Few grammars are designed in a straight line. During grammar prototyping, authors often want to run a partially settled rule before refactoring overlapping alternatives or regenerating parser code. Predictive parsers are effective for stable grammars, but their generation and compilation loop can slow down interactive grammar development. This paper presents MSLL, a lightweight runtime extension of recursive-descent LL parsing for edit-time grammar exploration. When a FIRST/FIRST conflict appears, MSLL keeps multiple live parsing stacks, lets each stack follow a different candidate production, and prunes a stack as soon as the input contradicts it. Ambiguity is therefore handled as a runtime state-management problem rather than as a condition that must be removed before execution. The prototype targets an ANTLR-style grammar workflow: run and inspect an evolving grammar directly, collect conflict traces, and later translate or adapt the stabilized grammar for a generated parser. Experiments on DSL-style workloads show that MSLL finishes three 1k-token benchmarks in 48, 49, and 59 ms; stays under one second on two ambiguous 50k-token workloads; and reaches 5.395 s on a 500k-token deeply nested stress case. A real-world JSON grammar case confirms interactive behavior on small and medium inputs while exposing prototype-level super-linear overhead at 500k tokens. The results position MSLL as a development-time companion to existing LL-based parser generators rather than a replacement for production parsers.
Large language models increasingly read and act on enterprise data, but SQL gives a late error signal: hallucinated fields or relations can execute and return plausible wrong answers, while incorrect writes cannot be safely assessed after execution. We present VirtualSet, a live, receiver-typed ontology-world interface and generation target for LLMs. Instead of SQL, the model emits set expressions over entity-edge worlds. Generic Constraint Projection (GCP) checks expressions before execution, while future this preserves concrete receiver types through collection chains, turning invalid fields, edges, receivers, and actions into token-anchored type errors. Type-clean reads use a SQL fast path or bounded stream interpretation, with a parity oracle checking both paths over the exercised operator space. The same substrate supports guarded decisions: actions run first in a simulated world, and world-change events require external approval before actualization. On BIRD, we lift relational schemas into typed worlds and compare VirtualSet with direct SQL while holding the model, evidence, values, zero-shot setting, timeout, glossary, repair/voting, and grader constant where possible. On a frozen 1,072-question split, VirtualSet achieves 67.5% accuracy versus 63.5% for glossary-matched direct SQL with repair and voting (+4.0 points; McNemar exact p = 0.00117) using deepseek-reasoner. Full-corpus analysis finds no engine mis-computation of a type-clean expression; remaining errors arise from model semantics or gold defects. In a 30-body guard corpus, the write chain intercepts 20/20 hallucinated action bodies with zero false positives. VirtualSet thus remains competitive on SQL's home benchmark while providing pre-execution semantics for guarded decisions.
Qunhui Zhang· arXiv.org· 0 citations
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