8. 147
    Agents Fail at the Body, Not the Brain: A Self-Rewriting Scaffold That Lifts a 9B Model 44 Points
    HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry
    Chen, Lu, Zhao et al. · ·30 min·Jun 15, 2026
  9. 146
    How an Innocent README Can Freeze an AI Agent's Safety Check for an Hour
    From Shield to Target: Denial-of-Service Attacks on LLM-Based Agent Guardrails
    Zhou, Wang, Ma et al. · Hong Kong University of Science and Technology·26 min·Jun 15, 2026
  10. 142
    Training a Tiny Model to Run the Plumbing Between an Agent and the World
    HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harness
    Wang, Wang, Taylor et al. · University of California·24 min·Jun 12, 2026
  11. 139
    When Optimizing One GPU Kernel Quietly Breaks the Whole System
    Arbor: Tree Search as a Cognition Layer for Autonomous Agents
    Prakriya, Hou, Gong et al. · AMD·30 min·Jun 12, 2026
  12. 132
    The Agent Failed — But Did the Instructions Deserve to Be Followed?
    SkillAxe: Sharpening LLM-Authored Agent Skills Through Evaluation-Guided Self-Refinement
    Gautam, Radhakrishna, Gulwani · Microsoft·30 min·Jun 11, 2026
  13. 131
    Why Autonomous Research Agents Forget Their Own Lessons, and Arbor's Fix
    Toward Generalist Autonomous Research via Hypothesis-Tree Refinement
    Jin, Hu, Qiu et al. · Renmin University of China·33 min·Jun 11, 2026
  14. 129
    How a Crowd of Anonymous AI Agents Broke a 40-Year Math Record
    Harnessing the Collective Intelligence of AI Agents in the Wild for New Discoveries
    Bianchi, Kwon, Pappu et al. · Together AI·29 min·Jun 11, 2026
  15. 125
    AI Coding Agents Run a Marathon, and Fewer Than One in Three Finish
    SWE-Marathon: Can Agents Autonomously Complete Ultra-Long-Horizon Software Work?
    Desai, Hu, Cabezas et al. · Abundant·27 min·Jun 09, 2026
  16. 121
    When the Agent Says It's Done But Nothing Happened: Debugging the Harness, Not the Model
    From Failed Trajectories to Reliable LLM Agents: Diagnosing and Repairing Harness Flaws
    Chen, Wang, Liu et al. · Institute of Software·27 min·Jun 05, 2026
  17. 120
    How an AI Agent Rewrites Its Own Tools, Without an Answer Key
    Retrospective Harness Optimization: Improving LLM Agents via Self-Preference over Trajectory Rollouts
    Pan, Liu, Lin et al. · City University of Hong Kong·30 min·Jun 05, 2026
  18. 110
    How an Agent Got 44 Points Better by Mining Its Own Scratch Paper
    Inducing Reasoning Primitives from Agent Traces
    Lei, Yan, Momo et al. · Carnegie Mellon University·27 min·Jun 03, 2026
  19. 107
    How a Market of Crippled AI Agents Outscored One Unrestricted Model
    Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions
    Qi, Su, Qu et al. · Harvard·26 min·Jun 03, 2026
  20. 102
    How to Catch an AI Attack That No Single Conversation Reveals
    Stateful Online Monitoring Catches Distributed Agent Attacks
    Brown, Bhargav, Santhanam et al. · University of Pennsylvania·24 min·Jun 01, 2026
  21. 100
    How a Prompt Wrapper Lets a Frontier Model Play Poker Like an Expert
    PokerSkill: LLMs Can Play Expert-Level Poker without Training or Solvers
    Li, Wang, Huang · IIIS·29 min·May 29, 2026
  22. 097
    Same Tokens, Same Cost, Wildly Different Results: What Actually Scales in AI Agents
    Scaling Laws for Agent Harnesses via Effective Feedback Compute
    Zhang, Wang, Xu et al. · Harbin Institute of Technology·25 min·May 29, 2026
  23. 096
    How Treating an AI Agent's Execution Like Git Recovers a Coordination Penalty
    Shepherd: A Runtime Substrate Empowering Meta-Agents with a Formalized Execution Trace
    Yu, Chong, Nandi et al. · Northeastern University·22 min·May 28, 2026
  24. 093
    A Calibrated Knob for Weak-to-Strong AI Oversight, Tested on Real Code
    Calibrating Conservatism for Scalable Oversight
    Overman, Bayati · Stanford Graduate School of Business·22 min·May 28, 2026
  25. 091
    When Better Fine-Tuning Can't Help: A Geometric Impossibility in LLM Causal Reasoning
    Why LLMs Fail at Causal Discovery and How Interventional Agents Escape
    Roy, Parbhoo · SIRE·24 min·May 28, 2026
  26. 088
    Two Levers for Self-Improving AI: When Rewriting Code Isn't Enough
    SIA: Self Improving AI with Harness & Weight Updates
    Hebbar, Manawat, Verboomen et al. · Hexo Labs·25 min·May 27, 2026
  27. 076
    Same Model, Organized Differently: How an Agent Architecture Beat Frontier Systems at Research Math
    RMA: an Agentic System for Research-Level Mathematical Problems
    Zhao, Yuan, Choi et al. · Georgia Institute of Technology·22 min·May 25, 2026
  28. 071
    When the Model Is Fine and the Plumbing Is Broken: Fixing Agents at the Interface
    Adapting the Interface, Not the Model: Runtime Harness Adaptation for Deterministic LLM Agents
    Xu, Wen, Li · Peking University·23 min·May 22, 2026
  29. 063
    Why Web Agents Are Slow: A Compiler-Style Fix for Computer-Use Latency
    Agent JIT Compilation for Latency-Optimizing Web Agent Planning and Scheduling
    Winston, Wang, Mirhoseini et al. · Stanford University·26 min·May 21, 2026
  30. 062
    Treating Hallucinations as Exploits: A Gate-Based Architecture for Agent Safety
    Hallucination as Exploit: Evidence-Carrying Multimodal Agents
    Zhang, Zheng, Yang · Shenzhen University·24 min·May 20, 2026
  31. 061
    When Helpful Agents Go Sideways: A 404 Error, Campus Security, and Why Alignment Misses This
    Agent Meltdowns: The Road to Hell Is Paved with Helpful Agents
    Jha, Triedman, Bhattacharya et al. · Cornell University·27 min·May 20, 2026
  32. 053
    An AI Agent Swapped In Focal Loss And Beat A Human-Tuned Training Script
    Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design
    Pepe, Lin, Magka et al. · FAIR at Meta·32 min·May 18, 2026
  33. 051
    Why Parallel Sampling Plateaus, And What Evidence Graphs Do Instead
    Argus: Evidence Assembly for Scalable Deep Research Agents
    Zhang, Su, Chen et al. · MiroMind AI·22 min·May 18, 2026
  34. 047
    When Agent Benchmarks Lie: The Harness Problem in Open-Source AI
    Orchard: An Open-Source Agentic Modeling Framework
    Peng, Yao, Wu et al. · Microsoft Research·28 min·May 15, 2026
  35. 046
    When the AI Optimizer Edits the Grade Book: Why Harnessing Evolution Needs a Wall
    Harnessing Agentic Evolution
    Zhang, Gu, Ruan et al. · The Hong Kong University of Science and Technology (Guangzhou) / DeepWisdom·24 min·May 15, 2026
  36. 034
    Catching Multi-Agent Deadlocks Before Deployment With a 40-Year-Old Tool
    TraceFix: Repairing Agent Coordination Protocols with TLA+ Counterexamples
    Xia, Li, Ehsan et al. · Rutgers University·30 min·May 11, 2026
  37. 030
    Why Your AI Agent Won't Stop Working — and Each Model Falls for a Different Trap
    LoopTrap: Termination Poisoning Attacks on LLM Agents
    Xu, Wang, Zhang et al. · Zhejiang University·30 min·May 09, 2026
  38. 027
    When AI Agents Build the Serving Stack: A Bet on Bespoke Infrastructure
    VibeServe: Can AI Agents Build Bespoke LLM Serving Systems?
    Kamahori, Li, Peter et al. · University of Washington·30 min·May 08, 2026
  39. 024
    An AI Agent That Found 28 Zero-Days in Windows — And What Made It Work
    Agentic Vulnerability Reasoning on Windows COM Binaries
    Lee, Kim, Zhang · University of Illinois at Urbana-Champaign·22 min·May 07, 2026
  40. 012
    Why AI Coding Agents Keep Trying to Debug Without a Debugger
    Dynamic analysis enhances issue resolution
    Liu, Wang, Chen et al. · Sun Yat-sen University·21 min·May 02, 2026

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