Towards Data Science
Aug 2
How to Apply Coding Agents to Non-Programming Tasks
Perform non-programming tasks with coding agents The post How to Apply Coding Agents to Non-Programming Tasks appeared first on Towards Data Science .
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
Towards Data Science
Aug 2
I Replaced a 15-Minute Booking Process with a LangGraph AI Agent
A step-by-step guide to building, running, and monitoring a stateful customer support agent using Python, LangGraph, and Langfuse. The post I Replaced a 15-Minute Booking Process with a LangGraph AI Agent appeared first on Towards Data Science .
Hugging Face Trending Papers
Aug 2
Control Under Compression: Reliability Frontiers for Tool-Using Agents
Tool-using language-model agents are governed not only by task prompts but also by persistent system-side instructions that specify tools, arguments, policies, execution protocols, and recovery. Compressing these agent control contexts (ACCs) can reduce input cost and context use, yet existing prompt-compression evaluations do not reveal whether the resulting control remains operationally reliable.
Hugging Face Trending Papers
Aug 2
TrajWiki: Source-Grounded Memory Trajectories for Long-Horizon Dialogue Agents
Large language model agents have shown strong capabilities in generating coherent and contextually appropriate responses, yet robust long-horizon dialogue remains limited by the lack of external memory that is traceable, updatable, and diagnostically transparent. Existing memory-augmented agents often store memories as isolated records or overwritable states, making it difficult to preserve how information originates, evolves, conflicts, or becomes obsolete over time.
Towards Data Science
Aug 1
Coding Agents Don’t Need Bigger Context Windows — They Need a Context Compiler
Most coding agents treat prompt construction like retrieval: gather more files, add more context, hope the model figures it out. But that approach breaks down fast.
Towards Data Science
Aug 1
Put the Agent Inside the Workflow
A hybrid LLM application pattern that combines a predefined workflow with adaptive agent behavior The post Put the Agent Inside the Workflow appeared first on Towards Data Science .
Towards Data Science
Jul 31
The 3× Token Bill We Didn’t See Coming
How a seemingly harmless move to a multi-agent architecture quietly tripled our LLM costs and what actually fixed it. The post The 3× Token Bill We Didn’t See Coming appeared first on Towards Data Science .
Towards Data Science
Jul 31
When the Code Becomes the CEO: Why Your Next Manager Might Be a Decentralized Agentic Loop
In five to ten years, the sharpest manager in your company might not be human, might not sleep, and might exist entirely in shared GPU memory. This is the systems-level view of the algorithmic corporation — why middle management collapses into a protocol, what breaks in the current AI stack, and what has to be built for autonomous agents to actually run a business.
Towards Data Science
Jul 31
How to Debug AI Coding Agents When They Change the Wrong Thing
A practical tutorial for recording model tool requests, real function results, patches, checks, screenshots, and a saved run log. The post How to Debug AI Coding Agents When They Change the Wrong Thing appeared first on Towards Data Science .
arXiv Machine Learning
Jul 31
Group-Reflective Self-Distillation for Agentic Reinforcement Learning
arXiv:2607. 28076v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is effective for training large language model agents.
arXiv AI
Jul 31
AgentGUI: An Interface for Observing and Steering Long-Running AI Agents
arXiv:2607. 26300v1 Announce Type: cross Abstract: AI agents are increasingly adept at tackling complex, long-running tasks.
arXiv AI
Jul 31
Exploring Structures in Physics Problems: Can AI Agents Discover Statistical Mechanical Mappings?
arXiv:2607. 26367v1 Announce Type: new Abstract: An important skill in theoretical physics is to recognize when a new problem can be transformed into a known model.
arXiv Machine Learning
Jul 31
Continuous-time reinforcement learning for optimal switching over multiple regimes
arXiv:2512. 04697v3 Announce Type: replace-cross Abstract: This paper studies the continuous-time reinforcement learning (RL) for optimal switching problems across multiple regimes.
arXiv Machine Learning
Jul 31
EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agents
arXiv:2607. 28229v1 Announce Type: cross Abstract: The web is increasingly accessed by AI agents rather than humans.
arXiv AI
Jul 31
SimpleWikiSearch: A Clean Offline Wikipedia Environment for Agentic Search
arXiv:2607. 26070v1 Announce Type: cross Abstract: Large language model (LLM)-based agentic search systems are often evaluated as if the underlying LLM were the only component that matters, yet their measured performance also depends on the surrounding search environment: the Wikipedia snapshot, preprocessing pipeline, chunking policy, retrieval backend, tool schema, observation format, and answer submission rule.
arXiv AI
Jul 31
A First Look at Coding Agents' Compliance with AI Contribution Rules in Open-Source Communities
arXiv:2607. 26819v1 Announce Type: cross Abstract: Open source communities have been flooded with AI-generated contributions.
arXiv AI
Jul 31
AI as Friction for Reflection Support in Ideation
arXiv:2607. 26827v1 Announce Type: cross Abstract: Generative AI tools for creative work tend to be designed around the goal of removing friction, on the assumption that smoother iteration and faster output translate into more value for the designer.
arXiv AI
Jul 31
Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork
arXiv:2607. 27177v1 Announce Type: new Abstract: Effective collaboration with novel and diverse partners is a crucial skill for autonomous agents.
arXiv AI
Jul 31
Large-Scale ChatBot Validation Through Customer Digital Twin Simulations
arXiv:2607. 26060v1 Announce Type: cross Abstract: LLM-based chatbots are transforming customer service in regulated domains such as banking, but scalable and cost-effective validation remains a critical barrier to safe deployment.
arXiv AI
Jul 31
The Age of AI Agents Demands A New Scientific Paradigm To Sustain Trustworthy Science
arXiv:2607. 26064v1 Announce Type: cross Abstract: AI systems are becoming autonomous research agents that generate hypotheses, design experiments, and produce discoveries at scales beyond human oversight.