Hugging Face Trending Papers
Using Grounded Theory for Agent Behavior Analysis at Scale
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arXiv AI
Sep 1
Using Grounded Theory for Agent Behavior Analysis at Scale
arXiv:2608.30391v1 Announce Type: cross Abstract: Understanding agent behavior requires methods that scale to thousands of trajectories and surface new patterns in long, often unfamiliar tasks where...
arXiv AI
Sep 1
AgentRx: Diagnosing AI Agent Failures from Execution Trajectories
arXiv:2602.02475v2 Announce Type: replace Abstract: AI agents often fail in ways that are difficult to localize because executions are probabilistic, long-horizon, multi-agent, and mediated by noisy...
arXiv AI
Jun 8
Insights Generator: Systematic Corpus-Level Trace Diagnostics for LLM Agents
arXiv:2605. 21347v3 Announce Type: replace Abstract: Diagnosing failures in LLM agents remains largely manual.
arXiv AI
Jun 9
Projecting the Emerging Mindset of SWE Agent by Launching a Wild Code Understanding Journey
arXiv:2606. 08500v1 Announce Type: cross Abstract: Software engineering agents (SWE agents) increasingly work through tool-mediated trajectories in real repositories, yet their behavior remains difficult to characterize in concrete, observable terms.
arXiv AI
Sep 11
Do Agents Know When They Succeed? Calibrating Agent Confidence from Internal Representations
The paper examines whether internal representations of agentic systems can better indicate task success than traditional confidence measures. It introduces two methods—Latent Trajectory Dynamics (LTD) and Action Representation Probe (ARP)—that analyze changes in residual-stream representations and action-level representations, respectively. Experiments on Bash, SQL, and Python benchmarks with Qwen and DeepSeek models show these methods outperform conventional surface-level and sequence-based calibration baselines, offering a zero‑overhead reliability monitor without prompt changes or multiple rollouts.
arXiv AI
Jul 15
Tracing Agentic Failure from the Flow of Success
arXiv:2607. 12747v1 Announce Type: new Abstract: Failure attribution for LLM-based agentic systems, i.