Theme · 100 episode(s)

Agentic AI

← all concepts

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. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. 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
  52. 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
  53. 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
  54. 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
  55. 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
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. 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
  63. 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
  64. 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
  65. 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
  66. 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
  67. 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
  68. 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
  69. 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
  70. 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
  71. 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
  72. 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
  73. 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
  74. 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
  75. 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
  76. 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
  77. 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
  78. 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
  79. 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
  80. 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
  81. 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
  82. 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
  83. 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
  84. 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
  85. 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
  86. 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
  87. 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
  88. 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
  89. 023
    Why a Small Agent Confidently Overwrites Memories It Doesn't Understand
    What Happens Inside Agent Memory? Circuit Analysis from Emergence to Diagnosis
    Mao, Zhao, Penn et al. · City University of Hong Kong·23 min·May 07, 2026
  90. 022
    Training the Model Spec Directly: An Alignment Lever Aimed at the Say-Do Gap
    Model Spec Midtraining: Improving How Alignment Training Generalizes
    Li, Price, Marks et al. · Anthropic Fellows Program·32 min·May 06, 2026
  91. 017
    When the Agent Grades Its Own Homework: A Brutal New Benchmark for AI Workers
    Gym-Anything: Turn any Software into an Agent Environment
    Aggarwal, Neubig, Welleck · CMU·31 min·May 03, 2026
  92. 016
    Why Your Coding Agent Stalls While the GPU Runs Hot
    MARS: Efficient, Adaptive Co-Scheduling for Heterogeneous Agentic Systems
    Wang, Ye, Xu et al. · Duke University·24 min·May 03, 2026
  93. 014
    Why a Constrained Pipeline Beat a Full Coding Agent at Finding Bugs 30-to-1
    Guiding Symbolic Execution with Static Analysis and LLMs for Vulnerability Discovery
    Shafiuzzaman, Desai, Guo et al. · University of California·32 min·May 03, 2026
  94. 013
    Why Search Keeps Rediscovering the Same Workflow, and What That Means
    Why Search When You Can Transfer? Amortized Agentic Workflow Design from Structural Priors
    Du, Liu, Du et al. · Carnegie Mellon University·22 min·May 03, 2026
  95. 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
  96. 011
    When RL Actually Teaches Agents Something New, And When It Doesn't
    Does RL Expand the Capability Boundary of LLM Agents? A PASS@(k,T) Analysis
    Zhai, Yan, Shao et al. · Fudan University·23 min·May 02, 2026
  97. 008
    Why Long-Horizon AI Agents Get Stuck, and a Milestone-Based Fix That Helps
    A Subgoal-driven Framework for Improving Long-Horizon LLM Agents
    Wang, Gooding, Hartmann et al. · Google DeepMind·24 min·May 02, 2026
  98. 005
    Why a Debugger Designed for Humans Is the Wrong Tool for an AI Agent
    Empowering Autonomous Debugging Agents with Efficient Dynamic Analysis
    Xiang, Xu, Chu et al. · Southern University of Science and Technology·22 min·May 01, 2026
  99. 003
    How to Pick the Best of Sixteen Coding Agent Rollouts
    Scaling Test-Time Compute for Agentic Coding
    Kim, Yang, Niu et al. · Meta Superintelligence Labs / University of Washington·17 min·May 01, 2026
  100. 002
    An AI Ran a Real Optics Lab for 21 Hours and Found a Transformer-Shaped Pattern in Light
    End-to-end autonomous scientific discovery on a real optical platform
    Yang, Chen, Zhao et al. · Zhejiang University·29 min·May 01, 2026

Worth reading next

Papers we haven't done a deep dive on yet, but would recommend on this topic.