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.
When Evidence is Sparse: Weakly Supervised Early Failure Alerting in Dialogs and LLM-Agent Trajectories
arXiv:2606. 05414v1 Announce Type: cross Abstract: Early failure alerting requires deciding, while a dialog or agent trajectory is still unfolding, whether to flag it as likely to fail.
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arXiv AI
Jul 23
Stateful Guardrails for Multi-Turn LLM Systems: A Conversational Risk Accumulation Framework
arXiv:2607. 19361v1 Announce Type: cross Abstract: Most safety guardrails for large language models (LLMs) evaluate each prompt-response pair in isolation, which misses failures that arise only over a dialogue as benign turns compose into harm.
arXiv AI
Jun 3
StepFinder: A Temporal Semantic Framework for Failure Attribution in Multi-Agent Systems
arXiv:2606. 03467v1 Announce Type: new Abstract: LLM-based multi-agent systems exhibit remarkable collaborative capabilities in complex multi-step tasks.
arXiv Machine Learning
Jul 30
Forecasting Trajectory-Level Safety Risks in Black-Box Multi-Turn Interactions
arXiv:2607. 26820v1 Announce Type: new Abstract: As large language models (LLMs) evolve from standalone assistants into autonomous agents, ensuring their safety requires shifting beyond pointwise risk assessment to understand how risks emerge and unfold over long-horizon trajectories.
arXiv Machine Learning
Jun 10
FailureScope: Cross-Regime Behavioral Diagnosis of Language Model Weaknesses
arXiv:2606. 09878v1 Announce Type: new Abstract: Standard benchmarks report aggregate accuracy, but practitioners need to know which specific capabilities a model lacks.
arXiv AI
Jun 2
FALAT: Tracing Failures in LLM Agent Trajectories via Dependency-Guided Search
arXiv:2606. 00765v1 Announce Type: new Abstract: LLM-based agents increasingly solve complex tasks through long trajectories involving reasoning steps, tool calls, and inter-agent communication.
Hugging Face Trending Papers
Jul 7
Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade
Large language model (LLM) agents solving multi-step tasks frequently commit to trajectories that are doomed to fail, yet continue to consume substantial inference compute before the failure becomes observable. We show that failure is predictable early from the agent's internal representations: lightweight per-round probes on hidden activations anticipate eventual episode failure as early as the first interaction round, where scorers reading only the agent's observable behavior are barely better than chance.
arXiv AI
Jul 8
Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade
arXiv:2607. 06503v1 Announce Type: new Abstract: Large language model (LLM) agents solving multi-step tasks frequently commit to trajectories that are doomed to fail, yet continue to consume substantial inference compute before the failure becomes observable.
arXiv Machine Learning
Jun 11
When is Your LLM Steerable?
arXiv:2606. 11599v1 Announce Type: cross Abstract: Activation steering offers a lightweight approach to control language models' behavior at inference time, but whether it succeeds or fails heavily depends on the prompt, concept, model, and steering configuration.
arXiv AI
1d ago
Intent-Driven Situation Tracking for User-Centric Multi-Turn Agents
arXiv:2608. 15755v1 Announce Type: new Abstract: User-centric multi-turn agents must act on an evolving task situation shaped by changing user intents, accumulated tool-grounded facts, missing information, and execution constraints.
arXiv Machine Learning
Jul 21
FAME: Failure-Aware Mixture-of-Experts for Message-Level Log Anomaly Detection
arXiv:2605. 22779v2 Announce Type: replace-cross Abstract: Production systems generate millions of log lines daily, yet most anomaly detectors operate at the session or window-level, flagging groups of lines rather than identifying the specific message responsible.
arXiv AI
Aug 10
Gated-BEPO: Confidence-Gated Bellman Credit Assignment for Large Language Model Agents
arXiv:2608. 06861v1 Announce Type: new Abstract: Training large language model agents in long-horizon environments requires assigning credit from sparse terminal outcomes to individual actions.