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Life sciences · Preprint
arXiv · September 4, 2026
Raises a question worth testing. It does not answer one.
This preprint proposes SiLR, a structure-preserving admission gate for LLM tool agents that uses product-order reasoning over branch-level violation states rather than scalar projection. Empirical results on Gym-ANM and CityLearn benchmarks show SiLR recovers 21/21 multi-action episodes versus 0/21 for terminal gates, and demonstrates robustness against magnitude-redistribution attacks. The work is unpublished and addresses a computer science problem with no direct medical or clinical relevance.
Preprint. Simulated power grid and urban energy management environments (Gym-ANM and CityLearn); three LLM model families evaluated.. Intervention: SiLR: structure-preserving admission gate using product order over branch-level violation state (overloaded-branch support and per-branch severity).. Compared with: Scalar projection gates, terminal gate, support-only baseline; scalar projection as GRPO process reward..
SiLR recovers 21/21 multi-action episodes on mined Gym-ANM scenarios versus 0/21 for terminal gate and 9/21 for best scalar gate With two constraint families active, scalar projection admits 63.2% of 42,410 physically unsafe actions; product order admits 0% SiLR as GRPO process reward achieves 0.844 ungated policy performance versus 0.778 untrained base
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
This is a preprint describing a novel algorithmic approach (SiLR) for LLM tool agents with empirical validation on benchmark tasks, but lacks peer review, clinical applicability, and independent replication.
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A runtime gate for an LLM tool agent is usually cast as a filter. In a ReAct loop a rejected proposal is followed by another at the same state, so the gate is a search operator over the proposal stream whose admission criterion shapes which trajectories are reachable. We study post-violation recovery admission, where progress must be admitted while the system is still in violation, and identify the scalar projection trap: an aggregate-score gate accepts a locally improving proposal and commits the trajectory to a plateau. SiLR instead shadow-executes each proposal and admits it under a product order over the branch-level violation state (overloaded-branch support and per-branch severity). We prove that no scalar surrogate is sound for this order, so the failure is representational, not a matter of threshold tuning. On mined Gym-ANM scenarios, SiLR recovers 21/21 multi-action episodes against 0/21 for terminal and 9/21 for the best scalar gate, significant across the full 24-scenario benchmark. The terminal-versus-structured dichotomy holds across three model families and in CityLearn. Because admission rests on deterministic simulation, the LLM lies outside the trust boundary: a magnitude-redistribution attack that defeats both scalar and support-only baselines is contained only by the full per-branch predicate. With two constraint families active, every tested scalar projection admits physically unsafe actions; support-only admits the largest fraction (63.2% of 42,410; product order 0). In the hardest dual-family traces, scalar gates recover only through that unsafe class. Reused as a GRPO process reward, it outperforms its count projection in every mined scenario and is the only tested reward whose ungated policy exceeds the untrained base (0.844 vs. 0.778). Scalar projection loses the violation geometry at both design points; only the full product order is structurally sufficient.
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