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The paper proposes that self‑evolving large‑language‑model agents can only be safely reviewed if their changes are confined to the runtime harness—instruction text, tool‑call logic, and primitive composition—while the model weights remain fixed. A dual‑loop engine with an admission gate records a hash‑chained diff before deployment, and simulation experiments show that this gate admits only safe changes, preventing error increases and maintaining false‑positive rates. When the gate is removed, many harmful changes slip through and missed flags rise, illustrating the gate’s protective role.
arXiv:2609.38512v1 Announce Type: new Abstract: Voice agents in production must handle several requests, background speech, and customers who lose patience. We introduce VAmoS Energy, a benchmark tha...
arXiv:2610.25458v2 Announce Type: replace Abstract: Small language models have improved to the point where a phone can plausibly screen its own calls, answering on an absent owner's behalf without se...
Self-improving LLM agents can adapt a credit pipeline to a changed rule, but an agent that rewrites itself destroys the artefact a supervisor reviews: a named change, a recorded test, an approval. We...
BENCHCOMPASS is a new payment‑domain benchmark that transforms typed evidence packs into scenario‑grounded tasks, applies LLM‑based quality checks, generates attack variants, and reserves final item admission for domain experts. It includes an expert‑reviewed Pro benchmark covering payment knowledge, context‑grounded scenario reasoning, and attacked open robustness, plus a lower‑assurance Normal pool. Across 16 model variants, BENCHCOMPASS reveals distinct failure modes—missing payment knowledge, incomplete reasoning, and failure to reject invalid workflows—while the best model scores 89.6% on open context‑grounded reasoning and 81.7% under attacked inputs. "whyItMatters":"The benchmark provides a structured way to isolate and evaluate specific weaknesses in LLMs for payment operations, a critical financial infrastructure where rules change rapidly and decisions depend on complex contextual factors."
arXiv:2607. 08028v1 Announce Type: new Abstract: Enterprise large language model (LLM) applications often begin as prototypes whose behavior is carried by prompts and retrieval context.