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cs.AI2026
Plans Don't Persist: Why Context Management Is Load Bearing for LLM Agents
Aman Mehta, Anupam Datta
Long-horizon agents depend on context management: systems compress, summarize, and evict old tokens so tasks can continue beyond finite windows. That is safe only when dropped info…
cs.AI2026
When Agents Commit Too Soon: Diagnosing Premature Commitment in LLM Agents
Aman Mehta
Long-horizon LLM agents can fail quietly: they settle on one reading of the evidence early, then spend the rest of the run defending it. We call this premature commitment. Final-an…
cs.AI2026
When Agents Disagree With Themselves: Behavioral Consistency as an Uncertainty Signal for LLM Agents
Aman Mehta
Running the same LLM agent on identical inputs yields 2.3-4.2 distinct action sequences per 10 runs; this behavioral variance constitutes a training-free, black-box uncertainty sig…