New Methods Use Agent Internal States to Predict Success in Multi‑Turn Tasks
Researchers have introduced two techniques—Latent Trajectory Dynamics (LTD) and Action Representation Probe (ARP)—to gauge the likelihood of success in agentic systems by examining internal model representations. The study focuses on multi‑turn interactions in three safety‑critical benchmarks: Bash, SQL, and Python, and evaluates three large language models: Qwen14B, Qwen7B, and DeepSeek6.7B.
Key points
- LTD tracks residual‑stream changes across interaction trajectories.
- ARP predicts success from representations at action decisions.
- Both methods beat surface‑level and sequence‑based baselines on Bash, SQL, and Python benchmarks.
LTD captures how residual‑stream representations evolve over an entire interaction trajectory, while ARP predicts success at each action decision point. Both methods operate without additional prompts or rollouts, offering a zero‑overhead reliability monitor.
Across all benchmarks and model families, LTD and ARP consistently outperform traditional surface‑level generation and sequence‑based calibration baselines, providing a more accurate confidence signal that could help safeguard agentic deployments in real‑world applications.
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