Theme · 124 episode(s)

Agentic AI

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Definition

Agentic AI refers to AI systems that take goal-directed actions over multiple steps in some environment — calling tools, browsing the web, editing files — rather than producing a single response to a single prompt. The shift introduces a new class of risks around autonomy, long horizons, and irreversible actions.

Episodes covering this

  1. 288
    An AI Agent Given Thirty Hours and No Goal, Then Tested on What It Learned
    Is this machine playing?
    Cloos, Norelli, Durbin et al. · MIT·14 min·Oct 07, 2026
  2. 287
    Can You Measure Research Taste If The AI Isn't Allowed To Code?
    TasteVal: Measuring the Experimental Research Taste of AI Systems Against Human Experts
    Jaffe, Sherburn · P-Zero Research·14 min·Oct 06, 2026
  3. 285
    What a Perfect Score Hides: Auditing an AI Agent That Scored 100
    Kepler: Auditable World Models for ARC-AGI-3
    Wu · Independent Researcher·14 min·Oct 02, 2026
  4. 284
    Four AI Models Steered a Real Corolla, and Only One Finished
    DrivingBench: Can Vision-Language Models Drive a Toyota Corolla?
    Ramabadran, Mahns, Gessler·14 min·Oct 01, 2026
  5. 282
    Fifty AI Agents Got One Warning and All Crowded the Same Road
    Warned alike, AI agents avoid the less-crowded road while people take it
    Ezaki, Imura, Nishinari · Research Center for Advanced Science and Technology·14 min·Sep 29, 2026
  6. 281
    When a Guardrail Blocks an Agent, It Goes Looking for Another Route
    Instrumental Monitor Evasion Emerges Under Ordinary Task Pressure
    Schmotz, Prinzhorn, Beurer-Kellner et al. · ELLIS Institute Tübingen·12 min·Sep 27, 2026
  7. 279
    Two Idle Agents, One Kill Switch, and a 38% Sabotage Rate
    Shutdown Sabotage Propensities in Multi-Agent Systems
    Knecht, Schaller, Summerfield et al. · AI Safety Research Group·12 min·Sep 26, 2026
  8. 278
    Every Agent Safety Study Reads a Log the Agent Could Edit
    LLM Agents Can Easily Tamper With Their Own Traces
    Qin, Schmotz, Prinzhorn et al. · ELLIS Institute Tübingen·17 min·Sep 25, 2026
  9. 276
    An AI Agent Rewrote Its Own Scaffolding For Eight Days. Here's What Survived
    Recursive self-improvement of AI research agents
    Srikanth, Zhao, Xu et al. · Weco AI·14 min·Sep 23, 2026
  10. 275
    Your AI Agent Read Your Inbox, Then Quoted a Higher Price
    Et Tu, Brute? Economic Misalignment in Personal AI Agents
    Priyanshu, Vijay, Jabarian et al. · FoundationAI·12 min·Sep 22, 2026
  11. 272
    How a Model Guesses Which Engine Is Running It, From a Wrong Date
    Inference-Engine Fingerprinting Attacks are Practical: Exploring Model-Driven Environmental Discovery, Exploitation, and Escape
    Radway, Cheng, Reddi et al. · Harvard University·11 min·Sep 20, 2026
  12. 270
    The Agent Said It Read 240 Files. The Log Says One.
    Quantifying Overclaiming Propensity in Frontier LLM Agents
    Smyth, Mantilla-Ramos, Notsawo et al. · Tara Research·14 min·Sep 19, 2026
  13. 269
    How a Forged Transcript Got Model Weights Past a Safety Monitor
    Red-Teaming Auto Mode: Improving Blocking Classifiers Against Malign Coding Agents
    Remedios, Storf, Roger et al. · Anthropic Fellows Program·18 min·Sep 18, 2026
  14. 268
    A Rigged Benchmark Taught a Self-Improving Agent to Always Disable SSL
    Reflections on Trusting Trust, Revisited: Contaminating Self-Modifying AI Coding Agents with Poisoned Benchmarks
    Roesner, Kohno · University of Washington·20 min·Sep 18, 2026
  15. 258
    The Same Weights Scored 291, Then 468 — What Changed Was the Loop
    Post-Training Language Models for Gold-Medal Performance in Coding Competitions
    Ficek, Narenthiran, Samadi et al.·25 min·Sep 03, 2026
  16. 257
    They Planted a Shortcut in the Data. Seven Coding Agents Took It.
    BAITBENCH: Measuring Agent Reward Hacking with Optional Shortcuts Planted in ML Tasks
    Prasad, Anto, Eshuijs et al. · National University of Singapore·23 min·Sep 01, 2026
  17. 256
    The Agent That Never Said It Failed, and the Monitor That Noticed
    CURA: Certified Runtime Alarms for Computer-Use Agents
    Kumar, Tayebati, Naik et al. · University of Illinois Chicago·24 min·Aug 31, 2026
  18. 254
    The Tool Description Was the Attack: How Agents Leak Their Own Context
    ContextLeak: Exfiltrating LLM Agent Context via Malicious Tools
    Jia, Wang, Li et al. · Duke University·21 min·Aug 31, 2026
  19. 252
    Stealing an AI Agent's Expertise Without Copying a Word of It
    Daydreaming: Stealing Hidden Agent Skills through Black-Box Task Interaction
    Tsai, Lu, Tsai et al. · UC Berkeley·23 min·Aug 31, 2026
  20. 250
    The Same Model Refused a Backdoor, Then Its Own Sub-Agent Ran It
    When Context Gets Root: Privilege Escalation in LLM Harnesses
    He, Chen, Qian et al. · Nanjing University·23 min·Aug 29, 2026
  21. 245
    Fifteen Models Ran Football Clubs for Twenty Years, and Size Didn't Decide It
    FM-Bench: A Benchmark for Long-Horizon Management with Competing Agents
    Wang, Gao, KezhенChen et al. · AnalogyAI·19 min·Aug 20, 2026
  22. 236
    Why a Printed 'OPERATOR OVERRIDE' Note Redirects Robot Planners
    Hijacking Robots with a Piece of Paper: A Systematic Study of Physical Prompt Injection in VLM-Controlled Robots
    Samarakoon, Muthugala, Sachinthana et al. · Singapore University of Technology and Design·21 min·Aug 07, 2026
  23. 227
    Poisoned Bug Reports Fooled Coding Agents Two Times Out of Three
    IssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests
    Singh, Yang, Chen · Concordia University·18 min·Jul 24, 2026
  24. 224
    The AI Agent That Found the Truth and Typed the Lie Anyway
    DRNOISE: Benchmarking Deep Research Agents in Misleading Evidence Environments
    Nie, Yang, Tang et al. · Hong Kong Baptist University·14 min·Jul 21, 2026
  25. 202
    How Do You Know an AI Agent Actually Refused? Check the World, Not the Words
    Safety Testing LLM Agents at Scale: From Risk Discovery to Evidence-Grounded Verification
    Feng, Lin, Wen et al. · AntGroup / Hunan Institute of Advanced Technology·18 min·Jul 06, 2026
  26. 200
    The One Mechanism That Turns Twenty AI Clones Into an Actual Team
    EVOCHAMBER: Test-Time Co-evolution of Multi-Agent System at Individual, Team, and Population Scales
    Zhang, Xu, Dai et al. · Oregon State University; AG2AI·19 min·Jul 04, 2026
  27. 196
    AI Agents Reached Opposite Conclusions From the Same Data — and Passed Review
    The Agentic Garden of Forking Paths
    Miao, Pritchard, Zou · Stanford University·18 min·Jul 03, 2026
  28. 194
    How a Robot Builds a Debugging Notebook It Can Read, Edit, and Hand to Another Robot
    ASPIRE: Agentic /Skills Discovery for Robotics
    Lu, Wu, Kou et al. · NVIDIA·24 min·Jul 02, 2026
  29. 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
  30. 190
    The Skill Every AI Manager Is Missing: Handing Out Exactly the Right Keys
    ClawArena-Team: Benchmarking Subagent Orchestration and Dynamic Workflows in Language-Model Agents
    Xiong, Ji, Qiu et al. · UNC Chapel Hill·21 min·Jul 02, 2026
  31. 189
    Why Phone Agents Ace the Test and Crash on Your Actual Phone
    Xiaomi-GUI-0 Technical Report
    Team, Qu, Luan · Xiaomi·24 min·Jul 02, 2026
  32. 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
  33. 187
    An 8-Billion Agent That Beats Models 80 Times Its Size By Looking Things Up
    An AI agent for treatment reasoning over a biomedical tool universe
    Gao, Noori, Zhu et al. · Department of Biomedical Informatics·19 min·Jun 30, 2026
  34. 186
    How a Frozen Model Went From 2% to 77% on Physics Puzzles — Without Retraining
    Hierarchical Experimentalist Agents
    Chandra, Vaidyanathan, Dhanuka et al. · University of Massachusetts Amherst·22 min·Jun 30, 2026
  35. 185
    Aligned to Refuse, Built to Tap: When Phone Agents Know the Task Is a Crime and Do It Anyway
    It Lied to a Doctor to Buy Poison Ingredients: Quantifying Real-World Misuse of Phone-use Agents
    Sun, Chen, Zhou et al. · Fudan University·27 min·Jun 30, 2026
  36. 183
    Why You Can't Fine-Tune Foresight Into an AI Agent
    Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning
    Zhang, Zhou, Qiao et al. · Fudan University / Shanghai Innovation Institute / Tencent Youtu Lab·23 min·Jun 29, 2026
  37. 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
  38. 176
    An AI Designed Its Own Psychology Studies, Then Confirmed What It Found
    Closing the Loop to Discover Psychological Theories with an Automated Cognitive Scientist
    Jagadish, Strittmatter, Jacoby et al. · Princeton University·31 min·Jun 26, 2026
  39. 175
    One Crosscoder Feature Flips a Stalling Chatbot Into a Working Agent
    Localizing RL-Induced Tool Use to a Single Crosscoder Feature
    Shportko, Bhokare, AlZahrani et al. · Northwestern University·26 min·Jun 26, 2026
  40. 168
    When Turning Experience Into Code Makes Your AI Agent Dumber
    Metis: Bridging Text and Code Memory for Self-Evolving Agents
    Dai, He, Li et al. · The Chinese University of Hong Kong·27 min·Jun 24, 2026
  41. 167
    How Teaching an AI to Predict, Not Act, Made It a Better Actor
    Qwen-AgentWorld: Language World Models for General Agents
    Team, Zuo, Xiao et al.·27 min·Jun 24, 2026
  42. 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
  43. 165
    A Free-Lunch Tweak That Lets a Tiny Agent Beat Frontier Giants
    Group-Graph Policy Optimization for Long-Horizon Agentic Reinforcement Learning
    Wang, Song, Zhang et al. · Peking University·22 min·Jun 23, 2026
  44. 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
  45. 161
    A Robot That Plays Before You Give It a Job, And Why That Beats Retrying
    Playful Agentic Robot Learning
    Zhang, Ge, Yoo et al. · University of California·19 min·Jun 19, 2026
  46. 160
    Training an AI to Take Its Own Notes, So Its Future Self Works Better
    Connect the Dots: Training LLMs for Long-Lifecycle Agents with Cross-Domain Generalization Via Reinforcement Learning
    Chen, Shi, Xie et al. · Alibaba Group·23 min·Jun 19, 2026
  47. 159
    Can a Coding Agent Run Its Own Robot Experiments Overnight, With No Human Resetting the Scene?
    ENPIRE: Agentic Robot Policy Self-Improvement in the Real World
    Xiao, Xie, Zhang et al. · NVIDIA·23 min·Jun 19, 2026
  48. 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
  49. 156
    Why More Human Demonstrations Made a Computer-Use Agent Worse
    ProCUA-SFT Technical Report
    Jung, Lu, Cui et al. · NVIDIA / University of Washington·20 min·Jun 18, 2026
  50. 155
    Why a Flawless Demo Makes a Worse Computer-Using Agent, And the Fix
    Skill-Guided Continuation Distillation for GUI Agents
    Fan, Yu, Shen et al. · StepFun·22 min·Jun 18, 2026
  51. 154
    How a 7B Model Out-Investigates a 72B One by Choosing What to Look At
    Native Active Perception as Reasoning for Omni-Modal Understanding
    Xing, Xu, Wang et al. · The Chinese University of Hong Kong·21 min·Jun 18, 2026
  52. 151
    Why More Experience Made This AI Agent Worse, And How to Fix It
    Not All Skills Help: Measuring and Repairing Agent Knowledge
    Wang, Zhou, Liang et al. · UNC Chapel Hill·28 min·Jun 16, 2026
  53. 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
  54. 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
  55. 144
    When an AI Agent Just Copies Its Tool — And Bigger Models Copy More
    When the Tool Decides: LLM Agents Defer Blindly to Graph Neural Network Tools, and Stronger Backbones Defer More
    Wang, Vemuri · raptorX.ai·15 min·Jun 15, 2026
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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
  61. 123
    Five Identical Worlds, One Swapped Model: What Happens When AI Agents Run for Fifteen Days
    Emergence World: A Platform for Evaluating Long-Horizon Multi-Agent Autonomy
    Akkil, Kokku, Vikram et al. · Emergence AI·30 min·Jun 09, 2026
  62. 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
  63. 119
    Beating Reinforcement Learning Without Ever Touching the Model's Weights
    Agentic Monte Carlo: Simulating Reinforcement Learning for Black-Box Agents
    Hwang, Suri, Villecroze et al. · Layer6 AI·22 min·Jun 05, 2026
  64. 114
    Agents That Rewrite Their Own Weights Instead of Just Taking Notes
    Scaling Self-Evolving Agents via Parametric Memory
    Ren, Luo, Yang et al. · Peking University / Alibaba Group·26 min·Jun 04, 2026
  65. 113
    What If a Prompt Injection Never Left? Attacks That Wait in Agent Memory
    What If Prompt Injection Never Left? Exploring Cross-Session Stored Prompt Injection in Agentic Systems
    Xie, Liu, Zhang et al. · Institute of Information Engineering·27 min·Jun 04, 2026
  66. 112
    When an AI Agent Cheats Without Being Told: Inside the Meta-Agent Challenge
    The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development?
    Lu, Wang, Wang et al. · Institute of Software·22 min·Jun 04, 2026
  67. 111
    How a 4B Web Agent Beat Models 60x Its Size on 500 Demonstrations
    OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents
    Yang, Wu, Chen et al. · UIUC·24 min·Jun 03, 2026
  68. 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
  69. 109
    An AI Got Caught Reading the Answer Key, And Why That Catch Matters
    EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
    Chen, Shi, Li et al. · Shenzhen Institutes of Advanced Technology·28 min·Jun 03, 2026
  70. 105
    The Trojan Is Your Agent's Memory: Why Single-Step Defenses Miss Persistent Attacks
    From Prompt Injection to Persistent Control: Defending Agentic Harness Against Trojan Backdoors
    Tan, Dou, Yang et al. · Gaoling School of Artificial Intelligence·26 min·Jun 01, 2026
  71. 104
    How Making a Research Agent Smarter Quietly Makes It Leak Your Secrets
    MosaicLeaks:Privacy Risks in Querying-in-the-Open for Deep Research Agents
    Gurung, Gella, Drouin et al. · University of Edinburgh·25 min·Jun 01, 2026
  72. 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
  73. 101
    Treating Math Formalization Like a Codebase, and Where the Agents Cheat
    Formalizing Mathematics at Scale
    Rammal, Patel, Gloeckle et al. · FAIR at Meta / CERMICS·27 min·May 29, 2026
  74. 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
  75. 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
  76. 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
  77. 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
  78. 090
    How MiniMax-M2 Bets That Sparsity Plus Verifiable Rewards Can Match Frontier Agents
    The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
    MiniMax · MiniMax·28 min·May 27, 2026
  79. 089
    When AI-Written Papers Read Well But the Evidence Underneath Is Broken
    ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence
    Meng, Mishra, Chen et al. · Google Cloud AI Research·32 min·May 27, 2026
  80. 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
  81. 083
    Training the Translator: How a Small Communication Model Lets Agent Teams Outperform Themselves
    AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning
    Hu, Qian, Wang et al. · GSAI·24 min·May 26, 2026
  82. 082
    Training a Deep Research Agent on 8,000 Synthetic Tasks: The Rubric Tree Trick
    QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks
    Xie, Lin, Wang et al. · The Ohio State University·31 min·May 26, 2026
  83. 080
    How a Two-Agent Trick Unlocked Large-Scale Training for Computer-Use Agents
    CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents
    Wang, Lu, Wang et al. · The University of Hong Kong·32 min·May 26, 2026
  84. 078
    Training a Markdown File: When LLM Self-Improvement Borrows the Discipline of Neural Net Training
    SkillOpt: Executive Strategy for Self-Evolving Agent Skills
    Yang, Gong, Huang et al. · Microsoft·28 min·May 25, 2026
  85. 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
  86. 072
    A Robot Made Graphene Without Help, And Caught Itself Hallucinating
    Qumus: Realization of An Embodied AI Quantum Material Experimentalist
    Shi, Zheng, Juan et al. · Princeton University·29 min·May 23, 2026
  87. 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
  88. 068
    The OS Trick That Makes Tree Search Practical for Coding Agents
    DeltaBox: Scaling Stateful AI Agents with Millisecond-Level Sandbox Checkpoint/Rollback
    Dong, He, Hou et al. · Institute of Parallel and Distributed Systems·27 min·May 22, 2026
  89. 067
    An AI Just Solved a 1996 Erdős Problem—and the Simplest Agent Won
    Advancing Mathematics Research with AI-Driven Formal Proof Search
    Tsoukalas, Kovsharov, Shirobokov et al. · Google DeepMind·31 min·May 22, 2026
  90. 066
    Why Giving an AI Agent More Tools Can Make It Worse at Using a Computer
    ToolCUA: Towards Optimal GUI-Tool Path Orchestration for Computer Use Agents
    Hu, Zhang, Xu et al. · Tongyi Lab·26 min·May 22, 2026
  91. 065
    One Loop to Optimize Them All: A Universal API for LLM-Driven Discovery
    optimize_anything: A Universal API for Optimizing any Text Parameter
    Agrawal, Lee, Tan et al. · UC Berkeley·27 min·May 22, 2026
  92. 064
    When Agent Memory Stops Being a Database and Starts Being a Skill
    Auto-Dreamer: Learning Offline Memory Consolidation for Language Agents
    Ye, Liu, Wang et al. · University of Illinois Urbana-Champaign·30 min·May 22, 2026
  93. 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
  94. 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
  95. 059
    Firefly's Inversion: Building Verified Tool-Call Training Data by Working Backward
    Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs
    Lu, Wang, Lu et al. · Northeastern University·22 min·May 20, 2026
  96. 058
    Why Upgrading Your AI Auditor to a Smarter Model Can Make Your System Less Safe
    The Capability Paradox: How Smarter Auditors Make Multi-Agent Systems Less Secure
    Liu, Holz, Ye et al. · University of Chinese Academy of Sciences·32 min·May 19, 2026
  97. 057
    How Uber Caught 206 Leaked Credentials With an LLM-Powered Security Stack
    ADR: An Agentic Detection System for Enterprise Agentic AI Security
    Li, Hu, Xu et al. · Uber Technologies·28 min·May 19, 2026
  98. 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
  99. 052
    An Old Reinforcement Learning Tradeoff Sneaks Back Into LLM Agents
    Look Before You Leap: Autonomous Exploration for LLM Agents
    Ye, Shi, Liu et al. · University of Science and Technology of China / Meituan·23 min·May 18, 2026
  100. 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
  101. 049
    An AI Agent Reached for Root in Twelve Minutes, Without Being Attacked
    Ambient Persuasion in a Deployed AI Agent: Unauthorized Escalation Following Routine Non-Adversarial Content Exposure
    Cuadros, Maiga · Digital Epidemiology Laboratory·28 min·May 17, 2026
  102. 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
  103. 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
  104. 044
    How One Sentence and a Forged History Flip the Most Aligned Models
    History Anchors: How Prior Behavior Steers LLM Decisions Toward Unsafe Actions
    Salgado · Independent Researcher·23 min·May 15, 2026
  105. 042
    An Agentic Scientific Computing System That Actually Remembers What It Learns
    GRAFT-ATHENA: Self-Improving Agentic Teams for Autonomous Discovery and Evolutionary Numerical Algorithms
    Toscano, Chai, Karniadakis · Division of Applied Mathematics·30 min·May 13, 2026
  106. 039
    When Smarter Agents Get Fooled by Three Extra Nodes in a Database
    Oracle Poisoning: Corrupting Knowledge Graphs to Weaponise AI Agent Reasoning
    Kereopa-Yorke, Diaz, Wright et al. · Microsoft·31 min·May 12, 2026
  107. 035
    Why Frontier Agents Ask for Clarification at Exactly the Wrong Moment
    Ask Early, Ask Late, Ask Right: When Does Clarification Timing Matter for Long-Horizon Agents?
    Gulati, Gupta, Lumer et al. · PricewaterhouseCoopers U.S.·29 min·May 11, 2026
  108. 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
  109. 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
  110. 029
    Why Forty-Eight Percent on FrontierMath Isn't the Real Story in DeepMind's New Math Paper
    AI Co-Mathematician: Accelerating Mathematicians with Agentic AI
    Zheng, Glehn, Zwols et al. · Google DeepMind·20 min·May 08, 2026
  111. 027
    When AI Agents Build the Serving Stack: A Bet on Bespoke Infrastructure
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  116. 016
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  119. 012
    Why AI Coding Agents Keep Trying to Debug Without a Debugger
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  120. 011
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  121. 008
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  122. 005
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  123. 003
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