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3 entries

1. 2026AI4GOOD and EIML workshops, I…PublishedCurated

   Authority, Truth, and Citation Bias: A Large-Scale Multi-Domain Benchmark for Studying Epistemic Susceptibility in Large Language Models

   Khurana et al.

   Introduces AuthorityBench, a 220,564-prompt multi-domain benchmark that isolates how citation-based authority signals influence epistemic behaviour in LLMs. It uses a fully balanced 2x2 factorial design crossing claim veracity with citation veracity across four domains, with controlled variation over 40 prompt templates, four venue prestige tiers and a country-coded author name dataset. Evaluating seven models, the authors find that citation presence, whether real or fabricated, consistently increases hallucination rates relative to a no-citation baseline, with the effect strongest when fabricated citations accompany true claims.

   EvaluationAlignment

   arXiv
2. 2026PreprintCurated

   IH-Benchmark: A Conflict-Centered Benchmark for Instruction-Hierarchy Robustness in LLM Applications

   McCauley et al.

   A benchmark for what happens when a model receives conflicting instructions from different priority levels, built from a human-authored taxonomy of 44 constraint families across generic, health, finance, retail and coding settings. Across 37 evaluated models, hierarchy compliance ranges from 98.2% to 20.5%. The authors report that strong system-over-user compliance is not a reliable proxy for user-over-tool robustness: several models hold system constraints under direct user conflict but degrade sharply when the conflicting instruction arrives inside a tool output.

   EvaluationAlignment

   arXiv
3. 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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