The papers behind how we build

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2 entries

1. 2026PreprintCurated

   SilentProbe: Measuring Silent Failure in Production APIs Used as Agent Tools

   Li et al.

   An audit of what happens when an agent calls a production API that answers 200 OK with a parsable body while having silently failed. Across 721,320 parameters in 2,501 independently published OpenAPI documents, 7.5% declare an enumeration and 15.2% declare any machine-checkable constraint at all, while 40.1% of documents state at least one constraint in prose their schema does not encode. Executing 219 schema-derived perturbations against live endpoints from 27 vendors, the authors find that constraint form rather than vendor identity predicts honesty: machine-checkable constraints produced an honest error in 111 of 111 cases, prose-only constraints failed silently in 44 of 61 (p = 2e-13). Twelve models across eight families then met these endpoints: a vocabulary the description merely exemplified was missed on 88 of 88 attempts, while vocabularies written out in full were used correctly 88 to 91% of the time. Running the full agent loop, models detected the silent failure in 12% of cases, repaired it in 0%, asserted a false negative to the user in 41%, and invented a figure in 12%. Promoting the vocabulary into the schema removes the failure, from 88 of 88 to 0 of 89.

   Agents & Tool UseEvaluation

   arXiv
2. 2026PreprintCurated

   Whose Side Is Your Agent On? Multi-Party Principal Loyalty in LLM Agents

   Li et al.

   A study of multi-party LLM agents, which act for a principal who briefs them and receives results while conversing in a separate channel with a counterparty whose interests may diverge, as in negotiating with a vendor, screening inbound requests or mediating between employees. The authors argue that "help whoever you are talking to" is the wrong objective and formalise the resulting loyalty problem. PrincipalBench is a 75-item multi-turn benchmark with leak probes, dual judges and an integrity-audit gate. Across 13 frontier subjects it exposes a sharp split, with one cluster at or below 20% harm and another at 53.6 to 75.3%, a split that single-turn safety evaluations do not surface: a selective cluster that declines adversarial probes while still following the principal's legitimate requests, and an over-refusing cluster that refuses broadly. A prompt-time loyalty scaffold of seven prioritised rules, open-coded from over 50 failure trajectories, holds Claude-Sonnet to 19.4% harm and all nine selective subjects at or below 20%. A per-token KL distillation recipe transfers a prompted Qwen3-32B teacher into 8B Qwen3 and Llama-3.1 students. Both mechanisms move along a common leak/over-refusal trade-off rather than crossing it.

   Agents & Tool UseAlignment

   arXiv

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