The authors studied terminal agents completing long-horizon software tasks.
Terminal agents struggle when familiar environmental assumptions stop holding
Across three terminal benchmarks, targeted environmental changes exposed failures to diagnose problems and revise otherwise successful strategies.
Big Tech
Janvijay Singh · Vaishnavi Shrivastava · Dilek Hakkani-Tur · Ece Kamar · Asli Celikyilmaz
University of Illinois Urbana-Champaign · Microsoft Research AI Frontiers
Research Digest··2 min read
Singh and colleagues introduce AGNI, a pipeline that identifies assumptions behind successful agent trajectories, invalidates them through controlled environmental changes, and verifies that the modified tasks remain solvable.
Why this paper
From Microsoft Research AI Frontiers and University of Illinois Urbana-Champaign
In one line
LLM agents fail to adapt when environmental assumptions change, but training on such novelties improves both adaptation and base task performance.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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